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 .
Nathan Wainstein is an Assistant Professor in the English Department at the University of Utah's College of Humanities, where he has been teaching since July 2023. His academic work bridges literary studies with contemporary digital media, particularly focusing on video game aesthetics alongside modernist literature. Wainstein maintains an active scholarly presence through publications, editorial work, and his podcast "Authors Die Twice" co-hosted with Bryan Counter. Wainstein's educational background includes: PhD from Stanford University (2013-2020) BA from Swarthmore College (2007-2011) His research interests span video game aesthetics, modernism, the novel, literary theory, narrative theory, and the intersection of literature and philosophy. Wainstein approaches these fields with a particular focus on paradox, difficulty, and aesthetic experiences that challenge conventional interpretations. His work often examines how seemingly simple literary devices like light descriptions can reveal deeper theoretical tensions within texts. Wainstein's recent publications demonstrate a consistent trajectory exploring the boundaries of literary aesthetics, from his analysis of James Joyce's photomimesis to his examination of narrative glitches in Faulkner's work. His scholarship connects historical literary theory with contemporary concerns, particularly in how he applies these frameworks to video game analysis. The upcoming book "Grant Us Eyes: The Art of Paradox in Bloodborne" exemplifies this interdisciplinary approach, bridging literary criticism with video game studies. Wainstein has held prestigious postdoctoral positions including an ACLS Emerging Voices Fellowship at the University of Utah and a Stanford Humanities and Sciences Dean's Fellowship. As an educator, Wainstein teaches innovative courses that reflect his research interests, including "On Difficulty," "Aesthetics of Violence in Video Games and Literature," "Games with Time," and "Videogame Storytelling." His teaching philosophy appears to emphasize the connections between traditional literary studies and contemporary digital media forms.
Tommi Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at the Massachusetts Institute of Technology. He received his MSc in theoretical physics from Helsinki University of Technology in 1992 and his PhD from MIT in computational neuroscience in 1997. After completing a postdoctoral position in computational molecular biology as a DOE/Sloan fellow at UCSC, he joined the MIT EECS faculty in 1998. His research advances how machines can learn, predict or control, and do so at scale in an efficient, principled, and interpretable manner. His work in machine learning extends from foundational theory to modern applications, focusing especially on statistical inference and estimation tasks that lie at the heart of complex learning problems. He designs new methods, theory and algorithms to automate the use and generation of semi-structured data such as natural language text, images, molecules, or strategies. Jaakkola applies and develops algorithms to solve multi-faceted recommender, retrieval, or inferential tasks (particularly in biomedical contexts), design and optimize molecules or reactions for drug design, and model strategic, game theoretic interactions. His recent work heavily focuses on diffusion models, protein structure prediction, molecular design, and generative AI, with significant publications in top conferences including ICML, NeurIPS, and ICLR. His scientific contributions span multiple disciplines with significant impact in both theoretical machine learning and practical applications in computational biology and chemistry, including notable work on antibiotic discovery published in Cell. Current advisees: Julia Balla, Bowen Jing, Hannes Stärk, Peter Holderrieth, Chenyu Wang Recent graduates: Gabriele Corso (Boltz PBC), Ezra Erives (DE Shaw), Jason Yim (Xaira) Jaakkola maintains an active research program through MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), with his office located in the Stata Center (32-G470). His work bridges theoretical machine learning with practical applications, making significant contributions to both the academic field and potential real-world impact in healthcare and drug discovery.
Osbert Bastani is an Associate Professor at the Department of Computer and Information Science, University of Pennsylvania, leading the trustml@Penn research group. He is affiliated with the ASSET , PRECISE , and PRiML centers, and the PLClub research group. His research focuses on Trustworthy Neurosymbolic Systems , Synthesizing Neurosymbolic Programs , and Machine Learning for Programmer Productivity , with applications in verification, fairness, and human-AI collaboration. He received the NSF CAREER Award in 2023. His recent publications (2024-2025) emphasize AI Safety , LLM Robustness , and Algorithmic Fairness , including work on adversarial robustness, conformal prediction, and program synthesis. Students he has advised include Sagnik Anupam, Stephen Mell, Jason Ma, Shuo Li, and others. Awards: NSF CAREER Award (2023)
Rachit Agarwal is an Associate Professor in the Department of Computer Science at Cornell University, with research focusing on systems, networking, and theoretical problems arising in practical systems. He leads a research group working on resource disaggregation, host architecture, secure cloud storage, and datacenter design. PhD in Computer Science, Cornell University Undergraduate, IIT Kanpur His research spans three major directions: Resource Disaggregation (with $3M NSF and Google awards), Host Architecture (exploring terabit interconnects), and PANCAKE (secure oblivious cloud storage with $1M NSF award). He also contributed to foundational work in Near-optimal Datacenter Design and Data Plane Monitoring . Awards include the Sloan Fellowship, NSF CAREER, IRTF Applied Networking Prize, and multiple best paper awards. His recent publications focus on host network architecture, congestion control, oblivious data access mechanisms, and secure cloud storage systems. He has advised multiple Ph.D. and postdoctoral researchers who now hold faculty positions at leading institutions. Sloan Research Fellowship NSF CAREER award Kavli Fellowship IRTF Applied Networking Research Prize SIGCOMM Best Student Paper Award Usenix Security Distinguished Paper Award Tau Beta Pi Professor of the Year 2025 Rachit has advised numerous students including current Cornell advisees like Midhul Vuppalapati, Shreyas Kharbanda, and Omar Eqbal. Former advisees include Saksham Agarwal (UIUC), Qizhe Cai (UVA), and Mina Tahmasbi Arashloo (University of Waterloo). His research is supported by large NSF grants and industry awards, with deployments in real-world systems and open-source contributions.
Robert Ghrist is the Andrea Mitchell University Professor at the University of Pennsylvania with dual appointments in the Department of Mathematics and the Department of Electrical and Systems Engineering. He serves as Associate Dean for Undergraduate Education for Penn Engineering. His educational background includes a B.S. in Mechanical Engineering from the University of Toledo (1991), and M.S. and Ph.D. degrees in Applied Mathematics from Cornell University (1994, 1995). Ghrist's research bridges pure and applied mathematics, focusing on applied algebraic topology , dynamical systems , and geometric methods in data science. His work extends to network theory, topological data analysis, and computational geometry, with applications spanning robotics, neuroscience, and social dynamics. Key innovations include developing sheaf-theoretic approaches for networked systems and persistence homology techniques for high-dimensional data. Analysis of his recent publications reveals a strong emphasis on lattice-theoretic frameworks , topological robotics , and network dynamics , with emerging applications in neural data interpretation and geometric computing. His research consistently integrates category theory with real-world engineering challenges. Significant scientific recognition includes: Presidential Early Career Award (PECASE, 2004) Scientific American 'Top 50' Research Leader (2007) Mathematical Association of America's Chauvenet Prize (2013) University of Pennsylvania Lindback Award for Distinguished Teaching (2015) DoD National Security Science and Engineering Faculty Fellowship (NSSEFF, 2015) Ghrist leads multiple federally funded research initiatives supported by AFOSR, DARPA, NSF, and ONR. He directs the development of educational tools including the Calculus BLUE/GREEN Project video series and custom GPTs for mathematical pedagogy. His open online courses have reached over 100,000 learners globally.
Clifford Stein is a Professor of Industrial Engineering and Operations Research (IEOR) and Computer Science at Columbia University, and Associate Director for Research at the Data Science Institute. He holds a Ph.D. (1992), M.S. (1989), and B.S.E. (1987) from MIT and Princeton University, respectively. His research focuses on algorithms, combinatorial optimization, operations research, scheduling, and computational biology. A co-author of the best-selling textbook Introduction to Algorithms , Stein has published widely in top venues and holds prestigious awards like ACM Fellow and NSF Career Award. His work includes foundational contributions to minimum cut algorithms, scheduling theory, and network optimization, supported by NSF and Sloan Foundation grants. Stein has advised over 40 graduate and undergraduate students, many now in academia and industry.
J. Andrew Bagnell is a Professor at the Robotics Institute of Carnegie Mellon University (CMU). His research bridges planning, control theory, and computational learning, focusing on systems that can self-optimize under partial models. Key projects include the LAIRLab (Learning Applied to Intelligent Robotics) and initiatives in the ARM-S and BIRD MURI programs. Research domains: Machine Learning, Robotics, Control Theory, Optimization, Probabilistic Modeling Key applications: Mobile Robotics, Intelligent Transportation Systems, Multi-Robot Decision Making Recent work emphasizes imitation learning, trajectory optimization, and game-theoretic algorithms for decision-making. His publications highlight collaborations with students and researchers on topics like online learning, planning under uncertainty, and autonomous systems. Notable affiliations include advising Gokul Swamy and past students such as Wen Sun and Anirudh Vemula. Labs: LAIRLab, ARM-S, BIRD MURI team.
Antonio Rangel is the Bing Professor of Neuroscience, Behavioral Biology, and Economics at the California Institute of Technology (Caltech), where he also serves as Head Faculty in Residence. He is a faculty member in the Division of Humanities and Social Sciences (HSS) with research focusing on the computational and neurobiological basis of value-based decision-making. Dr. Rangel received his educational training at prestigious institutions: B.Sc. from Caltech in 1993 M.S. from Harvard University in 1996 Ph.D. in 1998 Professor Rangel's research lies at the intersection of neuroscience, economics, and psychology, with a focus on understanding how the brain makes decisions. His work investigates the neural mechanisms underlying value computation, choice processes, self-control, and social decision-making. Using a multidisciplinary approach that combines functional magnetic resonance imaging (fMRI), eye-tracking, computational modeling, and behavioral experiments, his lab has made significant contributions to the field of neuroeconomics. His research has revealed how value signals are represented in the brain, how attention influences choice, and the neural basis of self-control failures. Professor Rangel has pioneered methods for studying decision processes with high temporal resolution using eye-tracking data, demonstrating how fixation patterns relate to value computations and choice outcomes. His work spans from theoretical frameworks of value-based decision-making to practical applications in behavioral public economics. Professor Rangel's work has been recognized with prestigious awards: 2019 NOMIS Distinguished Scientist Award 2018 Fellow of the Association for Psychological Science As an academic leader, Professor Rangel has mentored numerous students and researchers who have gone on to make their own contributions to neuroscience and economics. His lab, the Rangel Neuroeconomics Laboratory at Caltech, serves as a hub for interdisciplinary research, bringing together students and scholars from neuroscience, economics, psychology, and computer science. The lab has received significant funding to support its research on the neural basis of decision-making, including support from the NOMIS Foundation. The Rangel Neuroeconomics Laboratory is equipped with state-of-the-art facilities including fMRI analysis capabilities, eye-tracking systems, and computational resources for modeling decision processes. The lab fosters a collaborative environment where researchers apply methods from experimental economics and cognitive neuroscience to unravel the complexities of human decision-making.
Dr. Max Kwiek is an Associate Professor in the Department of Economics at the University of Southampton and serves as Deputy Head of School (Education). His research focuses on applied microeconomic theory, political economy, committee design, and auctions. He teaches microeconomics modules, including principles, game theory, and advanced topics. His research interests include voting systems, game theory applications, and experimental economics. Key projects involve analyzing voting mechanisms, intermediary communication, and conclave dynamics. He has supervised PhD student Yanchi Zhang and led a project funded by the British Academy on experimental investigations of conclaves. Externally, he is a committee member of the Virtual Seminars in Economic Theory and Poleconuk Webinars. His work bridges theoretical models with real-world applications in political and economic decision-making processes.
Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.
Andrew Postlewaite is the Harry P. Kamen Professor of Economics and Professor of Finance at the University of Pennsylvania's School of Arts and Sciences. He holds dual roles in the Department of Economics and the Ronald O. Perelman Center for Political Science and Economics. As a distinguished academic, he is a member of the American Academy of Arts and Sciences and serves as a director at the National Bureau of Economic Research (NBER). Additionally, he is the founding editor of the American Economic Journal: Microeconomics and a research associate at Penn's Population Studies Center and Institute for Law and Economics. Postlewaite's research focuses on game theory, behavioral economics, and social norms, with contributions to mechanism design, repeated games, and intergenerational transmission of preferences. His work bridges theoretical foundations with applied economic analysis, particularly in areas like consumer behavior, decision-making, and institutional design. He has held visiting positions at institutions including Caltech, Harvard, and Stanford, reflecting his academic prominence. His scholarly contributions include pioneering studies on future-oriented decision-making, interdependent valuations in markets, and equilibrium concepts in repeated games. Recent publications explore topics such as the impact of local media accessibility in the digital age and the welfare implications of consumption fluctuations. His editorial leadership at journals like Econometrica and International Economic Review underscores his influence in shaping economic discourse. Awarded prestigious fellowships and editorial roles, Postlewaite's career exemplifies a blend of rigorous theoretical research and practical applications in economics. His current research trends emphasize behavioral insights and the methodological underpinnings of economic models, reflecting a commitment to advancing both theory and real-world relevance.
Adam Zylbersztejn is an Assistant Professor (Maître de conférences HDR) at Université Lumière Lyon 2 and affiliated with GATE Lyon Saint-Etienne, a research group in economics. He holds a PhD from the Paris School of Economics (2013) and previously conducted postdoctoral research at WU Wien (Vienna University of Economics and Business). His research focuses on behavioral economics using experimental methods to study individual and interactive decision processes, particularly in contexts involving trust, strategic uncertainty, and cultural variability. Key research areas include behavioral game theory, experimental finance, and the impact of nonverbal communication in economic interactions. His work has been published in top journals such as Journal of Economic Psychology , Games and Economic Behavior , and Experimental Economics . Recent studies examine topics like anonymity effects in charitable giving, trust in market exchanges under oath, and cognitive processes in asset markets using eye-tracking technology. His ongoing projects include research on risk-incentive tradeoffs and ambiguity framing in decision-making. While no formal academic awards are listed, his work has received press coverage in outlets like Le Monde and CORTEX Mag . He collaborates widely with institutions such as GATE-LSE and the Paris School of Economics, contributing to both theoretical and applied economic inquiry.
Alexandre Jacquillat is the Maurice F. Strong Career Development Associate Professor and Associate Professor of Operations Research and Statistics at MIT Sloan School of Management. His research focuses on data-driven decision-making with applications in air traffic management, urban mobility, and decarbonization. He holds a PhD in Engineering and MS degrees from MIT and École Polytechnique. Education PhD in Engineering, MIT MS in Technology and Policy, MIT MS in Applied Mathematics, École Polytechnique Research Interests His work develops scalable optimization models for efficient, equitable, and sustainable operations. Key areas include stochastic optimization, large-scale systems design, and machine learning applications in transportation and public policy. Recent projects explore vertiport planning for urban aerial mobility and prescriptive analytics for pandemic response. Awards Harold W. Kuhn Award (2024) INFORMS Harvey Greenberg Research Award (2023) MIT Jamieson Prize for Excellence in Teaching (2023) Multiple INFORMS Best Paper Awards (2015-2023) Named Leading Academic Data Leader by Chief Data Officer Magazine (2021-2022) Teaching & Grants Teaches courses in optimization (15.093, 15.083) and analytics (15.072). His grants support work in robotic warehousing, air traffic scheduling, and disaster response logistics. Advises on transportation analytics for industry and government. Labs/Teams Leads MIT Sloan's operations research group, collaborating with industry partners like McKinsey & Co. and Booz Allen Hamilton on transportation analytics and optimization projects.
Jose Apesteguia is an ICREA Research Professor at the Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain. His research focuses on behavioral economics, decision theory, experimental economics, and game theory, with particular emphasis on topics such as behavioral heterogeneity, rationality measures, stochastic choice models, and social preferences. Apesteguia has collaborated extensively with scholars like Miguel A. Ballester and Jörg Oechssler, producing influential work on topics ranging from imitation dynamics to the impact of language on moral decisions. His research integrates theoretical frameworks with experimental methods, exploring how individuals make decisions under uncertainty, time preferences, and social contexts. Key contributions include the development of measures of rationality and welfare, as well as analyses of behavioral adaptation and the role of information in competitive environments. His work frequently bridges economic theory with empirical validation, addressing real-world issues such as rule compliance in public institutions and team performance in organizations. Apesteguia’s publications span top journals including the American Economic Journal: Microeconomics , Journal of Economic Theory , and Econometrica . He teaches advanced courses on bounded rationality and behavioral decision theory at the undergraduate and graduate levels. His research has been applied to fields like finance (e.g., copy trading behavior) and public policy (e.g., promoting compliance in libraries).
