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 .
Katerina Fragkiadaki is the JPMorgan Chase Associate Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. She works at the intersection of Artificial Intelligence, Computer Vision, Machine Learning, Language Understanding, and Robotics. PhD from GRASP Lab, University of Pennsylvania Postdoctoral researcher at UC Berkeley (with Jitendra Malik) and Google Research Recipient of NSF CAREER, DARPA Young Investigator, Amazon, Google, Sony, UPMC, and AFOSR awards Organizer of CoRL 2023 Workshop on Generalist Robots ICLR 2024 Program Chair, multiple area chair roles Her research group focuses on developing machines that autonomously improve world models through human-environment interactions, with specific emphasis on: Representation learning and video understanding 2D/3D unified vision-language models Generative simulation and reinforcement learning Real2Sim/Sim2Real robot learning Continual learning and spatial common sense 3D scene reconstruction and dynamics Recent publications highlight advancements in: 3D mesh generation with compositional transformers Unified 2D/3D perception frameworks Physics-aware generative models Diffusion-based robotic manipulation policies Embodied agents with memory prompting Awards include: 2024: DARPA Young Investigator Award 2023: Amazon Faculty Award 2022: Sony Faculty Research Award 2021: UPMC Faculty Research Award 2020: NSF CAREER Award 2019: Google Faculty Award Key collaborations span institutions including UC Berkeley, Google Research, Stanford, MIT, and University of Tsukuba. Her work bridges theoretical innovation with practical applications in: Autonomous robot manipulation 4D world modeling Language-grounded perception Visual dynamics prediction Embodied program synthesis Physics-based simulation engines
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)
Anca Dragan is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where she runs the InterACT Lab focused on algorithms for human-AI and human-robot interaction. Currently on leave from Berkeley, she leads AI Safety and Alignment at Google DeepMind, overseeing safety for Gemini models and preparing for future advancements. Dragan has been a co-PI of the Center for Human-Compatible AI and served on the steering committee for the Berkeley AI Research (BAIR) Lab. B.Sc. in Computer Science from Jacobs University Bremen, Germany Ph.D. from Carnegie Mellon University Dr. Dragan's research focuses on enabling AI agents to work effectively with, around, and in support of people. Her work bridges robotics, machine learning, and game theory to create systems that better understand human preferences and coordinate with users. Key areas include AI alignment (ensuring AI does what people actually want), learning reward functions from diverse human feedback forms, and developing algorithms for human-AI collaboration across domains like autonomous vehicles, brain-machine interfaces, and recommender systems. Her research emphasizes maintaining uncertainty about human preferences and accounting for the plurality of human values. Dr. Dragan's recent publications reveal a strong focus on addressing fundamental challenges in AI safety and alignment. Her work spans theoretical foundations of reward learning, practical implementations for human-AI coordination, and critical examinations of limitations in current approaches. There's a clear trajectory toward more robust, safe, and value-aligned AI systems that can handle complex human preferences while avoiding both present-day harms and potential catastrophic risks. IEEE RAS Early Academic Career Award in Robotics and Automation (2021) McEntyre Award for Excellence in Teaching (2020) PECASE (Presidential Early Career Award for Science and Engineering) (2019) Sloan Fellowship (2018) NSF CAREER Award (2017) Okawa Foundation Award (2017) MIT Tech Review 35 Innovators Under 35 (2017) Multiple best paper awards at top robotics and AI conferences Dr. Dragan has mentored numerous successful students who have gone on to faculty positions at MIT, Stanford, CMU, and Princeton, as well as industry roles at DeepMind, Waymo, and Meta. Her advising philosophy emphasizes both technical rigor and consideration of broader societal impacts. She has secured significant research funding including NSF CAREER, ONR Young Investigator, and Okawa Foundation awards, supporting work on human-AI interaction and alignment. Dragan has also consulted for Waymo for six years, helping develop roadmaps for deploying increasingly learning-based safety-critical systems. Dr. Dragan leads the InterACT Lab at UC Berkeley, which has produced influential work on Cooperative Inverse Reinforcement Learning, Inverse Reward Design, and other foundational concepts in human-AI interaction. The lab's research has significantly shaped the field of AI alignment, with applications spanning autonomous vehicles that coordinate with human drivers, brain-machine interfaces that adapt to user needs, and language models that better understand human preferences. Current work focuses on scaling safety approaches as AI capabilities advance, ensuring alignment keeps pace with technological progress.
Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a part-time role as Staff Research Scientist at Apple (MLR). He holds a Ph.D. in Electrical and Electronic Engineering from the University of Hong Kong (2018) and a B.Eng. in Electronic Engineering from Tsinghua University (2014). His research focuses on generative machine learning and AI agent interaction with the physical world, emphasizing multi-modal systems spanning language, images, videos, and 3D. Key themes include efficient modeling , flexible architecture design , and scalable decision-making frameworks . 2025: ICLR paper on DART framework 2024: TMLR work on GFlowNet alignment 2023: NeurIPS research on diffusion stability 2022: ACL papers on speech translation Recent publications explore diffusion models for text-to-image synthesis, 3D reconstruction, and efficient sampling techniques. His work addresses fundamental challenges in attention mechanisms, entropy collapse, and multi-stage distillation while advancing non-autoregressive translation and vision-language reasoning . Prospective students can apply through his recruitment process at UPenn. Prior affiliations include Meta AI (FAIR Labs) and academic collaborations with institutions like New York University's CILVR Lab.
Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML
Joseph Kable is the Jean-Marie Kneeley President's Distinguished Professor of Psychology at the University of Pennsylvania's Department of Psychology within the School of Arts and Sciences. His research integrates experimental economics, cognitive neuroscience, and personality psychology to investigate the neural and psychological mechanisms underlying decision-making. He explores how subjective value is represented in the brain, deviations from rational choice theory, and individual differences in decision processes. His lab employs fMRI and interdisciplinary methods to study topics like risk tolerance, impulsivity, and neural plasticity. Education: BS in Chemistry from Emory University (undergraduate), PhD in Neuroscience from the University of Pennsylvania (doctoral). Research focuses on behavioral and cognitive neuroscience mechanisms of choice, including studies on temporal predictions, value signals, and brain structure-function relationships. His recent work examines how neural markers correlate with decision-making variability across individuals. He currently advises graduate students in Psychology and has post-doctoral researchers in his lab. Associated with MindCORE, Penn's hub for integrative mind research. Lab activities include undergraduate research programs and active studies exploring decision neuroscience and neuroeconomics.
Brenden Lake is an Associate Professor of Computer Science and Psychology at Princeton University, starting Fall 2025. Previously, he was an Associate Professor of Psychology and Data Science at New York University. He is the principal investigator of the lab for Human & Machine Intelligence, which moved from NYU to Princeton in 2025 and is jointly affiliated with the Department of Computer Science and the Department of Psychology. His lab is located in Princeton's Peretsman Scully Hall, rooms 117, 120, and 121. Ph.D., Massachusetts Institute of Technology, 2014 Lake's research focuses on the intersection of human and machine intelligence, specifically examining human cognitive abilities that elude current AI systems. His work centers on few-shot learning of new concepts, learning by generating new goals, learning by asking questions, and learning by producing novel combinations of known components. He employs modern neural network modeling approaches including meta-learning, fine-tuning LLMs, neuro-symbolic modeling, and learning from child headcam videos. His research aims to advance both psychology and computer science by exploring what makes human intelligence unique and using those insights to develop more powerful AI systems. Lake's recent publications demonstrate significant trends in grounded language acquisition through child perspectives, systematic generalization in neural networks, and the intersection of developmental psychology with AI. His work has appeared in top-tier venues including Science (2024) and Nature (2023), with multiple publications exploring how insights from human cognition can improve machine learning systems. His research shows how incorporating human cognitive ingredients can make AI systems more powerful and human-like while addressing longstanding debates about neural network capabilities. Science publication (2024) on Grounded language acquisition through the eyes and ears of a single child Nature publication (2023) on Human-like systematic generalization through a meta-learning neural network Multiple publications covered by major media outlets including New York Times and Washington Post Lake advises Ph.D. students in computer science, psychology, and related fields through his lab. His research is supported by publications in top venues across computer science and cognitive science. He teaches courses including Computational Cognitive Modeling and Advancing AI through Cognitive Science, bridging the theoretical and practical aspects of his research. Lake leads the lab for Human & Machine Intelligence, which studies the ingredients of intelligence in humans and machines. The lab investigates human cognitive abilities that current AI systems cannot replicate, with the dual goal of advancing psychological understanding of human intelligence while developing more capable artificial intelligence systems. Current research focuses on few-shot concept learning, learning through goal generation, and learning by asking questions.
Professor Rebecca Lawson is a Professor of Neuroscience and Computational Psychiatry at the University of Cambridge, affiliated with the Department of Psychology and Bye-Fellow at Peterhouse College. Her research focuses on understanding how humans learn to make predictions under uncertainty, with applications to mental health conditions such as anxiety and depression. She leads the Prediction and Learning (PaL) Lab, which combines computational modeling, neuroimaging (e.g., 7T MRI), and behavioral experiments to study cognitive processes in typical and atypical populations. Key research interests include computational psychiatry, autism spectrum disorders, neuroimaging techniques, and the neurochemical basis of learning mechanisms. She has received significant funding, including a £4.3m Wellcome Mental Health Award and the Sir Henry Dale Fellowship. Notable contributions include advancing theories of neural gain and sensory expectations in autism, as well as studies on uncertainty processing in anxiety and depression. Professor Lawson holds academic awards such as the BNPA Lishmann Prize and the BAP Psychopharmacology Award. She actively contributes to public engagement, including initiatives like Knit-a-Neuron and involvement with PrideinSTEM. Her lab collaborates internationally, with projects like the CamRAA study investigating autism-anxiety overlaps and the Chemical and Brain Basis of Uncertainty (CBBU) study exploring pharmacological interventions. Education: PhD in Cognitive Neuroscience (University of Cambridge), BA (Hons) Psychology & Philosophy (University of Glasgow). Grants: £4.3m Wellcome Award, Parke-Davis Fellowship, Lister Institute Prize. Labs/Teams: Principal Investigator of the PaL Lab; collaborations with MRC Cognition and Brain Sciences Unit, Yale University, and Brown University.
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.
Dai Zhongxiang is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHKSZ), where he joined in August 2024. Previously, he was a Postdoctoral Associate at MIT's Laboratory for Information and Decision Systems (January-June 2024) and a Postdoctoral Fellow at the National University of Singapore's Department of Computer Science (April 2021-December 2023). He completed his Ph.D. in Artificial Intelligence at NUS under the supervision of Bryan Kian Hsiang Low and Patrick Jaillet. Dr. Dai's research focuses on the intersection of theoretical and practical AI, with particular emphasis on large language models (LLMs) and optimization techniques. His work spans both theoretical foundations of multi-armed bandits and Bayesian optimization, as well as practical applications in LLM inference, including prompt optimization, in-context learning, personalization of LLMs, LLM-based agents, and scaling up test-time computation of LLMs. His research approach often bridges theoretical principles with real-world applications, particularly in AI4Science problems. His recent publications demonstrate a clear trend toward advancing LLM capabilities through optimization techniques, with increasing focus on practical deployment challenges. The research spans both theoretical contributions to optimization theory and applied work on enhancing LLM performance in real-world scenarios. His work on dueling bandits, neural bandits, and zeroth-order optimization has been consistently published in top-tier venues including NeurIPS, ICML, ICLR, and ACL. Presidential Young Fellow, CUHKSZ (2024) Dean's Graduate Research Excellence Award, NUS (2021) Research Achievement Award × 2, NUS (2019 & 2020) Singapore-MIT Alliance Graduate Fellowship (2017) Dr. Dai actively mentors multiple Ph.D. students and research assistants, with several of his students' papers accepted to top conferences. His research has received significant attention, with invitations to serve as Area Chair for NeurIPS 2025 and ICLR 2025, reflecting his growing influence in the machine learning community. His work bridges theoretical machine learning with practical applications in large-scale AI systems.
LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.
Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.
Christopher Thomas Sege, PhD is an Assistant Professor in the Department of Psychiatry and Behavioral Sciences at the Medical University of South Carolina (MUSC) College of Medicine. His research focuses on the neurophysiological mechanisms underlying anxiety disorders, particularly examining escape, avoidance, and approach behaviors in the anxiety disorder spectrum. Dr. Sege's primary research interests include: Anxiety Disorders and Emotional Processing Defensive Behaviors and Coping Mechanisms Neurophysiological Correlates of Emotional Anticipation Startle Response and Autonomic Nervous System Functioning Transcranial Magnetic Stimulation (rTMS) Applications His work bridges clinical psychology with neuroscience to understand how individuals with anxiety disorders process and respond to emotional stimuli. Dr. Sege leads the research study "Modeling and Modulating Mechanisms of Escape, Avoidance, and Approach in the Anxiety Disorder Spectrum" which investigates whether repetitive transcranial magnetic stimulation (rTMS) can alter how people with anxiety cope with emotional situations. This study involves measuring brain activation while participants view emotional pictures and undergo rTMS procedures. His publication record demonstrates expertise across multiple domains: Neurophysiological mechanisms of defensive behaviors Emotional anticipation and perception Startle modulation in anxiety disorders Borderline personality disorder research Development of experimental paradigms for emotional processing studies His research employs multiple methodologies including psychophysiology, event-related potentials, and neurostimulation techniques. Dr. Sege collaborates with researchers including Lisa McTeague, Bernadette Cortese, Carla Danielson, Thomas Uhde, and Aicko Schumann. His work appears in journals such as Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Biological Psychology, and Psychophysiology.
Gauri Joshi is an Associate Professor in the Electrical and Computer Engineering (ECE) department at Carnegie Mellon University, with affiliate appointments in the Machine Learning Department and Robotics Institute. Her work focuses on system-aware algorithms for distributed machine learning, combining optimization, probability, and coding theory to address communication and computational constraints in edge networks. MIT Ph.D. in EECS (2016) IIT Bombay B.Tech/M.Tech in Electrical Engineering (2010) Research themes include federated learning with communication efficiency, erasure coding for non-linear computations, and reinforcement learning for heterogeneous queueing systems. She leads the Optimization, Probability and Learning (OPAL) lab , affiliated with the Parallel Data Lab (PDL), CyLab, and FLAME Center. Recent publications highlight advances in: federated fine-tuning with low-rank adaptation, robust PCA for model aggregation, privacy-preserving prediction mechanisms, and adaptive reinforcement learning for job dispatching systems. Her group has received 15+ paper awards across SIGMETRICS, MobiHoc, and NeurIPS workshops. Scientific recognition includes: IEEE Goldsmith Lecturer (2025), MIT Technology Review '35 Innovators Under 35' (2022), ONR Young Investigator Award (2023), NSF CAREER (2021), and ACM SIGMETRICS Best Paper (2020). She has advised 20+ graduate students, many now at tech giants like Google, Apple, and Meta. Service contributions span program co-chair roles (MLSys 2025), associate editorships (IEEE/ACM Transactions on Networking), and workshop organization (ICML, NeurIPS). Her NSF AI-EDGE Institute leadership (2021-present) drives next-generation intelligent edge networks for robotics and aerospace applications.
