Shivaram Kalyanakrishnan is an Associate Professor at the Department of Computer Science and Engineering , Indian Institute of Technology Bombay , specialising in Artificial Intelligence and Machine Learning . His research spans sequential decision making , multiagent learning , multi-armed bandits , and humanoid robotics , with applications in robot soccer , computer games , and online advertising . He teaches advanced courses like CS 747: Foundations of Intelligent and Learning Agents and CS 748: Advances in Intelligent and Learning Agents , focusing on end-to-end system design and theoretical analysis. His scientific awards include the Best Student Paper Award at RoboCup International Symposium 2006 and nomination for Best Student Paper Award at AAMAS 2007 . His work on reinforcement learning and policy iteration has been published in leading venues such as IJCAI , ICML , and COLT , with recent contributions to railway scheduling and bandit algorithms. While no explicit list of advisees is provided, his research projects and publications suggest mentorship of students in collaborative efforts. Contact : shivaram@cse.iitb.ac.in .
Kevin Jamieson is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering and an Adjunct Professor in the Department of Statistics at the University of Washington . His academic journey includes a B.S. (2009) , M.S. (2010) , and Ph.D. (2015) in electrical engineering from the University of Washington, Columbia University, and University of Wisconsin–Madison respectively. He completed a postdoc at UC Berkeley's AMP Lab before joining UW in 2017. Ph.D., Electrical Engineering, University of Wisconsin–Madison (2015) M.S., Electrical Engineering, Columbia University (2010) B.S., Electrical Engineering, University of Washington (2009) Jamieson's research lies at the intersection of interactive machine learning , active learning , and sequential decision making . His work focuses on: Adaptive sampling strategies in multi-armed bandits and reinforcement learning (RL) Developing instance-dependent optimal algorithms that adapt to problem difficulty Applications in robotics , human perception studies , and hyperparameter optimization Representation learning for large models and experimental design frameworks His 15 most recent publications (2025-2022) demonstrate expertise in bandit theory , contextual RL , and game-theoretic learning . Notable trends include sample-efficient optimization , adaptive A/B testing , and sim-to-real transfer in robotics. Jamieson has received: NSF CAREER award for foundational contributions Amazon Faculty Research award for innovation in learning systems He actively recruits graduate students and postdocs , emphasizing collaboration in areas like: Multi-agent RL and strategic actor learning Empirical process suprema and adaptive sampling theory Applications in robotics , large language model finetuning , and biomedical data analysis Jamieson leads the Washington AI Lab (WAIL) and develops open-source learning systems like the NEXT framework for real-world adaptive data collection. He serves as co-PI for the Institute for the Foundations of Data Science (IFDS) and co-organizes the Distinguished Seminar in Optimization & Data .
Furong Huang is an Associate Professor at the University of Maryland's Department of Computer Science, with affiliations at the Institute for Advanced Computer Studies, Center for Machine Learning, Maryland Robotics Center, and Applied Mathematics, Statistics, and Scientific Computation Program. Her research bridges trustworthy machine learning, sequential decision-making, and foundation models for robotics, emphasizing reliability, interpretability, and ethical standards. Research Interests: Trustworthy AI Generative AI Reinforcement Learning AI Security Algorithmic Fairness Foundation Models for Robotics Recent Publications span leading conferences (NeurIPS, ICML, ICLR, CVPR) and journals, focusing on: Robustness in Vision-Language Systems Trustworthy Generative AI Foundation Models for Sequential Decision-Making AI Security and Watermarking Scientific Awards MIT TR35 Innovator Under 35 (Asia Pacific 2022) Best Paper Award, AdvML Frontier Workshop, NeurIPS 2024 NSF NAIRR Pilot Awardee Microsoft Accelerate Foundation Models Research Award (2023) JP Morgan Faculty Research Awards (2019–2022) Advising and Grants : Her lab has graduated students to roles at OpenAI, Google, Meta, and Netflix. Research funded by DARPA, NSF, ONR, AFOSR, and industry partners like Microsoft, Adobe, and Capital One. Labs & Teams : Leads research groups focused on Trustworthy AI and Robotics at the University of Maryland, collaborating with the Maryland Robotics Center and Applied Mathematics Program.
Insup Lee is the Cecilia Fitler Moore Professor in the Department of Computer and Information Science and Director of the PRECISE Center at the University of Pennsylvania's School of Engineering and Applied Science. He holds a secondary appointment in the Department of Electrical and Systems Engineering and the Perelman School of Medicine’s Department of Biostatistics, Epidemiology, and Informatics. IEEE TCCPS Distinguished Leadership Award (2023) Fellow of the AAAS (2022) Test of Time Award, Runtime Verification (2019) Fellow of the ACM (2017) Best Paper Awards at IEEE ICPS, ACM/IEEE ICCPS, and MEMOCODE His research focuses on cyber-physical systems , real-time and embedded systems , safe autonomy , and internet of medical things , with applications in healthcare and connected systems. He advises PhD students including Eric Lu, Kaustubh Sridhar, Sooyong Jang, and Jean Park (co-advised with Kevin Johnson). Recent publications address safety monitoring for learning-enabled systems, model-free control synthesis using reinforcement learning, and multilingual toxicity guardrails for large language models. His team collaborates with institutions like Hillrom and Penn Nursing to optimize medical device usage in clinical settings.
Daniel M. Roy is a Full Professor at the University of Toronto, with cross-appointments in the Department of Computer Science, Department of Statistical Sciences, and Department of Electrical and Computer Engineering. He serves as Research Director at the Vector Institute and holds the CIFAR Canada AI Chair. Research Focus: Foundational principles of prediction, inference, and decision-making under uncertainty across machine learning, statistics, mathematical logic, applied probability, and computer science. Scientific Contributions: Key work in learning theory, statistical network analysis, probabilistic programming, and information-theoretic frameworks for generalization. Awards: ICML 2024 Best Paper Award for "Information Complexity of Stochastic Convex Optimization" and promotion to Full Professor in 2024. Student Advising: Actively mentors Ph.D. candidates and postdoctoral researchers with strong quantitative backgrounds, particularly at the intersection of machine learning, statistics, and computer science. Email: daniel.roy@utoronto.ca
Gareth Roberts is an Associate Professor in the Department of Linguistics at the University of Pennsylvania, where he serves as Graduate Chair. He is also a faculty member of the Psychology Graduate Group, founder and director of the Cultural Evolution of Language Lab, and co-director of the Social and Cultural Evolution Working Group at Penn. Roberts earned his PhD in Linguistics from the University of Edinburgh in 2010, following an MSc in Evolution of Language and Cognition (2006) and a BA in German and Russian (2003) from the University of Nottingham. His academic journey includes postdoctoral positions at Yeshiva University and the University of Stirling before joining Penn as an Assistant Professor in 2014, where he was promoted to Associate Professor in 2022. His research focuses on the role of social and communicative pressures in shaping language emergence and evolution. Roberts investigates fundamental questions about linguistic variant spread, phonological system structuring, and how communication shapes linguistic structure. His work bridges linguistics, cognitive science, and cultural evolution through innovative experimental approaches using artificial languages and laboratory simulations. Analysis of Roberts' recent publications reveals a consistent focus on experimental semiotics, with particular attention to social biases in language evolution, phonological organization, indexicality emergence, and the dynamics of linguistic variation. His work often combines computational modeling with human experiments to isolate specific mechanisms driving language change. Linguistic Society of America Cognitive Science Society Philological Society Cultural Evolution Society Roberts has successfully secured substantial research funding including an NSF PAC Grant ($102,648), Penn URF Research Grants ($12,155), MindCORE initiative grant ($600,000), and multiple smaller grants totaling over $20,000. His lab currently includes researchers investigating diverse topics from case and gender marking emergence to AI agent effects on group dynamics, linguistic and genetic data in British history, and phonological space organization. The Cultural Evolution of Language Lab, which Roberts founded and directs, conducts cutting-edge research using experimental semiotics methodologies. Current projects examine how social factors influence language change, the emergence of linguistic structure through communication, and the interaction between iconicity and combinatoriality in communication systems.
Fei-Fei Li is the Sequoia Capital Professor in Computer Science at Stanford University and Founding Co-Director of the Stanford Institute for Human-Centered AI (HAI). She holds courtesy appointments in the Graduate School of Business and is a Senior Fellow at HAI. Her work bridges AI research with interdisciplinary applications in healthcare, robotics, and policy. Dr. Li pioneered the ImageNet dataset, instrumental in the AI revolution, and co-founded World Labs to advance spatial intelligence and generative AI. Education: B.A. in Physics, Princeton University (1999) Ph.D. in Electrical Engineering, Caltech (2005) Doctorate (Honorary), Harvey Mudd College (2022) Research Interests: AI ethics, computer vision, robotic learning, healthcare applications, and human-AI collaboration. Her teams developed frameworks like MOMA for activity recognition and BEHAVIOR for embodied AI benchmarks. She advocates for diversity in tech and co-founded AI4All to mentor underrepresented students. Key Contributions: ImageNet and ImageNet Challenge Stanford Vision and Learning Lab (SVL) Policy advisory roles for U.S. Senate, UN Secretary-General, and California Governor Labs & Initiatives: Leads the People, AI & Robots Group (PAIR), Partnership in AI-Assisted Care (PAC), and the Human-Centered AI Institute. Her work emphasizes ethical AI deployment and societal impact. Awards: VinFuture Prize (2024), IEEE Fellow, National Academy memberships (Engineering, Medicine, Arts & Sciences), and recognition as one of Time’s AI100 Influencers.
Suguman Bansal is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology. Her research focuses on formal methods and their applications to artificial intelligence, programming languages, and machine learning. Previously, she held an NSF/CRA Computing Innovation Postdoctoral Fellowship at the University of Pennsylvania (2020-2022) and completed her PhD at Rice University (2016-2020). Education: PhD in Computer Science, Rice University (2016-2020) MS in Computer Science, Rice University (2014-2016) BSc (Honors) in Mathematics and Computer Science, Chennai Mathematical Institute (2011-2014) Research Interests: Formal methods, reinforcement learning, reactive synthesis, quantitative verification, and trustworthy AI systems. Her work bridges logic-based formal methods with modern AI challenges, emphasizing safety, reliability, and generalization in AI systems. Key Contributions: Pioneering work on specification-guided reinforcement learning, compositional synthesis algorithms, and formal verification of AI systems. Notable tools include Lisa (LTLf synthesis tool) and DiRL (compositional reinforcement learning framework). Awards: 2020 NSF CI Fellowship, 2021 MIT EECS Rising Star, 2023 ATVA Best Paper Award, and Keynote Speaker at SAS 2022. Advising & Grants: Advises PhD and Master’s students in reinforcement learning and formal methods. Lead PI on a collaboration grant with IIT Bombay (2024) and recipient of a ~$250K NSF/CRA postdoctoral grant. Labs/Teams: Leads the BansalLab at Georgia Tech, focusing on trustworthy AI through formal methods and algorithmic innovation.
Ronald Parr is a Professor of Computer Science in the Department of Computer Science at Duke University's Pratt School of Engineering. He has been at Duke since 2000, progressing from Assistant Professor to Associate Professor with tenure, and ultimately to Full Professor. From 2014 to 2017, he served as Department Chair and delivered graduation speeches in 2015, 2016, and 2017. Dr. Parr received his Ph.D. in Computer Science from the University of California, Berkeley in 1998, with a dissertation titled "Hierarchical Control and Learning in Markov Decision Processes" under advisor Stuart Russell. He earned his A.B. in Philosophy, cum laude, from Princeton University in 1990. His primary research focuses on methods for solving large stochastic planning problems using Markov Decision Processes and approximate dynamic programming techniques. His work spans reinforcement learning, value function approximation, game theory, sensing, and robotics. Dr. Parr's research has been consistently funded by major agencies including NSF, DARPA, and ARO, with recent projects focusing on feature encoding for reinforcement learning, neurosymbolic hierarchical reinforcement learning, and reasoning in large, structured, uncertain domains. His publication record demonstrates consistent contributions to top venues including NeurIPS, ICML, and AAAI, with work that bridges theoretical foundations and practical applications. Dr. Parr has received numerous honors including being elected as an AAAI Fellow in 2023, receiving an AAAI Outstanding Paper Honorable Mention in 2013, winning the IJCAI-JAIR Best Paper Award in 2007, receiving an NSF CAREER award in 2006, and being named an Alfred P. Sloan Fellow in 2003. He has advised ten graduate students to completion across various research areas within AI and robotics. His research has been supported by over $1.5 million in direct funding to his lab, with additional collaborative funding from multiple NSF, DARPA, and ARO grants. Dr. Parr has served extensively on program committees for major AI conferences including ICML, NeurIPS, AAAI, and UAI, and has held leadership roles such as Program Co-Chair and General Chair for UAI. Dr. Parr maintains an active research group focused on reinforcement learning and sequential decision making, with ongoing projects in neurosymbolic AI, hierarchical reinforcement learning, and interpretable machine learning models, continuing to bridge theoretical foundations with practical applications in robotics and AI systems.
Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.
Shai Ben-David is a Professor and University Research Chair at the Department of Computer Science, University of Waterloo. He is affiliated with the Cheriton School of Computer Science and can be reached at shai@uwaterloo.ca . His office is located in DC 2643. Education: Ph.D., Hebrew University, Jerusalem, Israel (1987) M.Sc., Hebrew University Jerusalem, Israel (1979) B.Sc., Hebrew University Jerusalem, Israel (1978) Research interests focus on foundational aspects of machine learning theory, including unsupervised learning (clustering), domain adaptation, fairness, interpretability, and alternative approaches to worst-case computational complexity. He also explores logic applications in computer science theory. His research trends emphasize theoretical challenges in machine learning, particularly clustering, fairness in representations, and the interplay between computational feasibility and learnability. He investigates how unlabeled data and sample compression techniques impact learning robustness and efficiency. No scientific awards are listed. His advising record shows no formal advisees listed here. Grants and funding details are not provided in the text. He has contributed to organizing events like the Dagstuhl Seminar on Foundations of Unsupervised Learning (2017) and co-edited MFCS 2016 proceedings. His work addresses both theoretical questions and practical gaps in ML implementation.
Constantinos Daskalakis is the Armen Avanessians (1982) Professor in the MIT Schwarzman College of Computing and the Department of Electrical Engineering and Computer Science (EECS). He joined MIT in 2009 and is a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), affiliated with the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center (ORC). His research focuses on theoretical computer science, with emphasis on game theory, machine learning, and high-dimensional statistics. Education: Ph.D. in Computer Science (not explicitly stated, but implied by tenure and awards). Research interests include computational complexity of Nash equilibria, multi-item auctions, machine learning algorithms, and causal inference. His work bridges game theory, economics, probability, and statistics, with applications in AI and healthcare. Key contributions include resolving long-standing problems in computational game theory and developing efficient methods for statistical hypothesis testing. He has been recognized with the 2018 Nevanlinna Prize, ACM Grace Murray Hopper Award, and the Kalai Game Theory Prize. Affiliations: CSAIL, LIDS, ORC, and the Foundations of Data Science Institute. Active in multi-agent learning, bias mitigation in data, and generative models.
John DeNero is the Giancarlo Teaching Fellow and Associate Teaching Professor in UC Berkeley's Electrical Engineering and Computer Sciences (EECS) department. He joined UC Berkeley in 2014 to focus on undergraduate education in computer science and data science. He teaches and co-develops introductory courses like CS 61A (Computer Science) and Data 8 (Data Science), which serve thousands of students annually. His research spans natural language processing and education innovation, with notable contributions to textbooks like Composing Programs and Computational and Inferential Thinking . Education: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley (2010) M.A. in Philosophy, Stanford University (2002) B.S. in Mathematical & Computational Science and Symbolic Systems, Stanford University (2001) Research interests focus on advancing AI education and curriculum design. His work emphasizes scalability, equity, and student success in large courses. Notable contributions include the Pac-Man projects for AI education and the Berkeley Data Science Education Program. Recent trends in publications highlight AI-assisted education tools, machine translation improvements, and large-scale student feedback systems. Awards include the UC Berkeley Distinguished Teaching Award (2018) and multiple accolades for teaching excellence. Advising and grants: While currently not taking new students, his team supports massive course infrastructures. Labs include the Berkeley Artificial Intelligence Research (BAIR) Lab and contributions to Data Science undergraduate studies.
Kwang-Sung Jun is an Assistant Professor at the University of Arizona, Department of Computer Science. His research spans interactive machine learning, reinforcement learning, and learning theory, with a focus on multi-armed bandits, Bayesian optimization, and generalized linear models. Education : Ph.D. in Computer Science from the University of Wisconsin-Madison (2015). Research Trends : Kwang-Sung's recent work (2023-2025) emphasizes bandit algorithms with second-order bounds, adaptive experimentation, and PAC-Bayes frameworks. He explores low-rank structures in regression, explainable reward shaping, and environmental risk modeling via probabilistic assessments of postfire debris-flows. His publications often bridge theoretical guarantees (e.g., regret bounds) with practical applications in machine learning and environmental hazards. Expertise : Interactive machine learning Multi-armed bandits Confidence sequences Reinforcement learning Human-machine hybrid systems
Dr. Vincent Fortuin is a tenure-track Assistant Professor at the Technical University of Munich (TUM) and a research group leader at Helmholtz AI in Munich. He leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group and holds multiple prestigious fellowships including the Branco Weiss Fellowship. His academic affiliations include the TUM School of Computation, Information and Technology, the Konrad Zuse School of Excellence in Reliable AI, and the Munich Center for Machine Learning. Dr. Fortuin earned his BSc in Molecular Life Sciences from the University of Hamburg (2012-2015), followed by an MSc in Computational Biology and Bioinformatics from ETH Zürich (2015-2017), where he received the ETH Excellence Scholarship and the Willi Studer Prize. He completed his PhD in Machine Learning at ETH Zürich (2017-2021) under the supervision of Gunnar Rätsch and Andreas Krause, supported by a Swiss Data Science Center PhD Fellowship. Prior to joining TUM, he was a Research Fellow at St. John's College, University of Cambridge (2022-2023). His research focuses on the intersection of Bayesian statistics and deep learning, specifically developing methods for more robust, data-efficient AI systems with reliable uncertainty estimates. His work addresses critical limitations in standard deep learning approaches, particularly their tendency to be overconfident in predictions and require large datasets for training. He investigates better priors and more efficient inference techniques for Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory, with applications in scientific and biomedical domains. Dr. Fortuin's recent publications demonstrate a consistent focus on improving uncertainty quantification in deep learning systems, with increasing emphasis on practical applications in scientific contexts. His work spans from theoretical foundations of Bayesian deep learning to practical implementations in protein design, materials science, and medical applications. A notable trend is his exploration of how to make Bayesian methods more scalable and applicable to modern large-scale AI systems while maintaining theoretical guarantees. Branco Weiss Fellowship (2023) St John's College Research Fellowship (2022) Swiss National Science Foundation Postdoc.Mobility Fellowship (2022) Swiss Data Science Center PhD Fellowship (2018) ETH Excellence Scholarship (2015) Willi Studer Award (2018) Dr. Fortuin actively supervises PhD and Master's students through his ELPIS research group at Helmholtz AI. He serves as a regular reviewer and area chair for major machine learning conferences and is an action editor for TMLR. He co-organizes the Symposium on Advances in Approximate Bayesian Inference (AABI) and the ICBINB initiative, demonstrating his commitment to advancing the field through community building. His research group receives funding from multiple sources including Helmholtz AI, the Branco Weiss Fellowship, and collaborations with international institutions. Dr. Fortuin leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group at Helmholtz AI, which focuses on fundamental machine learning research motivated by real-world scientific problems. The group collaborates extensively with researchers across Helmholtz centers and international institutions, particularly in biomedical applications where reliable uncertainty estimates are crucial.
