Dr. Ilai Bistritz is an Assistant Professor at the School of Industrial and Intelligent Systems Engineering and School of Electrical and Computer Engineering , Tel Aviv University , with a PhD in Electrical Engineering (2023) from Stanford University under Nicholas Bambos . His research bridges Game Theory , Distributed Control , and Multiagent Learning , focusing on decentralized decision-making in networked systems like autonomous vehicles , smart grids , and epidemic modeling . His work addresses challenges in Distributed Optimization where agents operate with limited communication and feedback, such as in multiplayer bandits and delayed adversarial environments . He has developed algorithms for max-min fairness , non-myopic informational cascades , and delay-robust regret minimization , achieving theoretical breakthroughs in Networked Artificial Intelligence . Scientific awards include the Best Student Paper Award at IEEE WCNC 2018 and Best Student Paper Finalist at WODES 2020 . His research emphasizes privacy-preserving protocols, scalable architectures, and applications in health monitoring , energy systems , and wireless networks .
Derong Liu is a Chair Professor at Southern University of Science and Technology (SUSTech) in Shenzhen, China, holding dual appointments as Full Professor of Electrical and Computer Engineering and Computer Science at the University of Illinois at Chicago. He is a distinguished scholar with multiple prestigious recognitions including Member of Academia Europaea, Fellow of IEEE, Fellow of International Neural Network Society, and Fellow of International Association for Pattern Recognition. His academic journey spans multiple institutions across China and the United States, with significant contributions to control theory and artificial intelligence. Ph.D. in Electrical Engineering from University of Notre Dame (1994) M.Sc. in Automatic Control Theory from Chinese Academy of Sciences (1987) B.Sc. in Mechanical Engineering from East China Institute of Technology (1982) Liu's research focuses on Adaptive Dynamic Programming and Reinforcement Learning, Intelligent Control and Information Processing, Modeling and Control of Complex Industrial Processes, Neural Networks and Computational Intelligence, and Smart Grid technologies. His work bridges theoretical foundations with practical applications in industrial control systems, particularly in automotive engine control and energy management. He has pioneered significant methodologies in neural network design and adaptive control systems that have become foundational in the field. His publication record demonstrates a consistent trajectory of high-impact research, with recent articles focusing on event-triggered control systems, neural architecture search, fault tolerant control, and multi-agent game theory applications. The research spans both theoretical advances in control algorithms and practical implementations in complex industrial systems, showing a clear evolution from foundational neural network research to sophisticated adaptive control frameworks. Member, Academia Europaea (2021) IEEE Computational Intelligence Society Neural Network Pioneer Award (2022) Dennis Gabor Award from International Neural Network Society (2018) Highly Cited Researcher by Clarivate (2017-present) Editor-in-Chief of Artificial Intelligence Review (2014-present) Liu has mentored numerous students and researchers throughout his career, serving as Editor-in-Chief for major journals and leading significant research initiatives. His work has been supported by multiple grants from the National Science Foundation of the United States and the National Natural Science Foundation of China. His research group continues to push boundaries in adaptive control systems and neural network applications. Currently, Liu leads a research group at SUSTech focusing on intelligent control systems, with active projects in adaptive dynamic programming, reinforcement learning applications, and smart grid technologies. His laboratory serves as a hub for interdisciplinary research connecting theoretical control frameworks with practical industrial implementations.
Wang-chien Lee is an active Associate Professor in Computer Science and Engineering, specializing in machine learning, data mining, and graph optimization. His work spans domains including social networks, wireless sensor systems, and location-based services. Key research focus areas: Recommendation systems, Graph neural networks, and Social network analysis Pioneering applications in traffic safety, VR configuration, and blockchain marketing His publications demonstrate expertise in transfer learning, deep learning frameworks, and heterogeneous network modeling. Recent work explores traffic crash prediction, social-aware VR systems, and NFT marketing optimization. Current projects include: Learning Latent Representations of Heterogeneous Information Networks Link Quality Estimation for Wireless Sensor Networks Community Clickthrough Model Development
Rakesh Venkat is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. His research focuses on Theoretical Computer Science, including approximation algorithms, hardness of approximation, and communication complexity. Education : Ph.D., Tata Institute of Fundamental Research (TIFR), Mumbai. Research Trends : His work addresses fundamental challenges in algorithm design, such as optimizing cache misses, improving clustering algorithms, analyzing graph expansion, and exploring embedding techniques. Publications span top-tier conferences like APPROX, FSTTCS, ICALP, and ITCS, with collaborations at institutions including HUJI, TIFR, and IIT-Bombay. Teaching : Courses taught include Approximation Algorithms, Advanced Data Structures, Discrete Mathematics, and Spectral Graph Theory.
Dr. Audrey Serna is a Senior Lecturer (HDR) at the National Institute of Applied Sciences of Lyon (INSA Lyon) and researcher at LIRIS laboratory, leading the SICAL team. She holds joint PhDs in Computer Science (University of Sherbrooke) and Cognitive Science (INP Grenoble), with postdoctoral experience at University of Grenoble. Her interdisciplinary work bridges Human-Computer Interaction and Cognitive Science. Research focuses on designing adaptive interactive systems through: User modeling: Analyzing activity patterns and engagement metrics Motivational design: Gamification, persuasive affordances Multi-device/VR environments: Classroom technologies and immersive learning Primary application domains are Education (adaptive learning, serious games) and Health (cognitive assistance, behavior change). Recent publications demonstrate strong focus on: Virtual Reality applications for chemistry education and safety training Longitudinal studies of adaptive gamification in learning systems Collaborative interfaces for multi-device classrooms Design frameworks for child-centered educational games Current PhD students include Anthony Basille (collaborative games) and former advisee Stuart Hallifax (adaptive gamification). Dr. Serna leads several ANR projects including LudiMoodle+ (adaptive gamification) and immersive chemical risk training systems. Her SICAL research group at LIRIS develops technologies including: VR environments for reflective learning Multi-surface collaborative systems Adaptive gamification engines Lab resources are accessible via LIRIS website .
Walker M. White serves as Stephen H. Weiss Provost's Teaching Fellow and Director of the Game Design Initiative at Cornell (GDIAC) within Cornell University's Department of Computer Science. He teaches core game design courses (CS/INFO 3152 and CS/INFO 4152), CS 1110 (Introduction to Computing in Python), and mentors independent study projects through CS/INFO 4999. His academic leadership spans curriculum development, career advising for game design students, and cross-departmental coordination for Cornell's game design minor. White's research centers on two interconnected domains: data-driven game development and data stream processing. In data-driven games, he pioneers declarative specification methods for non-player character behavior, addressing performance bottlenecks in massively multiplayer environments through innovations like the SGL language. His concurrent Cayuga project develops scalable data stream processing systems that balance expressive query capabilities with publish/subscribe system efficiency, yielding theoretical advances in temporal query semantics and practical implementations for event monitoring. His educational scholarship focuses on inquiry-based learning techniques adapted from mathematical logic to computer science pedagogy. White's publication record from 2006-2011 reveals a cohesive trajectory where database theory informs gaming innovation. Early work established foundational challenges in virtual world scalability, evolving into specialized techniques for checkpoint recovery, MapReduce-based behavioral simulation, and declarative game languages. Simultaneously, his Cayuga research advanced publish/subscribe systems through multi-query optimization and formal stream semantics, demonstrating consistent methodological rigor across both domains. Stephen H. Weiss Provost's Teaching Fellow As GDIAC Director, White advises undergraduate game design students through competitive independent study projects, with select student games featured at independent game festivals. He facilitates industry recruitment by major studios including Electronic Arts, Valve, and Bungie, while supporting student startups in the mobile gaming space. His career advising leverages strong industry connections cultivated through Cornell's game design program. White leads the Game Design Initiative at Cornell (GDIAC), coordinating game design education across multiple academic departments. He collaborates extensively with Cornell's database research group, particularly with Johannes Gehrke and Alan Demers on data stream processing and gaming projects. His educational initiatives include developing inquiry-based learning materials for computer science and mathematics bridge courses.
