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Sunita SarawagiView profile

Professor

• Machine Learning
• Data Analytics
• Databases
• +10 more

Sunita Sarawagi is a Professor at Computer Science and Engineering , IIT Bombay, and a member of AI Labs@CSE . She is also associated with the Center for Machine Intelligence and Data Science (CMInDS), which she founded in 2020. Education: PhD in Computer Science from UC Berkeley (Thesis: Query Processing in Tertiary Memory Databases), BTech in Computer Science from IIT Kharagpur Research Interests span machine learning , data analytics , graphical models , and structured learning , with applications in text segmentation, sequence modeling, domain adaptation, and human-in-the-loop systems. Her publications reveal a strong focus on integrating data mining with database systems , temporal data analysis , and information extraction using probabilistic methods. Professional Activities include serving on the IEEE John Von Neumann Medal committee (2017-), VLDB 2011 Research Track Co-chair , and multiple program committee roles at top conferences like ICML, KDD, and SIGMOD. Labs & Teams : Leads the SS Lab , a research group focused on probabilistic graphical models, sequence modeling, and data integration techniques.

Catherine GreenhillView profile

Professor

• Combinatorics
• Graph Theory
• Applied Probability
• +6 more

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.

• Machine Learning
• Probability
• Mathematical Physics
• +6 more

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 .

Yee Whye TehView profile

Professor

• Statistical Machine Learning
• Probabilistic Learning
• Deep Learning
• +4 more

Yee Whye Teh is a Professor at the Department of Statistics, University of Oxford, and a research scientist at DeepMind. His work focuses on statistical machine learning, including probabilistic learning, Bayesian nonparametrics, deep learning, and Monte Carlo methods. He co-directs the ELLIS programme on Robust Machine Learning and has held roles such as Programme Co-chair for ICML 2017. Teh has delivered keynotes at UAI 2019, an IMS Medallion Lecture at JSM 2019, and the Breiman Lecture in 2017. His research emphasizes scalable inference algorithms, hierarchical models, and applications in genetics and natural language processing. Teh's educational background includes a PhD from the University of Toronto (2003) and a Master's from the same institution (2000). He has contributed to widely used software tools like the Sequence Memoizer and has been recognized for his work through prestigious lectureships. Research interests span Bayesian nonparametric models, MCMC methods, and their applications in genetics and data compression. His lab collaborates on projects like fragmentation-coagulation processes for genetic variation modeling and Mondrian forests for online learning. Teh advises students through Oxford's graduate programs, though he notes high demand for mentorship. His work often bridges theory and practice, addressing challenges in big data learning and small data problems.

• Machine Learning
• Statistics
• Theoretical Machine Learning
• +7 more

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 .

Adrian WellerView profile

Research Professor

• Artificial Intelligence
• Machine Learning
• Explainability
• +9 more

Adrian Weller is a prominent researcher and academic at the University of Cambridge, serving as a Director of Research in Machine Learning within the Department of Engineering. He holds multiple significant leadership roles including Programme Director for Trust and Society at the Leverhulme Centre for the Future of Intelligence (CFI), and previously served as Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI. His work bridges theoretical machine learning research with practical applications and societal implications of artificial intelligence. Weller's research interests span a broad spectrum of AI and machine learning topics with a particular focus on ensuring beneficial societal outcomes. His work encompasses explainability, fairness, robustness, scalability, privacy, safety, and ethics in AI systems. He has made significant contributions to trustworthy machine learning, including developing frameworks for AI governance, certification, and human-AI collaboration. His research group actively investigates neuro-symbolic approaches, privacy-preserving techniques, and methods for improving the reliability and interpretability of AI systems. His recent publications demonstrate a strong trend toward addressing the practical challenges of deploying AI systems in real-world contexts, particularly focusing on certification frameworks, governance mechanisms, and human-centered approaches. His work spans theoretical advances in machine learning architectures while maintaining a strong connection to societal impact, with publications appearing in top venues across AI, machine learning, and interdisciplinary applications. Scientific Awards: MBE for services to digital innovation (2022 Queen's Birthday Honours) Turing AI Fellowship for Trustworthy Machine Learning Weller actively supervises a large group of PhD students and postdocs, with current students including Juyeon Heo, Yanzhi Chen, Katie Collins, Isaac Reid, Yichao Liang, Herbie Bradley, and Shoaib Siddiqui. His former students have gone on to positions at leading institutions including Google DeepMind, ETH Zurich, NYU, and MPI-IS Tübingen. He has served on numerous advisory boards including the Centre for Data Ethics and Innovation, UNESCO's expert group on AI ethics, and the World Economic Forum's Global Future Council on AI. His research has been supported through his Turing AI Fellowship and various collaborative projects focused on safe and ethical AI development. Weller leads a vibrant research group focused on trustworthy machine learning, which actively organizes workshops and conferences including ICML 2024 (where he served as Program Chair), multiple workshops on responsible AI, and events through the ELLIS network. His group collaborates extensively across disciplines, working with researchers in computer science, social sciences, law, and policy to address the multifaceted challenges of developing beneficial AI systems.

Amitabha BagchiView profile

Professor

• Data Algorithmics
• Probability and Networks
• Theoretical Computer Science
• +7 more

Amitabha Bagchi is a Professor in the Department of Computer Science and Engineering at IIT Delhi. His research spans data algorithmics, probability, networks, and theoretical computer science, with applications in distributed systems, social networks, and AI-driven platforms. He has published extensively in leading venues such as SIGMOD, VLDB, ICDE, AAAI, and KDD, often collaborating with students and researchers on problems involving graph algorithms, fairness, and large-scale data analysis. Research Interests: His primary research interests include Data Algorithmics, Probability and Networks, Theoretical Computer Science, Distributed Algorithms, Graph Algorithms, and Machine Learning Theory. He investigates algorithmic foundations for real-world problems such as food delivery optimization, social network analysis, and efficient data structures for streaming and large graphs. Publication Trends: Recent publications focus on fairness in gig economy platforms, efficient solvers for graph Laplacians, generalization in neural networks, and temporal graph querying. His work combines theoretical rigor with practical impact, often involving GPU acceleration, distributed computing, and data-aware algorithm design. Scientific Service: Editor, Algorithms (2020–present) Editor, Journal of Discrete Algorithms , Elsevier (2006–2018) Guest Editor, special issue on Algorithms for Shortest Paths in Dynamic and Evolving Networks , Algorithms (2021) Volume Editor for proceedings of ESA, ATMOS, COCOON, and others Conference Leadership: He has served on numerous program committees and as chair for conferences including ESA (Engineering Track, 2016), ATMOS (2020), and ICALP (2019). His involvement spans algorithmic engineering, transportation optimization, and theoretical computer science forums. Teaching: He currently teaches COL863: Special Topics in Theoretical Computer Science on concentration inequalities and their applications. He has previously taught advanced courses in algorithms and data structures.

• Mathematics of Data
• Numerical Linear Algebra
• Randomized Algorithms
• +5 more

Liza Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University's School of Engineering and Applied Science. She is also an associated faculty member of the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML). Her research focuses on randomized numerical linear algebra and the mathematics of data science, with connections to high-dimensional probability and stochastic optimization. She develops mathematically justified randomized algorithms for large-scale data, particularly interested in settings where data exhibits mathematical structure such as spectral decay, multi-modality, or non-negativity. Her work addresses challenges in data compression, recovery, and optimization under structured noise conditions. Dr. Rebrova's recent publications show a strong trend toward developing robust algorithms for linear systems, tensor data compression, and nonnegative matrix factorization. Her work bridges theoretical foundations with practical applications in machine learning and data science, with emphasis on memory efficiency and handling corrupted or incomplete data. NSF DMS-2309685 (Single PI, 2024-2026): "Outliers are not what they seem: data-aware, flexible, and robust randomized iterative methods" NSF DMS-2108479 (Collaborative, 2022-2024): "Fast, Low-Memory Embeddings for Tensor Data with Applications" (co-PIs Mark Iwen and Deanna Needell) She currently advises three PhD students (Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko) and has previously advised Abraar Chaudhry (PhD 2024) and Nicolo Grometto (Masters thesis 2023). At Princeton, she teaches graduate courses including ORF526 (Graduate Probability), ORF387 (Networks), and ORF523 (Convex and Conic Optimization).

Tal MalkinView profile

Professor

• Cryptography
• Computer Security
• Complexity Theory
• +8 more

Tal Malkin is a Professor of Computer Science at Columbia University, directing the Cryptography Lab and serving as inaugural chair of the Cybersecurity Center at Columbia's Data Science Institute. She holds a Ph.D. from MIT (2000), joined Columbia after AT&T Labs research experience, and focuses on cryptography, security, complexity theory with applications in secure computation, zero-knowledge proofs, and privacy-preserving systems . Education: B.S. in Math and Computer Science, Bar-Ilan University M.S. in Computer Science, Weizmann Institute of Science Ph.D. in Computer Science, MIT (2000) Research Interests: Malkin's work spans foundational and applied cryptography, including homomorphic encryption, lattice-based protocols, attribute-based encryption, and tamper-resilient systems . She explores intersections with machine learning and information theory , addressing challenges in multi-party computation , public-key encryption , and side-channel resistance . Scientific Contributions: Her publications include breakthroughs in non-malleable codes , secure computation , and privacy-preserving databases . Notable works involve continual leakage resilience , optimally-fair coin tossing , and garbled circuits for efficient cryptographic protocols. Awards & Grants: Recipient of the NSF CAREER award, IBM and Google faculty research awards, and the IACR Fellow designation. Her research is funded by NSF, NSA, DHS, NYSIA, IARPA, and industry partnerships with Google, IBM, Mitsubishi, and NEC. Professional Leadership: Former conference chairs at CRYPTO 2021 , CCS 2017 , and CT-RSA conferences. Active in program committees for over 20 leading cryptography and security events, including FOCS , Eurocrypt , and Real World Crypto . Advising: Supervised numerous Ph.D. students and postdoctoral researchers, including Marshall Ball , Chengyu Lin , and Negev Shekhel Nosatzki . Her lab has mentored graduates like Ghada Almashaqbeh (2019) and Fernando Krell (2016), focusing on decentralized networks , secure learning , and cryptographic primitives .

Maya RamanathView profile

Associate Professor

• Database Systems
• Information Retrieval
• Semantic Web Data Management
• +3 more

Maya Ramanath is an Associate Professor in the Department of Computer Science and Engineering at Indian Institute of Technology (IIT) Delhi. She joined IIT Delhi in 2011 after a postdoctoral research stint at the Max-Planck Institute for Informatics in Germany. Her research interests focus on database systems, information retrieval, semantic web technologies, and knowledge graph construction and applications. Education: PhD in Computer Science, Indian Institute of Science, Bangalore M.Sc.(Engg.) in Computer Science, Indian Institute of Science, Bangalore B.E. in Computer Science and Engineering, Bangalore University, Bangalore Her recent work emphasizes efficient query processing over large-scale graphs, knowledge graph applications, and natural language interfaces for semantic data. Notable contributions include algorithms for reachability approximation in web-scale graphs, speculative query planning for knowledge graphs, and exploratory querying techniques. She has collaborated extensively on projects like NAGA, ESTHETE, and KlusTree, advancing the state of the art in graph-based data management and semantic search. Publications span conferences such as ICDE, ECIR, EDBT, and VLDB, reflecting a strong focus on database systems, graph algorithms, and semantic web applications. Her work bridges theoretical foundations with practical implementations, addressing scalability and efficiency challenges in modern data management systems. Research and advising activities include supervision of projects on distributed graph processing, query optimization, and knowledge representation. She has contributed to open-source tools like LegoDB and StatiX, and her lab focuses on interdisciplinary approaches to data-centric AI.

Peter BühlmannView profile

Professor

• High-dimensional Statistics
• Causal Inference
• Machine Learning
• +3 more

Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik , focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg , mboost , and glmmlasso , impacting fields such as genomics, proteomics, and intensive care analytics. Key Contributions : Causal structure learning, stability selection, anchor regression, and deconfounding. Software : Developed widely used R packages for statistical modeling and causal inference. Teaching : Courses on high-dimensional statistics at ETH Zürich and international institutions. Research Trends : Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests. Scientific Recognition : Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.

Renjie FengView profile

Research Fellow

• Probability Theory
• Random Matrix Theory
• Machine Learning
• +5 more

Renjie Feng is a Research Fellow in Mathematics and AI at the School of Mathematics and Statistics and the Sydney Mathematical Research Institute , University of Sydney. His work bridges probability theory, statistics, and applications in machine learning, deep learning, and artificial intelligence. His research interests focus on probability theory and its applications to machine learning , random matrix theory , and statistical physics . He investigates extreme value problems, spectral properties of random matrices, and topological features of random fields over Riemannian manifolds. Recent publications highlight trends in random matrix theory (GUE, GOE, GSE), extreme gap problems , determinantal point processes , and Wiener chaos . Collaborative works with F. Götze, D. Yao, and R. Adler emphasize U-statistics , multivariate linear statistics , and random topology inspired by Poisson point process studies.

Smirnov StanislavView profile

Professor

• Mathematical Physics
• Probability
• Complex Analysis
• +3 more

Stanislav Smirnov is a Professor at the University of Geneva's Department of Mathematics since 2003 and Head of the Chebyshev Laboratory at St. Petersburg State University since 2010. He holds a Ph.D. from the California Institute of Technology (1996) and a B.Sc./M.Sc. from St. Petersburg State University (1992). His research focuses on mathematical physics, probability, complex analysis, and dynamical systems, with groundbreaking contributions to the understanding of critical phenomena in statistical physics. Key achievements include receiving the Fields Medal (2010), the highest honor in mathematics, for his work on percolation and the Ising model. He has organized major international conferences, such as the 'St. Petersburg School in Probability & Statistical Physics' (2012) and the '2D Statistical Physics Workshop' (2013). His academic career includes positions at Yale University, the Max-Planck Institute, and KTH Royal Institute of Technology. Education: Ph.D., Mathematics, California Institute of Technology (1996) B.Sc./M.Sc., Mathematics (with honors), St. Petersburg State University (1992) Awards: Fields Medal (2010) European Research Council Advanced Grants (2008, 2013) Rollo Davidson Prize (2002) Salem Prize (2001) Smirnov’s research bridges probability theory and complex analysis, with notable results on conformal invariance in two-dimensional models. His work has advanced understanding of critical exponents, percolation thresholds, and the universality of phase transitions. He advises doctoral students and collaborates internationally on projects funded by Swiss and European grants. He co-organized over 20 conferences worldwide, including plenary talks at the International Congress of Mathematicians (2006, 2010) and the World Congress in Probability and Statistics (2012). His lab fosters interdisciplinary research, linking mathematics to physics and computer science.

Paul GölzView profile

Assistant Professor

• Social Choice Theory
• Algorithmic Fairness
• AI Ethics
• +3 more

Paul Gölz is an Assistant Professor at Cornell University's School of Operations Research and Information Engineering (ORIE), with affiliations in Computer Science. His research focuses on computational social choice, algorithmic fairness, and AI ethics, addressing topics like democratic innovation, fair resource allocation, and AI systems for diverse users. Education: Undergraduate studies at Saarland University (Germany), PhD in Computer Science from Carnegie Mellon University, followed by postdoctoral research at Harvard University, UC Berkeley, and the Simons Laufer Mathematical Sciences Institute. His work has been recognized with awards including the JPMorgan Chase AI Research Fellowship (2021) and honorable mentions for prestigious dissertation awards (Dantzig and ACM SIGecom). Research Highlights : Developed Panelot, a tool for selecting citizens' assemblies using state-of-the-art algorithms. His work on fair refugee resettlement and apportionment methods has been featured in Operations Research and Nature . Recent projects include AI alignment distortion analysis and generative social choice mechanisms. Teaching : Teaches Optimized Democracy (Spring 2025) and Mathematical Programming (Fall 2024). Supervises PhD students in ORIE, focusing on interdisciplinary applications of optimization and social choice. Awards & Grants : Received a $40K Structural Democracy Fellowship (Crankstart) and an OpenAI grant for Democratic Inputs to AI. Active in policy briefs (e.g., mini-public selection strategies). Labs/Tools : Co-developed Panelot.org , a nonprofit platform for fair citizen assembly selection. Active in open-source tool development for social choice applications.

Sarah BillingtonView profile

Professor

• Sustainable Building Design
• Human Wellbeing in Built Environments
• Affordable Housing
• +3 more

Sarah Billington is the UPS Foundation Professor of Civil and Environmental Engineering at Stanford University and a Senior Fellow at the Woods Institute for the Environment. Her research program focuses on sustainable building design, human wellbeing, and ethical supply chain practices. Education: PhD, University of Texas at Austin, Structural Engineering (1997) MSE, University of Texas at Austin, Structural Engineering (1994) BSE, Princeton University, Civil Engineering & Operations Research (1990) Her lab employs interdisciplinary methods to study how built environments impact human physical, psychological, and social wellbeing. Current projects include developing tools to quantify nature exposure in buildings, exploring affordable housing's role in wellbeing, and using AI to assess forced labor risks in material supply chains. While no longer active in this area, her group previously pioneered research on sustainable construction materials like bio-based composites and ductile cement-based composites. Her recent publications address topics such as nature integration in architecture, workplace design optimization, and health impacts of building materials. The Billington Lab at Stanford fosters collaborative team science, welcoming partnerships to advance occupant-centric engineering solutions.