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Nathan LinialView profile

Professor

• Combinatorics
• Theoretical Computer Science
• Bioinformatics
• +6 more

Nathan (Nati) Linial is a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, where he has been a faculty member since completing his postdoctoral period at UCLA. He earned his undergraduate degree in mathematics from the Technion and his PhD in graph theory from the Hebrew University. His research spans multiple areas of theoretical computer science and mathematics, with primary focus on combinatorics, theoretical computer science, and bioinformatics. Linial's work has made significant contributions to high-dimensional combinatorics, expander graphs, metric embeddings, and computational molecular biology. His research often bridges geometry, analysis, and combinatorial structures, demonstrating deep connections between seemingly disparate mathematical fields. Linial's recent publications reveal a strong trend toward high-dimensional combinatorial structures, including simplicial complexes, hypertrees, and high-dimensional permutations. His work frequently employs probabilistic methods, linear programming techniques, and geometric approaches to solve fundamental combinatorial problems. The breadth of his research is evident in both pure mathematical contributions and applications to computational biology. Fellow of the American Mathematical Society ISI Highly Cited Researcher Conant Prize (2008) for the influential survey paper "Expander graphs and their applications" Linial has served on the editorial boards of several prestigious journals including the Israel Journal of Mathematics (as Chief Editor 2013-2017), Random Structures and Algorithms, and Combinatorica. His academic leadership extends to organizing conferences and workshops in combinatorics and theoretical computer science. He has mentored numerous students whose work spans theoretical computer science, combinatorics, and computational biology. Linial is associated with research projects including ProtoNet (for protein sequence classification) and EVEREST (for evolutionary conserved protein domains), demonstrating his commitment to interdisciplinary research that bridges computer science with molecular biology.

• Fast graph algorithms in different computation models
• Robust algorithms against adaptive adversaries
• Continuous optimization for combinatorial problems
• +3 more

Thatchaphol Saranurak is an Assistant Professor at the University of Michigan , specifically in the Computer Science and Engineering Division . Prior to this, he earned his PhD in Computer Science from KTH Royal Institute of Technology in 2018 under Danupon Nanongkai , followed by a postdoctoral research assistant professorship at Toyota Technological Institute at Chicago (2018-2020). Research Focus : His work bridges fundamental problems in graph theory, including Dynamic graph algorithms for max-flow and min-cut Expander graph decompositions and their applications Robust algorithms against adaptive adversaries Continuous optimization for combinatorial problems Scientific Contributions : He has made breakthroughs in deterministic graph algorithms, notably improving vertex connectivity bounds, developing near-linear time Gomory-Hu trees, and advancing dynamic matching algorithms. His research has been recognized by Sloan Research Fellowship NSF CAREER Award Presburger Award 2023 Teaching : He teaches courses like Expander and Graph Algorithms and Introduction to Algorithms (Winter 23, Winter 25). His lecture videos and notes are publicly available. Collaborations : He works with leading researchers including Sayan Bhattacharya , Joakim Blikstad , and Jason Li , with affiliations to institutions like TTIC , KTH , and SODA conferences.

Giorgis PetridisView profile

Associate Professor

• Arithmetic Combinatorics
• Combinatorial Geometry
• Number Theory
• +3 more

Giorgis Petridis is an Associate Professor at the University of Georgia, specializing in arithmetic combinatorics, a field rooted in combinatorial number theory with modern extensions into discrete analysis and finite field geometry. Born in Athens, Greece, he earned his PhD from the University of Cambridge under Tim Gowers and held a Visiting Assistant Professor position at the University of Rochester. He serves as an editor for Combinatorial Theory and is affiliated with the Number Theory and Arithmetic Geometry group, particularly its additive combinatorics and discrete analysis subgroup. Doctor of Philosophy (2011), University of Cambridge Certificate of Advanced Studies in Mathematics (2002), St John’s College, Cambridge BA (Hons) in Mathematics (2001), St John’s College, Cambridge His research focuses on additive combinatorics, exploring sumset estimates, polynomial configurations in prime lattices, and geometric incidence problems over finite fields. He investigates combinatorial geometry, including pinned distance problems and bisector arrangements, while also contributing to exponential sum bounds and expander graph theory. His work bridges theoretical mathematics with applications in pseudorandomness and discrete geometry. Recent publications highlight trends in finite field analysis, with 6 of 15 articles addressing arithmetic structures in prime-order fields. Key keywords include Combinatorics , Number Theory , and Finite Fields , with sub-fields spanning polynomial configurations, energy bounds, and geometric combinatorics. Scientific awards include the Creative Research Medal (2024) from the University of Georgia for mid-career research impact. Grants from the Simons Foundation (MPS-TSM-00007816) and multiple NSF DMS Awards (2054214, 1723016, 1500984, 1804049) support his work on discrete analysis and conferences. He co-advises five PhD students and has supervised multiple Master’s theses on topics like point-plane incidences and additive energy. Outreach includes leading high school math teams, organizing discrete analysis sessions, and contributing to public science communication guides.

Joachim RosenthalView profile

Professor

• Coding Theory
• Cryptography
• Algebraic Geometry
• +2 more

Joachim Rosenthal is a Professor of Applied Mathematics at the University of Zurich's Department of Mathematics, leading the Applied Algebra Group. His research integrates coding theory, cryptography, and algebraic geometry with applications in communications and security systems. His primary research explores algebraic coding theory including convolutional codes, subspace codes, and post-quantum cryptography. He develops algorithms for error correction, code optimization, and cryptographic protocol analysis, with emphasis on mathematical structures in finite fields and rings. Professor Rosenthal serves as President of the Swiss Mathematical Society (2024-2025) and sits on IEEE Information Theory Society's Board of Governors. He has organized multiple international conferences on coding theory and cryptography, including the Zurich COST Meeting and Workshop on Convolutional Codes. His editorial contributions include roles at Archiv der Mathematik and SIAM Journal on Applied Algebra and Geometry. Recent publications focus on code construction methods, complexity analysis in group-based cryptography, and algebraic approaches to error-correcting codes.

Sharon RozovskyView profile

Professor

• Oxidative Stress
• Protein Quality Control
• ERAD Pathway
• +5 more

Sharon Rozovsky is a Professor in the Department of Chemistry and Biochemistry at the University of Delaware's College of Arts & Sciences, where she leads research on oxidative stress response mechanisms and protein quality control pathways. Her work bridges biochemistry, chemical biology, and structural biology with direct implications for understanding neurodegenerative diseases and viral pathogenesis. Her academic foundation includes a B.S. from Tel Aviv University (1994) and a Ph.D. from Columbia University (2000), establishing her expertise in protein dynamics and redox biochemistry. These credentials underpin her innovative approaches to studying cellular stress responses. Rozovsky's research program centers on selenoproteins—proteins containing the rare amino acid selenocysteine—and their critical roles in endoplasmic reticulum (ER) stress resolution. She investigates how membrane-bound selenoproteins like Selenoprotein S and K regulate the ER-associated degradation (ERAD) pathway, with recent work revealing their surprising autoproteolytic activity and involvement in SARS-CoV-2 replication. Her lab pioneers chemical tools including expressed protein ligation and advanced 77Se NMR spectroscopy to characterize these systems at molecular resolution. Analysis of her 2021-2025 publications shows dominant themes in selenoprotein structure-function relationships, ER stress mechanisms, and viral interactions, alongside methodological innovations in cryo-EM grid technology and NMR. This body of work demonstrates consistent focus on redox biochemistry with expanding applications in virology and structural biology. No major scientific awards or fellowships were explicitly documented in the available materials, though her research impact is evident through high-impact publications and methodological contributions. She directs the active Rozovsky Research Group, mentoring graduate students and postdoctoral researchers in biochemical and biophysical techniques. Her laboratory operations are supported by competitive funding including an NSF CAREER award (2011) focused on selenoprotein reactivity, reflecting sustained recognition of her innovative research program.

Bryon AragamView profile

Associate Professor

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +6 more

Bryon Aragam is an Associate Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research focuses on the intersection of causality, statistical machine learning, and probabilistic modeling, with particular emphasis on applications to artificial intelligence systems including large language models like ChatGPT and generative models like DALL-E. Dr. Aragam completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. Research Focus: Causal structure learning in probabilistic generative models Key Areas: Causal machine learning, deep generative models, latent variable models, statistical learning theory Applications: AI interpretability, ethics, and fairness in artificial intelligence systems Teaching: Business Statistics, Econometrics and Statistics Colloquium His recent publications demonstrate a strong theoretical foundation combined with practical applications, particularly in understanding and improving AI systems. His work spans causal discovery, graphical models, deep learning, and latent variable modeling, with particular attention to the theoretical properties of these methods and their applications to real-world AI challenges. The research shows a progression toward increasingly complex problems in causal representation learning and AI interpretability. Scientific Awards: Robert H. Topel Faculty Scholar NSF Graduate Research Fellow Dr. Aragam's work has been published in top statistics and machine learning venues including the Annals of Statistics, Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the Journal of Machine Learning Research (JMLR). His research group publishes broadly across both statistical and machine learning communities, demonstrating the interdisciplinary nature of his work at the intersection of statistics, machine learning, and causal inference. As a data science consultant for technology and marketing firms, Dr. Aragam has applied his expertise to problems in survey design, customer retention, logistics, and ranking, bridging the gap between theoretical research and practical applications.

Yuxin ChenView profile

Professor

• statistics
• optimization
• reinforcement learning
• +5 more

Yuxin Chen is a Professor at the University of Pennsylvania , holding joint appointments in the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering . Prior to UPenn, he was an Assistant Professor at Princeton University (2017-2021) and a Postdoctoral Researcher at Stanford University (2015-2017). His research spans statistics, optimization, reinforcement learning theory, diffusion models, and information theory , with a focus on theoretical foundations and practical algorithms for machine learning. Education : Ph.D. in Electrical Engineering (Stanford, 2015), M.S. in Statistics (Stanford, 2013), M.S. in Electrical and Computer Engineering (UT Austin, 2010), B.E. in Electrical/Microelectronics (Tsinghua, 2008). Research Interests encompass theoretical and applied aspects of machine learning, including nonconvex optimization , sample complexity analysis , low-dimensional adaptation , and generative modeling . His work bridges mathematical rigor with real-world applications, particularly in scientific imaging and high-dimensional data analysis. Scientific Awards include the SIAM Activity Group on Imaging Science Best Paper Prize (2024) Alfred P. Sloan Fellowship (2022) NSF Career Award (2022) Google Research Scholar Award (2022) IEEE Transactions on Power Electronics Prize Paper Award (2024) Advising and Grants : He has mentored numerous students who have transitioned to academic roles at institutions like UIUC and UW-Madison. His research is supported by grants from the NSF , Amazon , and Google , with recent projects focusing on controllable diffusion models and efficient reinforcement learning algorithms .

Amit ChakrabartiView profile

Professor

• Theoretical Computer Science
• Computational Complexity
• Data Stream Algorithms
• +4 more

Amit Chakrabarti is a Professor in the Department of Computer Science at Dartmouth College, part of the School of Arts and Sciences. He holds a B.Tech. from IIT Bombay and a Ph.D. from Princeton University. His research focuses on theoretical computer science, emphasizing computational complexity, data stream algorithms, and approximation algorithms. He has contributed to foundational work in communication complexity, lower bounds, and graph algorithms. Chakrabarti has received prestigious awards including the NSF CAREER Award and the Karen E. Wetterhahn Award. He has organized workshops such as the Banff Communication Complexity and Applications conference and contributed to the IHP thematic program in Paris. He teaches courses like Data Stream Algorithms and Computational Complexity, and has advised numerous graduate and undergraduate students. His current research explores connections between information theory and complexity, memory-efficient graph algorithms, and algebraic techniques in computational complexity. Chakrabarti has served on committees for major conferences (e.g., FOCS, SODA) and editorial roles for Information Processing Letters.

Karl-Theodor SturmView profile

Professor

• Optimal transport
• Metric measure spaces
• Probability theory
• +4 more

Karl-Theodor Sturm is a Professor of Mathematics at the University of Bonn, holding this position since 1997. He is affiliated with the Institute for Applied Mathematics and leads the Cluster of Excellence Hausdorff Center for Mathematics. His academic journey includes a PhD (1989) and habilitation (1993) from the University of Erlangen-Nürnberg, followed by postdoctoral positions at Zurich, Erlangen-Nürnberg, and the Max Planck Institute for Mathematics in the Sciences (MPI Leipzig). He has held visiting professorships at Stanford, Toulouse, Paris, and Bonn. Sturm's research focuses on stochastic analysis and geometric analysis, particularly in optimal transport, metric measure spaces, synthetic curvature bounds, and diffusion processes. His work on synthetic Ricci curvature bounds, developed in competition with Cédric Villani, has been highly influential. He received the ERC Advanced Grant (2016-2022) for research on metric measure spaces and Ricci curvature, and was a Plenary Speaker at the 2020 European Congress of Mathematics. His leadership roles include Vice Chairman of Collaborative Research Center SFB 611 (2002–2012), Managing Director of the Institute for Applied Mathematics (2007–2010), and Coordinator of the Hausdorff Center for Mathematics (2012–2019). Awards include the Heisenberg Fellowship (1994) and recognition through numerous invited lectures and editorial roles. His mentorship has shaped the careers of prominent researchers such as Nicola Gigli and Jan Maas.

• Theoretical Computer Science
• Error-Correcting Codes
• Approximation Algorithms
• +5 more

Venkatesan Guruswami is a Chancellor's Professor in the Department of EECS and a Senior Scientist at the Simons Institute for the Theory of Computing at UC Berkeley . He also holds a Professor position in the Department of Mathematics . His academic journey began with a B.Tech in Computer Science from the Indian Institute of Technology, Madras (1997) , followed by a Ph.D. in Computer Science from the Massachusetts Institute of Technology (2001) . After a Miller Research Fellowship at UC Berkeley (2001–02), he held faculty roles at the University of Washington and Carnegie Mellon University before returning to UC Berkeley in January 2022. Education : B.Tech, IIT Madras (1997) Ph.D., MIT (2001) Professional Affiliations : Chancellor's Professor, UC Berkeley (EECS) Senior Scientist & Interim Director, Simons Institute Professor, UC Berkeley (Mathematics) Guruswami's research spans multiple domains within Theoretical Computer Science , focusing on Error-Correcting Codes , Approximation Algorithms , Randomness in Computing , Probabilistically Checkable Proofs , and Computational Complexity . His groundbreaking work in List Decoding has enabled codes with minimal redundancy for correcting worst-case errors, while recent advancements include Polar Codes , Deletion-Correcting Codes , and Constraint Satisfaction Problems . He has also contributed to Quantum Coding Theory , Locally Recoverable Codes , and Approximation Hardness in various computational contexts. His publications reflect a deep engagement with interdisciplinary topics. Key trends include: Quantum Information Theory : Quantum LDPC codes, transversal gates, and quantum storage. Algebraic Coding : Reed-Solomon codes, AG codes, and polynomial-based constructions. Computational Complexity : Hardness of approximation, CSPs, and parameterized intractability. Data Transmission : Polar codes, deletion channels, and feedback mechanisms. Algorithmic Techniques : Spectral methods, semirandom models, and Lasserre hierarchy applications. Guruswami has received numerous accolades, including the Simons Investigator Award , Presburger Award , Packard Fellowship , Sloan Research Fellowship , ACM Doctoral Dissertation Award , and the IEEE Information Theory Society Paper Award . He is an ACM Fellow (2017) and IEEE Fellow (2019) , with recent honors like the Guggenheim Fellowship (2023) and AMS Fellow (2023) . As an advisor, he has mentored over 25 PhD and postdoctoral researchers , including Atri Rudra , Prasad Raghavendra , and Peter Manohar , whose work has won awards like the Edmund M. Clarke Doctoral Dissertation Award and CRA Outstanding Undergraduate Researcher Award . His research is supported by grants from the National Science Foundation , Packard Foundation , and Sloan Foundation . He also serves as Editor-in-Chief of the Journal of the ACM and holds leadership roles in IEEE and arXiv moderation. Guruswami is actively involved in Simons Institute programs and co-organized workshops on Coded Computation and Information Theory . His work bridges theoretical advancements with practical applications in Cloud Storage , Quantum Computing , and Group Testing , including pandemic-era contributions like AC-DC: Amplification Curve Diagnostics for SARS-CoV-2 .

• Cryptography
• Computational Number Theory
• Computer Algebra
• +3 more

Damien Stehlé is a Professor at the École Normale Supérieure de Lyon (ENS Lyon), affiliated with the Laboratoire LIP (CNRS, ENSL, INRIA, UCBL, U. Lyon) and a member of the AriC team. He is also a member of the Institut Universitaire de France. His research focuses on cryptography, computational number theory, and computer algebra, particularly lattice-based cryptography and its applications in post-quantum security. He has held editorial roles at the Journal of Cryptology and Designs, Codes and Cryptography, and served on committees for major conferences like ASIACRYPT and CRYPTO. His work has led to advancements in lattice reduction algorithms, cryptographic protocols (e.g., signatures, encryption schemes), and security proofs in the quantum random oracle model. Stehlé has supervised numerous PhD students, including Alice Pellet-Mary and Miruna Rosca, and has contributed to open-source lattice reduction software like fplll. His awards include Best Paper Awards at ASIACRYPT 2021 and 2015. He teaches advanced courses on post-quantum cryptography and cryptanalysis at the Master’s level.

• Theoretical Computer Science
• Graph Algorithms
• Lower Bounds
• +7 more

Nicole Wein is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, where she is a member of the Theory of Computation Lab within the Computer Science and Engineering Division. Her research focuses on theoretical computer science, particularly graph algorithms and lower bounds across various domains including distance-estimation, dynamic, parameterized, distributed, and online algorithms. Education: PhD in Computer Science from MIT, advised by Virginia Vassilevska Williams Master's in Computer Science from Stanford University B.S. in Computer Science/Mathematics from Harvey Mudd College Nicole's research centers on theoretical aspects of graph algorithms and computational complexity. She investigates fundamental questions about how algorithms can efficiently handle changing data, extract information from graphs in linear time, and understand the structure of shortest paths, especially in directed graphs. Her work spans multiple algorithmic paradigms including dynamic algorithms that adapt to changing inputs, parameterized approaches for hard problems, and fine-grained complexity that establishes precise relationships between problem difficulty. Analysis of Nicole's recent publications reveals a strong focus on graph algorithms, particularly shortest path problems, spanners, and hardness results. Her work often bridges theoretical insights with practical implications, developing novel techniques for distance estimation, dynamic graph processing, and approximation algorithms. A significant portion of her research examines the structural properties of graphs that enable or constrain efficient computation, with applications across computer science. Nicole actively mentors students at various levels. She currently advises PhD student Jubayer Nirjhor and has worked with undergraduate researchers including Sam Hiken (now a pre-doc at MIT), Michael Wang, and Tony Zhang. Her teaching includes foundational courses like EECS 376: Foundations of Computer Science and specialized courses such as EECS 598: Graph Algorithms. Nicole contributes to the academic community through service as a program committee member for major conferences including SOSA 2025, FOCS 2025, SODA 2025, and others. She co-organized the June 2023 DIMACS workshop on Modern Techniques in Graph Algorithms and previously organized Algorithms Office Hours at MIT to improve communication between theory and applications of algorithms.

Hassan Z. AshtianiView profile

Associate Professor

• Machine Learning Theory
• Privacy-Preserving Algorithms
• Gaussian Mixture Models
• +5 more

Hassan Z. Ashtiani is an Associate Professor in the Department of Computing and Software within the Faculty of Engineering at McMaster University. His academic profile shows consistent engagement in both teaching and research activities, with evidence of active participation in major machine learning conferences and journals through 2025. Dr. Ashtiani's research focuses on the theoretical foundations of machine learning, with particular expertise in privacy-preserving algorithms, Gaussian mixture models, and adversarial robustness. His work bridges statistical learning theory with practical algorithm design, often addressing fundamental questions about sample complexity and computational efficiency in learning systems. A significant portion of his recent work explores the intersection of differential privacy with statistical learning, developing methods for private density estimation and distribution learning. Analysis of his publication record reveals a strong trend toward increasingly sophisticated theoretical frameworks for private and robust learning. His work consistently appears in top-tier venues including NeurIPS, ICML, COLT, and ALT, with recent contributions extending into agnostic private density estimation and robust learning with tolerance. The research demonstrates progression from foundational work on nearest neighbor search and clustering algorithms toward more complex problems in private learning of high-dimensional distributions. Dr. Ashtiani teaches across multiple levels of computer science education, including undergraduate courses in Automata and Computability (COMPSCI 2AC3) and Principles of Programming (COMPSCI 2S03), as well as graduate-level courses such as Fundamentals of Machine Learning (COMPSCI 4ML3) and Theoretical Foundations of Unsupervised Learning (CAS 775). His teaching portfolio shows consistent involvement in machine learning education since at least 2019, with evidence of teaching multiple sections each academic year. His scholarly impact is reflected in mentions across 3 news outlets, reference in 1 policy source, engagement from 7 X users, and 90 readers on Mendeley, suggesting growing recognition of his contributions to theoretical machine learning.

Chen LiuView profile

Assistant Professor

• Machine Learning
• Adversarial Robustness
• Privacy-Preserving Machine Learning
• +9 more

Chen Liu is an Assistant Professor in the Department of Computer Science at City University of Hong Kong and the Principal Investigator (PI) of the Machine Learning and Optimization (MLO) group. His research focuses on building reliable machine learning models, particularly studying robustness and privacy properties of deep neural networks from an optimization perspective. University: City University of Hong Kong Academic Rank: Assistant Professor Students: Supervises multiple PhD, MPhil, and postdoctoral researchers. Education: Holds a Ph.D. (2022) and MSc (2017) in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), and a BSc (2015) in Computer Science from Tsinghua University. Research Interests: Adversarial robustness, privacy-preserving machine learning, optimization algorithms, dataset distillation, generative models, and theoretical analysis of loss landscapes. His work addresses challenges like catastrophic overfitting, architecture overfitting in distilled data, and stable adversarial training methods. Article Trends: Recent publications explore adversarial robustness under l0/l1 norms, gradient inversion for data reconstruction, evolutionary factor searching in finance, and meta-tuning for out-of-domain few-shot learning. These works emphasize optimization techniques to enhance model reliability and generalization. Scientific Awards: Microsoft Research Ph.D. Scholarship Programme (2017–2019) Advising and Grants: Supervises a diverse team of current and former students, with collaborations across institutions like George Mason University and Zhejiang University. Research supported by academic and industry grants. Labs and Teams: Leads the MLO group, which investigates fundamental ML theory and algorithms to improve system reliability. The group's work spans adversarial training, dataset distillation, and generative model optimization.

Benjamin EysenbachView profile

Professor

• Reinforcement Learning
• Self-Supervised Learning
• Machine Learning
• +2 more

Benjamin Eysenbach leads the Princeton Reinforcement Learning Lab, where he designs algorithms that enable artificial intelligence systems to learn intelligent behaviors through trial-and-error, specializing in self-supervised methods that eliminate the need for human labels. He joined Princeton after completing his PhD in machine learning at Carnegie Mellon University under Ruslan Salakhutdinov and Sergey Levine, supported by the NSF Graduate Research Fellowship and Hertz Fellowship. His research bridges fundamental machine learning principles with practical applications in robotics and decision-making systems. Eysenbach's research focuses on developing self-supervised reinforcement learning algorithms that enable autonomous skill acquisition without external rewards. His investigations span contrastive learning methods, temporal abstraction techniques, and scalable architectures for goal-conditioned behaviors. These innovations aim to create more efficient and generalizable learning systems that can discover useful behaviors from unlabeled experience. Eysenbach's publications demonstrate consistent advancement in self-supervised RL methodologies, with recent work focusing increasingly on temporal abstraction and representation learning theory. His research shows progression from foundational contrastive RL frameworks toward more sophisticated analyses of generalization properties and uncertainty quantification. The 2025 works indicate expanding investigation into hierarchical control, probabilistic alignment, and hyper-deep network architectures. Eysenbach has been recognized with prestigious awards including the Hertz Fellowship and NSF Graduate Research Fellowship, supporting his doctoral research in self-supervised RL methodologies. His work has been presented at top machine learning conferences including NeurIPS, ICML, and ICLR. As director of the Princeton Reinforcement Learning Lab, Eysenbach oversees research initiatives in self-supervised RL, including projects on intention-conditioned modeling, horizon generalization, and contrastive learning frameworks. He has secured funding from the Princeton AI Lab to study neural correlates of temporal contrast in decision-making. Eysenbach teaches courses in reinforcement learning and has developed new benchmarks like JaxGCRL to accelerate research in goal-conditioned RL.