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.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۱-۱۵ از ۱٬۴۲۹ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
- Econometrics
- Labor Economics
- Causal Inference
- +۳ مورد دیگر
Joshua D. Angrist is the Ford Professor of Economics at the Massachusetts Institute of Technology, where he has been a faculty member since 1996. He is also a co-founder and director of MIT's Blueprint Labs and a Research Associate at the National Bureau of Economic Research. Angrist shares the 2021 Nobel Prize in Economic Sciences with David Card and Guido Imbens for their methodological contributions to the analysis of causal relationships. Angrist received his B.A. from Oberlin College in 1982 and completed his Ph.D. in Economics at Princeton University in 1989. Prior to joining MIT, he taught at Harvard University and the Hebrew University of Jerusalem. His academic journey began somewhat unconventionally, as he left high school early after 11th grade, worked for over a year, and only later discovered his passion for economics through an inspiring teacher at Oberlin. Angrist's research focuses on developing and applying innovative econometric methods to answer important economic questions using natural experiments. His work spans labor economics, education economics, and causal inference methodology. He is particularly known for his contributions to instrumental variables methods and the Local Average Treatment Effect (LATE) framework developed with Guido Imbens. His research explores the economics of education and school reform, the impact of social programs on labor markets, and the effects of immigration and regulation. His recent publications reveal a continued focus on causal inference methods applied to education policy questions, labor market issues, and health economics. The trend shows increasing sophistication in research design, with particular attention to addressing selection bias and developing methods for external validity. His work spans theoretical econometric contributions alongside empirical applications in education, labor markets, and health. Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (2021) Fama Prize for Graduate Education (2018) Fellow of the American Academy of Arts and Sciences Fellow of the Econometric Society Angrist is deeply committed to teaching and mentoring. He has developed influential econometrics textbooks including 'Mostly Harmless Econometrics' and 'Mastering Metrics' with Jörn-Steffen Pischke. At MIT, he teaches courses including Labor Economics I (14.661), Econometric Data Science (14.32), and Labor Economics and Public Policy (14.64). He emphasizes selecting UROP students who have mastered foundational economics through courses like 14.64 and 14.32. Beyond MIT, Angrist co-founded Avela, a software startup using cutting-edge research to help schools improve enrollment and operations. Angrist co-founded and directs MIT's Blueprint Labs, which brings together researchers from economics, computer science, and education to develop innovative solutions for educational challenges. Through Blueprint Labs and Avela, his work bridges academic research with practical applications in education policy and technology.
Tim Althoffمشاهده پروفایل
دانشیار
Tim Althoff is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering, University of Washington, specializing in Artificial Intelligence and Human-Centered Computing. His research focuses on behavioral data science, combining Data Science Natural Language Processing Social Computing Human-Centered AI Ethics & Fairness to extract insights about health and well-being. Recent publications highlight advancements in: Mental health support through AI Wearable sensor health monitoring Online community analysis Reproducibility in data science Public health interventions with notable papers in ACL, Nature Machine Intelligence, and NeurIPS. Scientific recognition includes: ACL 2023 Outstanding Paper Award WWW 2021 Best Paper Award Double ICWSM 2021 Best Paper Awards SIGKDD Dissertation Award 2019 Fulbright Scholarship German National Merit Foundation Actively mentoring postdoctoral researchers and seeking PhD students in areas like neural representation learning, NLP applications to psychology, and mobile health technologies through his Behavioral Data Science Group .
Kevin D. Ashley is a Professor of Law at the University of Pittsburgh School of Law. He is also a senior scientist at the Learning Research and Development Center and an adjunct professor of computer science at the University of Pittsburgh. His interdisciplinary work bridges artificial intelligence, legal analytics, and ethical reasoning. JD, Harvard Law School M.A. and PhD, University of Massachusetts BA, Princeton University His research focuses on computational modeling of legal reasoning , AI and ethics , legal text analytics , and case-based reasoning . He has pioneered AI applications for legal decision support, automated argumentation, and bias detection in legal corpora. Recent publications include studies on large language models in legal annotation, argument mining, and empirical legal analysis. His work has been supported by multiple National Science Foundation grants, emphasizing fairness in AI and access to justice. 2022 Codex Prize for Computational Law 2015 University of Massachusetts Outstanding Achievement Award 2002 AAAI Fellow for AI in Law contributions 2000 Chancellor’s Distinguished Research Award A former President of the International Association for Artificial Intelligence and Law, Ashley has held visiting roles at the University of Bologna, Stanford CodeX, and IBM Watson Research Center. He co-edits the journal Artificial Intelligence and Law and teaches courses on applied legal analytics.
Haibin Ling is the SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, part of the College of Engineering and Applied Sciences. His research focuses on computer vision, medical image analysis, augmented reality, and AI applications in science. He holds a Ph.D. from the University of Maryland (2006) and prior degrees from Peking University. Previously, he worked at Temple University (2008–2019) and held roles at Siemens Corporate Research, UCLA, and Microsoft Research Asia. Professor Ling's work spans biomedical imaging, AI for science, and human-computer interaction. He leads the CV Lab and collaborates with the AI Institute at Stony Brook. Awards include the NSF CAREER Award (2014), Best Student Paper (ACM UIST 2003), and IEEE Fellow (2020). He serves on editorial boards for IEEE Trans. PAMI, Pattern Recognition, and CVIU, and chairs major conferences like CVPR. His research group includes over 50 students and alumni, with active projects in tracking benchmarks (LaSOT), Leafsnap, and medical imaging tools. Notable publications address OCTA flow estimation, backdoor attacks on vision models, and topology-guided medical learning. Collaborations involve institutions like Temple University and Stony Brook's Department of Applied Mathematics and Statistics.
Lin Tan is a Professor of Computer Science at Purdue University , holding the Mary J. Elmore New Frontiers Professorship . She joined Purdue in 2019 after serving as a Canada Research Chair and associate professor at the University of Waterloo. She is an ACM Distinguished Member and IEEE Senior Member . Education: PhD in Computer Science, University of Illinois Urbana-Champaign BS in Computer Science and Technology, Zhejiang University Research Interests: Professor Tan’s research lies at the intersection of software engineering , artificial intelligence , and security . Her work focuses on software-AI synergy , software dependability , defect detection & repair , and software text analytics . She leverages machine learning and natural language processing to enhance software reliability, and conversely uses software techniques to improve the dependability of AI systems. Her recent projects include building binary foundation models (Nova), evaluating large language models for code generation and repair, and developing interactive debugging tools that reduce debugging time by one-third. She also explores robot task planning with LLMs and automated front-end development . Awards & Honors: Best Paper Award Finalist, ICRA 2025 ELATES Fellow, 2024-2025 ACM SIGSAC Distinguished Paper Award, CCS 2024 J.P.Morgan AI Faculty Research Awards (2020, 2021, 2022) ACM SIGSOFT Distinguished Paper Awards (ASE 2020, MSR 2018, FSE 2016) Canada Research Chair (2017) Ontario Early Researcher Award (2015) NSERC Discovery Accelerator Supplements Award (2015) Google Faculty Research Awards (2010, 2014) IEEE Micro Top Picks (2006) Advising & Funding: Professor Tan currently advises eight PhD students and has graduated 20+ PhD and Master’s students now thriving in academia (York University, Concordia University, University of Alberta) and industry (Microsoft, Meta, Amazon, Google). Her group is generously supported by NSF , Meta/Facebook Research Awards , J.P.Morgan AI Faculty Awards , and NSF REU programs. Labs & Teams: She leads the Software Reliability & AI Lab at Purdue, recruiting postdocs, PhD, MS, and undergraduate researchers year-round. Lab interests span binary recovery , LLM-based program repair , testing deep-learning libraries , and data-free model extraction .
Eduardo Azevedo is the John M. Bendheim and Thomas L. Bendheim Professor of Business Economics and Public Policy at the Wharton School , University of Pennsylvania. He holds a courtesy appointment as Professor of Economics and was awarded the 2016 Sloan Foundation Fellowship. His research integrates economic theory with practical applications across science and business domains. His research interests include: Market design Selection markets Social science genetics Experimental economics Game theory Recent publication trends focus on: Economic theory applications to healthcare and digital markets Empirical Bayes methods in A/B testing Adverse selection in insurance markets Strategic behavior in two-sided matching Evolutionary behavioral economics He serves as an instructor for BEPP2500 - Managerial Economics , emphasizing real-world application of microeconomic theory to business problems. His work also involves software development for economic research, including MATLAB-based empirical Bayes tools for analyzing treatment effects in large-scale experiments. Scientific awards : Sloan Foundation Fellow (2016)
Linyi Liمشاهده پروفایل
استادیار
Linyi Li is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), leading the Trustworthy Artificial Intelligence (TAI) Lab. His research focuses on certifiably trustworthy deep learning systems, combining machine learning and computer security. He holds a PhD from the University of Illinois Urbana-Champaign (UIUC) and a B.Eng. from Tsinghua University. Affiliations: Simon Fraser University, TAI Lab Education: PhD in Computer Science, UIUC, 2023 B.Eng (Cum Laude), Tsinghua University, 2018 His research interests include deep learning , trustworthy machine learning , large language models , and software engineering . He emphasizes rigorous certification of robustness, fairness, and numerical reliability in AI systems. Recent work includes the InfiBench benchmark for evaluating code LLMs and advancements in neural network verification. Recent Research Trends: His publications span certified robustness, fairness guarantees, and scalable verification techniques for deep learning models. He also explores scientific evaluation of foundation models and adversarial defense mechanisms. Awards: Rising Stars in Data Science AdvML Rising Star Award Wing Kai Cheng Fellowship Finalist: Qualcomm Innovation Fellowship (2022) Winner: VNN-COMP'23 Competition (Team α, β-CROWN) Advising & Grants: As a PI, he oversees the TAI Lab's research. Though no specific grants are listed, his work is funded through competitive awards and university resources. Labs/Teams: Leads the TAI Lab at SFU, focusing on foundational and applied research in trustworthy AI.
Bryan H. Choiمشاهده پروفایل
دانشیار
- Software Liability
- AI Safety
- Cybersecurity Law
- +۶ مورد دیگر
Bryan H. Choi is an Associate Professor of Law at the University of Colorado Law School , where he bridges law and computer science to address software and AI safety. His work on software liability has influenced national cybersecurity strategy discussions. As an Adviser for the ALI Principles Project on Civil Liability for Artificial Intelligence , he shapes legal frameworks for emerging technologies. Education : JD and AB in Computer Science from Harvard University; clerkships with U.S. Court of Appeals judges Leonard I. Garth and William C. Bryson. Roles : Former joint appointment at Ohio State University Law School and Computer Science Department; Faculty Fellow at UPenn's CTIC; Director of Law and Media at Yale's ISP. Research Focus : Choi's scholarship examines software liability , AI accountability , and privacy law through interdisciplinary lenses. He critiques institutional approaches to software safety and advocates for empirical legal frameworks over participation-based models. Recent Articles address AI malpractice , NIST software standards , and forensic tool validation , reflecting trends in AI regulation and cyber-physical system liability . His 2021 NSF grant funded technical-legal methods for safety-critical systems. Awards & Grants : National Science Foundation (NSF) Grant (2021) Adviser, ALI Principles Project on Civil Liability for Artificial Intelligence Community Engagement : Active in Law and Computer Science communities , serving on committees for the ACM Symposium , Cybersecurity Law and Policy Scholars Conference , and co-organizing the AAAI Bridge Program on AI and Law .
Song Meiمشاهده پروفایل
استادیار
Song Mei is an Assistant Professor in the Department of Statistics and Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. She received her Ph.D. from Stanford University in 2020 under Andrea Montanari and maintains active research collaborations with institutions including Amazon (as a 2023 Amazon Research Award recipient) and OpenAI (where she is currently on leave). Her research spans the intersection of statistics, machine learning, information theory, and computer science, with particular emphasis on foundational theories for modern AI systems. Key interests include language models, diffusion models, quantum algorithms, and high-dimensional statistics, often leveraging insights from statistical physics literature. Analysis of her recent publications reveals a strong focus on theoretical underpinnings of generative AI, with significant contributions to understanding contrastive pre-training (CLIP), attention mechanisms in LLMs, and mathematical foundations of diffusion models. Her work demonstrates consistent interdisciplinary connections between statistical theory and practical AI development. Sloan Research Fellowship (2025) Noether Early Career Scholar Award (2025) Google Research Scholar Award (2024) Amazon Research Award (2024) She actively advises graduate students through MA programs and has secured significant research grants including Amazon Research Awards. Her work with AGI Labs at Amazon demonstrates applied impact of theoretical research. Current projects focus on mechanistic interpretability of large language models and mathematical frameworks for generative AI. Professor Mei leads research on the statistical principles behind frontier AI models, with particular focus on developing rigorous theoretical frameworks for understanding emergent behaviors in large-scale systems.
Elaine M. Huang is an Associate Professor of Human-Computer Interaction at the Department of Informatics, University of Zurich, where she has served since 2010. She also leads the People and Computing Lab, focusing on the dynamic interplay between human practices and technological advancements. Her academic background includes: PhD in Computer Science, Georgia Institute of Technology (2006) Dr. Huang's research centers on human-computer interaction, examining the bidirectional relationship between technology and human practices. She is particularly known for her work on gender equality in technology, challenging assumptions about innate gender differences and investigating how AI systems may perpetuate biases. Her research also spans sustainable interaction design, mental health technologies, and chronic disease management, always emphasizing empirical data over intuition. Analysis of her recent publications (2023-2025) reveals a strong trajectory toward socially impactful HCI, with significant focus on health technologies (diabetes management, mental health), cultural sensitivity in design, and the ethical challenges of AI. Her methodology often involves field studies to understand real-world technology use, countering the industry's reliance on intuition. As head of the People and Computing Lab, Dr. Huang oversees a research group dedicated to designing and evaluating technologies that address complex human needs. The lab's work frequently involves co-design with diverse user communities to ensure relevance and inclusivity in technological solutions.
Daniel Varro is a Professor affiliated with McGill University (Faculty of Engineering, School of Computer Science), with strong ties to Budapest University of Technology and Economics and Linköping University. He is a leading researcher in model-driven engineering, cyber-physical systems, and software engineering, actively contributing to top-tier conferences such as MODELS, ICSE, and ASE. His research focuses on model-based systems engineering (MBSE) , automated model generation , model transformations , and constraint-based consistency checking . Recently, his work has expanded into integrating large language models (LLMs) and machine learning into modeling workflows, including model querying, domain modeling, and bug detection. The recent publications reveal a strong trend toward AI-augmented modeling, logic-based solvers (e.g., Refinery), and safety assurance of autonomous systems (e.g., COLREGs compliance). His work bridges formal methods with practical software engineering challenges in industrial and safety-critical domains. Scientific Awards: No specific awards mentioned in the text. Advising and Grants: While no explicit list of students or grants is provided, his mentorship in the Doctoral Symposium and repeated leadership roles suggest active supervision and likely grant funding. He has led projects on automated model generation, model quality, and AI integration in modeling. Labs and Teams: Daniel Varro is associated with research groups focused on model-driven engineering and software evolution, likely leading or co-leading teams working on the VIATRA and Refinery frameworks for model transformation and solving.
- Game Design
- Digital Media Studies
- Cultural Memory
- +۵ مورد دیگر
James Manning is a Lecturer in Digital Design at RMIT University's School of Design. His research focuses on game design, digital media, and cultural memory, exploring topics such as game preservation, livestreaming practices, and the intersection of play and technology. He is particularly interested in 'anarchaeological' approaches to defunct games and the documentation of everyday play practices. His teaching interests include game studies, interactive media, animation aesthetics, and design methodology. He actively supervises research in strategies for reflective shortform game design and has contributed to projects like the Astrosurf interactive dome experience. Key research themes include media archaeology, esports culture, and the sociocultural dimensions of digital games. His work bridges theoretical frameworks with empirical studies, examining how digital platforms shape media consumption and cultural production.
Stefania Dumbrava is an Associate Professor of Computer Science at ENSIIE (École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise) and a permanent member of the ACMES team in the SAMOVAR laboratory at Télécom SudParis, Institut Polytechnique de Paris. She is also actively involved in the Property Graph Schema Working Group and the European Research Network on Formal Proofs (EuroProofNet). Education PhD in Computer Science, Université Paris-Sud (2016) MSc in Computer Science, Jacobs University Bremen (2012) BSc in Mathematics, Jacobs University Bremen (2010) Research Interests Dumbrava's research lies at the intersection of formal methods and data management . She designs and verifies algorithms and systems for graph databases , with emphasis on property graphs , schema discovery , query optimization , and distributed graph processing . Recently, her work focuses on certifying large-scale distributed graph systems under the ANR JCJC VERDI project (2025–2029). Awards & Honors SIGMOD Best Paper Award 2023 – “PG-Schema: Schemas for Property Graphs” SIGMOD Research Highlight Award 2023 – “Threshold Queries” VLDB 2022 Best Regular Research Paper Runner-Up – “Threshold Queries in Theory and in the Wild” SIGMOD 2025 Distinguished Reviewer Award ICDE 2025 Best Program Committee Member Award EASST Best Software Science Paper Award, ICGT 2025 Students & Grants Dumbrava has supervised numerous research interns and is actively recruiting PhD students for her ANR VERDI project on verified foundations of large-scale distributed graph systems. She has also served on six PhD thesis committees as examiner since 2021. Labs & Teams She leads the ACMES research group within the SAMOVAR laboratory (Télécom SudParis, Institut Polytechnique de Paris), where her team develops formally verified graph-database engines and tools such as GRASP, VerDILog, and DatalogCert.
- Machine Learning
- Dynamical Systems
- Optimization
- +۳ مورد دیگر
Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.
فقط ۷۵۰ نتیجه اول نمایش داده میشود. برای رسیدن به نتایج دقیقتر، جستوجو را با فیلترها محدودتر کنید.