Shujiao Liao | Machine Learning | Best Researcher Award

Published on by Computer Vision Awards

Prof . Shujiao Liao | Machine Learning | Best Researcher Award

Professor at Minnan Normal University, China

Dr. Shujiao Liao is a full professor at the School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, Fujian, China. With a strong academic background in applied mathematics and software engineering, she has dedicated her career to advancing the fields of granular computing, data mining, and machine learning. Her work bridges theoretical mathematics and computational methodologies, enabling novel approaches to intelligent data analysis. Over the years, Dr. Liao has played a pivotal role in both academic teaching and research leadership, contributing significantly to her institution’s development and scholarly output. She has guided numerous students and collaborated across interdisciplinary research groups. Her commitment to innovation and academic excellence makes her a respected figure in her field. As a scholar deeply engaged in cutting-edge technologies and data science trends, she continues to contribute impactful research and strives to address complex problems with analytical precision and computational insight.

Professional Profile 

Education🎓

Dr. Shujiao Liao holds a strong interdisciplinary educational background that underpins her academic career. She earned her Master of Science degree in Applied Mathematics from Shantou University, Guangdong, China, in 2006, where she built a solid foundation in mathematical modeling and analytical reasoning. Her pursuit of advanced studies led her to obtain a Ph.D. degree in Software Engineering from the University of Electronic Science and Technology of China, Chengdu, Sichuan, in 2018. This advanced degree enabled her to integrate mathematical theory with practical software systems, contributing to her versatility in computational research. Her doctoral studies focused on bridging data-centric algorithms with intelligent systems, which now form the core of her research interests. This rich educational trajectory has allowed her to approach complex scientific questions from both a mathematical and engineering perspective, making her academic contributions particularly robust in the fields of data mining and machine learning.

Professional Experience📝

Dr. Shujiao Liao is currently a full professor at the School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, Fujian, China. With an academic career that spans over a decade, she has demonstrated excellence in teaching, research, and academic leadership. In her current role, she teaches advanced mathematics and computational theory courses, supervises postgraduate research projects, and actively engages in departmental development. She has led several internal and collaborative research initiatives in granular computing and machine learning, working closely with both academic and industrial partners. Her experience also includes conference presentations, curriculum development, and cross-disciplinary project coordination. She is recognized for her effective mentorship, contributing to the growth of young researchers and promoting high standards in academic inquiry. Through her consistent professional contributions, Dr. Liao has helped elevate her institution’s research standing and continues to serve as a vital resource for the academic community in mathematics and software research.

Research Interest🔎

Dr. Shujiao Liao’s research interests span several pivotal domains in computer science and applied mathematics, with a particular focus on granular computing, data mining, and machine learning. Her work in granular computing explores how knowledge can be structured and processed using information granules, improving the interpretability and efficiency of decision-making systems. In the area of data mining, she investigates algorithms for pattern discovery, classification, and clustering, contributing to improved data-driven strategies in scientific and industrial applications. Her interests in machine learning include developing intelligent models capable of adaptive learning and robust performance across complex datasets. Dr. Liao’s research bridges theory and application, aiming to solve real-world problems such as intelligent diagnostics, automated reasoning, and big data analysis. Her interdisciplinary focus allows her to work on innovative projects that combine mathematical rigor with computational techniques, positioning her as a contributor to the evolving field of intelligent systems and artificial intelligence.

Award and Honor🏆

While specific awards and honors for Dr. Shujiao Liao were not provided in the given information, her appointment as a full professor reflects recognition of her academic contributions and research leadership. Attaining such a role typically involves competitive peer-reviewed evaluations, consistent scholarly output, and excellence in teaching and mentorship. It is likely that she has received internal university-level commendations, research project funding awards, or participation in prestigious academic panels, common among professors of her standing. If available, details such as Best Paper Awards, Research Excellence Awards, or National Science Grants would further highlight her academic acclaim. Her long-standing role in the academic community and sustained focus on impactful research suggest she is a strong candidate for further honors at national or international levels. Formal acknowledgment through such accolades would complement her already impressive academic and research credentials, reinforcing her eligibility for broader recognitions such as the Best Researcher Award.

Research Skill🔬

Dr. Shujiao Liao possesses a robust set of research skills grounded in both theoretical understanding and practical application. She demonstrates strong expertise in mathematical modeling, algorithm development, and data analysis, which are essential for her work in granular computing and data mining. Her proficiency in applying machine learning techniques to complex datasets enables her to design predictive models with real-world relevance. She is adept at academic writing, literature review, and hypothesis-driven exploration, essential for high-quality publications and grant writing. Additionally, Dr. Liao has strong collaborative and project management skills, allowing her to lead interdisciplinary research teams and coordinate joint research initiatives. Her experience in supervising graduate theses further reflects her ability to guide rigorous research methodologies. She is also likely skilled in programming languages and tools used in data science, such as Python, MATLAB, or R, further supporting her contributions to computational research domains.

Conclusion💡

Dr. Shujiao Liao is a strong candidate for the Best Researcher Award, particularly within fields like granular computing and machine learning. Her academic background and full professorship position suggest a high level of expertise and leadership. To solidify her candidacy for top-tier recognition, showcasing quantifiable research outcomes, international influence, and broader impact will be important.

Publications Top Noted✍

  • Title: WrdaGAN: A text-to-image synthesis pipeline based on Wavelet Representation and Adaptive Sample Domain Constraint strategy
    Authors: Yongchao Qiao, Ya’nan Guan, Shujiao Liao, Wenyuan Yang, Weiping Ding, Lin Ouyang
    Year: 2025
    Citation: DOI: 10.1016/j.engappai.2025.111305

  • Title: Semisupervised Feature Selection With Multiscale Fuzzy Information Fusion: From Both Global and Local Perspectives
    Authors: Nan Zhou, Shujiao Liao, Hongmei Chen, Weiping Ding, Yaqian Lu
    Year: 2025
    Citation: DOI: 10.1109/TFUZZ.2025.3540884

  • Title: S-approximation spaces extension model based on item-polytomous perspective
    Authors: Xiaojie Xie, Shujiao Liao, Jinjin Li
    Year: 2024
    Citation: DOI: 10.21203/rs.3.rs-4447331/v1

  • Title: Multi-Target Rough Sets and Their Approximation Computation with Dynamic Target Sets
    Authors: Wenbin Zheng, Jinjin Li, Shujiao Liao
    Year: 2022
    Citation: DOI: 10.3390/info13080385

  • Title: Multi-Label Attribute Reduction Based on Neighborhood Multi-Target Rough Sets
    Authors: Wenbin Zheng, Jinjin Li, Shujiao Liao, Yidong Lin
    Year: 2022
    Citation: DOI: 10.3390/sym14081652

  • Title: Attribute‐scale selection for hybrid data with test cost constraint: The approach and uncertainty measures
    Authors: Shujiao Liao, Yidong Lin, Jinjin Li, Huiling Li, Yuhua Qian
    Year: 2022
    Citation: DOI: 10.1002/int.22678

  • Title: Feature–granularity selection with variable costs for hybrid data
    Authors: Shujiao Liao, Qingxin Zhu, Yuhua Qian
    Year: 2019
    Citation: DOI: 10.1007/s00500-019-03854-2

Divya Mishra | Machine Learning | Best Researcher Award

Published on by Computer Vision Awards

Assoc . Prof . Dr . Divya Mishra | Machine Learning | Best Researcher Award

Associate Professor at GL Bajaj Institute of Technology & Management, Greater Noida, India

Dr. Divya Mishra is a passionate and accomplished academician and AI researcher with over 13 years of cross-sectoral experience spanning academia, research, and industry. Currently serving as an Associate Professor in CSE-AIML at NIET and pursuing post-doctoral research remotely at Infrastructure University Kuala Lumpur (IUKL), her work centers on AI-driven sustainable e-governance. She brings deep expertise in machine learning, deep learning, and neural networks, underpinned by practical software development experience in Java and Python. Her PhD research addressed call drop prediction in mobile networks using an ANN-based model, resulting in near-perfect accuracy. Dr. Mishra is actively engaged in impactful research projects, including patents, edited books, and IEEE conference publications, while serving as a reviewer, session chair, and technical program committee member in prestigious forums. With a commitment to transparency, innovation, and sustainability in digital transformation, she is a leading voice in AI applications for public administration and smart solutions.

Professional Profile 

Education🎓

Dr. Divya Mishra holds a robust academic background in computer science and electronics. She earned her Ph.D. in Computer Science and Engineering from Noida International University in August 2021, with research focused on mitigating mobile network call drops using deep learning. She previously completed her M.Tech in Computer Science (Full-Time) from the same institution with a stellar CGPA of 9.2, securing a Gold Medal. Her postgraduate studies include an MCA from U.P. Technical University in 2011 with 77.4%, and a BCA from IGNOU, New Delhi. She also holds a Diploma in Electronics Engineering from B.T.E. Lucknow with 72.95%. Her academic journey reflects a consistent trajectory of academic excellence, technical competence, and multidisciplinary learning. Recognized for her honors during MCA by the Governor of Uttar Pradesh, Dr. Mishra’s educational path has equipped her with the theoretical and applied foundation required for her advanced research in AI, machine learning, sustainable systems, and digital governance.

Professional Experience📝

Dr. Divya Mishra boasts over 13 years of versatile professional experience across academia, industry, and research. She currently serves as an Associate Professor in the CSE-AIML Department at NIET, Greater Noida, since May 2025, while also pursuing post-doctoral research on AI-driven e-governance at IUKL, Malaysia. Her academic tenure includes Assistant Professor roles at GL Bajaj Institute and GNIOT, where she taught and mentored students in AI, ML, and data analytics. Previously, she was a Research Scholar at Noida International University, contributing significantly to AI-based telecom systems. Her industrial experience includes software development roles at Tripti e Solutions, Apex TG India Pvt. Ltd., and IIHT Ltd, where she also served as Center Head. She began her technical journey as a Diploma Trainee at Indian Telephone Industries Ltd. Her multifaceted experience enables her to seamlessly integrate theoretical concepts with practical applications in her teaching and research efforts.

Research Interest🔎

Dr. Divya Mishra’s research interests lie at the intersection of artificial intelligence, machine learning, deep learning, and sustainable governance systems. She is particularly passionate about developing intelligent, real-time AI-driven solutions for public administration, telecom, e-governance, and smart environmental monitoring. Her doctoral research focused on mitigating call drops in mobile networks through ANN-based models integrated into a real-time mobile application. Her post-doctoral focus extends into AI-powered sustainable e-governance frameworks, emphasizing transparency and accountability. She is also involved in multidisciplinary projects such as wildlife monitoring using deep learning, hand sign language recognition, waste classification, and emotion recognition from voice, reflecting her commitment to using AI for societal benefit. Dr. Mishra’s work spans practical AI implementations in healthcare, energy optimization, VANET security, and IoT systems. Through her edited books, patents, and publications, she continues to explore innovative intersections of AI with sustainability, data integrity, and policy, aligning her research with global digital transformation agendas.

Award and Honor🏆

Dr. Divya Mishra has received numerous accolades recognizing her academic excellence, impactful research, and leadership in AI. Notably, she was honored with the Shakti Award 2024 by Jansharnam NGO on Women’s Day for her outstanding contributions to technology and education. She also received the Gold Medal during her M.Tech, and her MCA degree was conferred by the Governor of Uttar Pradesh, recognizing her academic honors. She was appreciated for her contributions at international conferences like IICS 2021, and awarded the Quality Contribution Award by GNIOT, Greater Noida. Additionally, her leadership as an Innovation Ambassador at GL Bajaj’s Innovation Cell and roles as session chair and reviewer for multiple IEEE and Springer conferences further validate her active participation in shaping global research discourse. Her recognitions from institutional and national forums reflect her continuous drive toward academic excellence, innovative research, and meaningful community contributions in AI and governance.

Research Skill🔬

Dr. Divya Mishra possesses an extensive and dynamic research skill set across the AI landscape. She is proficient in programming languages like Python, Java, and C, and has a deep command over machine learning, deep learning, neural networks, and data analysis. Her expertise includes developing intelligent algorithms for real-time applications, evidenced by her ANN-based call drop prediction model and integration into the MyTelecomApp. She has published and reviewed numerous peer-reviewed papers, contributed to edited books, and filed multiple AI-driven patents across domains such as environment, health, and security. Dr. Mishra excels in research writing, patent drafting, project conceptualization, and conference management. She also has experience in hands-on technical training and mentoring, contributing to student development and curriculum design. Her interdisciplinary skills allow her to translate complex AI frameworks into socially impactful, sustainable solutions, making her a versatile and effective researcher in applied artificial intelligence and digital innovation ecosystems.

Conclusion💡

Dr. Divya Mishra demonstrates strong qualifications, multidisciplinary impact, and innovative leadership that make her a highly suitable candidate for the Best Researcher Award. Her ongoing postdoctoral work, numerous publications, patents, and reviewer engagements speak to her active and impactful research career. With minor enhancements in global collaborations, funding portfolios, and citation metrics, her candidacy would become even more compelling.

Publications Top Noted✍

  1. Title: Self-optimization in LTE: An approach to reduce call drops in mobile network
    Authors: D. Mishra, A. Mishra
    Year: 2018
    Citations: 8

  2. Title: Sentimental Voice Recognition: An Approach to Analyse the Emotion by Voice
    Authors: A. Gupta, D. Mishra
    Year: 2024
    Citations: 2

  3. Title: Neural Network: A Way to Know Consumer Satisfaction During Voice Call
    Authors: D. Mishra, S. Mishra
    Year: 2022
    Citations: 2

  4. Title: Performance Enhanced and Improvised Approach to Reduce Call Drops Using LTE-SON
    Authors: D. Mishra, A. Mishra
    Year: 2019
    Citations: 2

  5. Title: Drowsiness Alert System: An Approach To Save The Life
    Authors: A. Chandra, D. Mishra, B. Shaw, A. Gupta
    Year: 2023
    Citations: 1

  6. Title: Mobility Robustness Optimization Using ANN for Call Drop Prediction
    Authors: D. Mishra, S. Yadav
    Year: 2020
    Citations: 1

  7. Title: Fine tuning of MapReduce jobs using parallel K Map clustering
    Authors: D. Mishra, S. Yadav
    Year: 2019
    Citations: 1

  8. Title: Empowering Sustainable Waste Management: A Comparative Study of Machine Learning Models for Citizen Engagement
    Authors: D. Mishra, R. Kumar, A.B. bin Abdul Hamid
    Year: 2025

  9. Title: Machine Learning: A Self-Optimized Boon for Deaf and Mute to Recognize Real-Time Hand Sign Language
    Authors: P. Pandey, D. Mishra
    Year: 2025

  10. Title: Character Detection: An Approach to Clarify the Texts Using Machine Learning
    Authors: B. Shaw, D. Mishra
    Year: 2025

  11. Title: Intellicam: A Self-Optimizing Approach to Detect Burglary using Machine Learning
    Authors: A. Chandra, D. Mishra
    Year: 2025

  12. Title: Integrating Cryptographic Techniques with Machine Learning Algorithms for Enhanced Data Privacy and Information Security: A Mathematical Framework
    Authors: G. Merlin Florrence, D. Mishra, G. Ghule, P.K. Sahu, Singh
    Year: 2024

  13. Title: A Mathematical Framework for Enhancing IoT Security in VANETs: Optimizing Intrusion Detection Systems through Machine Learning Algorithms
    Authors: D. Mishra, S. Moudgi, D. Virmani, Y.P. Faniband, A.B. Nandyal, P.K. Sahu
    Year: 2024

  14. Title: YOLO: A way to identify gemstone and predict its relevant finger to wear
    Authors: D. Mishra, S. Mishra
    Year: 2023

  15. Title: Instant Energy Products: An Analysis
    Authors: D.M. Mohasin Haque, Irfan Ahamad
    Year: 2023

  16. Title: Mid–Point Sorting Algorithm: A New Way to Sort
    Authors: A. Garg, V. Patel, D. Mishra
    Year: 2022

  17. Title: A review on call drop
    Authors: D. Mishra, A. Mishra
    Year: 2016

Bharati Chaudhari | Edge Detection | Best Researcher Award

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Ms . Bharati Chaudhari | Edge Detection | Best Researcher Award

Assitstant Professor at Maharashtra Institute of Technology, Chh. Sambhajinagar, India

Ms. Bharati Prakash Chaudhari is an experienced academician and researcher with over 18 years of teaching experience in computer science and engineering. Currently serving as an Assistant Professor at MIT, Aurangabad, she has consistently demonstrated a strong commitment to research and education. Her expertise spans image processing, machine learning, and digital system development, with active contributions to both academic research and industry-oriented projects. She has authored multiple research papers in international journals and conferences, including Scopus-indexed publications and IEEE proceedings. Additionally, her involvement in intellectual property development through several copyrights underscores her original contributions to technical education. Ms. Chaudhari continues to pursue her Ph.D. in Computer Science and Engineering at Dr. Babasaheb Ambedkar Marathwada University, reflecting her dedication to academic growth. Her work bridges theoretical knowledge with practical application, particularly through collaborations with industry for digital tool development. She is a proactive, skilled, and forward-looking researcher shaping the field of computer engineering.

Professional Profile 

Education🎓

Ms. Bharati Prakash Chaudhari holds a Master of Engineering degree in Computer Science and Engineering from Government College of Engineering, Aurangabad, affiliated with Dr. Babasaheb Ambedkar Marathwada University (Dr. B.A.M.U.), where she graduated in 2010 with distinction, scoring 81.25%. She earned her Bachelor of Engineering in Computer Engineering from K.K. Wagh College of Engineering, Nashik under Pune University in 2003, securing first-class marks with 62.2%. Currently, she is pursuing her Ph.D. in Computer Science and Engineering from Dr. B.A.M.U., Aurangabad. Her academic background showcases a steady progression through well-regarded institutions and reflects a continuous pursuit of advanced knowledge in her domain. Her postgraduate studies have equipped her with a solid foundation in algorithm development, computational models, and system-level design. The ongoing doctoral research further strengthens her analytical and research capabilities, positioning her to contribute meaningfully to emerging trends in machine learning and image processing.

Professional Experience📝

Ms. Bharati Prakash Chaudhari has over 18 years of professional academic experience in engineering education. She began her teaching career in February 2003 at MIT IT College, Cidco, Aurangabad, serving as a Lecturer for over three years. Since July 2006, she has been affiliated with MIT, Aurangabad, initially as a Lecturer and later redesignated as an Assistant Professor. Throughout her tenure, she has taught various core subjects in computer science and engineering and actively engaged in curriculum development and mentoring students. Her long-standing commitment to teaching is complemented by her involvement in research, project guidance, and departmental responsibilities. She has also contributed to industry-academic collaboration through participation in projects like digital tool development for transformer design, under GIZ–MASSIA initiatives. Ms. Chaudhari’s experience demonstrates not only her academic dedication but also her ability to integrate applied engineering practices into her educational approach, enhancing student learning and research culture.

Research Interest🔎

Ms. Bharati Prakash Chaudhari’s research interests center around Image Processing, Machine Learning, and Optimization Algorithms, with a keen focus on applying intelligent computing methods to solve practical problems in healthcare and security. Her recent work on edge detection using Ant Colony Optimization for medical images illustrates her interest in bio-medical image analysis. She also explores areas such as reversible data hiding, digital watermarking, and encrypted image processing—topics that are critical to data security and digital forensics. Her Ph.D. research and publications reflect an effort to integrate biologically inspired algorithms into traditional image processing techniques. Moreover, she has shown a consistent interest in enhancing data representation, pattern recognition, and system intelligence. Through hybrid algorithm development and advanced segmentation techniques, Ms. Chaudhari aims to push the boundaries of image understanding and machine learning applications, particularly in domains where accurate visual interpretation is crucial, such as diagnostics, surveillance, and automation.

Award and Honor🏆

Ms. Bharati Prakash Chaudhari has been recognized for her scholarly contributions through multiple Intellectual Property Rights (IPRs) registrations, including copyrights on algorithmic learning materials and applied computer science concepts such as Dijkstra’s Algorithm, Histogram Equalization, and Finite Automata Design. These IPRs reflect her dedication to developing high-quality, original educational content and research outputs. While formal academic awards are not explicitly listed, her achievements in publishing papers in Scopus-indexed journals and prestigious conferences like IEEE and Elsevier Procedia signify academic excellence. Her active involvement in applied research projects, such as the Digital Tool Development for Transformer Design under a government-industry partnership (GIZ-MASSIA), further underscores her practical impact. Through these achievements, she has earned peer recognition within academic and industrial circles. Her participation in international events and successful collaborations with senior researchers demonstrate her growing reputation as a capable and emerging researcher in the field of computer engineering.

Research Skill🔬

Ms. Bharati Prakash Chaudhari possesses strong research skills across multiple domains of computer science, particularly in image analysis, optimization algorithms, and machine learning models. She is proficient in applying Ant Colony Optimization, ICA (Independent Component Analysis), and encryption-based data hiding techniques for real-world problems. Her skill set includes the ability to design experimental methodologies, simulate and validate results, and interpret complex datasets for image processing tasks. She is adept at using MATLAB and other relevant software tools for developing and testing algorithms. Additionally, she is capable of translating conceptual ideas into practical implementations, as evident in her industry collaboration for transformer design automation. Her copyright registrations for algorithmic content reflect her strength in educational research and tool development. With a foundation in both academic writing and hands-on experimentation, Ms. Chaudhari’s research competencies bridge theoretical understanding and applied problem-solving—making her a valuable contributor to innovation-driven computing research.

Conclusion💡

Ms. Bharati Prakash Chaudhari is a strong candidate for the Best Researcher Award, especially given her longevity in academia, publication record, IPRs, and participation in reputed conferences. However, to be a top-tier awardee, finalizing her Ph.D. and enhancing her presence in globally ranked journals, along with measurable citation metrics, would make her profile even more competitive.

Publications Top Noted✍

  • Title: Hepatoprotective activity of Hydroalcoholic extract of Momordica charantia Linn. leaves against Carbon tetrachloride induced Hepatopathy in Rats
    Authors: KRB, Chaudhari BP, VJ Chaware, YR Joshi
    Year: 2009
    Citations: 45

  • Title: Protective effect of the aqueous extract of Momordica charantia leaves on gentamicin induced nephrotoxicity in rats
    Authors: KRB, VJ Chaware, BP Chaudhary, MK Vaishnav
    Year: 2011
    Citations: 20

  • Title: Protective effect of the aqueous extract of Phaseolus radiatus seeds on gentamicin induced nephrotoxicity in rats
    Authors: VJ Chaware
    Year: 2012
    Citations: 16

  • Title: Quality by design (QbD) concept review in pharmaceuticals
    Authors: K Jagtap, B Chaudhari, V Redasani
    Year: 2022
    Citations: 11

  • Title: Development and validation of spectrophotometric method for simultaneous estimation of meclizine hydrochloride and pyridoxine hydrochloride in tablet dosage form
    Authors: SA Shinde, ZM Sayyed, BP Chaudhari, VJ Chaware, KR Biyani
    Year: 2016
    Citations: 10

  • Title: Development and validation of UV-spectrophotometric method for simultaneous estimation of amlodipine besylate and hydrochlorothiazide in combined dosage form
    Authors: Z Sayyed, S Shinde, V Chaware, B Chaudhari, K Biyani
    Year: 2015
    Citations: 9

  • Title: A cross-sectional prescription audit database for anti-anginal drugs with impact of essential drug list and standard treatment guidelines on prescription pattern in Nasik city
    Authors: V Chaudhari, B Chaudhari, A Khairnar
    Year: 2011
    Citations: 7

  • Title: Approaches of digital image watermarking using ICA
    Authors: BP Chaudhari, AK Gulve
    Year: 2010
    Citations: 7

  • Title: A Review on in situ Gel of Gastro Retentive Drug Delivery System
    Authors: BV Aiwale, BP Chaudhari, AB Velhal, VK Redasani
    Year: 2022
    Citations: 6

  • Title: Image segmentation using hybrid ant colony optimization: A review
    Authors: B Chaudhari, P Shetiye, A Gulve
    Year: 2021
    Citations: 6

  • Title: A Review on Diverging approaches to Fabricate Polymeric Nanoparticles
    Authors: S Deshmukh, B Chaudhari, A Velhal, V Redasani
    Year: 2022
    Citations: 5

  • Title: A Validated RP-HPLC Method for Simultaneous Estimation of Tizanidine and Nimesulide in Bulk and Pharmaceutical Formulation
    Authors: KD Bharatee Chaudhari
    Year: 2020
    Citations: 5

  • Title: Pharmacosome as a Vesicular Drug Delivery System
    Authors: RR Shinde, BP Chaudhari, AB Velhal, VK Redasani
    Year: 2022
    Citations: 4

  • Title: Influence of Newly Synthesized Superdisintegrant on Dissolution Rate Enhancement of Carbamazepine using Liquisolid Compact Technique
    Authors: GV Raut, PB Chaudhari, KV Redasani
    Year: 2022
    Citations: 4

  • Title: Development and validation of UV-spectrophotometric method for simultaneous estimation of spironolactone and hydrochlorothiazide in pharmaceutical formulation
    Authors: Z Sayyed, S Shinde, V Chaware, B Chaudhari, M Zuber, M Sayyed
    Year: 2015
    Citations: 4

  • Title: A compendious review on biodegradable polymeric nanoparticles
    Authors: S Deshmukh, B Chaudhari, A Velhal, V Redasani
    Year: 2022
    Citations: 3

  • Title: Cleaning Validation in Pharmaceutical Industry
    Authors: P Khalate, B Chaudhari, V Redasani
    Year: 2022
    Citations: 2

  • Title: A Novel Tool for Controlled Delivery: Transdermal Drug Delivery System
    Authors: AV Panval, BP Chaudhari, AB Velhal, VK Redasani
    Year: 2022
    Citations: 2

  • Title: A Review on Pharmaceutical Regulatory Authority of India, USA, UK, Australia
    Authors: AA Shinde, AS Gurav, BP Chaudhari, VK Redasani
    Year: 2024
    Citations: 1

  • Title: Review on Colon Targeted Drug Delivery System
    Authors: NB Waghmode, SV Dhanje, BP Chaudhari, VK Redasani
    Year: 2024
    Citations: 1

Liao Jun Guo | Object Detection | Best Researcher Award

Published on by Computer Vision Awards

Prof . Dr . Liao Jun Guo | Object Detection | Best Researcher Award

Teacher at Hunan University of Science and Technology, China

Prof. Dr. Jun Guo Liao is a distinguished academic and researcher serving as a Full Professor at the School of Computer Science and Engineering, Hunan University of Science and Technology, China. With a Ph.D. in Information Security earned from Huazhong University of Science and Technology in 2007, he brings over 15 years of scholarly excellence and pedagogical contribution to his field. His professional journey has been defined by a steadfast commitment to information security and the broader discipline of computer applications. Throughout his academic career, Prof. Liao has mentored numerous students, contributed to curriculum development, and engaged in research that addresses pressing issues in digital safety and technological advancement. His experience and leadership have made significant contributions to institutional growth, while his ongoing research aims to support the secure evolution of computing systems in a connected world. He continues to pursue innovative solutions to challenges in cybersecurity and digital system integration.

Professional Profile 

Education🎓

Prof. Dr. Jun Guo Liao has a strong educational background rooted in information technology and computer science. He earned his Ph.D. in Information Security from the prestigious Huazhong University of Science and Technology in 2007, one of China’s leading institutions in science and engineering. During his doctoral studies, he specialized in areas related to data protection, system vulnerabilities, cryptographic protocols, and secure computing systems. His academic training equipped him with a deep understanding of cybersecurity frameworks, cryptography, and network defense mechanisms. Prior to his doctoral studies, Prof. Liao likely completed a rigorous undergraduate and master’s education in computer science or related fields, building a solid foundation for his future research endeavors. His educational journey has not only shaped his technical expertise but also reinforced his ability to approach complex research problems with academic rigor and analytical depth. This strong academic foundation continues to underpin his success as a researcher and educator.

Professional Experience📝

Prof. Dr. Jun Guo Liao has accumulated extensive professional experience as a dedicated educator, researcher, and academic leader. He currently serves as a Full Professor at the School of Computer Science and Engineering at Hunan University of Science and Technology, where he has played a pivotal role in both teaching and research. His responsibilities span delivering advanced-level courses, supervising graduate students, and contributing to academic policy-making within the university. Since completing his Ph.D. in 2007, he has focused his career on advancing the field of information security and computer applications. Over the years, Prof. Liao has likely led funded research projects, participated in national-level research programs, and collaborated with industrial partners to translate theoretical work into practical solutions. His professional achievements reflect a sustained commitment to academic excellence, institutional development, and scientific contribution. His role as a faculty leader highlights his ability to foster research innovation and academic integrity.

Research Interest🔎

Prof. Dr. Jun Guo Liao’s research interests center on information security and computer applications, two domains of critical importance in the digital age. His work explores the development of secure computing environments, the design of cryptographic algorithms, and the protection of data across networks and systems. He is particularly interested in safeguarding sensitive information against cyber threats, improving authentication systems, and fortifying infrastructure against unauthorized access. Additionally, Prof. Liao’s interests likely extend into applied computer science areas such as secure software development, cloud computing security, and artificial intelligence in cybersecurity. His research strives to bridge theoretical computer science with practical applications, offering real-world solutions to modern digital challenges. Through his work, Prof. Liao contributes to building resilient and trustworthy computing environments. His interest in interdisciplinary collaboration enables him to address complex problems that intersect with data privacy, digital ethics, and secure communications, making his research highly impactful and timely.

Award and Honor🏆

While specific awards and honors were not listed in the available curriculum vitae, it is likely that Prof. Dr. Jun Guo Liao has received recognition at various institutional, regional, or national levels for his academic and research achievements. As a Full Professor with a Ph.D. in Information Security and a sustained academic career, he may have been honored with outstanding teaching awards, research excellence awards, or government-funded research grants. His contributions to the advancement of cybersecurity and academic mentorship position him as a valuable figure in the academic community, potentially earning him roles in review panels, conference committees, or research consortiums. Furthermore, his long-standing affiliation with Hunan University of Science and Technology suggests consistent internal recognition for academic leadership and service. Additional details on his recognitions would further affirm his suitability for prestigious awards such as the Best Researcher Award, reflecting his excellence and dedication in his field.

Research Skill🔬

Prof. Dr. Jun Guo Liao possesses advanced research skills in the domains of information security and computer applications, which encompass both theoretical and applied methodologies. His expertise includes cryptographic system design, vulnerability assessment, secure communication protocols, and data protection strategies. He demonstrates strong analytical thinking, problem-solving abilities, and a keen understanding of algorithmic implementation for secure systems. Over the years, he has likely developed skills in research project management, academic writing, peer reviewing, and mentoring graduate students. Additionally, his technical skill set may include programming, network analysis, penetration testing, and proficiency in tools related to cybersecurity. Prof. Liao is also adept at conducting literature reviews, designing experimental models, and evaluating system security in real-world applications. These research competencies enable him to contribute meaningfully to the academic discourse on digital safety while promoting innovation in technology. His continuous development of research skills supports his contributions to scholarly excellence and institutional impact.

Conclusion💡

Based on the limited available information, Prof. Dr. Jun Guo Liao appears to be a strong academic with expertise in information security, making him potentially eligible for the Best Researcher Award. However, to confidently support his nomination, it is highly recommended to provide:

  • A complete list of publications and citation metrics

  • Details of research projects, funding, and impactful contributions

  • Any national/international recognitions or awards

  • Evidence of research leadership and community involvement

Publications Top Noted✍

  • Title: MBB-YOLO: A comprehensively improved lightweight algorithm for crowded object detection
    Year: 2024
  • Title: A multikey fully homomorphic encryption privacy protection protocol based on blockchain for edge computing system
    Year: 2023
    Citations: 5
  • Title: DTSAC: Smart Contract-based Access Control with Delegation and Trust Management
  • Title: An adaptive traffic sign recognition scheme based on deep learning in complex environment




HAIFEI CHEN | Visual SLAM | Best Researcher Award

Published on by Computer Vision Awards

Assoc .Prof. Dr. HAIFEI CHEN | Visual SLAM | Best Researcher Award

Central South University of Forestry and Technology, China

Dr. Haifei Chen is an accomplished Associate Professor at the School of Mechanical and Intelligent Manufacturing, Central South University of Forestry and Technology. Born in August 1990 in Chenzhou, Hunan, he is a dedicated scholar in the field of robotics and intelligent control. With a strong academic background and robust research portfolio, he has led numerous national and provincial research projects focusing on teleoperation robotics, multi-agent coordination, SLAM, and networked control systems. His scholarly contributions include over 20 high-impact SCI-indexed journal articles and multiple patents. A recognized academic leader, Dr. Chen holds key roles in national research centers and professional societies and serves as a reviewer for top-tier IEEE journals. His research has significantly advanced space robotics and teleoperation technologies. Through innovation, leadership, and academic excellence, he has positioned himself as a promising and impactful researcher in the field of intelligent robotics.

Professional Profile 

Education🎓

Dr. Haifei Chen pursued his doctoral studies at the School of Astronautics, Northwestern Polytechnical University, beginning in September 2017. His Ph.D. specialization was in navigation, guidance, and control under the supervision of Professor Panfeng Huang, a prominent academician and expert affiliated with the National Distinguished Youth Fund and military science commissions. Dr. Chen’s doctoral research focused on time-delay modeling and compensation for space teleoperation systems, laying a strong theoretical foundation for his current work. Prior to his doctoral education, he had successfully cleared national English proficiency exams including CET-6 and achieved an IELTS score of 6.0, reflecting a solid command of the English language for academic communication. His educational experience has equipped him with expertise in robotics, control systems, and artificial intelligence, supporting his contributions to both theoretical research and engineering practice. His education forms the core of his academic identity and drives his research in intelligent robotic systems.

Professional Experience📝

Dr. Haifei Chen currently serves as an Associate Professor at the School of Mechanical and Intelligent Manufacturing, Central South University of Forestry and Technology, a position he has held since December 2023. Prior to this, he was a Lecturer at the School of Mechanical and Electrical Engineering at the same university from February 2022 to December 2023. His academic trajectory has been steadily progressive, with research and teaching roles focused on robotic teleoperation, intelligent control, and remote robotic systems. His leadership includes managing multiple funded projects, mentoring students, and contributing to institutional development as a discipline leader in forestry equipment and intelligent systems. He is also affiliated with key research centers, including the National Engineering Research Center for Economic Forest Harvesting Equipment. His experience bridges fundamental research and practical implementation, making him a vital contributor to the advancement of robotics in forestry and space technology domains.

Research Interest🔎

Dr. Haifei Chen’s research interests are deeply rooted in robotic teleoperation, intelligent control, and multi-agent system collaboration. His work explores the challenges of time-delay in remote control systems, particularly in space teleoperation, with the goal of enhancing the precision and reliability of robotic operations under communication constraints. He is also focused on visual SLAM (Simultaneous Localization and Mapping), predictive control, and networked control systems—all critical to the development of autonomous and semi-autonomous robotic platforms. His recent research involves developing teleoperation systems for agricultural and forestry robots, such as oil-tea camellia fruit picking, using advanced machine learning algorithms and sensor fusion techniques. Dr. Chen aims to bridge the gap between theoretical models and practical deployment by incorporating real-time data processing, human-robot interaction, and decision-making algorithms. These interests reflect a multidisciplinary approach, integrating robotics, AI, and control theory to create intelligent, adaptive, and scalable robotic systems.

Award and Honor🏆

Dr. Haifei Chen has earned recognition both as a researcher and a community contributor. He serves as an expert reviewer for the National Natural Science Foundation of China (NSFC), reflecting his authority in his research field. He is the Vice Director of the National Engineering Research Center for Economic Forest Harvesting Equipment and serves on the Youth Committee of the Chinese Society of Forestry and the Robotics Subcommittee. These positions highlight his leadership within the academic and scientific communities. Furthermore, Dr. Chen has served as a reviewer for several internationally renowned journals, including IEEE Transactions on Systems, Man and Cybernetics: Systems, IEEE Transactions on Mechatronics, IEEE RA-L, Acta Astronautica, and ISA Transactions. His contributions to academic peer review and research management are clear indicators of professional excellence. While individual awards are not specifically listed, his prestigious roles and responsibilities underscore his impact and growing recognition in the robotics field.

Research Skill🔬

Dr. Haifei Chen possesses a versatile and advanced set of research skills in robotics and intelligent systems. He is highly proficient in modeling and controlling time-delay in networked and remote robotic systems—skills critical for real-time space and teleoperation environments. He has strong command over predictive control algorithms, Smith predictors, and error loop correction methods. His expertise extends to multi-agent coordination, robot vision (including SLAM), and motion planning, where he integrates machine learning techniques such as CNNs, BiGRUs, and attention mechanisms for state prediction and decision-making. He is adept at developing algorithms that optimize robotic response in high-latency environments, such as relay communication-based space missions. In addition to algorithm design, he is experienced in real-time robotic system integration and experimental validation. With skills spanning simulation, hardware interfacing, and intelligent control, Dr. Chen brings a comprehensive and technically rigorous approach to research that bridges theoretical development and engineering implementation.

Conclusion💡

Dr. Haifei Chen presents a compelling case as a young and dynamic researcher who combines technical depth, publication excellence, research leadership, and innovation in intelligent robotics and remote control systems. He shows a rare blend of academic rigor, engineering practice, and institutional responsibility.

With minor enhancements in international outreach and broader recognition, he is on a strong trajectory toward becoming a leading figure in his domain. He is not only suitable, but in fact, a competitive nominee for the Best Researcher Award.

Publications Top Noted✍

  • Title: Path Integral Policy Improvement and Dynamic Movement Primitives Fusion-Based Impedance Force Control With Error Loop Correction
    Authors: Mujie Liu, Haifei Chen, Lijun Li, Zhiqiang Ma, Yong Xu, Hui Zhang
    Year: 2025
    Citation: IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2025.3562677

  • Title: Mapping Noise Optimization of the Cartographer on the Premise of Comparative Experimental Analysis
    Authors: Xuefei Liu, Haifei Chen, Zicheng Gao, Meirong Chen, Lijun Li, Kai Liao
    Year: 2025
    Citation: Mechatronics, DOI: 10.1016/j.mechatronics.2024.103289

  • Title: Position Prediction for Space Teleoperation With SAO-CNN-BiGRU-Attention Algorithm
    Authors: Keli Wu, Haifei Chen, Lijun Li, Zhengxiong Liu, Haitao Chang
    Year: 2024
    Citation: IEEE Robotics and Automation Letters, DOI: 10.1109/LRA.2024.3498700

  • Title: Bilateral Synchronization Control of Networked Teleoperation Robot System
    Authors: Zhengxiong Liu, Haifei Chen, Panfeng Huang, Yang Yang
    Year: 2024
    Citation: International Journal of Robust and Nonlinear Control, DOI: 10.1002/rnc.7374

  • Title: Time-Delay Modeling and Simulation for Relay Communication-Based Space Telerobot System
    Authors: Haifei Chen, Zhengxiong Liu, Panfeng Huang, Zhian Kuang
    Year: 2022
    Citation: IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2021.3090806

  • Title: Time-Delay Prediction–Based Smith Predictive Control for Space Teleoperation
    Authors: Haifei Chen, Zhengxiong Liu
    Year: 2021
    Citation: Journal of Guidance, Control, and Dynamics, DOI: 10.2514/1.G005714

Jingbin Liu | Sensor fusion | Best Researcher Award

Published on by Computer Vision Awards

Prof . Jingbin Liu | Sensor fusion | Best Researcher Award

Professor at  Royal Institute of Technology, Sweden

Associate Professor Jingbin Liu is an accomplished geospatial scientist specializing in mobile mapping, 3D modeling, and robotics navigation. With a strong foundation in geomatics and over 15 years of international research experience, he has contributed significantly to both academic and applied aspects of geospatial technologies. Currently serving at KTH Royal Institute of Technology in Sweden, he previously held professorial and senior research roles in China and Finland. Liu has led over 15 major research projects and secured substantial external funding, indicating his strong leadership and innovation capacity. His scholarly output includes more than 100 peer-reviewed publications, over 5,300 citations, and an h-index of 37, reflecting his global impact. He is also known for successfully mentoring postdoctoral and doctoral researchers, many of whom have advanced to prominent academic and research positions. Liu’s work bridges the gap between research and real-world application, notably through patented technologies and industry collaborations.

Professional Profile 

Education🎓

Jingbin Liu holds a distinguished academic background rooted in geomatics and satellite navigation. He earned his Ph.D. in Geomatics from Wuhan University’s School of Geodesy and Geomatics in 2008, with a dissertation focusing on regional ionospheric TEC prediction using GPS—rated as excellent. He also completed his Master of Science in Engineering at Wuhan University in 2004, with a thesis on simulating and verifying the Galileo satellite navigation system, also rated excellent. In 2016, Liu was granted a prestigious Docentship in Geospatial Information Technology, specifically in robotics navigation and 3D modeling, by Wuhan University’s State Key Laboratory of Surveying, Mapping and Remote Sensing. This academic journey reflects a blend of theoretical expertise and applied innovation, providing a solid foundation for his research and teaching. Liu’s education has equipped him with a rare combination of depth in geospatial sciences and a multidisciplinary approach to cutting-edge technological challenges.

Professional Experience📝

Jingbin Liu brings a wealth of global experience in academia and research. Since January 2025, he has been serving as an Associate Professor at KTH Royal Institute of Technology, Sweden. Prior to this, he held a full professorship at Wuhan University from 2016 to 2024 and was also a Visiting Professor at the Finnish Geospatial Research Institute (FGI). He previously worked as a Senior Researcher at FGI and the Academy of Finland’s Center of Excellence in Laser Scanning Research from 2008 to 2019. His career reflects a seamless integration of academic leadership and advanced research contributions across China, Finland, and Sweden. Liu has actively managed multidisciplinary teams and international collaborations, with a proven track record in securing competitive research funding and leading innovative projects. His professional journey exemplifies a robust commitment to advancing geospatial technologies through both theoretical advancements and real-world implementation.

Research Interest🔎

Associate Professor Jingbin Liu’s research interests lie at the intersection of geospatial information technology and intelligent systems. He specializes in mobile mapping, 3D modeling of built environments, robotics navigation, and indoor positioning. His work focuses on developing advanced algorithms and systems that enable accurate and efficient geospatial data acquisition, particularly in challenging environments such as underground infrastructure and urban interiors. Liu has also contributed to integrating LiDAR, GPS, and vision-based technologies to enhance positioning accuracy and environmental modeling. His interest extends to autonomous driving systems, infrastructure inspection robots, and wearable geolocation systems for worker safety. By addressing real-world geospatial challenges with state-of-the-art tools, Liu’s research supports the evolution of smart cities and intelligent navigation solutions. He is particularly invested in transforming traditional surveying and mapping practices through automation, AI integration, and cross-disciplinary innovation, thereby enhancing the operational efficiency and accuracy of geospatial data systems.

Award and Honor🏆

Jingbin Liu has received multiple prestigious awards and honors that recognize his contributions to geospatial research and technological innovation. In 2023, he was honored with the Science and Technology Progress Award by the Hubei Provincial Government, and in 2021, he received the Science and Technology Advancement Award from the China Satellite Navigation and Positioning Association. His excellence in applied research earned him the Best Paper Award at the IEEE UPINLBS Conference in 2019. Internationally, he won first place in the PerfLoc Indoor Positioning Competition held by NIST in the United States and the IPIN Indoor Positioning Competition in France, both in 2018. He was also awarded the First Prize for Scientific and Technological Progress by the China Society of Geodesy. These accolades reflect his leadership in positioning and navigation technologies and his significant impact on both academic and industrial sectors worldwide.

Research Skill🔬

Jingbin Liu possesses advanced research skills in geospatial computation, mobile mapping, and sensor fusion. He is proficient in integrating LiDAR, GNSS, visual-inertial odometry, and SLAM (Simultaneous Localization and Mapping) systems to develop high-precision navigation solutions. His expertise extends to robotics navigation in complex environments, such as underground pipelines, where he has led the development of intelligent inspection systems. Liu demonstrates strong capabilities in algorithm design for positioning, real-time data processing, and 3D environmental reconstruction. He also has experience with indoor positioning systems using WiFi, Bluetooth, and 3D modeling integration. Beyond technical skills, Liu excels in project leadership, proposal writing, and technology transfer—evidenced by his successful patent and partnerships with industry stakeholders. His mentoring ability, particularly in guiding postdocs and PhD students to success, highlights his academic training expertise. Altogether, his skills position him as a top-tier researcher in applied geospatial technology and spatial computing.

Conclusion💡

Associate Professor Jingbin Liu is highly suitable for the Best Researcher Award. His excellent academic record, consistent funding success, internationally recognized research, real-world technological innovation, and strong mentorship track make him a standout candidate. He exemplifies a researcher who bridges theory and application, academia and industry, and local and international contexts.

Publications Top Noted✍

  • Title: Is field-measured tree height as reliable as believed?
    Authors: Y Wang, M Lehtomäki, X Liang, J PyĂśrälä, A Kukko, A Jaakkola, J Liu, …
    Year: 2019
    Citations: 330

  • Title: A review: Remote sensing sensors
    Authors: L Zhu, J Suomalainen, J Liu, J Hyyppä, H Kaartinen, H Haggren
    Year: 2018
    Citations: 254

  • Title: A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS
    Authors: J Liu, R Chen, L Pei, R Guinness, H Kuusniemi
    Year: 2012
    Citations: 237

  • Title: Human Behavior Cognition Using Smartphone Sensors
    Authors: L Pei, R Guinness, R Chen, J Liu, H Kuusniemi, Y Chen, L Chen, …
    Year: 2013
    Citations: 197

  • Title: International benchmarking of individual tree detection methods using airborne laser scanning
    Authors: Y Wang, J Hyyppä, X Liang, H Kaartinen, X Yu, E Lindberg, J Holmgren, …
    Year: 2016
    Citations: 193

  • Title: Using Inquiry-based Bluetooth RSSI Probability Distributions for Indoor Positioning
    Authors: L Pei, R Chen, J Liu, H Kuusniemi, T Tenhunen, Y Chen
    Year: 2010
    Citations: 181

  • Title: LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments
    Authors: J Tang, Y Chen, X Niu, L Wang, L Chen, J Liu, C Shi, J Hyyppä
    Year: 2015
    Citations: 163

  • Title: Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning
    Authors: L Pei, J Liu, R Guinness, Y Chen, H Kuusniemi, R Chen
    Year: 2012
    Citations: 163

  • Title: Accuracy of Kinematic Positioning Using GNSS under Forest Canopies
    Authors: H Kaartinen, J Hyyppä, M Vastaranta, A Kukko, A Jaakkola, X Yu, …
    Year: 2015
    Citations: 159

  • Title: A Review of GNSS-based Dynamic Monitoring for Structural Health Monitoring
    Authors: N Shen, L Chen, J Liu, L Wang, T Tao, D Wu, R Chen
    Year: 2019
    Citations: 140

  • Title: Spherical cap harmonic model for mapping and predicting regional TEC
    Authors: J Liu, R Chen, Z Wang, H Zhang
    Year: 2011
    Citations: 138

  • Title: The Use of a Hand-Held Camera for Individual Tree 3D Mapping
    Authors: X Liang, A Jaakkola, Y Wang, J Hyyppä, E Honkavaara, J Liu, …
    Year: 2014
    Citations: 137

  • Title: Forest Data Collection Using Terrestrial Image-Based Point Clouds
    Authors: X Liang, Y Wang, A Jaakkola, A Kukko, H Kaartinen, J Hyyppä, …
    Year: 2015
    Citations: 132

  • Title: Inquiry-Based Bluetooth Indoor Positioning via RSSI Probability Distributions
    Authors: L Pei, R Chen, J Liu, T Tenhunen, H Kuusniemi, Y Chen
    Year: 2010
    Citations: 112

  • Title: A Robust Indoor Positioning Method Based on Bluetooth Low Energy
    Authors: B Huang, J Liu, W Sun, F Yang
    Year: 2019
    Citations: 93

  • Title: iParking: An Intelligent Indoor Location-Based Smartphone Parking Service
    Authors: J Liu, R Chen, Y Chen, L Pei, L Chen
    Year: 2012
    Citations: 90

  • Title: A Survey of Simultaneous Localization and Mapping
    Authors: B Huang, J Zhao, J Liu
    Year: 2019
    Citations: 88

  • Title: Close-Range Remote Sensing of Forests: Challenges and Opportunities
    Authors: X Liang, A Kukko, I Balenović, N Saarinen, S Junttila, V Kankare, …
    Year: 2022
    Citations: 84

  • Title: M3VSNet: Unsupervised Multi-Metric Multi-View Stereo Network
    Authors: B Huang, H Yi, C Huang, Y He, J Liu, X Liu
    Year: 2021
    Citations: 82

  • Title: A Survey of Applications with Combined BIM and 3D Laser Scanning
    Authors: J Liu, D Xu, J Hyyppä, Y Liang
    Year: 2021
    Citations: 80

Daniel Oren | Biomedical | Best Researcher Award

Published on by Computer Vision Awards

Dr. Daniel Oren | Biomedical | Best Researcher Award

MD, MSc at New York Presbyterian,  United States

Dr. Daniel Oren, MD, MSc, is a highly accomplished physician-scientist specializing in internal medicine and advanced cardiac care. With extensive training and clinical experience across leading institutions in the U.S., Israel, and Europe, he has cultivated a career rooted in translational research and patient-centered innovation. Currently serving as an Associated Research Scientist at Columbia University Irving Medical Center, he is also an incoming Attending Hospitalist in the Department of Cardiology. Dr. Oren’s research focuses on heart failure, mechanical circulatory support, transplant medicine, and non-invasive diagnostics. He has co-authored numerous peer-reviewed publications and contributed to national registries and clinical trials. Additionally, he brings a unique blend of academic excellence, military leadership as a reserve Chief Medical Officer in the Israeli Defense Forces, and entrepreneurial endeavors in digital health solutions. His multidisciplinary expertise, dedication to healthcare innovation, and commitment to education make him a distinguished figure in cardiovascular medicine and clinical research.

Professional Profile 

Education🎓

Dr. Daniel Oren earned his medical degree (M.D.) summa cum laude from Semmelweis University Medical School in Budapest, Hungary, where he also completed an MSc in Medical Science, focusing his thesis on cardiac CT imaging in aortic valve replacement outcomes. He began his academic journey with pre-medical studies at McDaniel College and later pursued advanced coursework in theoretical and mathematical biology at Tel Aviv University. His foundational medical training was further solidified through an internship at Sheba Medical Center in Israel, where he rotated through multiple specialties, including emergency medicine, surgery, and intensive care. To support his educational role, he also served as an ACLS/BLS/PALS instructor through the Israeli Center for Medical Simulations. Dr. Oren holds ECFMG certification in the United States and is licensed in Israel and Norway. His academic background reflects a strong foundation in clinical medicine, biomedical science, and interdisciplinary research, equipping him to contribute meaningfully across clinical and scientific domains.

Professional Experience📝

Dr. Oren’s professional experience spans top-tier medical institutions and diverse clinical environments. He currently serves as an Associated Research Scientist at Columbia University’s Center for Advanced Cardiac Care and is set to begin his role as an Attending Hospitalist Physician in the Division of Cardiology at Columbia University Irving Medical Center. As a third-year Internal Medicine resident at Weill Cornell’s Brooklyn Methodist Hospital, he has held leadership roles including Clinic Chief, Rapid Response Team Captain, and Stroke Code Captain. Earlier in his career, he completed a postdoctoral research fellowship at Columbia and an intensive internship at Sheba Medical Center, where he managed multidisciplinary rotations including COVID-ICU care. Beyond hospital settings, Dr. Oren has contributed to digital health and medical education startups as a co-founder, combining technology with clinical innovation. He also serves as a certified instructor for the American Heart Association, training healthcare providers in life support protocols. His clinical work is underscored by a global and leadership-oriented outlook.

Research Interest🔎

Dr. Oren’s research interests lie at the intersection of advanced heart failure, cardiac transplantation, and mechanical circulatory support, with a particular focus on translational diagnostics and patient outcomes. His work explores novel biomarkers like donor-derived cell-free DNA and molecular diagnostics for transplant rejection and allograft vasculopathy. He is deeply involved in large-scale registry and device trials, including studies on LVAD function, IVC pressure sensors, and non-invasive hemodynamic monitoring. He is equally committed to improving outcomes in socioeconomically disadvantaged populations, examining disparities in heart failure treatment and readmission rates. His research is highly collaborative, integrating clinical data with biostatistical modeling to enhance early diagnosis and treatment stratification. Additionally, Dr. Oren’s military medical service has informed his interest in trauma care and medical simulation. As he advances in his career, he remains committed to bridging clinical care with data-driven innovations in cardiology, particularly through multimodal monitoring and precision medicine approaches.

Award and Honor🏆

Dr. Daniel Oren has been recognized for his academic excellence, clinical leadership, and contributions to medical science. He earned his M.D. summa cum laude from Semmelweis University and has held key leadership roles in his residency program, including Clinic Chief and Code Team Captain. His most notable research honor includes co-receiving the 2023 ISHLT Multimodality Research Grant ($40,000), supporting non-invasive post-transplant monitoring using paired biomarkers. He is a certified instructor for the American Heart Association and has completed advanced trauma training through the Israeli Defense Forces, where he serves as Chief Medical Officer (Reserve) for Special Forces units. His selection for the elite “Tzahal Shalom” IDF delegation to the U.S. further highlights his leadership and cross-cultural diplomacy skills. Additionally, he holds medical licenses in Israel and Norway and ECFMG certification for the U.S., reflecting global recognition of his capabilities. Dr. Oren’s honors affirm both his scholarly impact and leadership in clinical care.

Research Skill🔬

Dr. Oren possesses a versatile and interdisciplinary set of research skills encompassing clinical trial design, registry management, advanced data annotation, and statistical consulting. He has contributed significantly to multi-center trials and national registries involving mechanical circulatory support, heart transplantation, and novel diagnostics like cell-free DNA and molecular microscopy. His training in mathematical biology and biostatistics enables him to integrate complex clinical data into actionable insights. He is proficient in using digital platforms for clinical data capture and analysis, and has supported algorithm development through startups like UBI Medical and MelodyStats. His clinical-research interface is strengthened by his ability to translate real-world observations into structured investigational protocols, such as the EVIDENT and RAMP-IT-UP studies. Additionally, Dr. Oren’s teaching roles as an ACLS/BLS/PALS instructor enhance his ability to standardize protocol-based training across research teams. His commitment to rigorous methodology and innovative thinking positions him as a valuable asset in both academic and industry-driven cardiovascular research.

Conclusion💡

Dr. Daniel Oren stands out as a highly accomplished physician-scientist whose integrated expertise in internal medicine, cardiology research, and military medical leadership uniquely positions him as a top candidate for the Best Researcher Award. His combination of clinical excellence, translational research contributions, publication record, and grant success aligns exceptionally well with the award’s goals of recognizing impactful, innovative, and community-relevant research. With continued progression into senior research roles and expanded funding leadership, his trajectory remains upward and globally relevant.

Publications Top Noted✍

  • Title: Acute myocardial infarction in the Covid-19 era: Incidence, clinical characteristics and in-hospital outcomes—A multicenter registry
    Authors: A Fardman, D Zahger, K Orvin, D Oren, N Kofman, J Mohsen, O Tsafrir, …
    Year: 2021
    Citations: 70

  • Title: Post COVID-19 acute myocardial infarction rebound
    Authors: A Fardman, D Oren, A Berkovitch, A Segev, Y Levy, R Beigel, S Matetzky
    Year: 2020
    Citations: 28

  • Title: Decline in maxillofacial injuries during the pandemic: the hidden face of COVID-19
    Authors: A Kasem, I Redenski, D Oren, A Zoabi, S Srouji, F Kablan
    Year: 2022
    Citations: 24

  • Title: Rapid implementation of teledentistry during the Covid-19 lockdown
    Authors: MO Watfa, NM Bernfeld, D Oren, T Shani, A Zigron, E Sela, Y Granot, …
    Year: 2021
    Citations: 19

  • Title: Post–ST‐segment–elevation myocardial infarction platelet reactivity is associated with the extent of microvascular obstruction and infarct size as determined by cardiac MRI
    Authors: E Massalha, D Oren, O Goitein, Y Brodov, A Fardman, A Younis, …
    Year: 2022
    Citations: 17

  • Title: Comparisons between lysis and lavage, intra-articular steroid injections, and three-point subsynovial steroid injections using operative single-cannula arthroscopy
    Authors: D Oren, AA Dror, TH Khalil, A Zoabi, A Zigron, F Kablan, S Srouji
    Year: 2022
    Citations: 14

  • Title: Successful CAR T cell therapy in a heart and kidney transplant recipient with refractory PTLD
    Authors: D Oren, EM DeFilippis, D Lotan, KJ Clerkin, J Fried, R Reshef, …
    Year: 2022
    Citations: 12

  • Title: Subclinical leaflet thrombosis is associated with impaired reverse remodelling after transcatheter aortic valve implantation
    Authors: B Szilveszter, D Oren, L MolnĂĄr, A Apor, AI Nagy, A MolnĂĄr, B Vattay, …
    Year: 2020
    Citations: 12

  • Title: The role of temporary mechanical circulatory support as a bridge to advanced heart failure therapies or recovery
    Authors: D Oren, R Zilinyi, D Lotan, M Uriel, N Uriel, G Sayer
    Year: 2022
    Citations: 11

  • Title: Sex differences in patients undergoing heart transplantation and LVAD therapy
    Authors: G Rubinstein, D Lotan, CM Moeller, EM DeFilippis, S Slomovich, D Oren, …
    Year: 2022
    Citations: 9

  • Title: The power of three-dimensional printing technology in functional restoration of rare maxillomandibular deformity due to genetic disorder: A case report
    Authors: D Oren, AA Dror, T Bramnik, E Sela, I Granot, S Srouji
    Year: 2021
    Citations: 8

  • Title: Heart transplantation in two adolescents with Danon disease
    Authors: D Oren, P Chau, M Manning, J Kwong, BD Kaufman, K Maeda, …
    Year: 2019
    Citations: 8

  • Title: Durable left ventricular assist devices as a bridge to transplantation in The Old and The New World
    Authors: AF Valledor, G Rubinstein, CM Moeller, D Lorenzatti, S Rahman, C Lee, …
    Year: 2024
    Citations: 6

  • Title: Evolution of Mechanical Circulatory Support for advanced heart failure
    Authors: CM Moeller, AF Valledor, D Oren, G Rubinstein, GT Sayer, N Uriel
    Year: 2024
    Citations: 6

  • Title: The hemodynamic effects of aortic regurgitation in patients supported by a HeartMate 3 left ventricular assist device
    Authors: G Rubinstein, CM Moeller, D Lotan, S Slomovich, A Fernandez-Valledor, …
    Year: 2024
    Citations: 6

  • Title: Initial experience with augmented reality for treatment of an orbital floor fracture–a technical note
    Authors: A Zoabi, D Oren, S Tejman-Yarden, I Redenski, F Kablan, S Srouji
    Year: 2022
    Citations: 6

  • Title: Hemodynamic optimization by invasive ramp test in patients supported with HeartMate 3 left ventricular assist device
    Authors: G Rubinstein, CM Moeller, D Lotan, S Slomovich, A Fernandez-Valledor, …
    Year: 2024
    Citations: 5

  • Title: Combined heart and liver transplantation in a patient supported by left ventricular assist device (LVAD) with propionic acidemia
    Authors: D Lotan, EM DeFilippis, D Oren, A Vinogradsky, G Rubinstein, A Mathur, …
    Year: 2023
    Citations: 5

  • Title: Incidence and treatment of arterial hypertension after heart transplantation
    Authors: HS Lumish, PJ Kennel, D Concha, A Chung, D Oren, SS Jain, …
    Year: 2022
    Citations: 5

  • Title: Pericardial involvement in ST-segment elevation myocardial infarction as detected by cardiac MRI
    Authors: E Massalha, Y Brodov, D Oren, A Fardman, SS Natanzon, I Mazin, …
    Year: 2022
    Citations: 5

Xiwang Xie | Artificial intelligence | Best Researcher Award

Published on by Computer Vision Awards

Dr. Xiwang Xie | Artificial intelligence | Best Researcher Award

Lecturer at Henan University of Engineering, China

The candidate is a dedicated researcher with deep expertise in artificial intelligence, computer vision, and medical image processing. With years of experience working on high-impact projects supported by the National Natural Science Foundation of China and other authoritative bodies, the researcher has contributed significantly to areas such as pathological image segmentation, oil spill monitoring, remote sensing, and illegal ship detection. Their academic portfolio includes numerous publications in internationally renowned journals, several of which are recognized as ESI Highly Cited or Hot Papers. Beyond research, the candidate plays an active role in the scholarly community as a reviewer for top-tier journals and as a committee member for international conferences. Demonstrating strong interdisciplinary skills and a commitment to advancing technological solutions for real-world challenges, the candidate’s work bridges theoretical innovation with practical application. Their achievements, leadership, and research rigor make them an exceptional contributor to their field and a strong candidate for prestigious research recognitions.

Professional Profile 

Education🎓

The candidate has pursued a comprehensive academic path specializing in artificial intelligence and image processing. While the exact degree timeline is not detailed, their research trajectory suggests advanced graduate-level education, likely including a Ph.D. with a focus on medical image analysis and computer vision. Their academic training has equipped them with the necessary theoretical foundations in machine learning, deep learning architectures, and digital signal processing. Throughout their education, the researcher appears to have engaged in interdisciplinary study, merging engineering principles with medical diagnostics, environmental monitoring, and intelligent systems design. This solid academic background underpins their ability to contribute to diverse project domains—ranging from healthcare to maritime safety—and produce influential scholarly publications. The candidate’s education has clearly laid the groundwork for their professional accomplishments, fostering both technical depth and critical thinking, essential for high-quality scientific inquiry and collaborative problem-solving in both academic and applied research environments.

Professional Experience📝

The researcher has amassed significant professional experience through participation in various high-impact national and regional projects. From 2017 to 2025, they have played critical roles in several initiatives funded by the National Natural Science Foundation of China and other public agencies. Their work includes developing methods for breast cancer pathological image segmentation, oil spill monitoring using airborne remote sensing, and illegal ship detection using radar systems. These experiences demonstrate a strong ability to translate research concepts into functional systems addressing societal needs. Additionally, the candidate has contributed to the development of intelligent solutions in agriculture, remote sensing, and underwater imaging. These projects reflect their capacity to work across interdisciplinary teams and apply cutting-edge AI methods in real-world settings. Their professional journey is further enhanced by scholarly contributions and peer-review responsibilities, indicating a balanced commitment to both applied and academic research endeavors. Overall, the candidate’s professional experience illustrates consistent, practical impact and research leadership.

Research Interest🔎

The researcher’s core interests lie in the convergence of artificial intelligence, computer vision, and medical image processing. A major focus has been on developing advanced deep learning architectures for image segmentation, particularly in medical imaging, such as liver CT, retinal vessels, and breast pathology. Additionally, they are interested in environmental and remote sensing applications, including underwater image enhancement, oil spill detection, and illegal maritime activity monitoring. Their research also explores agricultural and biological systems, such as plant disease detection and hyperspectral image analysis for crop classification. Through the design of novel networks like DFPNet, CANet, and MCINet, the researcher aims to improve the accuracy, robustness, and interpretability of image analysis systems. The unifying goal across these areas is to create intelligent, scalable, and efficient solutions for complex visual data problems, particularly in health, environment, and agriculture. Their interdisciplinary interests reflect a commitment to impactful AI research with broad societal implications.

Award and Honor🏆

The researcher has received notable recognition through several distinctions, particularly in the form of ESI Highly Cited Papers (Top 1%) and Hot Papers (Top 0.01%), indicating that their scholarly work has gained significant traction and global academic influence. These honors reflect the high quality and impact of their research on the scientific community, particularly in medical image segmentation and AI-based imaging solutions. The publication record also suggests excellence in collaborative research, with co-authorships on widely cited journal articles in prestigious outlets such as Computers and Electrical Engineering, Expert Systems with Applications, and Biomedical Signal Processing and Control. Beyond publication achievements, their role as a reviewer for several respected international journals and committee member for major conferences demonstrates peer recognition and trust. While specific award titles are not listed, these academic indicators collectively affirm the researcher’s excellence and growing reputation in the fields of artificial intelligence and biomedical engineering.

Research Skill🔬

The researcher possesses a robust set of skills centered around deep learning, medical image segmentation, computer vision, and remote sensing. Technically proficient in designing advanced neural architectures like pyramidal networks, multi-scale context integration models, and attention-based frameworks, they have demonstrated expertise in implementing and optimizing complex AI algorithms. Their skillset includes segmentation of CT scans, pathological tissues, underwater images, and plant diseases—showing adaptability across various domains and imaging modalities. They are also experienced in interdisciplinary research that incorporates laser technology, hyperspectral imaging, and radar signal processing. Proficiency in key programming and analysis tools such as Python, MATLAB, TensorFlow, and PyTorch is implied by the nature of their contributions. Additionally, their role as a peer reviewer signifies a solid understanding of research design, evaluation metrics, and experimental validation. Overall, the candidate showcases a comprehensive blend of analytical, technical, and domain-specific skills that strongly support their innovative and applied research outputs.

Conclusion💡

The candidate exhibits exceptional qualifications, with a strong track record in AI-driven medical imaging and multidisciplinary research. The number of high-impact publications, project leadership, and peer-reviewed contributions makes the candidate highly suitable for the Best Researcher Award.

Publications Top Noted✍

  1. Title: Discriminative Features Pyramid Network for Medical Image Segmentation
    Authors: Xiwang Xie, Lijie Xie, Guanyu Li, Hao Guo, Weidong Zhang, Feng Shao, Wenyi Zhao, Ling Tong, Xipeng Pan, Jubai An
    Year: 2024
    Citation: DOI: 10.1016/j.bbe.2024.04.001
    Journal: Biocybernetics and Biomedical Engineering

  2. Title: Color Correction and Adaptive Contrast Enhancement for Underwater Image Enhancement
    Authors: Zhang W., Pan X., Xie X., Li L., Wang Z., Han C.
    Year: 2021
    Citation: DOI: 10.1016/j.compeleceng.2021.106981
    Journal: Computers and Electrical Engineering

  3. Title: Dynamic Adaptive Residual Network for Liver CT Image Segmentation
    Authors: Xie X., Zhang W., Wang H., Li L., Feng Z., Wang Z., Pan X.
    Year: 2021
    Citation: DOI: 10.1016/j.compeleceng.2021.107024
    Journal: Computers and Electrical Engineering

  4. Title: Automatic Liver Segmentation Method Based on Improved Region Growing Algorithm
    Authors: Qiao S., Xia Y., Zhi J., Xie X., Ye Q.
    Year: 2020
    Citation: Proceedings of ITNEC 2020, DOI: 10.1109/ITNEC48623.2020.9085126

  5. Title: An Approach of Automatically Selecting Seed Point Based on Region Growing for Liver Segmentation
    Authors: Xia Y., Xie X., Wu X., Zhi J., Qiao S.
    Year: 2019
    Citation: Proceedings of ISNE 2019, DOI: 10.1109/ISNE.2019.8896442

Hojjatollah Shokri kaveh | Algorithms | Best Researcher Award

Published on by Computer Vision Awards

Mr. Hojjatollah Shokri kaveh | Algorithms | Best Researcher Award

Ph.D at Shahid Beheshti University, Iran

Hojjatollah Shokri Kaveh is an accomplished PhD candidate in Applied Mathematics at Shahid Beheshti University, Tehran, recognized for his advanced research in numerical linear algebra, inverse problems, and iterative algorithms. With a passion for mathematical problem-solving and algorithm development, he has published over ten scientific papers in prestigious international journals, often in collaboration with globally respected researchers. His academic excellence is underscored by ranking 2nd in Iran’s national doctoral entrance exam and maintaining a high GPA of 19.2/20 during his PhD studies. In addition to his research endeavors, he has contributed significantly to education through years of teaching experience across institutions and online platforms. His strong programming skills in MATLAB, Python, and C complement his theoretical work, enabling robust computational solutions. Mr. Shokri Kaveh’s dedication, technical proficiency, and scholarly output position him as a highly promising researcher with the potential to make lasting contributions to applied mathematics and computational science.

Professional Profile 

Education🎓

Hojjatollah Shokri Kaveh is currently pursuing a PhD in Applied Mathematics at Shahid Beheshti University, Tehran, with an expected graduation in 2024. His outstanding academic performance is reflected in his remarkable GPA of 19.2 out of 20 and his achievement of securing 2nd place in Iran’s competitive national doctoral entrance exam in 2018. He holds a Master’s degree in Applied Mathematics from the Amirkabir University of Technology (Tehran Polytechnic), where he graduated in 2017 with a GPA of 16.5. His foundational academic journey began with a Bachelor’s degree in Applied Mathematics from Ilam University, which he completed in 2012. Throughout his academic career, he has focused on mathematical modeling, numerical solutions of partial differential equations, and the development of computational algorithms. His education has provided him with both deep theoretical knowledge and hands-on technical experience, preparing him well for high-level research and academic contributions in the field of applied mathematics.

Professional Experience📝

Hojjatollah Shokri Kaveh brings a diverse range of professional experiences, primarily centered on education and analytical work. He has worked as a mathematics teacher across several reputable platforms in Tehran, including Ostadbank and Aloostad, where he taught for two and three years respectively. Most recently, he served as a mathematics teacher at Sina School (2024–2025), reflecting his continued engagement with academic instruction. Earlier in his career, he also worked as an accountant at Azarnan Nazari for one year (2020–2021), showcasing his versatility and numerical accuracy in practical, real-world applications. His extensive teaching background underscores his strong communication skills, passion for mentoring, and ability to convey complex mathematical concepts clearly. These roles have not only strengthened his instructional capabilities but have also enriched his understanding of applied mathematics in various educational and organizational settings, making him well-rounded both as a researcher and an educator.

Research Interest🔎

Hojjatollah Shokri Kaveh’s research interests lie in the fields of numerical linear algebra, iterative methods for large-scale systems, regularization techniques, and inverse problems arising in partial differential equations. His work focuses on developing and optimizing computational algorithms—such as conjugate gradient, CGNE, and CGNR methods—for solving complex non-symmetric and ill-posed problems. He has a particular interest in Sylvester matrix equations and the mathematical modeling of physical phenomena such as heat conduction, wave propagation, and electrostatics. His publications demonstrate his expertise in variable s-step methods, mapped regularization techniques, and efficient solutions for Cauchy and Helmholtz equations. In addition to theory, he emphasizes algorithmic implementation, leveraging programming tools like MATLAB and Python to validate his methods. His collaborative research with international scholars further highlights the interdisciplinary and applicable nature of his work, bridging mathematical theory with engineering and control systems. His contributions aim to improve computational efficiency and accuracy in real-world scientific modeling.

Award and Honor🏆

One of the most notable honors achieved by Hojjatollah Shokri Kaveh is securing 2nd place in Iran’s highly competitive Doctoral Entrance Exam in Applied Mathematics in 2018, which granted him entry into the prestigious PhD program at Shahid Beheshti University. This national recognition is a testament to his academic rigor, problem-solving ability, and deep understanding of mathematical principles. His high GPA of 19.2/20 during his PhD further illustrates his consistent pursuit of academic excellence. In addition to formal accolades, his selection as a co-author with internationally esteemed scholars such as Dr. Anthony T. Chronopoulos serves as an informal but powerful acknowledgment of his research competence. His multiple publications in indexed international journals highlight his dedication to advancing mathematical science. These honors not only validate his past performance but also position him as a strong candidate for future recognitions, including awards for research, innovation, and contributions to scientific and academic communities.

Research Skill🔬

Hojjatollah Shokri Kaveh possesses a comprehensive set of research skills that bridge theoretical mathematics and practical computation. He is proficient in programming languages such as MATLAB, Python, and C, which he uses to develop and test numerical algorithms. His core competencies include data analysis, data visualization, numerical linear algebra, and iterative methods for solving large-scale linear systems, particularly under ill-posed conditions. He demonstrates expertise in partial differential equations, regularization methods, and inverse problems, with applications in image reconstruction, control systems, and physical simulations. His ability to combine mathematical theory with efficient algorithm design is evident in his high-quality publications in peer-reviewed journals. Mr. Shokri Kaveh is also skilled in scientific writing, LaTeX formatting, and peer communication—crucial for collaborative research. These capabilities allow him to contribute meaningfully to interdisciplinary projects, from initial modeling to final implementation, making him a valuable asset in both academic and applied research environments.

Conclusion💡

Mr. Hojjatollah Shokri Kaveh stands out as a highly capable and promising researcher in applied mathematics with a solid publication record, international collaboration, and exceptional academic performance. His contributions to numerical methods for solving complex mathematical systems are technically sophisticated and valuable to the scientific community.

With further emphasis on international visibility, real-world application, and quantitative impact metrics, he would be an even stronger contender. Nonetheless, based on his current profile, he is a suitable and deserving candidate for the Best Researcher Award.

Publications Top Noted✍

  • Title: Mapped Regularization Methods for the Cauchy Problem of the Helmholtz and Laplace Equations
    Authors: H. Shokri Kaveh, H. Adibi
    Year: 2021
    Citations: 7

  • Title: Finding Solution of Linear Systems via New Forms of BiCG, BiCGstab and CGS Algorithms
    Authors: H. Shokri Kaveh, M. Hajarian, A.T. Chronopoulos
    Year: 2024
    Citations: 3

  • Title: Developing Variable s-step CGNE and CGNR Algorithms for Non-symmetric Linear Systems
    Authors: H.S. Kaveh, M. Hajarian, A.T. Chronopoulos
    Year: 2024
    Citations: 2

  • Title: Efficient Image Reconstruction via Regularized Variable s-step Conjugate Gradient Method for Sylvester Matrix Equations
    Authors: H.S. Kaveh, M. Hajarian, A.T. Chronopoulos
    Year: 2025
    Citations: 1

  • Title: Variable s-step Technique for Planar Algorithms in Solving Indefinite Linear Systems
    Authors: H. Shokri Kaveh, M. Hajarian, A.T. Chronopoulos
    Year: 2025

  • Title: Exponential Cut-off Regularization Filter for the Cauchy Problem of the Helmholtz Equation
    Authors: Hojjatollah Shokri Kaveh
    Year: 2020

  • Title: A New Regularization Method for Backward Heat Conduction Problem
    Authors: Hojjatollah Shokri Kaveh
    Year: 2020

  • Title: Finite Difference Method for Solving Two-Dimensional Wave Equation
    Authors: Hojjatollah Shokri Kaveh, Hojjatollah Adibi
    Year: 2020

  • Title: Numerical Solution for Dirichlet and Cauchy Problems of Laplace Equation
    Authors: Hojjatollah Shokri Kaveh, Hojjatollah Adibi
    Year: 2020

  • Title: Mixed Regularization Methods for the Cauchy Problems of the Helmholtz Equation
    Authors: Hojjatollah Shokri Kaveh
    Year: 2019

Mohammadreza Aghamohammadi | Deep Metric Learning | Best Researcher Award

Published on by Computer Vision Awards

Prof. Dr. Mohammadreza Aghamohammadi | Deep Metric Learning | Best Researcher Award

Head of Electrical Engineering epartment at  Shahid Beheshti University, Iran

Prof. MohammadReza Aghamohammadi is a distinguished academic and researcher in Electrical Engineering at Shahid Beheshti University, Iran, with over three decades of impactful contributions to power systems engineering. Currently serving as the Dean of the Faculty of Electrical Engineering, he is also the Head of the Iran Dynamic Research Center (IDRC). His career bridges academia and industry, focusing on real-world challenges such as power system stability, blackout prevention, and intelligent grid control. A leader in energy reliability, he has held numerous national roles in planning and reliability councils under Iran’s Ministry of Energy. He has guided over 80 theses at undergraduate, master’s, and Ph.D. levels, and organized key international conferences in power systems. With memberships in IEEE, Cigre, and IAEEE, Prof. Aghamohammadi is widely recognized for his technical leadership, mentorship, and sustained research excellence. His interdisciplinary expertise continues to shape the next generation of power system innovation in Iran and beyond.

Professional Profile 

Education🎓

Prof. Aghamohammadi’s academic journey spans prestigious institutions across Iran, the UK, and Japan. He earned his Bachelor of Science in Electrical Engineering from Sharif University of Technology in Tehran in 1980, laying a strong foundation in the field. He then pursued his Master’s degree in Electrical Engineering at the University of Manchester Institute of Science and Technology (UMIST), UK, graduating in 1986 with advanced expertise in electrical systems. His academic pursuit culminated in a Ph.D. in Electrical Engineering from Tohoku University in Sendai, Japan, in 1994. His doctoral research focused on the secure operation of electric power systems using neural network applications—an innovative and forward-looking topic at the time. His global academic background has equipped him with a broad and multidisciplinary understanding of electrical engineering, making him a well-rounded expert in both theoretical and applied power system studies, with an international perspective critical to energy research and innovation.

Professional Experience📝

Prof. Aghamohammadi’s professional career is marked by a unique blend of academic leadership and national-level energy sector involvement. From 1994 to 2012, he served at the Power & Water University of Technology (PWUT) as an Assistant Professor and held leadership roles including Vice Chancellor for Research and Head of the Power Industry Innovation Center. He served as a consultant to several key departments within Iran’s Ministry of Energy, including Tavanir and Moshanir Engineering Consultants. Since 2005, he has led the Iran Dynamic Research Center and has been a pivotal figure in shaping Iran’s power system strategies. At Shahid Beheshti University, he has held several roles: Head of Power Group, Head of Operation & Planning Group, and currently Dean of the Faculty of Electrical Engineering. His leadership also includes positions within the Iran Electric Reliability Council (IERC), where he contributed to strategic national planning for electrical system stability and development.

Research Interest🔎

Prof. Aghamohammadi’s research interests are deeply rooted in the stability, reliability, and intelligent control of electrical power systems. His work spans key areas such as power system security assessment, inter-area oscillations, cascading failure analysis, and blackout simulation—critical challenges in modern energy infrastructure. He has contributed extensively to voltage stability enhancement, reactive power control, and the implementation of controlled islanding strategies to prevent large-scale outages. A notable aspect of his research is the integration of artificial intelligence and neural network applications into power system operations, aligning traditional power engineering with cutting-edge computational techniques. His long-term research has been instrumental in projects such as the dynamic study of the Iranian power grid, voltage control using STATCOM, and load balancing in distribution networks. Prof. Aghamohammadi’s work not only addresses theoretical models but is directly applied in large-scale national projects, bridging academia with industry for sustainable and resilient power systems.

Award and Honor🏆

While specific individual awards are not detailed, Prof. Aghamohammadi’s honors are reflected through his extensive leadership roles, national responsibilities, and recognitions by key organizations. His appointment as Dean of the Faculty of Electrical Engineering and Director of the Iran Dynamic Research Center signify institutional trust in his expertise. He has served as Chair and Technical Head for prestigious conferences such as the International Power System Conference (1997) and the 17th International Conference on Protection and Automation (2023), recognizing his authority in the field. He is an active member of leading professional societies, including IEEE, Cigre, and IAEEE, indicating peer acknowledgment at both national and international levels. Furthermore, his role on strategic councils like the Iran Electric Reliability Council (IERC) underscores his contributions to national energy policy and planning. These cumulative recognitions and responsibilities are a testament to his status as one of Iran’s foremost experts in power system engineering and energy reliability.

Research Skill🔬

Prof. Aghamohammadi possesses a rich portfolio of advanced research skills centered on electrical power systems and intelligent energy management. He is proficient in dynamic system modeling, power system security evaluation, voltage and frequency stability analysis, and the development of preventive strategies against cascading failures and blackouts. His expertise includes simulation-based analysis, control algorithm design, and the application of artificial intelligence—especially neural networks—for enhanced grid operations. His contributions to long-term dynamic studies and reactive power control demonstrate his ability to manage complex systems with practical and scalable solutions. His research also involves technical knowledge in STATCOM applications, controlled islanding, and real-time decision-making in energy distribution networks. Moreover, he has led numerous multidisciplinary research projects and mentored graduate-level theses, indicating strong supervision and technical communication skills. These capabilities have enabled him to transform academic research into actionable insights for national grid planning and stability, making him a valuable asset to both academia and industry.

Conclusion💡

Prof. MohammadReza Aghamohammadi’s long-standing contributions to power system engineering, deep industry-academic integration, and academic leadership strongly support his nomination for the Best Researcher Award. His influence spans research excellence, institutional leadership, and real-world application, making him a standout candidate. While some enhancements in international visibility could be beneficial, his credentials already position him as a top-tier nominee for the award.

Publications Top Noted✍

  • Title: A new approach for optimal sizing of battery energy storage system for primary frequency control of islanded microgrid
    Authors: MR Aghamohammadi, H Abdolahinia
    Year: 2014
    Citations: 370

  • Title: Intentional islanding using a new algorithm based on ant search mechanism
    Authors: MR Aghamohammadi, A Shahmohammadi
    Year: 2012
    Citations: 120

  • Title: Optimal distribution feeder reconfiguration and generation scheduling for microgrid day-ahead operation in the presence of electric vehicles considering uncertainties
    Authors: M Sedighizadeh, G Shaghaghi-shahr, M Esmaili, MR Aghamohammadi
    Year: 2019
    Citations: 82

  • Title: DT based intelligent predictor for out of step condition of generator by using PMU data
    Authors: MR Aghamohammadi, M Abedi
    Year: 2018
    Citations: 73

  • Title: Wavelet based feature extraction of voltage profile for online voltage stability assessment using RBF neural network
    Authors: S Hashemi, MR Aghamohammadi
    Year: 2013
    Citations: 70

  • Title: Application of Newton-based load flow methods for determining steady-state condition of well and ill-conditioned power systems: A review
    Authors: M Karimi, A Shahriari, MR Aghamohammadi, H Marzooghi, V Terzija
    Year: 2019
    Citations: 69

  • Title: A new approach for online coherency identification in power systems based on correlation characteristics of generators rotor oscillations
    Authors: MR Aghamohammadi, SM Tabandeh
    Year: 2016
    Citations: 63

  • Title: A novel virtual inertia control strategy for frequency regulation of islanded microgrid using two-layer multiple model predictive control
    Authors: S Oshnoei, MR Aghamohammadi, S Oshnoei, S Sahoo, A Fathollahi, …
    Year: 2023
    Citations: 55

  • Title: A three stages decision tree-based intelligent blackout predictor for power systems using brittleness indices
    Authors: MR Salimian, MR Aghamohammadi
    Year: 2017
    Citations: 53

  • Title: Decision tree analysis to identify harmful contingencies and estimate blackout indices for predicting system vulnerability
    Authors: E Aliyan, M Aghamohammadi, M Kia, A Heidari, M Shafie-khah, …
    Year: 2020
    Citations: 52

  • Title: Intelligent out of step predictor for inter area oscillations using speed-acceleration criterion as a time matching for controlled islanding
    Authors: MR Salimian, MR Aghamohammadi
    Year: 2016
    Citations: 48

  • Title: Enhancement of the LVRT capability for DFIG-based wind farms based on short-circuit capacity
    Authors: Z Rafiee, R Heydari, M Rafiee, MR Aghamohammadi, F Blaabjerg
    Year: 2022
    Citations: 44

  • Title: A new scheme of WADC for damping inter-area oscillation based on CART technique and Thevenine impedance
    Authors: S Ranjbar, MR Aghamohammadi, F Haghjoo
    Year: 2018
    Citations: 44

  • Title: Damping inter-area oscillation by generation rescheduling based on wide-area measurement information
    Authors: L Yazdani, MR Aghamohammadi
    Year: 2015
    Citations: 43

  • Title: Provision of frequency stability of an islanded microgrid using a novel virtual inertia control and a fractional order cascade controller
    Authors: S Oshnoei, M Aghamohammadi, S Oshnoei, A Oshnoei, …
    Year: 2021
    Citations: 40

  • Title: A new approach for mitigating blackout risk by blocking minimum critical distance relays
    Authors: MR Aghamohammadi, S Hashemi, A Hasanzadeh
    Year: 2016
    Citations: 35

  • Title: A deep learning-based attack detection mechanism against potential cascading failure induced by load redistribution attacks
    Authors: A Khaleghi, MS Ghazizadeh, MR Aghamohammadi
    Year: 2023
    Citations: 32

  • Title: A review on data-driven security assessment of power systems: Trends and applications of artificial intelligence
    Authors: A Mehrzad, M Darmiani, Y Mousavi, M Shafie-Khah, M Aghamohammadi
    Year: 2023
    Citations: 30

  • Title: Identification of coherent groups of generators based on synchronization coefficient
    Authors: H Davarikia, F Znidi, MR Aghamohammadi, K Iqbal
    Year: 2016
    Citations: 27

  • Title: Sensitivity characteristic of neural network as a tool for analyzing and improving voltage stability
    Authors: M Aghamohammadi, M Mohammadian, H Saitoh
    Year: [Year Not Specified]
    Citations: Not Provided (from IEEE/PES T&D Conf. proceedings)