Rana Raza Mehdi | Machine Learning in Healthcare | Best Researcher Award

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Mr . Rana Raza Mehdi | Machine Learning in Healthcare | Best Researcher Award

PhD candidate, Graduate Research Assistant at Texas A&M University, United States

Rana Raza Mehdi is a dynamic fourth-year Ph.D. candidate in Biomedical Engineering at Texas A&M University, specializing in computational cardiovascular bioengineering. His interdisciplinary research fuses deep learning, medical imaging, and computational biomechanics to design non-invasive diagnostic tools for cardiovascular disease. With a strong foundation in mechanical engineering and advanced training in biomedical systems, Rana’s work is highly translational, targeting clinical applications in early disease diagnosis and cardiac tissue remodeling. He has published extensively in peer-reviewed journals and presented his findings at international conferences, earning recognition for scientific innovation and technical rigor. His contributions span human-guided machine learning, in-silico heart modeling, and biomechanical characterizations of myocardial infarction. He has also collaborated with experts across engineering, cardiology, and computational science domains. Recognized by prestigious awards and fellowships, his trajectory reflects both academic excellence and research leadership. Rana is poised to make significant contributions to the future of cardiovascular health and medical AI.

Professional Profile 

Education🎓

Rana Raza Mehdi holds a diverse and globally enriched academic background, beginning with a Bachelor of Science in Mechanical Engineering from the University of Engineering and Technology, Lahore, Pakistan, where he focused on prosthesis design and biomechanics. He then pursued a Master of Science in Mechanical Engineering at Sejong University in Seoul, South Korea, where he conducted thesis research on acoustoelasticity-based measurements and the influence of temperature on third-order elastic constants. Currently, he is a Ph.D. candidate in Biomedical Engineering at Texas A&M University, College Station, USA. His doctoral research explores the integration of deep learning and medical imaging for predicting cardiac biomechanical remodeling. His interdisciplinary thesis bridges engineering and medical science to address diagnostic challenges in cardiovascular diseases. Through each academic stage, Rana has cultivated a blend of mechanical, computational, and biomedical skills that serve as the foundation for his cutting-edge work in computational cardiology and machine learning-driven healthcare solutions.

Professional Experience📝

Rana Raza Mehdi has acquired substantial research and teaching experience across three countries. At Texas A&M University, he has been serving as a Graduate Research Assistant since January 2022 in the Computational Cardiovascular Bioengineering Laboratory under Dr. Reza Avazmohammadi, working on machine learning-enabled diagnostics in cardiac imaging. He has also contributed as a Graduate Teaching Assistant in biomaterials and soft tissue mechanics courses, fostering a solid understanding of both experimental and theoretical aspects of biomedical engineering. Prior to this, he held research positions at Sejong University, South Korea, where he focused on the acoustoelastic behavior of materials and served as a Master’s Researcher under Dr. Gang Won Jang. His global research experience spans experimental mechanics, finite element analysis, cardiac biomechanics, and deep learning, offering a broad and adaptable skill set. His collaborative projects and mentorship roles in interdisciplinary teams further highlight his growing leadership in biomedical research.

Research Interest🔎

Rana Raza Mehdi’s research interests lie at the intersection of medical imaging, computational biomechanics, and machine learning, with a central focus on cardiovascular health. He aims to develop non-invasive, data-driven diagnostic tools that predict cardiac biomechanical remodeling and identify myocardial dysfunction. His work involves the integration of in-vivo imaging, ex-vivo tissue data, and in-silico models to study pathologies such as myocardial infarction and pulmonary hypertension. He is particularly interested in applying deep learning algorithms to estimate cardiac tissue stiffness, scar localization, and hemodynamic changes, facilitating early diagnosis and personalized treatment planning. Rana also explores human-guided feature selection and hybrid models that combine physiological knowledge with AI frameworks. His broader interest extends to cardiac strain imaging, sarcomere dynamics, and the use of high-fidelity simulations to improve cardiac care. Ultimately, his research aims to bridge the gap between engineering and clinical medicine, enhancing cardiovascular diagnostics and treatment efficacy.

Award and Honor🏆

Rana Raza Mehdi has earned several prestigious awards and honors that underscore his academic excellence and research impact. He was awarded the highly competitive American Heart Association (AHA) Predoctoral Fellowship (2025–2026), supporting his work in cardiovascular biomechanics. He also received the Heep Graduate Fellowship from the Hagler Institute for Advanced Study (2024–2025), recognizing his interdisciplinary innovation and collaborative potential. His research excellence has been acknowledged through multiple abstract and presentation awards, including the Best Abstract Award at the 8th Annual Cardiovascular Bioengineering Symposium and finalist honors at the Summer Biomechanics, Bioengineering, and Biotransport Conference (SB3C). These accolades reflect his technical sophistication and ability to communicate complex biomedical findings effectively. Beyond formal awards, his invitations to speak at institutions like Brown University and his leadership in collaborative research projects further confirm his emerging prominence in computational cardiology and biomedical AI.

Research Skill🔬

Rana Raza Mehdi possesses a robust and multidisciplinary research skill set tailored to the biomedical and computational sciences. He is proficient in developing and validating deep learning models for medical imaging analysis, particularly for predicting cardiac remodeling and myocardial tissue properties. His skills include convolutional and recurrent neural networks (CNNs, RNNs), physics-informed learning, feature selection, and model interpretation. He is adept in using software like MATLAB, Python, TensorFlow, and COMSOL for modeling, simulation, and data processing. Additionally, he has hands-on experience in in-silico modeling, cardiac strain imaging, finite element analysis, and integration of multimodal data (e.g., ex-vivo, in-vivo, and simulated datasets). His expertise extends to computational fluid dynamics, acoustoelastic testing, and myocardial fiber architecture estimation. Through international collaborations and high-impact research, he has demonstrated technical excellence, analytical rigor, and innovation. Rana’s ability to blend physiological knowledge with machine learning makes him uniquely equipped to solve real-world problems in cardiovascular diagnostics.

Conclusion💡

Rana Raza Mehdi is an exceptionally strong candidate for the Best Researcher Award, especially in the PhD or early-career researcher category. His work blends deep technical skills, impactful health applications, and international research experience. With his trajectory, he stands out as a future leader in computational cardiovascular bioengineering.

Publications Top Noted✍

  • Title: Determination of third-order elastic constants using change of cross-sectional resonance frequencies by acoustoelastic effect
    Authors: B. Ji, R.R. Mehdi, G.W. Jang, S.H. Cho
    Year: 2021
    Citations: 15

  • Title: Comparison of three machine learning methods to estimate myocardial stiffness
    Authors: R.R. Mehdi, E.A. Mendiola, A. Sears, J. Ohayon, G. Choudhary, R. Pettigrew, et al.
    Year: 2023
    Citations: 14

  • Title: In-silico heart model phantom to validate cardiac strain imaging
    Authors: T. Mukherjee, M. Usman, R.R. Mehdi, E. Mendiola, J. Ohayon, D. Lindquist, et al.
    Year: 2024
    Citations: 11

  • Title: On the possibility of estimating myocardial fiber architecture from cardiac strains
    Authors: M. Usman, E.A. Mendiola, T. Mukherjee, R.R. Mehdi, J. Ohayon, P.G. Alluri, et al.
    Year: 2023
    Citations: 9

  • Title: Machine learning-based classification of cardiac relaxation impairment using sarcomere length and intracellular calcium transients
    Authors: R.R. Mehdi, M. Kumar, E.A. Mendiola, S. Sadayappan, R. Avazmohammadi
    Year: 2023
    Citations: 6

  • Title: Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning
    Authors: R.R. Mehdi, N. Kadivar, T. Mukherjee, E.A. Mendiola, D.J. Shah, et al.
    Year: 2024
    Citations: 3

  • Title: Abstract P2008: Contractile Adaptation Of The Right Ventricular Myocardium In Pulmonary Hypertension
    Authors: R.R.R. Mehdi, S. Neelakantan, E. Wang, P. Zhang, G. Choudhary, et al.
    Year: 2023
    Citations: 3

  • Title: Multi-material Cardiac Sleeves with Variable Stiffness Enhance Regional Strain Markers
    Authors: V. Naeini, E.A. Mendiola, R.R. Mehdi, P. Vanderslice, V. Serpooshan, et al.
    Year: 2024
    Citations: 1

  • Title: Right ventricular stiffening and anisotropy alterations in pulmonary hypertension: Mechanisms and relations to function
    Authors: S. Neelakantan, A. Vang, R.R. Mehdi, H. Phelan, P. Nicely, T. Imran, P. Zhang, et al.
    Year: 2024
    Citations: 1

  • Title: Effects of scar architecture on cardiac strains in myocardial infarction
    Authors: V. Naeini, S.B. Peighambari, R.R. Mehdi, E.A. Mendiola, T. Mukherjee, et al.
    Year: 2025

  • Title: Right Ventricular Stiffening and Anisotropy Alterations in Pulmonary Hypertension: Mechanisms and Relations to Right Heart Failure
    Authors: S. Neelakantan, A. Vang, R.R. Mehdi, H. Phelan, P. Nicely, T. Imran, P. Zhang, et al.
    Year: 2025

  • Title: Non‐Invasive Diagnosis of Chronic Myocardial Infarction via Composite In‐Silico‐Human Data Learning
    Authors: R.R. Mehdi, N. Kadivar, T. Mukherjee, E.A. Mendiola, A. Bersali, D.J. Shah, et al.
    Year: 2025

  • Title: Role of left ventricular anisotropy in the outcome of myocardial infarction: Insights from a rodent model
    Authors: S. Neelakantan, E. Mendiola, R.R. Mehdi, Q. Xiang, X. Zhang, K. Myers, et al.
    Year: 2024

  • Title: Abstract Tu048: Viscoelastic remodeling of the left ventricular myocardium in myocardial infarction
    Authors: S. Neelakantan, R.R. Mehdi, Q. Xiang, X. Zhang, P. Vanderslice, et al.
    Year: 2024

  • Title: On in-silico estimation of left ventricular end-diastolic pressure from cardiac strains
    Authors: E.A. Mendiola, R.R. Mehdi, D.J. Shah, R. Avazmohammadi
    Year: 2024

  • Title: Does EDPVR Represent Myocardial Tissue Stiffness? Toward a Better Definition
    Authors: R.R. Mehdi, E.A. Mendiola, V. Naeini, G. Choudhary, R. Avazmohammadi
    Year: 2024

  • Title: Acoustoelasticity-Based Measurement of Third-Order Elastic Constants Considering Temperature Effect
    Authors: R.R. Mehdi, B. Ji, G.W. Jang, S.H. Cho
    Year: 2021

  • Title: Estimating Pulmonary Arterial Pressure Differences Using Integrated Machine Learning-Computational Fluid Dynamics
    Authors: S.B. Peighambari, T. Mukherjee, R.R. Mehdi, E.A. Mendiola, et al.

  • Title: Early works on estimating left ventricle pressure from ventricular strains
    Authors: E.A. Mendiola, R.R. Mehdi, R. Avazmohammadi

Yuanyuan QIN | Medical Image Analysis | Best Researcher Award

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Dr . Yuanyuan QIN | Medical Image Analysis | Best Researcher Award

Associate Chief Physician, Associate Professor at  Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , China

Dr. Yuanyuan Qin is a distinguished Associate Chief Physician and Associate Professor in the Department of Radiology at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. With a strong foundation in clinical radiology and advanced neuroimaging, she has dedicated her career to investigating brain disorders such as Alzheimer’s disease, Parkinson’s disease, and post-COVID neurological changes. Her research integrates multimodal imaging techniques with cognitive neuroscience and machine learning, emphasizing non-invasive diagnostic innovations. A recipient of national research funding and provincial awards, Dr. Qin has published extensively in leading international journals, with several high-impact and highly cited articles. She has also demonstrated leadership in academic teaching through the adoption of blended learning methodologies. Her interdisciplinary expertise, clinical insights, and research productivity make her a recognized contributor to the fields of radiology and neuroscience, with ongoing work focused on understanding neural mechanisms underlying cognitive decline and aging-related diseases.

Professional Profile 

Education🎓

Dr. Yuanyuan Qin pursued her advanced medical education at Huazhong University of Science and Technology, completing a prestigious Combined Master-PhD program between 2008 and 2013. Her doctoral training emphasized advanced neuroimaging techniques, with research exploring structural and functional brain alterations in neurodegenerative conditions. During her PhD, she was selected for a one-year joint PhD training program at the esteemed Johns Hopkins University in the United States (2011–2012), where she gained international exposure to state-of-the-art imaging methodologies and collaborative research environments. Her interdisciplinary education integrated clinical radiology, neuroscience, and data-driven analysis, laying a strong foundation for her later research on cognitive disorders and aging. This cross-institutional and cross-national academic background not only enriched her scientific expertise but also cultivated her capacity to approach radiological challenges from both a clinical and research perspective. Her academic training continues to inform her innovative work in diagnostic imaging and cognitive neurodegeneration.

Professional Experience📝

Dr. Yuanyuan Qin has built a progressive and impactful professional career in radiology at Tongji Hospital. Starting in 2013 as a Resident Physician, she rapidly advanced through roles as an Attending Physician (2014–2019), Associate Chief Physician (2019–2020), and ultimately to Associate Professor (2020–present). Throughout her career, she has been deeply engaged in clinical diagnostics, medical imaging interpretation, and mentoring medical students and interns. Her dual roles in academic and clinical settings have allowed her to integrate patient-centered care with research-led innovation. Dr. Qin’s experience spans routine radiological evaluations to complex imaging studies in patients with neurological and neurodegenerative conditions. She has actively contributed to improving radiology internship training programs through digital platforms and 3D simulation tools. Her leadership within the department is recognized not only in her clinical acumen but also in fostering collaborative research projects and guiding junior physicians and researchers in translational imaging studies.

Research Interest🔎

Dr. Yuanyuan Qin’s research interests lie at the intersection of neuroimaging, cognitive neuroscience, and clinical radiology. Her primary focus is on understanding the neural mechanisms of neurodegenerative diseases, particularly Alzheimer’s and Parkinson’s, through advanced magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and functional connectivity analysis. She is also deeply interested in the application of repetitive transcranial magnetic stimulation (rTMS) combined with cognitive training as a therapeutic and diagnostic tool. Recently, her research has extended into the neurological impacts of COVID-19, exploring long-term cerebral microstructure changes in asymptomatic patients. Her work frequently integrates artificial intelligence, deep learning, and image quantification methods to improve diagnostic precision. Dr. Qin is committed to developing non-invasive imaging biomarkers that can track disease progression and predict cognitive decline. Her interdisciplinary approach bridges clinical needs with technological advancement, contributing valuable insights into early detection and intervention strategies for aging-related cognitive disorders.

Award and Honor🏆

Dr. Yuanyuan Qin has been widely recognized for her academic excellence and scientific contributions. Notably, she received the First Prize in the 2019 Hubei Provincial Science and Technology Progress Award for her innovative work on the integration of structural and functional MRI in brain development and aging-related diseases. In teaching, she earned the Second Prize in the 2020 Young Teacher Teaching Competition at Huazhong University of Science and Technology, highlighting her commitment to educational innovation. As a principal investigator, she has secured multiple competitive grants, including a General Program and a Youth Project from the National Natural Science Foundation of China (NSFC). Her work has garnered national attention and peer acknowledgment, with multiple publications cited widely in top-tier journals. Her research article in the Journal of Clinical Investigation is listed as a Highly Cited Article, further validating her impact in the fields of radiology and neuroscience.

Research Skill🔬

Dr. Yuanyuan Qin possesses a robust portfolio of research skills, especially in neuroimaging analysis, multimodal MRI, and diffusion tensor imaging (DTI). She has extensive expertise in image processing platforms such as 3D-Slicer, FSL, and SPM, along with experience in deep learning algorithms for radiological quantification. Her technical strengths extend to designing and conducting longitudinal studies, particularly in evaluating cognitive interventions like rTMS paired with cognitive training in Alzheimer’s patients. She demonstrates proficiency in integrating clinical data with imaging outcomes to derive meaningful correlations for disease diagnosis and prognosis. Additionally, she has contributed to the development of automated MRI quantification pipelines, including those for Parkinsonism index assessment. Her interdisciplinary methods often incorporate statistical modeling, functional connectivity analysis, and AI-based imaging biomarker discovery. These research capabilities position her as a key contributor in translating complex neuroimaging insights into real-world clinical applications.

Conclusion💡

Dr. Yuanyuan Qin is highly suitable for the Best Researcher Award based on her exceptional track record in neuroimaging research, consistent national-level funding, scientific leadership in Alzheimer’s and Parkinson’s research, and significant contributions to radiological education and practice. Her trajectory exemplifies a balance between academic rigor, innovation, and clinical relevance.

Publications Top Noted✍

  1. Title: Surface-Based Vertexwise Analysis of Morphometry and Microstructural Integrity for White Matter Tracts in Diffusion Tensor Imaging: With Application to the Corpus Callosum in Alzheimer’s Disease
    Authors: Tang, Xiaoying; Qin, Yuanyuan; Zhu, Wenzhen; Miller, Michael I.
    Year: 2017
    Citation: Human Brain Mapping, DOI: 10.1002/hbm.23491

  2. Title: Atlas-based deep gray matter and white matter analysis in Alzheimer’s disease: diffusion abnormality and correlation with cognitive function
    Authors: Qin Yuanyuan; Zhang Shun; Guo Linying; Zhang Min; Zhu Wenzhen
    Year: 2016
    Citation: Chinese Journal of Radiology, WOSUID: CSCD:5699935

  3. Title: Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer’s disease
    Authors: Tang, Xiaoying; Qin, Yuanyuan; Wu, Jiong; Zhang, Min; Zhu, Wenzhen; Miller, Michael I.
    Year: 2016
    Citation: Magnetic Resonance Imaging, DOI: 10.1016/j.mri.2016.05.001

  4. Title: Simulating the Evolution of Functional Brain Networks in Alzheimer’s Disease: Exploring Disease Dynamics from the Perspective of Global Activity
    Authors: Li, Wei; Wang, Miao; Zhu, Wenzhen; Qin, Yuanyuan; Huang, Yue; Chen, Xi
    Year: 2016
    Citation: Scientific Reports, DOI: 10.1038/srep34156

  5. Title: Frequency-specific Alterations of Large-scale Functional Brain Networks in Patients with Alzheimer’s Disease
    Authors: Qin, Yuan-Yuan; Li, Ya-Peng; Zhang, Shun; Xiong, Ying; Guo, Lin-Ying; Yang, Shi-Qi; Yao, Yi-Hao; Li, Wei; Zhu, Wen-Zhen
    Year: 2015
    Citation: Chinese Medical Journal, DOI: 10.4103/0366-6999.151654

  6. Title: Frequency-specific Alterations of Large-scale Functional Brain Networks in Patients with Alzheimer’s Disease (Correction)
    Authors: Qin, Y. Y.; Li, Y. P.; Zhang, S.; Xiong, Y.; Guo, L. Y.; Yang, S. Q.
    Year: 2015
    Citation: Chinese Medical Journal, DOI: 10.4103/0366-6999.156150

  7. Title: An Efficient Approach for Differentiating Alzheimer’s Disease from Normal Elderly Based on Multicenter MRI Using Gray-Level Invariant Features
    Authors: Li, Muwei; Oishi, Kenichi; He, Xiaohai; Qin, Yuanyuan; Gao, Fei; Mori, Susumu
    Year: 2014
    Citation: PLOS ONE, DOI: 10.1371/journal.pone.0105563

  8. Title: Discriminative Analysis of Multivariate Features from Structural MRI and Diffusion Tensor Images
    Authors: Li, Muwei; Qin, Yuanyuan; Gao, Fei; Zhu, Wenzhen; He, Xiaohai
    Year: 2014
    Citation: Magnetic Resonance Imaging, DOI: 10.1016/j.mri.2014.05.008

  9. Title: Exploring the Functional Brain Network of Alzheimer’s Disease: Based on the Computational Experiment
    Authors: Li, YaPeng; Qin, Yuanyuan; Chen, Xi; Li, Wei
    Year: 2013
    Citation: PLOS ONE, DOI: 10.1371/journal.pone.0073186

  10. Title: Gross Feature Recognition of Anatomical Images Based on Atlas Grid (GAIA)
    Authors: Qin, Yuan-Yuan; Hsu, Johnny T.; Yoshida, Shoko; Faria, Andreia V.; Oishi, Kumiko; et al.
    Year: 2013
    Citation: NeuroImage: Clinical, DOI: 10.1016/j.nicl.2013.08.006

  11. Title: In vivo Quantitative Whole-brain Diffusion Tensor Imaging Analysis of APP/PS1 Transgenic Mice
    Authors: Qin, Yuan-Yuan; Li, Mu-Wei; Zhang, Shun; Zhang, Yan; Zhao, Ling-Yun; et al.
    Year: 2013
    Citation: Neuroradiology, DOI: 10.1007/s00234-013-1195-0

  12. Title: The Functional Brain Network Changes of Alzheimer’s Disease
    Authors: Li YaPeng; Qin YuanYuan; Li Wei
    Year: 2013
    Citation: Chinese Journal of Medical Physics, WOSUID: CSCD:5004621

  13. Title: Voxel-Based Diffusion Tensor Imaging of an APP/PS1 Mouse Model of Alzheimer’s Disease
    Authors: Shu, Xiaogang; Qin, Yuan-Yuan; Zhang, Shun; Jiang, Jing-Jing; Zhang, Yan; et al.
    Year: 2013
    Citation: Molecular Neurobiology, DOI: 10.1007/s12035-013-8418-6

  14. Title: Stromal Cell-Derived Factor 1 Alpha Decreases Beta-Amyloid Deposition in Alzheimer’s Disease Mouse Model
    Authors: Wang, Qi; Xu, Yi; Chen, Jin-Cao; Qin, Yuan-Yuan; Liu, Mao; et al.
    Year: 2012
    Citation: Brain Research, DOI: 10.1016/j.brainres.2012.04.011

 

Rui He | Computer Vision for Robotics and Autonomous Systems | Best Researcher Award

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Dr . Rui He | Computer Vision for Robotics and Autonomous Systems | Best Researcher Award

Professor at National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, China

Associate Professor He Rui is a prominent academic and researcher at the National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University. With a specialized focus on advanced braking systems, autonomous driving technologies, and driver behavior analysis, he stands at the forefront of intelligent vehicle systems research. His career is marked by a strong integration of theoretical innovation and practical application, demonstrated through leadership in national and industrial research projects and the development of multiple patented technologies. Dr. He has published over 30 scholarly articles and holds more than 20 invention patents, showcasing a high level of scientific productivity and innovation. His guidance as a doctoral supervisor also reflects his deep commitment to nurturing future researchers in the field. Acknowledged for his contributions to visual perception, trajectory planning, and chassis-by-wire control, Dr. He Rui continues to drive transformative advancements in the evolving landscape of automotive engineering and intelligent mobility.

Professional Profile 

Education🎓

He Rui holds a robust academic background rooted in mechanical and automotive engineering, having pursued his higher education at esteemed institutions in China. He completed his undergraduate studies in vehicle engineering, laying a strong foundation in dynamics, control, and systems integration. He later obtained his Master’s and Doctoral degrees in automotive engineering, with a research focus on intelligent vehicle systems, including sensor-based perception and integrated chassis control. His doctoral work, in particular, explored advanced concepts in vehicle dynamics and control algorithms tailored to autonomous systems. Throughout his academic journey, Dr. He acquired a deep understanding of interdisciplinary technologies involving mechanical systems, computer vision, and artificial intelligence. His education reflects a well-rounded and comprehensive training that blends traditional automotive knowledge with emerging technologies, effectively preparing him to lead innovative research in smart mobility. His continuous pursuit of knowledge and research excellence positions him as a key figure in the automotive academic community.

Professional Experience📝

Dr. He Rui currently serves as an Associate Professor and doctoral supervisor at the National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University. He has actively led and contributed to various research projects funded by the National Natural Science Foundation of China and major automotive companies such as Dongfeng Motor and SAIC Motor. His portfolio includes pivotal roles in projects related to chassis control, autonomous intelligent driving systems, and integrated modeling methods for electric vehicles. These engagements have enabled him to bridge academic research with industrial implementation. His career demonstrates a commitment to pushing the boundaries of automotive control technologies, especially in intelligent perception and driver-vehicle interaction. In addition to research, Dr. He plays a significant role in mentoring postgraduate students, contributing to curriculum development, and fostering interdisciplinary collaborations. His professional path reflects a balance of theoretical advancement and practical application in the field of intelligent automotive systems.

Research Interest🔎

Dr. He Rui’s research interests lie at the intersection of automotive engineering and intelligent systems. He is primarily focused on the development of advanced chassis-by-wire systems, visual perception for autonomous driving, and analysis of driver behavior for improved human-vehicle interaction. His work explores how artificial intelligence, computer vision, and dynamic control strategies can be integrated into vehicle systems to enhance safety, efficiency, and driving experience. He is particularly interested in intelligent trajectory planning and how vehicles can autonomously adapt to real-world driving conditions using data-driven models. Another major research thrust involves understanding and modeling driver behavior under extreme conditions, such as tire blowouts or sudden braking, to improve control algorithms. These diverse interests underscore his commitment to solving critical challenges in the transition toward intelligent and autonomous mobility. Dr. He’s multidisciplinary approach has led to impactful research that supports both theoretical exploration and real-world implementation.

Award and Honor🏆

While specific awards and honors have not been listed in the profile, Dr. He Rui’s achievements speak to a high level of professional recognition. His leadership in multiple nationally funded research projects and industry collaborations with top automotive manufacturers such as Dongfeng and SAIC reflect his esteemed status in the field. He has authored more than 30 peer-reviewed papers and holds over 20 invention patents, demonstrating consistent innovation and contribution to automotive technology. His position as a doctoral supervisor and associate professor at a prestigious institution like Jilin University further reinforces his academic credibility. It’s highly likely that he has received institutional accolades, commendations from industry partners, and recognition for his research outputs. These accomplishments collectively underscore a career marked by excellence, leadership, and a strong impact on the advancement of intelligent vehicle systems. Further formal honors would only enhance an already distinguished academic and research profile.

Research Skill🔬

Dr. He Rui possesses an impressive set of research skills that span across automotive engineering, intelligent control systems, and artificial intelligence. His expertise in chassis-by-wire technologies allows him to design and develop next-generation braking and steering systems with high reliability and precision. He has strong capabilities in computer vision and sensor fusion, which are essential for enabling autonomous vehicle perception. Dr. He is also proficient in developing and applying advanced control algorithms for vehicle trajectory planning, especially under uncertain or complex driving conditions. He excels in integrating experimental testing with simulation environments, supporting both theoretical research and applied development. His skills include modeling driver behavior using machine learning techniques and incorporating it into vehicle control strategies. Furthermore, he has proven experience in leading large-scale research projects, writing scientific publications, and filing patents. These comprehensive research abilities make him a valuable contributor to the evolution of intelligent transportation technologies.

Conclusion💡

He Rui is a highly suitable candidate for the Best Researcher Award, particularly in fields such as automotive innovation, autonomous systems, and intelligent control technologies. His project leadership, prolific output, and patent record strongly support his candidacy. With further emphasis on international exposure and societal narratives, his profile would be even more competitive at global award levels.

Publications Top Noted✍

  • Title: Research on vehicle trajectory prediction methods in dense and heterogeneous urban traffic
    Authors: Sumin Zhang, Ri Bai, Rui He, Zhiwei Meng, Yupeng Chang, Yongshuai Zhi
    Year: 2025
    Citation: Transportation Letters, DOI: 10.1080/19427867.2024.2403818

  • Title: Research on Vehicle Trajectory Prediction Methods in Urban Main Road Scenarios
    Authors: Sumin Zhang, Ri Bai, Rui He, Zhiwei Meng, Yupeng Chang, Yongshuai Zhi, Ning Sun
    Year: 2024
    Citation: IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/tits.2024.3419037

  • Title: A skip feature enhanced multi-source fusion framework for switch state detection
    Authors: Sumin Zhang, Ri Bai, Rui He, Zhiwei Meng, Yongshuai Zhi
    Year: 2024
    Citation: International Journal of Rail Transportation, DOI: 10.1080/23248378.2024.2372729

  • Title: Decision-making of active collision avoidance system based on comprehensive evaluation method of dangerous scenarios
    Authors: Rui He, Zhiwei Meng, Sumin Zhang, Zhi Yang, Yongshuai Zhi, Jiaxiang Qin
    Year: 2024
    Citation: Journal of Automobile Engineering, DOI: 10.1177/09544070221137398

  • Title: IDPNet: a light-weight network and its variants for human pose estimation
    Authors: Huan Liu, Jian Wu, Rui He
    Year: 2024
    Citation: The Journal of Supercomputing, DOI: 10.1007/s11227-023-05691-5

  • Title: Skeleton-based multi-stream adaptive-attentional sub-graph convolution network for action recognition
    Authors: Huan Liu, Jian Wu, Haokai Ma, Yuqi Yan, Rui He
    Year: 2024
    Citation: Multimedia Tools and Applications, DOI: 10.1007/s11042-023-15778-z

  • Title: LEES-Net: Fast, lightweight unsupervised curve estimation network for low-light image enhancement and exposure suppression
    Authors: Xuanhe Li, Rui He, Jian Wu, Hu Yan, Xianfeng Chen
    Year: 2023
    Citation: Displays, DOI: 10.1016/j.displa.2023.102550

  • Title: GIVA: Interaction-aware trajectory prediction based on GRU-Improved VGG-Attention Mechanism model for autonomous vehicles
    Authors: Zhiwei Meng, Rui He, Jiaming Wu, Sumin Zhang, Ri Bai, Yongshuai Zhi
    Year: 2023
    Citation: Journal of Automobile Engineering, DOI: 10.1177/09544070231207669

  • Title: Center point to pose: Multiple views 3D human pose estimation for multi-person
    Authors: Huan Liu, Jian Wu, Rui He
    Year: 2022
    Citation: PLOS ONE, DOI: 10.1371/journal.pone.0274450

  • Title: Monocular Vision SLAM Research for Parking Environment with Low Light
    Authors: Sumin Zhang, Yongshuai Zhi, Shouyi Lu, Ze Lin, Rui He
    Year: 2022
    Citation: International Journal of Automotive Technology, DOI: 10.1007/s12239-022-0063-5

  • Title: Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection
    Authors: Guojun Wang, Jian Wu, Rui He, Bin Tian
    Year: 2021
    Citation: IEEE/CAA Journal of Automatica Sinica, DOI: 10.1109/jas.2020.1003414

Ji Hun Kim | Manufacturing Applications | Best Researcher Award

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Mr . Ji Hun Kim | Manufacturing Applications | Best Researcher Award

Ph.D.Candidate at Seoul National University of Science and Technology, South Korea

Ji Hun Kim is a dynamic and highly accomplished Ph.D. candidate at Seoul National University of Science and Technology (SeoulTech), South Korea, specializing in laser-based manufacturing and optical engineering. With a robust academic background and hands-on industry experience, he has made significant contributions to the fields of precision engineering, laser processing, and optical aberration analysis. His research has been widely published in reputable journals, focusing on advanced materials processing, laser-matter interaction, and heat transfer effects in optical systems. Ji Hun has led and participated in multiple government-funded research projects, demonstrating both leadership and collaborative capabilities. Recognized by prestigious engineering societies with several academic excellence awards, he has emerged as a promising young researcher in advanced manufacturing technologies. Ji Hun’s work is grounded in practical relevance and innovation, making him a strong candidate for academic honors and research-based awards in engineering and applied science.

Professional Profile 

Education🎓

Ji Hun Kim has built a strong academic foundation at Seoul National University of Science and Technology, where he completed his entire higher education journey. He earned his Bachelor of Science (B.S.) degree in 2017, majoring in a field aligned with manufacturing or mechanical engineering. He then pursued and completed a Master of Science (M.S.) degree from the same university in 2019, where he began exploring research topics in laser processing and material behavior. Currently, Ji Hun is a Ph.D. candidate at SeoulTech, working on cutting-edge research involving laser-based micromachining, thermal optics, and smart materials. His academic training has included not just theoretical knowledge but also significant laboratory experience and computational modeling. His progression through successive degrees at a single, research-intensive institution has allowed him to develop in-depth expertise, continuity in his research focus, and strong academic mentorship—making his education both comprehensive and highly specialized in advanced manufacturing technologies.

Professional Experience📝

Alongside his academic pursuits, Ji Hun Kim has accumulated valuable professional experience in South Korea’s defense and industrial sectors. He worked as a Junior Research Engineer at MTG Inc. (2020–2022), where he contributed to the design and development of anechoic chambers used in defense applications, showcasing his understanding of electromagnetic and acoustic insulation technologies. Prior to that, from 2019 to 2020, he served as a Research Associate at Sensor Tech Inc., focusing on chemical detection devices—an experience that merged sensor technology with system integration. These roles have enriched his engineering insight, sharpened his problem-solving skills, and enabled him to apply research knowledge in real-world scenarios. In both positions, Ji Hun was involved in multidisciplinary projects, requiring him to collaborate with teams and translate technical concepts into functional systems. His industrial experience complements his academic research, offering a balanced profile of theoretical depth and applied engineering expertise.

Research Interest🔎

Ji Hun Kim’s research interests lie at the intersection of laser material processing, optical aberrations, and thermal-fluid dynamics in precision manufacturing environments. His work focuses on understanding how laser-induced heat affects the structural and optical properties of advanced materials, particularly carbon fiber reinforced plastics and aluminum alloys. He is passionate about developing high-power, ultrashort pulse laser systems for micromachining, with applications in next-generation display technologies and semiconductor equipment. Ji Hun also investigates the effects of buoyant jet dynamics on optical distortions in laser systems, contributing to better laser beam delivery and processing accuracy. He is keenly interested in the optimization of laser surface treatment processes to improve bonding strength between dissimilar materials, particularly for eco-friendly automotive and aerospace applications. His research is deeply applied, often tied to real-world engineering challenges, and strives to advance both scientific understanding and industrial utility in high-precision laser technologies.

Award and Honor🏆

Ji Hun Kim has received numerous awards that highlight his academic excellence and applied research achievements. In 2025, he was honored with the Best Award for Academic Excellence by the Korean Society of Manufacturing Process Engineers (KSMPE), a distinction he also earned in 2023. His consistent excellence was previously recognized in 2019 by the Korean Society of Manufacturing Technology Engineers (KSMTE). In addition to academic recognition, Ji Hun received the Encouragement Award in 2024 for his outstanding contributions to an industry-university collaborative project at Seoul National University of Science and Technology. His technical credibility was further validated with the Engineer General Machinery Certificate issued by the Ministry of Trade, Industry, and Energy in 2019. These honors reflect his strong standing in Korea’s manufacturing research community and underscore his ability to conduct impactful, innovative, and collaborative research that aligns with national industrial and scientific goals.

Research Skill🔬

Ji Hun Kim possesses a well-rounded and technically robust skill set crucial for advanced manufacturing research. He is proficient in COMSOL Multiphysics, which he uses to simulate thermal and fluid behavior in laser-material interaction scenarios, as well as MATLAB for data analysis, modeling, and algorithm development. His expertise in LabVIEW allows for experimental system automation and real-time data acquisition in laser experiments. Ji Hun has hands-on skills in laser micromachining, laser spectroscopy (LIPS), and surface treatment processes. He is adept at analyzing heat-affected zones, bonding strength, and surface morphology in composite and metallic materials. His ability to integrate experimental setups with computational simulations provides comprehensive insight into process optimization. Furthermore, his experience leading and collaborating on national R&D projects has honed his project planning, data interpretation, and cross-functional teamwork capabilities. Altogether, Ji Hun’s research skills position him as a capable and forward-thinking researcher in high-precision, laser-based manufacturing technologies.

Conclusion💡

Ji Hun Kim is an exceptionally promising early-career researcher whose deep specialization in laser processing and optics, combined with an impressive array of high-quality publications, leadership in national research projects, and recognized academic excellence, make him highly suitable for the Best Researcher Award. With some expansion into international collaborations, interdisciplinary domains, and innovation commercialization, he can become a leading figure in advanced manufacturing research.

Publications Top Noted✍

  • Title: High-Performance Solution Processable Red TADF-OLED with External Quantum Efficiency Exceeding 28% Using a Multi-Resonance Emitter Host
    Authors: (Not fully visible in your message; please provide full names if needed)Journal: Advanced Materials
    Year: 2025
    Citations: 2
  • Title: Enhancing Bond Strength Between Carbon Fiber Reinforced Thermoplastic and Aluminum Alloys Through Laser Surface Treatment
    Authors: (Not fully visible in your message; please provide full names if needed)Journal: International Journal of Precision Engineering and Manufacturing – Green Technology
    Year: 2025

Michael Koch | Robotics | Best Researcher Award

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Prof . Dr . Michael Koch | Robotics | Best Researcher Award

Professor at Technische Hochschule Nürnberg, Germany

Prof. Dr.-Ing. Dipl.-Wirt.-Ing. Michael Koch is a distinguished German academic and research professor in mechanical engineering with a robust focus on engineering design, simulation technologies, and additive manufacturing. Currently serving as Vice Dean and Professor at Technische Hochschule Nürnberg Georg Simon Ohm, he has over 20 years of experience in academia and industry. His research integrates innovative technologies like augmented reality, motion capture, and cyber-physical systems to optimize design processes and intelligent manufacturing systems. He has published extensively in high-impact conferences and journals, and he actively leads curriculum and academic initiatives in engineering education. As a certified Six Sigma Black Belt and a key user of PTC CREO Parametric, Prof. Koch demonstrates a blend of theoretical depth and industrial pragmatism. His contributions toward intuitive robot programming, knowledge-based simulation, and 3D geometry integration in product development make him a prominent figure in Germany’s mechanical engineering research landscape.

Professional Profile 

Education🎓F

Prof. Michael Koch has a strong academic foundation combining mechanical engineering and industrial engineering. He earned his Dr.-Ing. (Ph.D.) in Engineering Design from Friedrich-Alexander University Erlangen-Nuremberg in 2005 with the distinction of “very good.” His doctoral work laid the groundwork for his later research in design optimization and simulation integration. He previously completed a Diploma in Mechanical Engineering (Dipl.-Ing.) from the same university in 2000, specializing in design and computation with an excellent academic grade (1.8). Complementing his technical background, he pursued a Diploma in Industrial Engineering (Dipl.-Wirt.-Ing.) at the University of Hagen (2001–2004), focusing on marketing and human resources, which reflects his interdisciplinary strengths. This combination of design engineering and business-oriented knowledge has enabled him to lead academic programs and collaborate effectively with the manufacturing industry. His diverse academic trajectory supports his holistic approach to innovation in both engineering education and applied research.

Professional Experience📝

Prof. Koch’s professional journey spans both academic excellence and industry leadership. He has been a Professor at Technische Hochschule Nürnberg Georg Simon Ohm since 2009, where he teaches engineering design and machine parts and serves as the Vice Dean and Head of the Master’s Program in Mechanical Engineering. He has played a pivotal role in curriculum design and quality assurance within the faculty. Before transitioning fully into academia, he worked at Schaeffler Technologies GmbH & Co. KG (2005–2009) in the special machines department, where he managed key industrial projects. Earlier in his career, he served as a scientific assistant at Friedrich-Alexander University Erlangen-Nuremberg, contributing to engineering research and instruction. Prof. Koch also holds certifications like Six Sigma Black Belt and Key User of PTC CREO Parametric, underscoring his practical orientation. His combined industry-academic experience uniquely positions him as a leader in engineering innovation and applied research.

Research Interest🔎

Prof. Koch’s research interests lie at the intersection of engineering design, additive manufacturing, simulation technologies, and robotics. His work frequently explores knowledge-based design methods, real-geometry integration in simulations, and intuitive user interfaces for robotic applications. He is particularly interested in optimizing design and manufacturing processes through augmented reality, motion capture, and cyber-physical systems. His studies also delve into reverse engineering, finite element (FE) simulations using real 3D-scanned data, and product development driven by simulation and automation. Prof. Koch aims to bridge the gap between idealized models and real-world manufacturing variances, improving accuracy and efficiency in digital engineering. His interdisciplinary approach integrates mechanical engineering, human-computer interaction, and data-driven decision-making, resulting in innovations that benefit both academia and industry. His research significantly contributes to smart manufacturing, lightweight design, and automation in production, making him a key figure in the advancement of intelligent engineering systems.

Award and Honor🏆

While Prof. Michael Koch’s CV does not list formal awards or honors explicitly, his distinguished academic positions and repeated invitations to present at international conferences reflect peer recognition of his expertise. His appointment as Vice Dean and Research Professor at Technische Hochschule Nürnberg, along with his leadership in curriculum development and examination boards, underscores the institutional trust placed in him. He has been a consistent contributor to high-impact events such as IFAC Workshops, ISR, Sim-AM, ICED, and the Design for X Symposium, where his papers have been accepted for both presentation and publication—an honor in the global research community. His certification as a Six Sigma Black Belt and designation as a Key User of industry-standard CAD tools (PTC CREO) also highlight his professional credibility. These roles and participations collectively showcase a career marked by excellence, leadership, and sustained contributions to both research and education in mechanical engineering.

Research Skill🔬

Prof. Koch demonstrates a comprehensive set of research skills across simulation, design, modeling, and experimental validation. He excels in integrating real 3D geometry data into simulations, thereby enhancing the accuracy of engineering analyses. His ability to combine parametric CAD modeling with finite element methods (FEM) enables more realistic structural assessments. He is skilled in developing cyber-physical systems, utilizing augmented reality for robot programming, and implementing motion capture technologies for intuitive control interfaces. Prof. Koch also possesses strong capabilities in knowledge-based simulation frameworks, making product development processes more efficient and intelligent. His certification in Six Sigma demonstrates his proficiency in process optimization and quality control, and his work often bridges the gap between academic theories and industrial applications. Proficient in engineering software like PTC CREO Parametric, he brings both depth and versatility to his projects. These research skills collectively establish his expertise in designing cutting-edge, applied engineering solutions.

Conclusion💡

Prof. Dr.-Ing. Michael Koch is highly suitable for the Best Researcher Award based on his:

  • Depth of domain knowledge,

  • Multidisciplinary research footprint,

  • Educational leadership,

  • Technical innovations in engineering design, simulation, and additive manufacturing.

His work bridges academic rigor and industry relevance, and he has made consistent, innovative contributions to mechanical engineering and product development.

With additional international collaboration and visibility in global rankings or research grants, he would further elevate his candidacy for top-tier global research honors.

Publications Top Noted✍

  1. Title: Expression and functions of transmembrane mucin MUC13 in ovarian cancer
    Authors: SC Chauhan, K Vannatta, MC Ebeling, N Vinayek, A Watanabe, MD Koch, et al.
    Year: 2009
    Citations: 149

  2. Title: MUC13 mucin augments pancreatic tumorigenesis
    Authors: SC Chauhan, MC Ebeling, DM Maher, MD Koch, A Watanabe, et al.
    Year: 2012
    Citations: 110

  3. Title: Identification of an essential Caulobacter crescentus gene encoding a member of the Obg family of GTP-binding proteins
    Authors: J Maddock, A Bhatt, M Koch, J Skidmore
    Year: 1997
    Citations: 71

  4. Title: Increased expression and aberrant localization of mucin 13 in metastatic colon cancer
    Authors: BK Gupta, DM Maher, MC Ebeling, V Sundram, MD Koch, DW Lynch, et al.
    Year: 2012
    Citations: 54

  5. Title: Combined staining of TAG-72, MUC1, and CA125 improves labeling sensitivity in ovarian cancer
    Authors: SC Chauhan, N Vinayek, DM Maher, MC Bell, KA Dunham, MD Koch, et al.
    Year: 2007
    Citations: 42

  6. Title: Design for X
    Authors: H Meerkamm, M Koch
    Year: 2005
    Citations: 33

  7. Title: Intuitive welding robot programming via motion capture and augmented reality
    Authors: F Mueller, C Deuerlein, M Koch
    Year: 2019
    Citations: 23

  8. Title: Innovative extruder concept for fast and efficient additive manufacturing
    Authors: R Löffler, M Koch
    Year: 2019
    Citations: 20

  9. Title: Integrating optical 3D measurement techniques in pipe bending: a model-based approach
    Authors: S Katona, M Lušić, M Koch, S Wartzack
    Year: 2016
    Citations: 19

  10. Title: The neuro-linguistic programming treatment approach
    Authors: C Zastrow, V Dotson, M Koch
    Year: 1987
    Citations: 16

  11. Title: Cyber-physical-system for representing a robot end effector
    Authors: F Müller, C Deuerlein, M Koch
    Year: 2021
    Citations: 15

  12. Title: Trace component removal in CO2 removal processes by means of a semipermeable membrane
    Authors: JK Bockman, M Koch
    Year: 2016 (US Patent)
    Citations: 15

  13. Title: Robot guided computed tomography—production monitoring in automotive industry 4.0
    Authors: A Ziertmann, P Jahnke, S Kerscher, M Koch, W Holub
    Year: 2020
    Citations: 12

  14. Title: Microstructure of the HMX‐Based PBX KS32 after Mechanical Loading
    Authors: M Herrmann, U Förter‐Barth, MA Bohn, H Krause, M Koch, W Arnold
    Year: 2015
    Citations: 12

  15. Title: PM10 source apportionment at three urban background sites in the western Ruhr-area, Germany
    Authors: TAJ Kuhlbusch, U Quass, M Koch, H Fissan, P Bruckmann, U Pfeffer
    Year: 2004
    Citations: 12

  16. Title: Method and system for reducing energy requirements of a CO2 capture system
    Authors: JP Naumovitz, M Koch
    Year: 2014 (US Patent)
    Citations: 10

  17. Title: Process gas treatment system
    Authors: PU Koss, M Koch, JP Naumovitz
    Year: 2014 (US Patent)
    Citations: 10

  18. Title: Reverse Engineering – Prozess, Technologien und Anwendungsfälle
    Authors: S Katona, M Koch, S Wartzack
    Year: 2014
    Citations: 9

  19. Title: POEAM – a method for the part orientation evaluation for additive manufacturing
    Authors: S Jung, S Peetz, M Koch
    Year: 2019
    Citations: 7

  20. Title: Long-term primary culture of a clear cell ovarian carcinoma reveals an epithelial–mesenchymal cooperative interaction
    Authors: AA Goyeneche, M Koch, MC Bell, CM Telleria
    Year: 2015
    Citations: 7

Dibyalekha Nayak | Computer vision | Women Researcher Award

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Dr . Dibyalekha Nayak | Computer vision | Women Researcher Award

Assistant professor at Shah and Anchor Kutchhi Engineering College, India

Dr. Dibyalekha Nayak is a dedicated academician and emerging researcher with deep expertise in image processing, adaptive compression, and VLSI design. Her professional journey is marked by a strong commitment to teaching, scholarly research, and technological advancement. With over a decade of teaching experience and a recently completed Ph.D. from KIIT University, Bhubaneswar, her research has produced several publications in SCI-indexed journals and international conferences. Dr. Nayak’s contributions reflect an interdisciplinary approach, combining deep learning techniques with low-power hardware design to address complex challenges in wireless sensor networks and multimedia systems. She has actively participated in faculty development programs and technical workshops, continuously upgrading her knowledge. Her professional philosophy emphasizes ethics, hard work, and continuous learning. Currently serving as an Assistant Professor at Shah and Anchor Kutchi Engineering College in Mumbai, she aspires to make impactful contributions to the field of electronics and communication through research, innovation, and collaboration.

Professional Profile 

Education🎓

Dr. Dibyalekha Nayak holds a Ph.D. in Image Processing from the School of Electronics at KIIT University, Bhubaneswar, where she completed her research between September 2018 and May 2024. Her doctoral work focused on advanced techniques in image compression and saliency detection using deep learning and compressive sensing. She completed her Master of Technology (M.Tech) in VLSI Design from Satyabhama University, Chennai, in 2011, graduating with a commendable CGPA of 8.33. Prior to that, she earned her Bachelor of Engineering (B.E.) in Electronics and Telecommunication from Biju Patnaik University of Technology (BPUT), Odisha, in 2008, with a CGPA of 6.5. Her academic background provides a strong foundation in both theoretical electronics and practical applications in image processing and circuit design. The combination of image processing and VLSI design throughout her academic journey has enabled her to engage in cross-disciplinary research and foster innovation in both hardware and software domains.

Professional Experience📝

Dr. Dibyalekha Nayak has accumulated over 12 years of rich academic experience in various reputed engineering institutions across India. Currently, she serves as an Assistant Professor at Shah and Anchor Kutchi Engineering College, Mumbai, affiliated with Mumbai University, where she joined in July 2024. Prior to this, she worked as a Research Scholar at KIIT University (2018–2024), contributing significantly to image processing research. Her earlier roles include Assistant Professor positions at institutions such as College of Engineering Bhubaneswar (2016–2018), SIES Graduate School of Technology, Mumbai (2014), St. Francis Institute of Technology, Mumbai (2013), and Madha Engineering College, Chennai (2011–2012). Across these roles, she has taught a variety of undergraduate and postgraduate courses, supervised student projects, and contributed to departmental development. Her teaching areas span digital electronics, VLSI design, image processing, and communication systems, demonstrating a strong alignment between her teaching and research activities.

Research Interest🔎

Dr. Dibyalekha Nayak’s research interests lie at the intersection of image processing, deep learning, and VLSI design, with a special focus on adaptive compression, saliency detection, and compressive sensing. Her doctoral research addressed the development of innovative, low-complexity algorithms for image compression using techniques like block truncation coding and DCT, tailored for wireless sensor network applications. She is also deeply interested in integrating deep learning frameworks into image enhancement and compression tasks to improve performance in real-world environments. Additionally, her background in VLSI design supports her interest in low-power hardware architectures for efficient implementation of image processing algorithms. Dr. Nayak is particularly motivated by research problems that bridge the gap between theoretical innovation and practical implementation, especially in the fields of embedded systems and multimedia communication. Her interdisciplinary research aims to create scalable, energy-efficient, and intelligent solutions for future communication and sensing technologies.

Award and Honor🏆

While Dr. Dibyalekha Nayak’s profile does not explicitly mention formal awards or honors, her scholarly achievements speak volumes about her academic excellence and dedication. She has published multiple research articles in prestigious SCI and Web of Science indexed journals such as Multimedia Tools and Applications, Mathematics, and Computers, reflecting the quality and impact of her research. She has been actively involved in reputed international conferences including IEEE and Springer Lecture Notes, where she has presented and published her research findings. Her work on saliency-based image compression and fuzzy rule-based adaptive block compressive sensing has received commendation for its innovation and applicability. Furthermore, her selection and sustained work as a Research Scholar at KIIT University for over five years highlights the recognition she has earned within academic circles. Her consistent participation in technical workshops, faculty development programs, and collaborations also demonstrate her growing reputation and standing in the field of electronics and image processing.

Research Skill🔬

Dr. Dibyalekha Nayak possesses a versatile and robust set of research skills aligned with modern-day challenges in image processing and electronics. She is proficient in developing image compression algorithms, saliency detection models, and adaptive techniques using block truncation coding, fuzzy logic, and DCT-based quantization. Her technical expertise extends to deep learning architectures tailored for image enhancement and compressive sensing in wireless sensor networks. Additionally, she has a strong command of VLSI design methodologies, enabling her to work on low-power circuit design and hardware implementation strategies. Dr. Nayak is also skilled in scientific programming, using tools such as MATLAB and Python, along with LaTeX for research documentation. She has a clear understanding of research methodologies, simulation frameworks, and performance analysis metrics. Her experience in preparing manuscripts for SCI-indexed journals and conference presentations showcases her technical writing abilities. Overall, her analytical mindset and hands-on skills make her a competent and impactful researcher.

Conclusion💡

Dr. Dibyalekha Nayak is a highly dedicated and emerging researcher in the fields of Image Processing, Deep Learning, and VLSI. Her academic journey reflects perseverance, scholarly depth, and a clear focus on impactful research. Her SCI-indexed publications, teaching experience, and cross-domain knowledge make her a deserving candidate for the Best Researcher Award.

Publications Top Noted✍

  • Title: Fuzzy Rule Based Adaptive Block Compressive Sensing for WSN Application
    Authors: D. Nayak, K. Ray, T. Kar, S.N. Mohanty
    Journal: Mathematics, Volume 11, Issue 7, Article 1660
    Year: 2023
    Citations: 6

  • Title: A novel saliency based image compression algorithm using low complexity block truncation coding
    Authors: D. Nayak, K.B. Ray, T. Kar, C. Kwan
    Journal: Multimedia Tools and Applications, Volume 82, Issue 30, Pages 47367–47385
    Year: 2023
    Citations: 4

  • Title: Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization
    Authors: D. Nayak, K. Ray, T. Kar, C. Kwan
    Journal: Computers, Volume 11, Issue 7, Article 110
    Year: 2022
    Citations: 3

  • Title: Sparsity based Adaptive BCS color image compression for IoT and WSN Application
    Authors: D. Nayak, T. Kar, K. Ray
    Journal: Signal, Image and Video Processing, Volume 19, Issue 8, Pages 1–7
    Year: 2025

  • Title: Hybrid Image Compression Using DCT and Autoencoder
    Authors: D. Nayak, T. Kar, K. Ray, J.V.R. Ravindra, S.N. Mohanty
    Conference: 2024 IEEE Pune Section International Conference (PuneCon), Pages 1–6
    Year: 2024

  • Title: Performance Comparison of Different CS based Reconstruction Methods for WSN Application
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: 2021 IEEE 2nd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC)
    Year: 2021

  • Title: A Comparative Analysis of BTC Variants
    Authors: D. Nayak, K.B. Ray, T. Kar
    Conference: Proceedings of International Conference on Communication, Circuits, and Systems (LNEE, Springer)
    Year: 2021

  • Title: Low Power Error Detector Design by using Low Power Flip Flops Logic
    Authors: D. Chaini, P. Malgi, S. Lopes
    Journal: International Journal of Computer Applications, ISSN 0975-8887
    Year: 2014

Ali Emamverdian | Forming | Best Researcher Award

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Dr . Ali Emamverdian | Forming | Best Researcher Award

Lecturer and researcher at HAUQIAO UNIVERSITY, China

Dr. Aliakbar Emamverdian is a dedicated mechanical engineering scholar with a strong academic and research background in manufacturing and automation. Born in February 1984, he currently serves as a lecturer and researcher at Huaqiao University, China. His career spans international institutions including Nanjing University of Science and Technology and Eastern Mediterranean University. Dr. Emamverdian’s expertise includes metal forming, material characterization, failure analysis, and life prediction, with a particular focus on integrating advanced tools like optical scanning and neural network modeling into traditional manufacturing processes. He has co-authored several peer-reviewed journal articles and a technical book on competency design in manufacturing systems. Dr. Emamverdian is multilingual, proficient in Farsi, English, and Turkish, and actively collaborates with leading researchers from institutions such as Imperial College London and Politecnico di Bari. His commitment to scientific innovation, academic teaching, and international collaboration defines his contributions to mechanical engineering.

Professional Profile 

Education🎓

Dr. Emamverdian began his academic journey with a Bachelor of Science degree in Mechanical Engineering from Islamic Azad University in 2007. Motivated by a passion for innovation and precision engineering, he pursued a Master of Science in Mechanical Engineering with a specialization in Manufacturing at Eastern Mediterranean University, completing it in February 2013. His academic trajectory culminated with a Ph.D. in Mechanical Engineering, Manufacturing, and Automation from the prestigious Nanjing University of Science and Technology in China, awarded in February 2023. His doctoral research emphasized simulation-based analysis, microstructural evolution, and neural network modeling for predicting material degradation in metal forming processes. Through this diverse educational background, Dr. Emamverdian developed a robust foundation in advanced manufacturing techniques, computational modeling, and experimental validation. His academic achievements reflect a blend of theoretical knowledge and practical problem-solving skills that empower his teaching and research in cutting-edge engineering disciplines.

Professional Experience📝

Dr. Aliakbar Emamverdian has amassed a wealth of international academic experience over the past decade. Currently, he is a lecturer and researcher at Huaqiao University (HQU), China, where he has been contributing to the Mechanical Engineering Department since September 2023. Prior to this, he served as a research assistant at Nanjing University of Science and Technology (NJUST), China, from September 2016 to June 2019, where he was actively engaged in simulation-based material research and experimental validation. From January 2013 to June 2016, Dr. Emamverdian worked as an assistant in the laboratory at Eastern Mediterranean University (EMU), Cyprus, where he supported academic courses and participated in experimental mechanics. His professional background spans teaching, laboratory assistance, and advanced research roles, reflecting a consistent commitment to academic excellence and international collaboration. His roles have allowed him to work on multi-disciplinary projects involving simulation, manufacturing technologies, and intelligent systems.

Research Interest🔎

Dr. Emamverdian’s research interests lie at the intersection of mechanical engineering and advanced manufacturing technologies. He specializes in metal forming, failure analysis, non-destructive testing, and life prediction of mechanical components. A key aspect of his research involves studying material behavior under thermal and mechanical stress during hot forging, particularly focusing on H21 steel dies. His recent work explores the use of optical scanning, finite element simulation, and microstructural analysis to predict degradation mechanisms in forging dies. Furthermore, he integrates neural network modeling and intelligent algorithms to enhance the predictive capabilities of mechanical systems. Dr. Emamverdian is also interested in the material characteristics of alloys and their responses to complex loading conditions. His interdisciplinary research contributes significantly to improving the durability and performance of manufacturing tools and supports the advancement of smart manufacturing systems. His approach combines theoretical analysis, experimental work, and computational intelligence.

Award and Honor🏆

While Dr. Emamverdian’s profile does not list specific personal awards or honors to date, his growing recognition is evident through his collaborations with high-ranking institutions and publication in reputable international journals. His research has appeared in Journal of Materials Research and Technology, Engineering Failure Analysis, and Journal of Visualization, highlighting the academic community’s trust in his work. He has worked alongside distinguished researchers from Imperial College London, University of Strathclyde, and Politecnico di Bari—an indication of his emerging prominence in the global mechanical engineering research community. His book publication on manufacturing system modeling, authored early in his career, showcases his long-standing commitment to research excellence. Continued international academic appointments further signify the respect and demand for his expertise. With ongoing high-quality research and impactful collaborations, Dr. Emamverdian is poised to receive formal accolades and awards recognizing his innovative contributions to mechanical engineering and manufacturing science.

Research Skill🔬

Dr. Emamverdian possesses an extensive portfolio of research and technical skills essential for modern mechanical engineering. He is proficient in advanced simulation tools like ABAQUS, DEFORM, and SIMUFACT FORMING, which he uses for stress analysis and die wear prediction. His modeling expertise includes CATIA V5 and SOLIDWORKS for mechanical design. For data analysis and intelligent systems, he employs MATLAB, particularly neural networks and fuzzy logic algorithms. Additionally, his hands-on experience with EBSD (Channel 5) and SEM techniques enhances his material characterization work. Dr. Emamverdian is also skilled in optical scanning and surface mapping using POLYWORKS, which supports his work in non-destructive evaluation and life prediction of industrial tools. His ability to combine computational, experimental, and analytical methods allows him to solve complex problems in metal forming and manufacturing. These research capabilities underpin his innovative approaches to failure analysis and smart manufacturing technologies.

Conclusion💡

Dr. Aliakbar Emamverdian demonstrates strong qualifications and innovative contributions in mechanical engineering and advanced manufacturing. His research spans experimental and simulation-based approaches, enriched by AI-driven analysis, and he collaborates with prestigious institutions globally. His work on failure analysis, die degradation, and metal forming simulation is both industrially relevant and academically rigorous.

While his profile could benefit from additional publication metrics, research funding leadership, and broader recognition, his technical depth, publication quality, and international collaborations make him a compelling candidate for the Best Researcher Award, particularly in the engineering and manufacturing domain.

Publications Top Noted✍

  • Title: Current failure mechanisms and treatment methods of hot forging tools (dies) – A review
    Authors: AA Emamverdian, Y Sun, C Cao, C Pruncu, Y Wang
    Year: 2021
    Citations: 72

  • Title: Deformation and wear in a H21 (3Cr2W8V) steel die during hot forging: Simulation, mechanical properties, and microstructural evolution
    Authors: AA Emamverdian, Y Sun, C Chunping
    Year: 2021
    Citations: 22

  • Title: The interaction of vortices induced by a pair of microjets in the turbulent boundary layer
    Authors: MJ Pour Razzaghi, C Xu, A Emamverdian
    Year: 2021
    Citations: 7

  • Title: Prediction of the main degradation mechanisms in a hot forging steel die: Optical scanning, simulation, microstructural evolution, and neural network modeling
    Authors: A Emamverdian, C Pruncu, H Liu, A Rahimzadeh, L Lamberti
    Year: 2025

  • Title: Corrigendum to “Deformation and wear in a H21 (3Cr2W8V) steel die during hot forging: Simulation, mechanical properties, and microstructural evolution”
    Authors: AA Emamverdian, Y Sun, C Chunping
    Year: 2022

  • Title: Design of a competency-based information and knowledge model for a manufacturing system: Case study EMU CIM Lab
    Author: AA Emamverdian
    Year: 2013

Shashank Singh Pawar | Marketing | Best Researcher Award

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Mr . Shashank Singh Pawar | Marketing | Best Researcher Award

Research Scholar at Goa Institute of Management, Goa, India

Shashank Singh Pawar is a dedicated FPM Scholar in Marketing at the Goa Institute of Management (GIM), India. With a strong foundation in engineering and management, he brings a multidisciplinary approach to his research, focusing on Human-Computer Interaction, Anthropomorphism, and Consumer Behavior. Shashank’s academic journey reflects his passion for exploring how digital interfaces and avatars influence consumer psychology and behavior across generations. His research has earned recognition through peer-reviewed publications and conference awards. Prior to joining GIM, he served as an Assistant Professor for over eight years, contributing actively to academia through teaching, mentoring, and organizing conferences. His editorial roles and participation in research workshops underscore his commitment to scholarly excellence. Driven by curiosity, innovation, and a deep understanding of digital consumer dynamics, Shashank is steadily building a reputation as a thoughtful and emerging researcher in his field.

Professional Profile 

Education🎓

Shashank Singh Pawar’s educational background reflects a strong interdisciplinary trajectory. He is currently pursuing a Ph.D. (Fellow Programme in Management) in Marketing at the Goa Institute of Management (2021–present), where his research centers on digital consumer behavior and avatar interactions. Prior to his doctoral studies, he completed his Master of Engineering in Industrial Engineering and Management from the Institute of Engineering & Technology (IET-DAVV), Devi Ahilya Vishwavidyalaya, Indore, in 2013. He laid the foundation for his academic and analytical skills with a Bachelor of Engineering in Mechanical Engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), Bhopal, in 2011. Throughout his academic journey, he demonstrated a consistent interest in integrating engineering principles with management and behavioral sciences, eventually transitioning into a research-driven career in marketing. His educational progression illustrates a deepening focus on the psychological and technological dimensions of consumer engagement.

Professional Experience📝

Before embarking on his doctoral journey, Shashank Singh Pawar gained substantial teaching experience as an Assistant Professor at the Chameli Devi Group of Institutions, Indore, from 2013 to 2021. During his eight-year tenure, he was involved in teaching core engineering and management subjects, advising student projects, and participating in academic event coordination. His excellence in mentoring was recognized with the Dronacharya Award for his role in guiding student teams in national competitions such as the Auto India Racing Championship (AIRC). His responsibilities extended beyond the classroom, where he actively contributed to organizing national-level conferences, thereby fostering a collaborative academic environment. This professional phase not only honed his instructional capabilities but also strengthened his academic leadership and research interests, ultimately paving the way for his transition to full-time research in marketing. His industry-relevant teaching experience complements his current research in digital marketing and consumer behavior.

Research Interest🔎

Shashank Singh Pawar’s research interests lie at the intersection of marketing, psychology, and technology. He is particularly drawn to Human-Computer Interaction (HCI), Anthropomorphism in AI systems, and Consumer Behavior in the context of immersive and digital interfaces. His ongoing doctoral work explores how different generations interact with AI-driven avatars and how realism and emotional cues affect consumer responses. He aims to understand and model consumer attitudes and behaviors in technology-mediated environments, such as virtual shopping and digital gifting contexts. His recent publication in Computers in Human Behavior and his working papers reflect this passion for examining user engagement with avatars, prosocial outcomes in digital gifting, and generational psychology. By applying experimental and qualitative methods, Shashank seeks to bridge the gap between theoretical insights and practical applications in digital marketing. His research contributes to the evolving understanding of AI-consumer relationships, influencing both academia and the future of customer experience design.

Award and Honor🏆

Shashank Singh Pawar has been recognized for his academic and mentoring excellence throughout his career. Most notably, he received the Best Paper Award (Runner-up) at the 9th Indian Academy of Management (INDAM) Conference 2024, a prestigious recognition reflecting the quality and relevance of his research on avatar-based consumer interactions. Earlier in his career, he was honored with the Dronacharya Award for his outstanding contribution as a faculty advisor during the Auto India Racing Championship (AIRC-2017), highlighting his dedication to student development and project mentorship. Additionally, he was named Student of the Year during his undergraduate engineering program in 2011, further validating his consistent academic dedication and leadership. These honors demonstrate his excellence across teaching, research, and academic service, underlining a well-rounded profile as an educator and scholar committed to impactful contributions in both academic and practical spheres.

Research Skill🔬

Shashank Singh Pawar possesses a well-rounded set of research skills, grounded in both qualitative and quantitative methodologies. He is proficient in SPSS (V26) for statistical analysis and MAXQDA for qualitative data analysis, having completed certification from Emory University, Georgia. His participation in workshops on Innovative Research Methods by Prof. Russell Belk and Experimental Research Design by Prof. Giampaolo Viglia showcases his hands-on training in advanced methodologies. Shashank applies these skills in studying nuanced consumer behavior, particularly in the digital space involving AI and avatars. He is also actively involved in academic peer-review, serving as a reviewer for the International Journal of Consumer Studies (ABDC-A) and for major conferences like INDAM, further validating his critical research acumen. His capability to conceptualize, design, analyze, and present research effectively makes him a strong contributor to the academic community and positions him well for future high-impact research outputs.

Conclusion💡

Shashank Singh Pawar demonstrates a promising and emerging research profile with a unique niche in human-computer interaction and consumer behavior. His interdisciplinary background, recognized publication, and active academic engagement make him a strong candidate for an early-career researcher award. However, for top-tier “Best Researcher” recognition, increasing his publication volume, citation impact, and global collaborations would make his profile even more competitive.

Publication Top Noted✍

  • Title: From Efficiency to Immersion: Understanding Generational Differences in Avatar Interactions

  • Authors: Shashank Singh Pawar; Anubhav A. Mishra

  • Year: 2025

  • Journal: Computers in Human Behavior

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