Tushar Kafare | Artificial Intelligence | Best Researcher Award

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Dr. Tushar Kafare | Artificial Intelligence | Best Researcher Award

Assistant Professor at Sinhgad College of Engineering, India

Dr. Tushar Vaman Kafare is an Assistant Professor in the Department of Electronics and Telecommunication (E&TC) at the Sinhgad Technical Education Society (STES). With over 14 years of experience in teaching, he has made a significant impact in the field of Electronics and Telecommunication. His research and expertise span across machine learning, deep learning, computer vision, embedded systems, and various programming languages like Python, MATLAB, C, and Embedded C. Dr. Kafare is known for his dedication to teaching and research, having guided numerous student projects and published research work, focusing particularly on machine learning applications in plant disease analysis.

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Education

Dr. Kafare holds an M.E. degree in Electronics and Telecommunication, as well as a B.E. in Electronics. His strong academic background has been further reinforced by his ranking 6th in his graduation. His academic qualifications, combined with extensive practical and theoretical knowledge, make him a highly skilled educator and researcher. His ongoing Ph.D. research focuses on plant disease analysis using machine learning models, showcasing his commitment to advancing technological applications in agriculture.

Experience

Having joined STES on September 7, 2022, Dr. Kafare brings with him a wealth of experience in academia and industry. His teaching career spans over 14 years, during which he has mentored undergraduate and postgraduate students. He has contributed significantly to course development and the enhancement of educational experiences for students, incorporating advanced techniques in machine learning and embedded systems. Additionally, Dr. Kafare has served as a resource person for numerous workshops and faculty development programs, further demonstrating his expertise and commitment to professional growth.

Research Interests

Dr. Kafare’s primary research interest lies in the application of machine learning and image processing for agricultural advancements. His Ph.D. research focuses on using machine learning models to analyze plant diseases, particularly in grape and apple plants, through advanced image processing techniques. He is also interested in deep learning, computer vision, and embedded systems, areas that allow for the development of innovative solutions for real-world problems. Through his research, he aims to contribute to the growing field of agri-tech by leveraging modern computational techniques to assist in plant disease diagnostics and management.

Awards

Dr. Kafare has been recognized for his outstanding contributions in teaching and research. He received the prestigious Digital Teacher Award from ICT Academy, highlighting his exceptional use of technology in education. Additionally, his academic excellence is reflected in his university ranking, securing 6th place in his graduation. In 2024, he was honored with the Best Paper Award at the International Conference on Machine Learning in Jaipur, India, acknowledging the high impact and relevance of his research in the machine learning community.

Publications

Dr. Kafare has made significant contributions to the field of machine learning and telecommunication through his publications. His work has been widely cited, demonstrating the importance of his research. Below is a list of selected publications:

Kafare, T.V. et al., “Analysis on Plant Disease Diagnosis Using Convolution Neural Networks,” International Journal of Machine Learning, 2023, Scopus/SCI.

Kafare, T.V. et al., “Segmentation Techniques for Plant Disease Detection,” Journal of Image Processing, 2022, Scopus.

Kafare, T.V., “Double Convolution in CNN for Improved Plant Disease Classification,” International Conference on Machine Learning, 2024, Conference paper.

Kafare, T.V., et al., “Fungal Disease Detection in Grapes Using Machine Learning,” Journal of Agricultural Technology, 2021, Scopus.

Conclusion

Dr. Tushar Vaman Kafare’s career is marked by his dedication to both teaching and research, with a clear focus on applying machine learning and image processing to solve practical problems in agriculture. With over 14 years of teaching experience, he has proven himself as a skilled educator and researcher. His ongoing Ph.D. research, along with his numerous publications and awards, highlights his expertise in his field. As an active participant in academic and professional activities, he continues to contribute to the development of students and the academic community at large, particularly in the domains of machine learning and embedded systems.

Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

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Assist. Prof. Dr. Quanming Yao | Automated Machine Learning (AutoML) | AI & Machine Learning Award

Assistant Professor at Department of Electronic Engineering, Tsinghua University, China

Quanming Yao is a world-class researcher in the field of machine learning, holding the position of Assistant Professor in the Department of Electronic Engineering at Tsinghua University. With a strong academic background and extensive experience in deep learning, Yao’s research focuses on creating efficient and parsimonious solutions in machine learning, particularly in deep networks and graph learning. His work aims to enhance interpretability in AI models and has led to groundbreaking advancements, such as the development of EmerGNN, the first deep learning model that interprets drug-drug interaction predictions for new drugs. His contributions have significantly impacted both academia and industry, leading to the commercialization of his methods in the AI unicorn 4Paradigm.

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Education

Yao earned his Ph.D. in Computer Science and Engineering from the Hong Kong University of Science and Technology (HKUST) between 2013 and 2018. Prior to this, he completed his undergraduate studies at Huazhong University of Science and Technology, where he obtained a degree in Electronic and Information Engineering in 2013.

Experience

Before becoming an assistant professor at Tsinghua University in 2021, Yao worked as a researcher and senior scientist at 4Paradigm Inc. in Hong Kong, from June 2018 to May 2021. In his current academic role, he serves as a Ph.D. advisor, leading research in machine learning and AI, with a specific focus on making deep learning models more efficient and interpretable.

Research Interests

Yao’s research interests revolve around the concept of “parsimonious deep learning,” wherein he explores how simple solutions can lead to substantial improvements in machine learning models. His work is especially notable for its emphasis on automated graph learning methods, which has earned him first place in the Open Graph Benchmark, an equivalent to ImageNet in graph learning. He is also dedicated to the development of deep learning methods that provide interpretable results, particularly in domains like drug discovery, where his innovations have had a direct impact on creating a synthetic biology startup, Kongfoo Technology.

Awards

Yao’s exceptional contributions to the field of machine learning have earned him numerous prestigious awards. These include the Inaugural Intech Prize in 2024, the Aharon Katzir Young Investigator Award in 2023, Forbes 30 Under 30 in the Science & Healthcare Category (China) in 2020, and the Google Ph.D. Fellowship in 2016. He was also recognized as one of the World’s Top 2% Scientists in 2023, highlighting his influence in the global research community.

Publications

Yao has published over 100 papers in top-tier international journals and conferences, with a significant citation record (around 12,000 citations and an h-index of 36). His work includes several landmark papers, such as:

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network, Nature Computational Science, 2023.

AutoBLM: Bilinear Scoring Function Search for Knowledge Graph Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.

Efficient Low-rank Tensor Learning with Nonconvex Regularization, Journal of Machine Learning Research (JMLR), 2022.

Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels, Advance in Neural Information Processing Systems (NeurIPS), 2018.

These papers showcase his innovative work in the areas of drug interaction prediction, knowledge graph learning, and robust training of deep neural networks, significantly impacting both theoretical and practical aspects of AI.

Conclusion

Quanming Yao stands out as a leader in machine learning, particularly in deep learning, graph learning, and AI applications in drug discovery. His exceptional academic journey, impactful research, and numerous awards reflect his profound influence in the field. Yao’s contributions to AI are reshaping industries, and his future work promises to continue pushing the boundaries of what is possible with machine learning.

Qizhi He | Reinforcement Learning | Best Researcher Award

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Dr. Qizhi He | Reinforcement Learning | Best Researcher Award

Associate Researcher | DJI Innovation Technology Co., Ltd. | China

Dr. Qizhi He is an accomplished engineer and researcher specializing in navigation, guidance, and control systems. His academic and professional journey has been characterized by excellence and innovation, contributing significantly to the fields of multi-sensor information fusion, aircraft damage reconstruction, and autonomous vehicle localization. With a Doctor of Engineering degree from Northwestern Polytechnical University and a Master’s with Distinction from the University of Leicester, Dr. He has consistently demonstrated expertise in both theoretical research and practical application. His work spans prominent roles in academia, industry-leading companies, and national projects, underscoring his versatility and dedication to advancing technological solutions.

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Scholar

Education

Dr. He’s academic journey began with a Bachelor of Engineering degree at Northwestern Polytechnical University, where he participated in an integrated undergraduate, master’s, and doctoral program. He later pursued a Master of Science in Advanced Engineering at the University of Leicester, achieving a distinction and excelling in dynamics of mechanical systems. His doctoral research at Northwestern Polytechnical University focused on multi-sensor information fusion and aircraft damage reconstruction, culminating in groundbreaking contributions to Shaanxi Key Laboratory of Aircraft Control and Simulation. Throughout his education, Dr. He earned numerous scholarships and accolades, reflecting his exceptional academic performance.

Experience

Dr. He’s professional experience spans both academia and industry. At DJI Innovation Technology Co., Ltd., he led localization modules for agricultural drones, logistics drones, and automatic parachutes, optimizing sensor fusion algorithms to enhance system performance. He also contributed to autonomous vehicle localization at XPENG Motors and developed advanced robotics algorithms during his tenure at Limx Dynamics. His current role as an assistant researcher at the Yangtze River Delta Research Institute focuses on unmanned systems, leveraging his expertise to innovate in multi-sensor fusion and localization technologies.

Research Interests

Dr. He’s research interests lie at the intersection of multi-sensor information fusion, robust control systems, and autonomous navigation technologies. He has contributed to advancing the understanding of information fusion through Kalman filters, observer-based methods, and manifold theory, with applications in unmanned aerial vehicles (UAVs), autonomous driving, and robotics. His work emphasizes the development of vibration-resistant and interference-free algorithms, pushing the boundaries of GPS-denied localization and fault-tolerant systems for aircraft and underwater vehicles.

Awards

Dr. He’s achievements have earned him prestigious recognitions, including the “Belt and Road” Special Scholarship, Outstanding Talent Scholarship, and several academic excellence awards. His exceptional performance in circuit experiments and his distinction at the University of Leicester further attest to his technical and intellectual prowess.

Publications

Dr. Qizhi He has authored over 20 SCI/EI papers, including influential articles in top-tier journals. Below are a selection of his publications:

“Robust Adaptive Flight Control for Faulty Aircraft” (2020) – Published in Aerospace Science and Technology, cited by 15 articles.

“Multi-Sensor Information Fusion for UAV Localization” (2021) – Published in Journal of Navigation, cited by 12 articles.

“Dynamic Modeling of Aircraft Wing Damage Control” (2019) – Published in Control Engineering Practice, cited by 10 articles.

“Innovations in AHRS Algorithm Design” (2022) – Published in IEEE Transactions on Aerospace and Electronic Systems, cited by 20 articles.

“Error State Kalman Filter on SO(3) for Robotics” (2023) – Published in Robotics and Autonomous Systems, cited by 8 articles.

“Reconfigurable Control Systems for Civil Aircraft” (2021) – Published in Aerospace Systems Design, cited by 6 articles.

“Vision-Based Localization in GPS-Denied Environments” (2022) – Published in Sensors, cited by 18 articles.

Conclusion

Dr. Qizhi He embodies the fusion of rigorous academic research with practical engineering applications. His expertise in navigation and control systems, combined with his dedication to innovation, has made him a valuable contributor to both industrial advancements and scholarly research. As he continues his journey, Dr. He remains committed to addressing critical challenges in unmanned systems and autonomous technologies, advancing the state of the art in multi-sensor information fusion and robust control systems.