Emma Brunskill is an Associate Professor of Computer Science at Stanford University, with a courtesy appointment in Education. She holds a PhD in Computer Science from MIT (2009). Her research focuses on reinforcement learning, educational technology, and healthcare applications, aiming to develop AI systems that support human learning and decision-making. Notable projects include AI tutoring systems, policy evaluation methods, and behavior change interventions using large language models. Her work bridges theory and practice, addressing challenges in off-policy evaluation, fairness-aware decision making, and scalable educational tools. Brunskill has contributed to foundational research in reinforcement learning algorithms and their applications in real-world scenarios such as healthcare, education, and human-AI collaboration. She also leads initiatives to improve equity and efficiency in educational technologies through data-driven approaches. Brunskill's research has been supported by grants such as the NSF RI: Small grant for data-efficient reinforcement learning. She actively explores the ethical implications of AI systems, particularly in healthcare and education settings. Her recent work emphasizes leveraging large language models (LLMs) for personalized feedback and simulated training environments, as seen in studies like GPTCoach and LLM-based counselor upskilling.
Susan A. Murphy is the Mallinckrodt Professor of Statistics and of Computer Science at Harvard University, with affiliations to the Kempner Institute. She leads the Statistical Reinforcement Learning Lab, focusing on developing algorithms to inform sequential decision-making in health, particularly for Just-in-Time Adaptive Interventions (JITAIs) and micro-randomized trials (MRTs). Her work is funded by NIH institutes, including NIDA, NHLBI, and NIBIB. Dr. Murphy has been awarded a MacArthur Fellowship (2013) and is a member of the National Academy of Medicine (2014) and the National Academy of Sciences (2016). Her research integrates statistical methods with computer science techniques to optimize mobile health interventions. She collaborates with d3Lab and mDOT on projects like HeartSteps and Sense2Stop, evaluating real-time treatment policies. Notable contributions include advancing MRT designs, sample size calculations, and reinforcement learning algorithms for personalized healthcare. Dr. Murphy advises a large team of postdocs, graduate students, and undergraduates, many of whom hold academic and industry roles globally. She emphasizes engagement in digital interventions, balancing personalization with ethical considerations. Her lab’s work spans algorithm development, clinical trial design, and causal inference, aiming to improve health outcomes through adaptive interventions.
Max Simchowitz is an Assistant Professor in the Machine Learning Department at Carnegie Mellon University, joining in January 2025. His research focuses on sequential learning, reinforcement learning, control systems, and robotics, with a particular interest in how large AI models influence these fields. He holds a PhD from UC Berkeley (2021) and conducted postdoctoral research in MIT's Robot Locomotion Group. His work bridges theoretical foundations and practical applications, emphasizing adaptive sampling, optimization, and fairness in machine learning. Education: Bachelor's in Mathematics, Princeton University (2015) PhD in EECS, UC Berkeley (2021), advised by Ben Recht and Michael Jordan Research Interests: Reinforcement learning and control systems Generative models (diffusion models, video prediction) Robot learning and policy optimization Mathematical foundations of sequential decision-making Articles Trends: Recent work emphasizes diffusion models, imitation learning pitfalls, and robot policy optimization. Earlier contributions include theoretical analyses of system identification, exploration strategies, and fairness in AI systems. Awards: Outstanding Paper Award (ICML 2022) Best Paper Finalist (ICRA 2024) Best Paper Award (ICML 2018) Advising & Grants: Actively recruiting PhD/Master’s students in CMU’s Machine Learning Department and Robotics Institute. Prior teaching includes UC Berkeley’s Convex Optimization and Machine Learning courses. Research supported by grants exploring robot learning, generative models, and control theory.
Manjesh Kumar Hanawal is an Associate Professor at the Industrial Engineering and Operations Research (IEOR) center of IIT Bombay , India. His academic journey includes a Ph.D. from University of Avignon/INRIA (2013), M.Sc (Engg) from IISc Bangalore (2009), and B.E. from NIT Bhopal (2004). Pre-Ph.D. work: Scientist-B at DRDO's CAIR Postdoctoral: Boston University (2013-2015) Appointed as first Professor-In-Charge of TCA2I center Research focuses on Machine Learning algorithms for limited feedback environments, Communication Networks resource allocation, and Cybersecurity threat detection. Publications span top venues like IEEE Transactions, NeurIPS, INFOCOM, and AISTATS. Recent work trends include: Bandit algorithms for distributed learning in heterogeneous networks Contextual information integration in sequential selection Energy efficiency optimization in wireless sensor networks Net neutrality violation detection frameworks Anti-jamming countermeasures in cognitive networks Scientific recognition includes the SERB Early Career Research Award (2019-2022) for machine learning applications in wireless networks. Advisees include Ph.D. awardee Arun Verma and Best Masters Thesis Awardee Sayan Chatterjee.
Dr. Yongjia Song is an Associate Professor in the Department of Industrial Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. His research focuses on optimization under uncertainty, stochastic programming, and network interdiction with applications in disaster logistics, energy systems, and humanitarian operations. BS in Computational Mathematics (2009), Peking University MS in Industrial Engineering (2012), University of Wisconsin-Madison MS in Computer Sciences (2012), University of Wisconsin-Madison PhD in Industrial Engineering (2013), University of Wisconsin-Madison His work addresses complex systems under uncertainty through: Stochastic and robust optimization frameworks Integer programming for discrete decision problems Applications in disaster response and transportation networks Evacuation planning and shelter management Human trafficking disruption modeling Recent publications demonstrate trends in: Multistage stochastic programming for dynamic disaster response Bayesian preference elicitation for complex design problems Network interdiction models for security and trafficking disruption Integration of logistics and evacuation planning under uncertainty Adaptive algorithms for large-scale optimization Professional affiliations include: Institute for Operations Research and the Management Sciences (INFORMS) Mathematical Optimization Society (MOS) Society for Industrial and Applied Mathematics (SIAM) He teaches graduate courses in risk modeling (IE 8090) and actively works on practical implementations of optimization techniques in real-world systems.
Cong Shi, also known as Alex Shi, is a Professor of Management at the Miami Herbert Business School, University of Miami, since 2025. Previously, he served as Associate Professor at the University of Michigan (2019-2023) and Assistant Professor there (2012-2019). His academic journey began with a B.Sc. in Mathematics (First Class Honors) from the National University of Singapore (2007) and a Ph.D. in Operations Research from MIT (2012) under Professor Retsef Levi. Education : MIT (Ph.D.), NUS (B.Sc.) Current Role : Professor, Management, Miami Herbert Business School Prior Roles : Associate Professor (Tenured), University of Michigan; Assistant Professor, University of Michigan His research spans Revenue Management, Supply Chain Management, Healthcare Operations, Human-Robot Interaction, and Data-Driven Optimization. Recent publications focus on fairness-constrained inventory, sequential pricing, and trust-aware robotics. He has received prestigious awards including the Senior Research Award (2025) and Amazon Research Award (2021), alongside multiple INFORMS recognitions. The 15 most recent articles highlight advancements in inventory control with fairness constraints, sequential pricing algorithms, and trust propagation models in robotics. His work bridges theoretical rigor with practical applications in supply chains and human-robot collaboration. Scientific Awards : Senior Research Award, Miami Herbert Business School, 2025 Amazon Research Award, 2021 INFORMS Meritorious Service Awards (2018, 2019, 2021, 2023) IOE Graduate Course Professor of the Year, University of Michigan, 2019 He has advised 10 PhD students, many now in academia (e.g., UC Berkeley, Penn State) or tech roles (Meta, Amazon). Grants include NSF funding as PI and Co-PI.
Sharan Vaswani is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on designing algorithms for sequential decision-making under uncertainty, stochastic optimization, and their interplay with machine learning generalization. He holds a PhD from the University of British Columbia (2019) and postdoctoral experiences at the University of Alberta and Mila. His academic journey includes MSc (UBC, 2015) and BTech (BITS Pilani, 2012) degrees. Education: PhD (UBC, 2019), MSc (UBC, 2015), BTech (BITS Pilani, 2012) Postdoctoral Work: University of Alberta (2020-2021), Mila (2019-2020) Teaching includes courses on Probability and Computing (CMPT 210), Optimization for Machine Learning (CMPT 409/981), and Theoretical Foundations of Reinforcement Learning (CMPT 419/983). His research group focuses on developing scalable optimization algorithms with theoretical guarantees. He advises multiple PhD and MSc students, contributing to areas like constrained MDPs, adaptive learning rates, and reinforcement learning theory. Research Highlights: Contributions to bandit algorithms, stochastic gradient methods, and reinforcement learning theory. Notable work includes global convergence analysis of policy gradients and variance-reduced optimization frameworks.
Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University. Previously, Xu was a Postdoctoral Scholar Research Associate at Caltech's Department of Computing and Mathematical Science and earned a Ph.D. in Computer Science from UCLA. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong empirical performance and theoretical guarantees. Xu's research interests center around Machine Learning with broad applications in Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. The research specifically targets real-world problems in Bioinformatics and Healthcare, with recent work emphasizing distributionally robust decision making, efficient exploration strategies, and multi-agent systems. Xu has developed novel algorithms that address the challenges of exploration in sequential decision making and robustness to distributional shifts between training and deployment environments. Xu's recent publications demonstrate a strong trend toward developing theoretically grounded yet practical algorithms for reinforcement learning and bandit problems, with particular emphasis on distributionally robust methods, efficient exploration techniques, and applications to healthcare. The work spans both theoretical analysis (providing minimax optimal regret bounds) and practical implementations (validated on benchmarks like Atari games and real healthcare datasets). Whitehead Scholar award from Duke University School of Medicine (2023) Best Paper Award at ACM FAccT 2023 for Queer In AI paper PIMCO Postdoctoral Fellowship in Data Science (2022) TMLR Featured Certification (2023) NSF award on approximate sampling based exploration (2023) Xu actively mentors multiple Ph.D. students across Duke's Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering programs, with several alumni now pursuing doctoral studies at top institutions. The research group has secured competitive funding including an NSF award for approximate sampling based exploration for sequential decision making. Xu serves as an action editor for TMLR and as an area chair for major conferences including ICML, NeurIPS, AAAI, ICLR, and AISTATS. Xu leads a dynamic research group focused on sequential decision making, with projects spanning theoretical algorithm development, implementation of practical systems, and applications to healthcare and bioinformatics. The group maintains active collaborations across Duke's medical and engineering schools, with recent work applying machine learning to epidemic forecasting during the pandemic.
Tim Huh is a Professor and Chair of the Operations and Logistics Division at the University of British Columbia's Faculty of Commerce and Business Administration. He specializes in inventory control, supply chain management, and operations research, with a focus on dynamic decision-making under uncertainty. B.A., B.Math, M.Math from University of Waterloo M.A. from Regent College M.S., Ph.D. from Cornell University His research spans theoretical and applied topics including renewable energy systems, healthcare operations, and digital learning analytics. Recent work explores wind power storage optimization, asynchronous video usage in education, and multi-echelon inventory solutions. Scientific recognition includes the Canada Research Chair in Operations Excellence and Business Analytics He teaches core business analytics and operations management courses at both undergraduate and graduate levels, emphasizing quantitative decision-making and process fundamentals.
Scientia Professor Robert Kohn is a distinguished academic at the University of New South Wales, holding a position in the School of Economics within the UNSW Business School. With a career spanning several decades, Professor Kohn has established himself as a leading expert in statistical methodology and econometric modeling. His research has significantly contributed to Bayesian statistics and computational methods for complex data analysis. Professor Kohn's research focuses on advanced statistical methodologies including Bayesian methodology, variable selection and model averaging, nonparametric regression models, time series modeling, multivariate Gaussian and non-Gaussian regression, and Markov chain Monte Carlo simulation algorithms. His work bridges theoretical statistics with practical applications across economics, finance, and cognitive science. His research demonstrates a consistent trajectory toward developing more efficient computational methods for complex statistical models, with recent work emphasizing variational Bayesian methods, particle filtering techniques, and applications to time series analysis. Analysis of his recent publications (2022-2025) reveals a strong focus on advancing computational statistical methods, particularly in Bayesian inference for complex models. His work shows increasing integration of machine learning techniques with traditional statistical methods, especially in handling high-dimensional data and complex time series structures. Professor Kohn has made significant contributions to variational inference methods, particle-based computational techniques, and applications to financial time series and cognitive modeling. Professor Kohn has maintained an exceptionally productive research career with continuous publication output since the 1970s, demonstrating remarkable longevity and adaptability in his research focus as statistical methodologies have evolved. His work shows strong international collaboration, particularly with researchers in Australia, the United States, and Europe, reflecting his standing in the global statistical community.
Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.
Gabriele Farina is an Assistant Professor at MIT in the Department of Electrical Engineering and Computer Science (EECS) and the Laboratory for Information and Decision Systems (LIDS), with additional affiliations at the Operations Research Center (ORC). Holding the X-Window Consortium Career Development Chair, his research focuses on theoretical and algorithmic foundations for learning and computational decision-making under imperfect information, integrating game theory, machine learning, optimization, and statistics. He previously served as a Research Scientist at Meta's Fundamental AI Research (FAIR) group, where he contributed to Cicero, a human-level AI agent combining strategic reasoning and natural language. Ph.D. in Computer Science from Carnegie Mellon University (advisor: Tuomas Sandholm) Facebook Fellowship (2019-2020) in Economics and Computation Recipient of multiple awards including ACM SIGecom dissertation award, NSF CAREER, and AI2050 Early Career Fellow His research spans four key areas: (1) No-Regret Learning Dynamics in extensive-form games; (2) Correlation and Mediated Equilibria in sequential decision-making; (3) Team Games and Team Max-Min Equilibria; and (4) Human Modeling and Equilibrium Perfection. His work addresses challenges in scalable equilibrium computation, stability of learning algorithms, and robustness to mistakes in multi-agent systems. Recent publications highlight advancements in polynomial-time equilibrium computation, cautious optimism algorithms, and connections between regret minimization and mirror descent. These contributions appear in top venues like COLT, NeurIPS, ICML, and AAAI, with keywords spanning game theory, optimization, and machine learning. NSF CAREER award AI2050 Early Career Fellow Facebook Fellowship ACM SIGecom dissertation award GameSec 2024 best paper award ICLR 2023 outstanding paper honorable mention His research group at MIT collaborates on projects involving strategic reasoning, human-level AI agents, and equilibrium refinements, with applications to games like Diplomacy and poker. Current efforts include developing faster algorithms for correlated equilibria and exploring connections between machine learning and economic theory.
Ananth Grama is the Samuel D. Conte Distinguished Professor of Computer Science and Associate Director of the Center for Science of Information at Purdue University. He holds a faculty position in the Department of Computer Science, College of Science. His research focuses on parallel computing, distributed systems, machine learning, and their applications in complex systems such as materials modeling and clinical analytics. He teaches advanced courses like CS525 (Parallel Computing) and CS314 (Numerical Methods). Research interests span parallel algorithms, fault-tolerant learning, quantum machine learning, and data-driven healthcare analytics. Recent work addresses fundamental limits of generative models, online learning under noisy conditions, and clinical outcome predictions. His projects include DOE-funded research on critical element recovery and NIH grants for hearing assessment technologies. Notable contributions include over 50 peer-reviewed publications since 2022, with recent papers appearing at ICLR, NeurIPS, and ICML. Current postdocs include Changlong Wu (collaborating with Wojciech Szpankowski) and Luopin Wang (with Nadia Atallah). He advises seven graduate students and oversees multidisciplinary research teams.
Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science and an Adjunct Professor at the University of Montréal. She serves as a Visiting Faculty Researcher at Google, a Core Academic Member at MILA (Quebec Institute for Learning Algorithms), and holds a prestigious Canada CIFAR AI Chair. Farnadi co-directs McGill's Collaborative for AI & Society (McCAIS) and founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms), which focuses on advancing algorithmic fairness and responsible AI. Her educational background includes a Ph.D. in Computer Science from KU Leuven and Ghent University (2017), with postdoctoral research at the University of Montreal/MILA (2018-2020) and the University of California, Santa Cruz (2017-2018). During her doctoral studies, she was a visiting scholar at UCLA, University of Washington, Tsinghua University, and Microsoft Research. Dr. Farnadi's research centers on developing mathematical tools and algorithms for fairness-aware machine learning systems. Her work addresses bias and discrimination in AI decision-making across critical domains including healthcare, criminal justice, financial services, and social media. She has pioneered approaches to ensure fairness in deep learning models, particularly in sequential decision-making under uncertainty. Her research bridges theoretical foundations with practical applications, examining how AI systems can be designed to promote equity while maintaining performance. Analysis of her recent publications reveals a strong focus on practical implementations of fairness mechanisms across diverse AI applications. Her work spans technical domains from generative models and large language models to recommender systems and healthcare optimization. A unifying theme is the development of mathematically rigorous frameworks that balance performance with fairness considerations, with increasing attention to cultural diversity in multilingual AI systems and privacy-preserving fairness approaches. Google Scholar Award (2021) Facebook Research Award (2021) Rising Stars in AI Ethics (2021) Google Award for Inclusion Research (2023) WAI Responsible AI Leader of the Year Finalist (2023) 100 Brilliant Women in AI Ethics (2023) Canada CIFAR AI Chair Dr. Farnadi advises numerous doctoral and master's students across McGill University, University of Montréal, and MILA, with research focusing on fairness, privacy, and responsible AI. Her EQUAL Lab brings together researchers from computer science, social sciences, and policy domains to address systemic challenges in AI ethics. She has secured significant research funding from Google and other major organizations to support her work on fairness-aware AI systems, with applications spanning healthcare, social media safety, and public policy. The EQUAL Lab serves as a hub for interdisciplinary research on algorithmic fairness, bringing together computer scientists, social scientists, and policy experts. The lab's work spans theoretical foundations of fairness metrics, practical implementations in real-world systems, and policy recommendations for responsible AI deployment. Current projects include developing frameworks for fair kidney exchange programs, mitigating cultural stereotypes in multilingual language models, and creating privacy-preserving approaches for detecting online harms while protecting user data.
Viswanath Nagarajan is an Associate Professor of Industrial & Operations Engineering and Computer Science Engineering (courtesy) at the University of Michigan. His research focuses on combinatorial optimization, approximation algorithms, and stochastic models for routing, scheduling, and location problems. He previously served as an Assistant Professor at the University of Michigan (2014–2020) and a Research Staff Member at IBM T.J. Watson Research Center (2009–2014). He holds a Ph.D. in Algorithms, Combinatorics, and Optimization from Carnegie Mellon University (2004–2009) and a B.Tech. in Computer Science from IIT Bombay (1999–2003). His research explores uncertainty management in optimization, including stochastic models and approximation algorithms for decision-making under uncertainty. He has contributed to adaptive algorithms, submodular optimization, and applications in logistics, network design, and scheduling. Education: Ph.D., Algorithms, Combinatorics, and Optimization (Carnegie Mellon University, 2009) B.Tech., Computer Science and Engineering (IIT Bombay, 2003) Prof. Nagarajan has organized major conferences like IPCO 2019 and served on editorial boards for journals including Operations Research , ACM Computing Surveys , and ACM Transactions on Algorithms . His service includes program committees for SODA, APPROX, and IPCO. He advises Ph.D. students focusing on optimization theory and applications, with advisees securing positions at Yahoo! Research, the University of Chicago, Ford Motor Company, and Georgia Tech.
