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Milind TambeView profile

Professor

• Artificial Intelligence
• Public Health
• Wildlife Conservation
• +3 more

Milind Tambe is a Gordon McKay Professor of Computer Science at Harvard University and Director of the Center for Research on Computation and Society. He concurrently serves as Principal Scientist and Director for 'AI for Social Good' at Google DeepMind. His research focuses on applying AI to societal challenges like public health, conservation, and security. Tambe holds a PhD from Carnegie Mellon University and an undergraduate degree from BITS Pilani, India. Key roles include: Founding the Center for Artificial Intelligence in Society at USC (2016) Developing PAWS for wildlife conservation and ARMOR for security games Leading AI projects with NGOs like ARMMAN for maternal health Research interests span AI for social impact, multiagent systems, and game theory. Notable awards include the AAAI Feigenbaum Prize (2023) and the IJCAI John McCarthy Award (2023). He has advised 38 PhD students and 16 postdocs, fostering interdisciplinary collaboration across Harvard's departments. Current projects include: AI-driven maternal health interventions (e.g., mMitra and Kilkari programs) Wildlife conservation via PAWS deployment in 800+ parks AI for tuberculosis testing and HIV prevention His lab, Teamcore, emphasizes real-world impact, with deployments saving over $100M for US agencies and improving global health outcomes.

• Autonomous Navigation for Robots
• Deep Learning
• Artificial Intelligence
• +7 more

Jesús Tordesillas Torres is an Assistant Professor in the Department of Electronics, Automation, and Communications at the School of Engineering, Comillas Pontifical University. He joined the institution in June 2024, bringing extensive experience from postdoctoral research at MIT and ETH Zurich, and prior academic training from MIT and the Polytechnic University of Madrid. Education: PhD in Aeronautics and Astronautics, Massachusetts Institute of Technology (MIT), 2022 MS in Aeronautics and Astronautics, MIT, 2019 MS in Industrial Engineering, Polytechnic University of Madrid, 2019 BS in Industrial Engineering, Polytechnic University of Madrid, 2016 His research focuses on robotics, particularly autonomous navigation, trajectory planning, and optimization under uncertainty. He integrates deep learning and control theory to develop systems capable of safe, fast, and perception-aware navigation in dynamic and unknown environments. His work spans aerial and ground robots, multiagent systems, and challenging terrains, with strong emphasis on real-world deployment and robustness. The recent publications highlight a consistent trend in trajectory optimization, perception-aware planning, and multiagent coordination. His work bridges theoretical advances in optimization and learning with practical robotic applications, especially in safety-critical and communication-constrained scenarios. Scientific Awards: Best Paper Award, IEEE ICRA 2023 1st Place, Urban Circuit, DARPA Subterranean Challenge (2020) 2nd Place, Tunnel Circuit, DARPA Subterranean Challenge (2021) Finalist, Best Paper, IEEE IROS 2019 Jesús Tordesillas Torres has been actively involved in research grants and projects, including the ADS FERRARI WP-2 Project funded by Airbus Defence and Space (2025–2025). He mentors students and collaborates with leading institutions such as MIT, ETH Zurich, and the University of Pennsylvania. He also serves as a reviewer for top-tier journals including IEEE Transactions on Robotics , International Journal of Robotics Research , and IEEE Robotics and Automation Letters , as well as major conferences like ICRA and IROS. He leads and participates in research teams focused on autonomous systems, with affiliations to the Institute for Research in Technology (IIT) at Comillas. His invited talks at ETH Robotics Summer School and University of Pennsylvania reflect his growing influence in the robotics community.

Dennis GrinwaldView profile

Researcher

• Probabilistic Machine Learning
• Distributed Deep Learning
• Explainable AI
• +3 more

Dennis Grinwald is a doctoral researcher at the Technical University of Berlin affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a Master of Science in Computer Science from TU Berlin (2022) and focuses on machine learning applications in collaborative multiagent systems, distributed deep learning, and explainable AI. His technical work includes open-source projects like Hanabi game-solving with reinforcement learning, DQN-based Lunar Lander automation, and speech command prediction using LSTMs. M.Sc. in Computer Science (2022, TU Berlin) Current focus areas: Probabilistic ML, distributed learning architectures, and XAI Active GitHub contributor (22 repositories, 8 followers) Notable projects: Hanabi multiagent reinforcement learning ML applications in natural sciences DQN for autonomous lunar lander environments LSTM-based speech command recognition

Ilai BistritzView profile

Assistant Professor

• Game Theory
• Distributed Control
• Multiagent Learning
• +10 more

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 .

• Computer Science
• Mathematics
• Social Sciences
• +6 more

J. M. Meylahn is an Assistant Professor at the University of Twente, affiliated with the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) and the Applied Mathematics Department's Stochastic Operations Research group (EEMCS-AM-MOR). Their research focuses on multiagent learning and interdisciplinary intersections of computer science, mathematics, and social sciences. Expertise: Reinforcement Learning, Social Dilemma, Learning Algorithm, Interaction, Organisations Education: Theoretical Physics (Stellenbosch University), Mathematics PhD (Leiden University, 2019) Research interests: Multiagent systems, stochastic operations research, learning algorithms in social contexts

• Time delayed systems
• Cooperative control
• Infinite delayed systems
• +4 more

Xiang Xu is an Associate Professor at the School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech in December 2022 as an Associate Professor in the Department of Electronic and Electrical Engineering, following a postdoctoral fellowship at City University of Hong Kong from 2018 to 2021. His educational background includes: Ph.D. in Mechanical and Biomedical Engineering from City University of Hong Kong (2014-2018) Bachelor of Engineering in Automation from Nanjing University of Science and Technology (2010-2014) Xu's research focuses on advanced control theory, particularly addressing complex challenges in time-delayed systems. His work has significantly contributed to the understanding and control of systems with infinite delays, which are notoriously difficult to analyze and stabilize. He has developed novel Lyapunov-based methods and predictor feedback techniques that have extended the theoretical foundations of control theory for systems with unbounded delays. His research bridges theoretical advancements with practical applications in multi-agent systems, traffic control, and reinforcement learning. Xu has been recognized with several prestigious awards, including: National Science Fund for Excellent Young Scholars (Overseas) in 2022 Shenzhen Peacock Plan Class C in 2022 2023 All-Wheel Drive System Theory and Application Conference Outstanding Young Paper Award City University of Hong Kong Outstanding Doctoral Dissertation Award in 2018 Shortlisted for the 2018 Guan Zhaozhi Award Xu has mentored four Ph.D. students during his postdoctoral period, all of whom have published papers in top control journals. Two of his mentees have secured assistant professor positions at other universities. His research has attracted significant funding, including two RGC grants totaling over HK$1.7 million that his supervisor successfully applied for based on Xu's work. He is actively recruiting PhD students, postdoctoral fellows, and research assistants to continue his research in time-delay systems and multi-agent control. Xu leads a research group focused on control theory at SUSTech, with ongoing projects in multi-agent systems, time-delay systems, and partial differential equations. His team collaborates with leading researchers worldwide, including IEEE Fellow Miroslav Krstic from the University of California, San Diego. The research group provides comprehensive training for students and postdocs, with strong support for publishing in top-tier journals and attending international conferences.

Lina LiView profile

Professor

• Control Theory
• Reinforcement Learning
• Optimization
• +5 more

Lina Li is the Winokur Family Professor of Electrical Engineering and Applied Mathematics at Harvard University's School of Engineering and Applied Sciences (SEAS), where she serves as Area Chair for Electrical Engineering. She is an active researcher and advisor in control systems, learning algorithms, and their applications to cyber-physical systems. Research Focus: Integrates control theory, reinforcement learning, and optimization with applications in robotics, neuroscience, energy systems, and physical AI. Key Themes: Model-based and model-free learning, diffusion models, scalable multiagent-learning, and interdisciplinary methodologies bridging mathematics, engineering, and economics. Recent Awards: IFAC Manfred Thoma Medal (2022) Antonio Ruberti Young Researcher Prize (2024) NSF CAREER Award ONR YIP Award ASHRAE Best Paper Award (2024) Advising & Impact: Mentored former PhD students Xin Chen (Texas A&M) and Yingying Li (UIUC), both now in academic positions. Organized workshops at NeurIPS (2025) and NSF (2025) to advance AI applications in smart cities and reinforcement learning.

Britton D. WolfeView profile

Professor

• Machine Learning
• Computer Security
• 3D Vision
• +3 more

Britton D. Wolfe is a Professor of Computer Science at Grove City College's College of Engineering and Business. His career spans academic research, industry collaboration, and teaching across multiple CS domains. Ph.D. in Computer Science and Engineering, University of Michigan (2009) M.S. in Computer Science and Engineering, University of Michigan (2005) B.S. in Computer Science, Carnegie Mellon University (2003) Research focuses on applying machine learning to diverse challenges: Android malware detection using Google Play data, 3D vision for robotics, and collaborative tracking systems for biological research. He develops deep learning techniques for ant-tracking robots in natural habitats, working with students to refine computer vision algorithms. His publications (2005-2017) demonstrate sustained expertise in malware analysis , tracking algorithms , and predictive state modeling . Current work combines AI with biology studies through interdisciplinary robotics projects. Contact: bdwolfe@gcc.edu

Farhad MohsinView profile

Assistant Professor

• Computational Social Choice
• Fairness in Machine Learning
• Preference Aggregation
• +4 more

Farhad Mohsin is an Assistant Professor in the Department of Math and Computer Science at College of the Holy Cross in Worcester, MA. He earned his PhD in Computer Science from Rensselaer Polytechnic Institute (RPI) between 2018-2023, working with Professor Lirong Xia, and completed his BSc in Electrical and Electronic Engineering at Bangladesh University of Engineering and Technology (BUET) from 2010-2015. Prior to his academic career, he worked as a Telecommunications Engineer/Data Analyst at Grameenphone Ltd, Bangladesh. Dr. Mohsin's research focuses on computational social choice, particularly preference aggregation and fair decision-making. He explores ML-based techniques for designing economic mechanisms, with specific interest in fairer voting rules. His broader research interests include natural language processing, interpretable machine learning, and multi-agent reinforcement learning. His recent publications (2021-2024) examine computational complexity of voting paradoxes, election data generation using deep learning, and natural language-based preference aggregation. His work has appeared in top venues including IJCAI, AAMAS, and JAIR. Dr. Mohsin teaches courses including Data Mining, Data Structures, Analysis of Algorithms, Discrete Structures, and Advanced Algorithms. He supervises undergraduate research projects, with recent honors theses on multi-agent reinforcement learning and fairness in zoning laws.

• Artificial Intelligence
• Reinforcement Learning
• Multi-agent Systems
• +7 more

Jun Wang is a Professor of Information and Data Science in the Department of Computer Science at University College London. He serves as the Founding Director of the MSc Web Science and Big Data Analytics program and is the Co-founder and Chief Scientist of MediaGamma Ltd (acquired), a UCL startup focused on AI for intelligent audience decision-making. His educational background includes: Doctor of Philosophy from Technische Universiteit Delft (2007) Master of Science from National University of Singapore (2003) Bachelor's degree from Southeast University Nanjing (1997) Professor Wang's primary research focuses on AI and intelligent systems, with particular expertise in multiagent reinforcement learning, deep generative models, and their applications across several domains. His work spans information retrieval, recommender systems and personalization, data mining, smart cities, bot planning, and computational advertising. His research has practical implications in healthcare, robotics, and intelligent systems for decision-making, aligning with Sustainable Development Goal 3 (Good Health and Well-Being). His recent publications demonstrate a strong trend toward integrating large language models with reinforcement learning techniques to create more efficient and capable systems. The research spans healthcare applications, robotics, information systems, and strategic decision-making, with an emphasis on privacy preservation, efficient computation, and multi-agent coordination. Professor Wang has received several prestigious awards: Beyond Search – Semantic Computing and Internet Economics award from Microsoft Research Yahoo! FREP Faculty award UCLB One-to-Watch award 2016 for MediaGamma First place in global real-time bidding algorithm contest (80+ participants worldwide) Multiple Best Paper awards With over 15 years of experience, Professor Wang has advised numerous startups including Last.Fm, Passiv Systems, Massive Analytic, Context Scout, and Polecat. He has also collaborated with major industry players such as BT, Microsoft, Yahoo!, Alibaba, and Didi. His knowledge transfer activities bridge academic research with practical industry applications. Professor Wang has developed the Information Retrieval and Data Mining MSc module and the MSc/MRes programme on Web Science and Big Data Analytics. He also teaches a multiagent AI MSc module, sharing his expertise in cutting-edge AI techniques with the next generation of computer scientists.

Takayuki ItoView profile

Professor

• Multi-agent systems
• Automated negotiation
• Argumentation frameworks
• +7 more

Dr. Takayuki Ito is Professor at Nagoya Institute of Technology in the Computer Science & Engineering school. He earned his Doctor of Engineering from Nagoya Institute of Technology in 2000. His academic journey includes positions as a JSPS research fellow, associate professor at JAIST, and visiting scholar at prestigious institutions including USC/ISI, Harvard University, and MIT (visited twice). He has served as a board member of IFAAMAS (International Foundation for Autonomous Agents and Multiagent Systems). Dr. Ito's research primarily focuses on multi-agent systems, automated negotiation, argumentation frameworks, and AI-mediated discussion platforms. His work spans theoretical foundations of argumentation semantics to practical applications in sustainable development, urban planning, and online citizen engagement. He has developed the D-Agree platform for facilitating large-scale online discussions, with notable implementations in Afghanistan for municipal policy-making and SDG implementation. His publication record demonstrates significant contributions to understanding how AI can mediate human discussions, with experiments involving thousands of participants in countries like Afghanistan. His research shows how argumentative agents can improve responsiveness in discussions while also potentially polarizing debates by reinforcing initial stances. His work bridges theoretical computer science with practical social applications, particularly in contexts with challenging participation constraints. Board member of IFAAMAS Developer of D-Agree discussion support system Conducted large-scale experiments in Afghanistan with over 1,000 participants Expert in multi-agent negotiation protocols Dr. Ito's research has substantial implications for democratic processes, particularly in contexts where traditional face-to-face meetings are problematic due to security concerns, cultural restrictions, or pandemic conditions. His work demonstrates how AI mediation can overcome barriers to equal participation, especially for women and religious minorities in restrictive societies.