Claire Bowern is Professor of Linguistics at Yale University specializing in historical linguistics, language documentation, and Australian Indigenous languages. Her research employs computational phylogenetics to study language evolution and supports language revitalization through digital archives and fieldwork methodologies. Recent publications address: Phylogenetic signal in lexical evolution across language families Digital infrastructure for endangered language documentation (FLEx software analysis) Decolonizing linguistics pedagogy and research practices Her work consistently integrates linguistic, anthropological, and computational approaches to analyze language diversity and change. She contributes to global databases including Grambank and D-PLACE, examining links between linguistic, cultural, and environmental patterns.
David Silver is a Professor of Computer Science at University College London and Principal Research Scientist at DeepMind, leading the Reinforcement Learning Research Group. His pioneering work in deep reinforcement learning has revolutionized artificial intelligence through breakthroughs in computer game-playing algorithms. His educational background includes: Bachelor's and Master's degrees from Cambridge University (1997, 2000) PhD in Computer Science from the University of Alberta (2009) Silver specializes in deep reinforcement learning where algorithms learn through trial-and-error in interactive environments. His research combines deep neural networks with reinforcement learning strategies to solve complex decision-making problems. He is renowned for developing AlphaGo (defeating Go world champion Lee Sedol in 2016), AlphaZero (mastering Chess, Shogi and Go through self-play), and AlphaStar (conquering Starcraft II). His work demonstrates unprecedented generality in game-playing AI and has catalyzed industry-wide adoption of reinforcement learning techniques. Analysis of his publications reveals a consistent trajectory toward self-supervised learning systems. His 2015-2016 Nature papers established foundational architectures combining neural networks with Monte Carlo tree search, shifting the field from human-data dependency toward pure self-play methodologies. This evolution enabled superhuman performance across diverse game domains while minimizing domain-specific knowledge. His scientific awards include: ACM Prize in Computing (2019) for breakthrough advances in computer game-playing Marvin Minsky Medal (2018) for outstanding achievements in AI Royal Academy of Engineering Silver Medal (2017) for UK engineering contributions Mensa Foundation Prize (2017) for best AI scientific discovery Silver's research has generated significant real-world impact beyond gaming, including optimizing the UK power grid, reducing Google data center energy consumption, and planning European Space Agency probe trajectories. While not explicitly documented in advising roles, his leadership at DeepMind fosters collaborative research environments that train next-generation AI scientists through high-impact projects. As lead of DeepMind's Reinforcement Learning Research Group, Silver directs cutting-edge work extending deep reinforcement learning to robotics, complex real-world systems, and multi-agent environments. Current initiatives focus on transferring game-playing breakthroughs to industrial automation and scientific discovery applications.
Professor Catherine Greenhill is a faculty member at the School of Mathematics and Statistics, UNSW Sydney , where she serves as Professor and head of the Combinatorics group. Her academic career spans institutions including the University of Queensland, University of Oxford, University of Leeds, University of Melbourne, and Australian National University. D.Phil., University of Oxford (1996) M.Sc. (Research) in Combinatorics (1992) B.Sc. (Hons) in Pure Mathematics (1991) Her research focuses on the intersection of discrete mathematics , theoretical computer science , and probability , particularly in asymptotic combinatorics , probabilistic methods , and analysis of algorithms . Her work includes asymptotic enumeration of combinatorial structures and design of randomized algorithms for graph sampling and counting. Her recent publications (2025–2021) center on random graphs and hypergraphs , with key contributions to switch Markov chains , degree sequence analysis , and chromatic number bounds . These works reflect her expertise in probabilistic combinatorics and algorithmic complexity . Scientific Awards: Fellow of the Australian Academy of Science (2022) Christopher Heyde Medal in Pure Mathematics (2015) June Griffith Fellowship (2013) Hall Medal (2010) Advising and Grants: She has supervised numerous PhD/Masters students and secured multiple ARC Discovery Grants (2019–2021, 2014–2016, 2012–2014). Her grants address topics like hypergraph modeling, random discrete structures, and network analysis in illicit drug trafficking.
Prof. Ingmar Posner is a leading figure in applied artificial intelligence at the University of Oxford, where he serves as Principal Investigator for the Applied Artificial Intelligence Lab (A2I) and founding Director of the Oxford Robotics Institute. His work focuses on enabling robots to operate effectively in complex real-world environments through experience-driven learning. Key research areas: robot learning, scene interpretation, data-efficient learning, and transfer learning Applications in manipulation, autonomous driving, logistics, and space exploration His team has produced groundbreaking work in world models, sim-to-real transfer, and constraint-based manipulation systems (e.g., COMBO-Grasp). Notable contributions include the TWIST distillation framework and foundational research in tactile data generation (TactGen). He has received multiple best paper awards at top robotics venues. Publications reveal evolving research themes: 2025 work emphasizes language-conditioned learning (Lumos) and multi-agent decision-making, while 2024 focused on diffusion models for locomotion and differentiable simulators. Earlier work spans from urban scene analysis to physically plausible scene synthesis (RELATE).
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 .
Boris Hanin is an Associate Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE) and Associated Faculty at the Program in Applied and Computational Mathematics (PACM). Prior to Princeton, he held academic positions at Texas A&M University (Assistant Professor of Mathematics), MIT (NSF Postdoctoral Fellow), and Northwestern University (PhD in Mathematics under Steve Zelditch). He works part-time at Foundry, an AI/computing startup, leading the Foundry Institute. PhD in Mathematics, Northwestern University NSF Postdoctoral Fellowship, MIT Mathematics Associate Professor, Princeton ORFE Part-Time Leader, Foundry Institute His research spans machine learning, probability theory, and mathematical physics, focusing on neural network theory (approximation power, optimization guarantees), random matrix theory, and spectral asymptotics. He has made foundational contributions to understanding gradient behavior, initialization, and infinite-width limits in deep learning. Boris has supervised numerous PhD students and postdocs, with former members securing prestigious positions at Harvard, MIT, and Huawei. He serves as Associate Editor for journals like Pure and Applied Analysis and Mathematics of Operations Research , and has taught short courses at Oxford, Luxembourg, and Tor Vergata on statistical physics of neural networks and deep learning theory. 2024 Sloan Fellowship in Mathematics NSF CAREER grant DMS-2143754 NSF grant DMS-2133806 His research group collaborates on topics including hyperparameter transfer, architecture-aware scaling, and spectral analysis of random waves and neural networks. He actively contributes to theoretical machine learning through foundational publications in venues like Probability Theory and Related Fields , Journal of Machine Learning Research , and Communications in Mathematical Physics .
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 .
Robert West is an Associate Professor at EPFL (École polytechnique fédérale de Lausanne) in the School of Computer and Communication Sciences , leading the Data Science Lab (dlab) . His research focuses on Natural Language Processing , Machine Learning , and Computational Social Science , analyzing human-generated data from the web, social media, and online platforms. Education : PhD in Computer Science (2016) - Stanford University MSc in Computer Science (2010) - McGill University BSc in Computer Science (2007) - Technische Universität München Research Interests : West develops algorithms for analyzing large-scale web data, with emphasis on multilingual NLP , social network analysis , and AI ethics . His work bridges machine learning with social science to understand digital human behavior. Scientific Awards : ICWSM’22 Adamic–Glance Distinguished Young Researcher Award Google Faculty Research Award Facebook Research Award Multiple Outstanding Paper Awards at ICWSM and WWW Advising & Grants : He advises 12 PhD students and has secured funding from the Swiss National Science Foundation , Swiss Data Science Center , and industry partners. His lab maintains collaborations with Microsoft Research and CROSS . Labs & Collaborations : West leads the Data Science Lab at EPFL, which focuses on web-scale data analysis , privacy-preserving machine learning , and AI for social good . The lab develops tools like Wikispeedia and Quotebank for public data exploration.
Shimon Whiteson is Professor of Computer Science at the University of Oxford, leading the Whiteson Research Lab focused on reinforcement learning, multi-agent systems, and deep learning. His research develops algorithms for efficient learning in complex environments. Current work explores meta-reinforcement learning frameworks that enable agents to rapidly adapt to new tasks, with applications in autonomous driving simulation and robotics. Recent innovations include novel methods for offline reinforcement learning, multi-agent coordination, and morphology-aware control. Publications demonstrate advances in: Meta-RL algorithm design for few-shot adaptation Multi-agent reinforcement learning environments and benchmarks Imitation learning in autonomous driving Bayesian methods for sample-efficient learning Research outputs include widely used benchmarks and tools including JaxMARL for accelerated multi-agent RL research. Current doctoral supervision focuses on temporal abstraction in RL, multi-agent coordination, and reinforcement learning theory.
Jason D. Lee is an associate professor of Electrical Engineering and Computer Sciences (EECS) and Statistics at the University of California, Berkeley. Previously, he held academic positions at Princeton University as an associate professor, and was a research scientist at Google DeepMind. He completed his Ph.D. in Computational and Mathematical Engineering at Stanford University under the advisement of Trevor Hastie and Jonathan Taylor. For students and collaborators, his primary contact email is jasonlee@princeton.edu, though prospective students and postdocs are asked to include "filter_student" in the subject line. Ph.D., Computational and Mathematical Engineering, Stanford University (2015) B.Sc., Mathematics, Duke University (2010) Lee's research lies at the intersection of machine learning, statistics, and optimization, focusing on the theoretical foundations of artificial intelligence. His work addresses fundamental questions in deep learning, including optimization landscapes, representation learning, and reinforcement learning theory. He has made significant contributions to understanding how gradient descent operates in neural network training and has developed provably efficient algorithms for various learning scenarios. His ten most recent publications represent a diverse yet coherent body of work across machine learning theory, focusing on topics such as Gaussian multi-index models, transformer learning capabilities, shallow neural networks, and optimization techniques. These publications appear in top venues including COLT, ICML, NeurIPS, and JMLR. Among his notable accolades are: Samsung AI Researcher of the Year Award (2023) NSF Career Award (2022) ONR Young Investigator Award (2021) Sloan Research Fellow in Computer Science (2019) NIPS Best Student Paper Award (2016) Princeton Commendation for Outstanding Teaching (ECE538B) Lee has advised numerous students including Alex Damian, Wenhao Zhan, Eshaan Nichani, Tianle Cai, Zixuan Wang, and Yunwei Ren. Former advisees have gone on to positions at institutions like NYU Courant, Facebook AI Research, UW, MIT, Duke, and Microsoft Research. His research group and collaborators span multiple institutions, working on theoretical and applied aspects of machine learning and artificial intelligence, with a particular focus on the optimization and learning dynamics of neural networks and transformer models.
Abhishek Jain is an Associate Professor in the Department of Computer Science at Johns Hopkins University and a Senior Scientist at NTT Research. He is affiliated with the Data Science and AI Institute, the Information Security Institute, and the Algorithms and Complexity group. University: Johns Hopkins University Department: Computer Science Academic Rank: Associate Professor Institutional Roles: Co-director of the Advanced Research in Cryptography group, member of the Theory Group, and co-leader of the Cryptography Group Education: Ph.D. in Computer Science from the University of California Los Angeles (2012), advised by Amit Sahai and Rafail Ostrovsky. Recipient of the Symantec Outstanding Graduate Student Award during his Ph.D. Research Interests Cryptography Secure computation Proof systems Program obfuscation Privacy Blockchain Theoretical computer science Research Trends: His recent publications emphasize cryptographic protocols for homomorphic encryption, zero-knowledge proofs, and secure multi-party computation, alongside applications in blockchain and privacy-preserving systems. Key themes include scalability, verifiable evaluation, and adapting cryptographic techniques to dynamic and distributed environments. Scientific Awards 2020 NSF CAREER Award Best Paper Awards at Eurocrypt and the International Conference on the Theory and Applications of Cryptographic Techniques Advising & Grants: Mentors Ph.D. students and interns/postdocs at NTT Research. Research funded by NSF, DARPA, JP Morgan, Ethereum Foundation, Stellar, Cisco, Samsung, and JHU Catalyst awards. Labs & Teams: Co-leads the Cryptography Group at Johns Hopkins, participates in the DC Area Crypto Day, and organizes the weekly Theory Seminar. Collaborates with institutions including MIT CSAIL, BUSEC, and Microsoft Research New England.
Fanny Yang is an Assistant Professor in the Computer Science Department at ETH Zurich . She previously held postdoctoral positions at Stanford University and a Junior Fellowship at the Institute for Theoretical Studies at ETH Zurich, advised by Nicolai Meinshausen . Her PhD was completed at the EECS Department of UC Berkeley , supervised by Martin Wainwright . Research Interests : Theoretical foundations of machine learning and statistics , particularly focusing on overparameterized models and high-dimensional data . Developing trustworthy ML models with emphasis on distributional robustness , domain generalization , and interpretability . Applications in medical diagnostics and treatment effect analysis , aiming to address reliability issues in real-world domains. Recent Work Trends : 2025 Articles : Theoretical analysis of semi-supervised multi-objective learning , test-time scaling with verifiers, and foundation models for efficient randomized experiments . 2024 Articles : Studies on robust mixture learning , privacy-preserving data synthesis via optimal transport , confounding quantification in causal inference , and semi-private learning frameworks. 2023 Articles : Investigations into active vs. passive learning in high dimensions, inductive bias in noisy interpolation , and semi-supervised novelty detection using model ensembles .
Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.
Sewoong Oh is a Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he has been faculty since 2019. His research focuses on the foundations of machine learning with particular emphasis on differential privacy, secure and robust machine learning, and federated learning. Prior to joining UW, he was at the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign from 2012-2019. He is affiliated with multiple NSF AI Institutes including ACTION (Agent-based Cyber Threat Intelligence and Operation), AI-EDGE (Future Edge Networks and Distributed Intelligence), and IFML (Foundations of Machine Learning). Oh received his PhD in Electrical Engineering from Stanford University in 2011 under Andrea Montanari, followed by postdoctoral work at MIT's Laboratory for Information and Decision Systems under Devavrat Shah. His educational background spans top institutions in theoretical computer science and electrical engineering. His research spans the critical intersection of machine learning security, privacy, and robustness. Oh's work addresses fundamental challenges in making AI systems reliable and trustworthy, with particular focus on defending against backdoor attacks, developing privacy-preserving algorithms, and creating efficient tokenization methods for language models. His recent SuperBPE work demonstrates how moving beyond traditional subword tokenization can significantly improve language model efficiency and performance. His research consistently bridges theoretical foundations with practical implementations, as evidenced by numerous open-source repositories containing production-ready code. Oh's publications reveal a consistent trajectory toward addressing security and privacy concerns in increasingly complex machine learning systems, with recent work focusing on language model tokenization, data-centric AI, and federated learning frameworks. His research shows strong interdisciplinary connections between theoretical computer science, statistics, and practical machine learning systems. ACM SIGMETRICS best paper award (2015) NSF CAREER award (2016) ACM SIGMETRICS rising star award (2017) GOOGLE Faculty Research Awards (2017, 2020) 2024 ICML Best Paper Award (for work by advisee Jon Hayase) Professor Oh maintains an active research group with numerous PhD students, postdocs, and undergraduate researchers. His students have secured prestigious positions at leading tech companies including Amazon, Google, and Snap, as well as academic appointments at institutions like University of Wisconsin-Madison and Shanghai Jiao Tong University. He has received substantial research funding through his participation in multiple NSF AI Institutes and industry research awards from Google. His lab maintains several active GitHub repositories implementing cutting-edge research in backdoor defense, robust statistics, and privacy-preserving machine learning.
Dr. Guillermo Amador is an Assistant Professor in the Experimental Zoology department at Wageningen University & Research. His research focuses on understanding how animals interact with complex environments through locomotion, adhesion, and fluid dynamics. He investigates biological systems like insects, plants, and marine organisms to inspire bio-engineered solutions for robotics, microfluidics, and material science. Amador received his PhD in Mechanical Engineering from Georgia Institute of Technology (USA), followed by postdoctoral research at the Max Planck Institute for Intelligent Systems (Germany) and a Marie Sklodowska-Curie fellowship at TU Delft (Netherlands). His expertise spans biophysics, biomaterials, and biomechanics, with a focus on self-cleaning mechanisms and bioadhesion. He collaborates with the 4TU consortium on Dutch Soft Robotics to develop bio-inspired designs. His work bridges fundamental biology with engineering applications, emphasizing interdisciplinary approaches to solve challenges in robotics and environmental science. Amador teaches courses including Biomimetics and Functional Zoology , integrating his research into education. His research highlights include studies on cuttlefish suction cups, stick insect adhesion, and pollen transport mechanisms in pollinators.
