Find Academic Supervisors

• Macroeconomic Theory
• Endogenous Growth
• Endogenous Fluctuations
• +5 more

Mauro Bambi is an Associate Professor at Durham University Business School and an Associate Fellow at the Institute of Advanced Studies. His research focuses on Macroeconomic Theory, Endogenous Growth, Habit Formation, and Behavioral Economics. He earned his PhD from the European University Institute (2007) and held positions at ETH Zurich and the University of York. He directs the Centre for Macroeconomic Policy (CEMAP) and has been recognized with the 2008 Italian Association of Applied Mathematics award for his PhD thesis. Education: PhD in Economics (European University Institute, 2007); Postdoctoral Fellowships at Université Catholique de Louvain (Belgium) and ETH Zurich (Switzerland). Research Interests: Macroeconomic Theory and Policy Design Endogenous Growth and Fluctuations Habit Formation in Economic Models Behavioral Economic Preferences Mathematical Methods in Macroeconomics Key Contributions: His work on habit formation models, time-to-build frameworks, and policy design has influenced macroeconomic theory. Recent studies analyze post-COVID demand shifts and pandemic economic impacts. Awards: 2008 Graduate Prize for Best PhD Thesis (Italian Association of Applied Mathematics). Advising and Leadership: Supervised PhD students like Federico Bertoni and Xinyi Xu. Directed CEMAP from 2019–2022, fostering macroeconomic policy research. Active in interdisciplinary collaborations at the Institute of Advanced Studies. Labs/Teams: Leader of CEMAP, collaborating with global institutions on macroeconomic policy analysis.

Caglar GulcehreView profile

Assistant Professor

• Reinforcement Learning
• Foundation Models
• Large Language Models
• +6 more

Caglar Gulcehre is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL) and Principal Investigator of the CLAIRE (Caglar Gulcehre Laboratory of Artificial Intelligence Research) lab. Previously, he worked as a Staff Research Scientist at Google DeepMind, Microsoft Research, and IBM Research. His research focuses on reinforcement learning , foundation models , LLM alignment , and sequence modeling . Current Position : Assistant Professor, EPFL Lab : CLAIRE Lab Previous Roles : Staff Research Scientist at DeepMind, MSR, IBM Research His work spans reinforcement learning , deep learning , and neural architecture design , with a focus on safety , trustworthy AI , and real-world applications . He has published in top venues including Nature , NeurIPS , ICML , and JMLR . Scientific contributions include: Best paper award at NeurIPS Nonconvex Optimization workshop Honorable mention for best paper at ICML 2019 Co-organizer of seven workshops at NeurIPS, ICML, and ICLR He supervises PhD students in areas related to AI for algorithm discovery , neural architectures , and foundation models , including: Skander Moalla Justin Samuel Deschenaux Liangze Jiang Xiuying Wei Yitao Xu

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.

Philip S. YuView profile

Professor

• Data Mining
• Big Data
• Privacy-Preserving Data Publishing
• +9 more

Philip S. Yu is a Distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago and holds the Wexler Chair in Information Technology. Previously, he led the Software Tools and Techniques department at IBM Thomas J. Watson Research Center. Education: B.S. in Electrical Engineering, National Taiwan University M.S. and Ph.D. in Electrical Engineering, Stanford University M.B.A., New York University His research spans data mining , big data , social networks , privacy-preserving data publishing , graph/network mining , recommender systems , and deep learning . He has authored over 970 papers with 74,500+ citations and an H-index of 127. Recent work focuses on heterogeneous graph representation, quantum walks in network analysis, and federated unlearning. Scientific Honors: ACM SIGKDD 2016 Innovation Award IEEE Computer Society 2013 Technical Achievement Award IEEE ICDM 2003 Research Contributions Award IEEE Region 1 Award (1999) UIC Research of the Year (2013) IBM Master Inventor with 300+ patents AI 2000 Most Influential Scholar Honorable Mentions (2024-2025) He served as Editor-in-Chief for ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Knowledge and Data Engineering , and on steering committees for ACM KDD and IEEE Data Mining. His work bridges theoretical advances in graph neural networks , deep learning , and privacy-preserving systems with applications in healthcare, social media, and enterprise analytics.

• Household Finance
• Banking
• Behavioral Economics
• +4 more

Claire Célérier is an Associate Professor of Finance at the Rotman School of Management, University of Toronto, where she holds the Canada Research Chair in Household Finance. Her academic career spans prestigious institutions including the University of Toronto (where she advanced from Assistant to Associate Professor between 2016-2022) and the University of Zurich (2014-2016). She maintains an affiliation with the People's History Lab and actively contributes to academic discourse through conference organization including 'Race, Equity and Public Policy' (2023) and 'Banks, States and the People' (2024). Dr. Célérier's research explores how finance can benefit households through multiple lenses including behavioral economics, asset pricing, and financial history. Her work investigates innovation in finance, diversity and inclusion impacts, and historical perspectives on financial systems. She has developed significant expertise in household finance, banking, and the intersection of finance with social issues, particularly racial disparities in financial markets as demonstrated in her award-winning research on the Freedman's Savings Bank. Her scholarly output shows a clear trajectory from foundational work on security design and investor behavior toward increasingly socially relevant research on financial inclusion, racial equity, and household financial decision-making. Recent publications demonstrate her growing focus on historical contexts of financial systems and their contemporary implications, particularly regarding marginalized communities. Canada Research Chair in Household Finance (2024) Bank of Canada Governor's Award (2022) University of Toronto Top 5% Merit Award (2021) Petro Canada Young Innovators Award Program (2021) Multiple Rotman School of Management Teaching Awards (2019-2021) Ieke van den Burg Prize for Research on Systemic Risk (2015) Dr. Célérier has successfully secured substantial research funding including Insight Development Grants (CAD 54,500), Connaught Research Grant (CAD 10,000), and the Marianne and Marcus Wallenberg Foundation Grant (EUR 400,000). She mentors several PhD students including Andrada Bilan (2020), Alexey Vasilenko (2023), and Purnoor Tak (Expected 2026), and has supervised numerous master's theses at both University of Toronto and University of Zurich. Her service contributions include Rotman School of Management MBA program committee, executive committee, seminar coordination, and faculty recruiting committee.

• Public Economics
• Industrial Organization
• Government Procurement
• +3 more

Rodrigo Carril is an Assistant Professor at the Department of Economics and Business at Universitat Pompeu Fabra (UPF) and an Affiliated Professor at the Barcelona School of Economics (BSE). His research focuses on Public Economics and Industrial Organization, particularly examining public procurement policies and their economic impacts. He holds a PhD in Economics from Stanford University (2020) and is a Juan de la Cierva Researcher. Education: PhD in Economics, Stanford University (2020). His work explores topics such as pharmaceutical market dynamics, defense contracting efficiency, and regulatory frameworks for public procurement. He has received prestigious awards including the Claire and Ralph Landau Prize (2020) and the Young Economists' Essay Award (2022). Key research trends include analyzing procurement policies' effects on competition, evaluating preference programs for disadvantaged groups, and methodological contributions to econometric techniques like regression discontinuity designs. Awards: Claire and Ralph Landau Prize 2020 Young Economists' Essay Award 2022 Advising and Grants: While specific grants aren’t listed, his collaborative work involves co-authors like Claudia Allende, Mark Duggan, and Andres Gonzalez-Lira, indicating active academic partnerships. He is affiliated with the BSE and contributes to policy-oriented research initiatives. Labs/Teams: Engaged in interdisciplinary projects at UPF and BSE, focusing on public sector efficiency and regulatory economics.

Haneen FarahView profile

Associate Professor

• road infrastructure design
• road user behavior
• traffic safety
• +7 more

Dr. Haneen Farah is an Associate Professor in the Department of Transport & Planning at Delft University of Technology and co-director of the Traffic and Transportation Safety Lab. She also serves as head of the Traffic Systems Engineering section. Her research focuses on road infrastructure design, road user behavior, and traffic safety, integrating transportation engineering, human factors, and econometrics. Prior to TU Delft, she was a postdoc at KTH Royal Institute of Technology and earned her M.Sc. and Ph.D. in Transportation Engineering from the Technion-Israel Institute of Technology. Her work includes national/international projects like SAMEN (mixed automated/human traffic implications), AfroSAFE (road safety in Africa), and XCARCITY (sustainable city mobility). She teaches undergraduate and graduate courses on road design and traffic safety, including online programs for low/middle-income countries. Farah supervises multiple PhD and Master students in her research areas, contributing to over 50 peer-reviewed publications. Key research themes include infrastructure design for automated vehicles, driver behavior modeling, cyclist safety, and policy implementation of the Safe System approach. Her interdisciplinary approach bridges engineering and psychology to enhance traffic safety and efficiency through advanced analytics and simulation models.

Kamalika ChaudhuriView profile

Professor

• Machine Learning
• Trustworthy Machine Learning
• Differential Privacy
• +4 more

Kamalika Chaudhuri is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD), and also serves as a Director and Research Scientist with the FAIR team at Meta AI. Her research focuses on the foundations of trustworthy machine learning, including robust machine learning, learning with privacy, and out-of-distribution generalization. Dr. Chaudhuri has earned her PhD from UC Berkeley in 2007 with a dissertation on "Learning Mixtures of Distributions." Her academic journey has led her to become a leading researcher in machine learning theory with a particular emphasis on privacy and robustness. Her research interests span machine learning foundations with a strong focus on trustworthy AI . She investigates problems at the intersection of differential privacy , adversarial robustness , and out-of-distribution generalization . Her work addresses critical challenges in developing machine learning systems that maintain privacy while preserving utility, resist adversarial attacks, and generalize effectively beyond training data distributions. She has pioneered approaches in privacy-preserving machine learning, robust learning theory, and methods for detecting and mitigating data memorization in models. An analysis of her recent publications (2024-2025) reveals a strong focus on the intersection of privacy, security, and machine learning. Her work spans differential privacy mechanisms, membership inference attacks, memorization detection, and fairness certification. She has been particularly active in developing methods for privacy-preserving foundation models, with several papers on differentially private computer vision and language models. Her research demonstrates a consistent thread of addressing fundamental challenges in trustworthy AI while developing practical solutions that balance privacy, accuracy, and utility. Best Award at the ICLR 2024 Workshop on Privacy Regulation and Protection in Machine Learning Distinguished Paper Award at IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2024 Dr. Chaudhuri has advised numerous PhD students who have gone on to prominent positions at Google, DeepMind, Microsoft Research, and other leading AI institutions. Her group maintains an active research blog with guest posts from UCSD researchers. She has served in significant leadership roles including General Chair for ICML 2022 and Program Co-Chair for both ICML 2019 and AISTATS 2019, where she pioneered initiatives to improve reproducibility in machine learning research. Her research group at UCSD focuses on trustworthy machine learning, with current projects spanning privacy-preserving AI, robustness against adversarial attacks, and methods for ensuring reliable out-of-distribution generalization. The group collaborates closely with the FAIR team at Meta AI, where Dr. Chaudhuri serves as a Research Scientist.

Pascal PoupartView profile

Professor

• Reinforcement Learning
• Probabilistic Models
• Natural Language Processing
• +3 more

Pascal Poupart is a Professor and Canada CIFAR AI Chair at the Vector Institute, affiliated with the David R. Cheriton School of Computer Science at the University of Waterloo. He leads research in reinforcement learning, probabilistic models, and federated learning systems. Research spans: Bayesian optimization efficiency improvements Inverse constraint learning from demonstrations Uncertainty quantification in neural networks Federated learning architectures Recent publications show 70% focus on reinforcement learning applications, with new methods developed for confident inverse constraint learning and preference-based generation. Manages the AI research group developing algorithms for material design and conversational agents.

Zhun DengView profile

Assistant Professor

• Machine Learning
• Statistics
• Theoretical Computer Science
• +4 more

Zhun Deng is a tenure-track Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. His research bridges machine learning, statistics, and theoretical computer science, focusing on rigorous frameworks for responsible AI systems. He previously held postdoctoral positions at Columbia University and completed his Ph.D. at Harvard's Theory of Computation group under Cynthia Dwork. Ph.D. in Computer Science, Harvard University (2022) B.Sc. in Mathematics, Chu Kochen Honors College, Zhejiang University His research spans theoretical foundations of machine learning, including: Quantile-based risk control and conformal prediction Fairness guarantees in algorithmic decision-making Uncertainty quantification for LLMs Copyright frameworks for generative AI Physics-informed hybrid models Multi-agent reinforcement learning with constraints Recent work analyzes LLM alignment through distribution-free methods (ICML 2025), explores performativity challenges (ICML 2025), and develops calibration techniques (ICLR 2024). Collaborations include research interns from Stanford, MIT, and NYU. Students in his group include: Ruomeng Ding (Ph.D., UNC) Xiaowei Yin (Ph.D., UNC) Kaicheng Zhang (Ph.D., UNC)

Andreas VlachosView profile

Professor

• Natural Language Processing
• Machine Learning
• Dialogue Modelling
• +10 more

Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.

Daniel Dominic Kaplan SleatorView profile

Professor

• Algorithms
• Data Structures
• Theoretical Computer Science
• +5 more

Daniel Dominic Kaplan Sleator is a Professor of Computer Science at Carnegie Mellon University's School of Computer Science. He maintains an office in the Gates-Hillman Center (7205 Gates-Hillman) and teaches various courses in algorithms and theoretical computer science. Professor Sleator's research spans several areas of theoretical computer science and algorithms. His primary interests include: Algorithms and Data Structures Amortized Analysis and Competitive Analysis Persistent and Self-Adjusting Data Structures Computational Geometry and Combinatorial Optimization Combinatorial Game Theory and Mathematical Games Music Analysis using Computational Methods His extensive publication record shows a consistent focus on efficient data structures and algorithms. Over the years, his work has evolved from foundational data structures like splay trees and skew heaps to applications in diverse areas such as music analysis and combinatorial games. A notable trend in his work is the development of self-adjusting data structures that achieve excellent amortized performance without maintaining explicit structural constraints. His papers on splay trees, skew heaps, and persistent data structures have become classics in the field. Professor Sleator has made significant contributions across multiple domains of computer science. His work on competitive algorithms for paging and list update problems has been particularly influential, establishing fundamental results in online algorithms. His research extends beyond traditional computer science into interdisciplinary areas like computational music theory, demonstrating the broad applicability of algorithmic thinking. He teaches a variety of courses including Algorithms 15-451/651, Competition Programming 15-295, and specialized topics like mathematical games.

Sarah DeanView profile

Assistant Professor

• Machine Learning
• Data-Driven Control
• Decision-Making Systems
• +6 more

Sarah Dean is an Assistant Professor in the Computer Science Department at Cornell University, affiliated with the College of Engineering. Her research focuses on the interplay of machine learning, optimization, and dynamics in real-world systems, particularly in control theory, recommendation systems, and ethical AI. Education: PhD in EECS, University of California, Berkeley (2021) Postdoctoral Research, University of Washington (2021-2022) Research Interests: Data-driven control systems, reinforcement learning, recommendation systems, user dynamics, algorithmic fairness, and the societal impacts of AI. She emphasizes foundational understanding of how learning systems interact with human and social processes. Recent Work Trends: Her articles explore topics like bilinear system identification, user participation dynamics in recommendation platforms, and ethical considerations in AI development. Recent work includes harm mitigation strategies and mathematical modeling of AI-human feedback loops. Awards: AI2050 Early Career Fellow (2024) Best Paper at ICML 2018 (Delayed Impact of Fair Machine Learning) Best Student Paper in Imaging Systems (OSA Congress 2018) Advising & Labs: Advises over 15 graduate and undergraduate students. Leads research on interactive ML systems, with contributions to projects like the 'MSGD' repository for streaming data learning. Active in the GEESE group, promoting socially responsible computing.

• Natural Language Processing
• Large Language Models
• Digital Libraries
• +7 more

Min Yen Kan is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore's School of Computing, Department of Computer Science. With a PhD from Columbia University (2002), he leads the Web Information Retrieval / Natural Language Processing Group (WING.NUS) and serves as ACL Ethics Committee co-chair. His research spans Natural Language Processing , Large Language Models , Digital Libraries , and Information Retrieval , with specific focus on scientific discourse analysis, fact verification, and multimodal systems. Current projects include Scholarly Document Information Extraction (TRL 6), Task-Oriented Dialogue Systems (TRL 4), and Recommendation Systems (TRL 5). Recent publications reveal strong trends in LLM limitations (bias, hallucination, evaluation), conversational recommendation systems , and misinformation detection . His work consistently bridges theoretical NLP with real-world applications in digital libraries and scientific communication. Award highlights include: CIKM 2019 Best Paper Award ACL Distinguished Service Awards Vannevar Bush Best Paper Award (JCDL 2012) ACM Distinguished Speaker designation Kan mentors PhD students with placements at Google and USTC, and serves as associate editor for Information Retrieval and survey editor for Journal of AI Research . His lab WING.NUS develops practical tools like SciWING for scientific document processing and FANG for fake news detection. Media engagements include commentary on AI regulations in Southeast Asia and workforce implications in the AI era.

Huaxiu YaoView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Robotics
• +7 more

Huaxiu Yao is an Assistant Professor at the University of North Carolina at Chapel Hill, holding a joint appointment in the School of Data Science and Society and the Department of Computer Science (College of Arts & Sciences). His research focuses on building reliable large-scale AI models (foundation models) with applications in healthcare, robotics, genomics, and transportation. He leads the AIMING Lab, which explores adaptive intelligence through alignment, interaction, and learning. Education: Ph.D. from Pennsylvania State University (2021), Postdoctoral Scholar at Stanford University (hosted by Chelsea Finn). Research Interests: Generalizable AI agents, preference alignment, out-of-distribution generalization, embodied AI, and multimodal reasoning. Key applications include biomedicine, robotics, and vision-language systems. Notable Awards: KDD Best Paper Award (2024), Amazon Research Awards (2025), TMLR Outstanding Paper Award (2024). Advising & Labs: Recruits Ph.D. and intern students. Leads the AIMING Lab, affiliated with UNC NLP Group. Organizes workshops on foundation models (ICML 2024) and trustworthy AI systems. Publications: Over 40 peer-reviewed papers, including top venues like ICLR, NeurIPS, and ICML. Focuses on AI alignment, multimodal systems, and domain generalization.