Rohan Duppala | Machine Learning | Young Researcher Award

Mr. Rohan Duppala | Machine Learning | Young Researcher Award

Student at VIT-AP University, India

Rohan Duppala is an emerging researcher and technologist with a strong foundation in artificial intelligence, machine learning, deep learning, and natural language processing. As a final-year B.Tech Computer Science student at VIT-AP University, he has demonstrated exceptional research capabilities, developing innovative solutions in healthcare, education, and smart transportation. Rohan has published multiple papers in reputed journals, including Scientific Reports and MDPI, and has worked on diverse AI-driven projects, from infant cry classification and Alzheimer’s detection to generative AI-based educational tools. His ability to integrate advanced AI models with real-world applications reflects a rare combination of academic rigor and practical insight. In addition to academic work, Rohan has engaged with leading technologies like Gemini, Llama 3, and Weights & Biases, earning several certifications and accolades. With a forward-thinking mindset and a passion for impactful research, he aspires to contribute meaningfully to global challenges through AI and interdisciplinary innovation.

Professional Profile 

Education🎓

Rohan Duppala is currently pursuing his Bachelor of Technology in Computer Science and Engineering at VIT-AP University in Andhra Pradesh. His education has provided a rigorous grounding in core computer science principles while enabling him to explore advanced technologies such as artificial intelligence, machine learning, and natural language processing. Prior to his undergraduate studies, he completed his intermediate education in the Mathematics, Physics, and Chemistry (MPC) stream at Narayana Junior College in Visakhapatnam. He also completed his schooling at Sri Chaitanya School in Palasa, Andhra Pradesh. Throughout his academic journey, Rohan has demonstrated consistent excellence and a strong inclination toward analytical thinking and computational problem-solving. This solid educational background has laid the foundation for his research endeavors and technical accomplishments in AI, edge computing, and intelligent systems.

Professional Experience📝

Rohan gained hands-on industry experience as an IoT Specialist at Prayana Electric between June and August 2024. During his tenure, he was instrumental in integrating IoT solutions into electric bicycles, leveraging microcontrollers, GPS, and LoRa technologies to enable real-time monitoring and smart navigation. He also spearheaded the development of a pothole detection system, optimized specifically for edge deployment on Raspberry Pi Pico, seamlessly integrating it into the e-bike ecosystem. This professional experience not only expanded his understanding of smart transportation and embedded systems but also allowed him to apply theoretical AI knowledge to practical, scalable solutions. Rohan’s work at Prayana Electric reflects his ability to bridge the gap between academic research and industry requirements, highlighting his skills in system design, data analysis, and sensor integration. His initiative, problem-solving abilities, and adaptability in a real-world setting underscore his potential as a well-rounded researcher and future technology leader.

Research Interest🔎

Rohan Duppala’s research interests lie at the intersection of artificial intelligence, healthcare, education, and smart systems. He is particularly focused on building explainable and ethically sound AI systems that can be deployed in real-world settings. His work in medical diagnostics, including projects on Alzheimer’s and Parkinson’s disease detection using deep learning, underscores a commitment to socially impactful research. Rohan also explores Generative AI and Large Language Models, applying them to applications such as automated script evaluation, infant care, and educational feedback systems. His interest in edge AI and IoT-enabled smart devices reflects a drive to create scalable, efficient, and context-aware solutions for real-time environments. By combining transformer models, retrieval augmented generation (RAG), signal processing, and explainable AI (XAI) techniques, Rohan aims to push the boundaries of intelligent automation in human-centric domains. His interdisciplinary approach and ethical consideration make his research both innovative and future-ready.

Award and Honor🏆

Rohan Duppala has been recognized for his academic and technical excellence with several awards and honors. He received a Certificate of Honor from OpenCV University in recognition of his outstanding performance in deep learning applications and project execution. Additionally, he was awarded a unique NFT (Non-Fungible Token) honor from The Hashgraph Association, celebrating his innovation and engagement in the decentralized technology community. His publications in reputed platforms such as Scientific Reports (Nature) and MDPI further reflect the quality and impact of his research. These accolades highlight not only his technical achievements but also his ability to stand out in competitive, global academic and developer communities. His participation in specialized bootcamps, advanced workshops, and certificate programs offered by Google, AWS, Cisco, and Weights & Biases has further solidified his position as an accomplished early-career researcher poised for excellence in AI-driven innovation.

Research Skill🔬

Rohan Duppala possesses an advanced and versatile skill set tailored for research in artificial intelligence and related domains. He is proficient in Python and Java, with hands-on experience in building custom convolutional neural networks, implementing transformer models, and deploying real-time deep learning systems on edge devices like Raspberry Pi. His practical expertise spans data preprocessing, hyperparameter tuning, model evaluation, explainable AI (XAI) techniques like LIME and Saliency Maps, and fine-tuning large models like LLaMA 3 8b using LoRA. He has also worked with tools such as Hugging Face Transformers, Weights & Biases, and Streamlit for model development and deployment. Rohan is skilled in retrieval augmented generation (RAG), multimodal data processing, and prompt engineering. His ability to combine AI techniques with IoT, computer vision, and NLP enables him to develop interdisciplinary solutions. These research skills, backed by strong implementation and critical thinking abilities, make him a technically mature and innovation-ready researcher.

Conclusion💡

Rohan Duppala is a highly deserving candidate for the Best Researcher Award, owing to his exceptional drive, technical acumen, and impactful research contributions at an early stage of his academic journey. His work spans critical societal applications—from medical diagnostics using deep learning to educational tools powered by large language models—demonstrating both depth and relevance. With peer-reviewed publications in reputed journals like Scientific Reports and MDPI, and hands-on innovation in smart technologies and AI systems, he has already laid a strong foundation for a distinguished research career. Given his dedication, continuous learning, and visionary approach to solving real-world problems, Rohan holds immense potential for future leadership in the fields of artificial intelligence and intelligent healthcare systems.

Publications Top Noted✍

  • Title: An extensive experimental analysis for heart disease prediction using artificial intelligence techniques
    Authors: D. Rohan, G.P. Reddy, Y.V.P. Kumar, K.P. Prakash, C.P. Reddy
    Year: 2025
    Citations: 4

  • Title: A Custom Convolutional Neural Network Model-Based Bioimaging Technique for Enhanced Accuracy of Alzheimer’s Disease Detection
    Authors: P. Reddy G., S.M.A. Kareem, Y.V.P. Kumar, P.P. Kasaraneni, M. Janapati
    Year: 2025
    Citations: 1

  • Title: Artificial intelligence-based effective detection of Parkinson’s disease using voice measurements
    Authors: G. Pradeep Reddy, D. Rohan, Y.V.P. Kumar, K.P. Prakash, M. Srikanth
    Year: 2024
    Citations: 1

Shujiao Liao | Machine Learning | Best Researcher Award

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

Prof. Igor Belenichev | Machine Learning | Excellence in Innovation

Prof. Igor Belenichev | Machine Learning | Excellence in Innovation

Professor at Zaporizhzhia State Medical University, Ukraine

Profiles

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📚 Summary

Prof. Igor Fedorovich Belenichev is a distinguished Full Professor and Head of the Department of Pharmacology and Medical Formulation at Zaporizhzhia State Medical University. Renowned for his innovative research in neuroprotection and pharmacology, he is a laureate of the Cabinet of Ministers of Ukraine Prize for the development and implementation of groundbreaking technologies.

Education

  • Zaporizhzhia State Medical Institute (1988): Graduated with a degree in medicine.
  • Postgraduate studies (1988), professor assistant (1991), senior teacher (1999), associate professor (2004), and full professor (2006).

💼 Professional Experience

  • Zaporizhzhia State Medical University: Head of the Department of Pharmacology and Medical Formulation since 2005.
  • Main Scientific Researcher at «Pharmatrone» (since 1993).
  • Head of the regional branch of the Association of Pharmacologists of Ukraine.
  • Co-worker of the regional group of the National Expert Centre of the Ministry of Health of Ukraine.

🔬 Research Interests

Prof. Belenichev’s research focuses on the molecular and biochemical mechanisms of ischemic brain damage and the development of effective neuroprotectors. His work explores the roles of reactive oxygen and nitrogen species, thiol-disulfide systems, pro-/anti-apoptotic proteins, estrogen receptors, and endogenous neuroprotection factors. He also investigates drugs for CNS pathologies and effective neuro- or cardioprotectors from derivatives of 1,2,4-triazole, chinazoline, and xanthine.

🏆 Achievements

  • Scientific Works: Authored and co-authored 715 scientific publications.
  • Patents: Holder of 182 patents in Ukraine and the Russian Federation.
  • Theses: Supervised 3 Dr. Habs and 7 Ph.D. theses.
  • Drug Development: Contributed to the creation of drugs like Thiotriazoline, Thiocetam, and Thiodarone.
  • Awards: Token of the Bibliographical Society of America (2003), Regional Program “Zoryaniy Shlyakh” Prize (2000), and Cabinet of Ministers of Ukraine Prize (2017).

 

Publications

5+1-Heterocyclization as preparative approach for carboxy-containing triazolo[1,5-c]quinazolines with anti-inflammatory activity

  • Authors: Krasovska, Natalya; Berest, Galina; Belenichev, Igor; Severina, Hanna; Nosulenko, Inna; Voskoboinik, Oleksii; Okovytyy, Sergiy; Kovalenko, Serhii
  • Journal: European Journal of Medicinal Chemistry
  • Year: 2024

Beta-Blockers of Different Generations: Features of Influence on the Disturbances of Myocardial Energy Metabolism in Doxorubicin-Induced Chronic Heart Failure in Rats

  • Authors: Igor Belenichev; Olexiy Goncharov; Nina Bukhtiyarova; Oleh Kuchkovskyi; Victor Ryzhenko; Lyudmyla Makyeyeva; Valentyn Oksenych; Oleksandr Kamyshnyi
  • Journal: Biomedicines
  • Year: 2024

Characteristics of HIF-1α and HSP70 mRNA Expression, Level, and Interleukins in Experimental Chronic Generalized Periodontitis

  • Authors: Parkhomenko Daria; Igor Belenichev; Kuchkovskyi Oleh; Ryzhenko Victor
  • Journal: MicroRNA
  • Year: 2024

Comparative Analysis of the Effect of Beta Blockers of Different Generations on the Parameters of Myocardial Energy Metabolism in Experimental Doxorubicin-Induced Chronic Heart Failure

  • Authors: Igor Belenichev; Olexiy Goncharov; Nina Bukhtiyarova; Oleh Kuchkovskyi; Victor Ryzhenko; Lyudmyla Makyeyeva; Valentyn Oksenych; Oleksandr Kamyshnyi
  • Journal: Preprint
  • Year: 2024

Development and Optimization of Nasal Composition of a Neuroprotective Agent for Use in Neonatology after Prenatal Hypoxia

  • Authors: Igor Belenichev; Olena Aliyeva; Bogdan Burlaka; Kristina Burlaka; Oleh Kuchkovskyi; Dmytro Savchenko; Valentyn Oksenych; Oleksandr Kamyshnyi
  • Journal: Pharmaceuticals
  • Year: 2024