جستجوی استاد راهنمای دانشگاهی

Sara Ngمشاهده پروفایل

استادیار مهمان

• Computational Linguistics
• Speech Prosody
• Phonetics
• +۶ مورد دیگر

Sara Ng is a Visiting Assistant Professor in the Department of Linguistics at Western Washington University. She holds a PhD in Linguistics from the University of Washington (2024) and an MS in Computational Linguistics (2023), alongside a B.A. in Linguistics and B.S. in Applied Mathematics from the University of Utah (2017). Her research focuses on computational models of prosody, speech perception, and their integration with speech technologies like automatic speech recognition (ASR). She investigates how prosody conveys pragmatic meaning, influences conversational dynamics, and impacts listeners with hearing impairments. Her work bridges computational methods and linguistic theory, addressing challenges in clinical and technological applications. Education PhD in Linguistics, University of Washington, 2024 MS in Computational Linguistics, University of Washington, 2023 B.A. in Linguistics (Honors), B.S. in Applied Mathematics, University of Utah, 2017 Research Interests Ng’s research explores computational linguistics, prosody modeling, speech technology, and hearing impairment studies. She develops methods to leverage prosodic cues for tasks like punctuation prediction and investigates the impact of hearing loss on speech perception. Grants & Awards 2024: Nominated for UW Excellence in Teaching Award 2023: Excellence in Linguistic Research Graduate Fellowship 2017: University of Utah Top Scholar Award Teaching Ng teaches courses in phonetics, computational linguistics, and linguistics for honors students. She emphasizes pedagogical innovation and inclusivity, with experience as an instructor of record and teaching assistant at both Western Washington University and the University of Washington. Labs & Collaborations She collaborates with the TIAL Lab (University of Washington), Phonetics Lab, Hearing Aid Laboratory (Northwestern University), and CLILLAC-ARP (Université Paris Cité). Her work intersects with clinical and engineering domains, addressing real-world applications of speech technology.

Sewoong Ohمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Security & Privacy
• +۴ مورد دیگر

Sewoong Oh is a Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he has been faculty since 2019. His research focuses on the foundations of machine learning with particular emphasis on differential privacy, secure and robust machine learning, and federated learning. Prior to joining UW, he was at the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign from 2012-2019. He is affiliated with multiple NSF AI Institutes including ACTION (Agent-based Cyber Threat Intelligence and Operation), AI-EDGE (Future Edge Networks and Distributed Intelligence), and IFML (Foundations of Machine Learning). Oh received his PhD in Electrical Engineering from Stanford University in 2011 under Andrea Montanari, followed by postdoctoral work at MIT's Laboratory for Information and Decision Systems under Devavrat Shah. His educational background spans top institutions in theoretical computer science and electrical engineering. His research spans the critical intersection of machine learning security, privacy, and robustness. Oh's work addresses fundamental challenges in making AI systems reliable and trustworthy, with particular focus on defending against backdoor attacks, developing privacy-preserving algorithms, and creating efficient tokenization methods for language models. His recent SuperBPE work demonstrates how moving beyond traditional subword tokenization can significantly improve language model efficiency and performance. His research consistently bridges theoretical foundations with practical implementations, as evidenced by numerous open-source repositories containing production-ready code. Oh's publications reveal a consistent trajectory toward addressing security and privacy concerns in increasingly complex machine learning systems, with recent work focusing on language model tokenization, data-centric AI, and federated learning frameworks. His research shows strong interdisciplinary connections between theoretical computer science, statistics, and practical machine learning systems. ACM SIGMETRICS best paper award (2015) NSF CAREER award (2016) ACM SIGMETRICS rising star award (2017) GOOGLE Faculty Research Awards (2017, 2020) 2024 ICML Best Paper Award (for work by advisee Jon Hayase) Professor Oh maintains an active research group with numerous PhD students, postdocs, and undergraduate researchers. His students have secured prestigious positions at leading tech companies including Amazon, Google, and Snap, as well as academic appointments at institutions like University of Wisconsin-Madison and Shanghai Jiao Tong University. He has received substantial research funding through his participation in multiple NSF AI Institutes and industry research awards from Google. His lab maintains several active GitHub repositories implementing cutting-edge research in backdoor defense, robust statistics, and privacy-preserving machine learning.

Xin Guoمشاهده پروفایل

استاد

• Mathematical Finance
• Financial Engineering
• FinTech
• +۷ مورد دیگر

Xin Guo is Professor and Department Chair of Industrial Engineering and Operations Research (IEOR) at UC Berkeley's College of Engineering, holding the Coleman Fung Chair in Financial Modeling. Her research bridges mathematical finance, stochastic control, and machine learning with applications in risk analytics and quantitative trading. Education: Ph.D. in Mathematics, Rutgers University (1999) Research Interests: Professor Guo's work centers on mathematical finance , stochastic games , and reinforcement learning . She develops theoretical frameworks for α-potential games and mean-field systems while applying signature methods and GANs to financial data. Her research addresses critical problems in portfolio optimization, fraud detection (e.g., Medicare analytics), and market forecasting, emphasizing the intersection of stochastic control with machine learning for real-world decision-making under uncertainty. Publication Trends: Recent work (2023-2025) shows increasing focus on multi-agent reinforcement learning through mean-field game theory, with applications spanning finance (corporate bonds, trading), healthcare (fraud detection), and transportation (rate forecasting). Key innovations include BSDE approaches for stochastic games, signature-based time series analysis, and theoretical guarantees for GAN training dynamics. Scientific Awards: Holds the prestigious Coleman Fung Chair in Financial Modeling, reflecting significant contributions to quantitative finance research. Advising and Grants: As IEOR Department Chair, Professor Guo mentors graduate students in stochastic modeling and financial engineering. Her research is supported by the Coleman Fung Endowment Fund, with collaborations spanning finance, healthcare, and transportation sectors through industry partnerships. Labs and Teams: Leads the Risk Analytics & Data Analysis Research (RADAResearch) Lab ( https://risklab.ieor.berkeley.edu/ ), which develops cutting-edge methodologies for risk assessment, data-driven decision-making, and game-theoretic solutions to complex systems. The lab fosters interdisciplinary work connecting mathematical theory with practical applications in FinTech and beyond.

Andrew Stuartمشاهده پروفایل

استاد

• Applied and Computational Mathematics
• Bayesian Inverse Problems
• Data Assimilation
• +۶ مورد دیگر

Andrew Stuart is the Bren Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech), joining in 2016. He previously held faculty positions at the University of Warwick (1999–2016), Stanford University (1992–1999), and Bath University (1989–1992). He earned his PhD from the University of Oxford's Computing Laboratory in 1986. Professor Stuart's research focuses on applied and computational mathematics , particularly Bayesian inverse problems , data assimilation for dynamical systems , and stochastic modeling . His work bridges mathematical theory, algorithm development, and applications in geophysics, materials science, and biological systems. His recent publications emphasize operator learning , machine learning for PDEs , and uncertainty quantification . Key areas include ensemble Kalman methods , Gaussian processes , and neural operators for solving and learning from complex systems. Scientific awards include the Vannevar Bush Faculty Fellowship and election to the Royal Society of Great Britain . He advises graduate students in applied mathematics, computational science, and geophysics, including Edoardo Calvello , Hojjat Kaveh , and Florian Wolf .

Jackie Chit Kit Cheungمشاهده پروفایل

دانشیار

• Natural Language Processing
• Natural Language Generation
• Automatic Summarization
• +۱۲ مورد دیگر

Jackie Chit Kit Cheung is an Associate Professor in the School of Computer Science at McGill University, where he co-directs the Reasoning and Learning Lab. He holds the Canada CIFAR AI Chair and serves as an Associate Scientific Co-Director at the Mila Quebec AI Institute. He is also a consulting researcher at Microsoft Research Montreal. Academic Background: Ph.D. in Computer Science, University of Toronto (2010–2014) M.Sc. in Computer Science, University of Toronto (2008–2010) B.Sc. (Honours) in Computer Science, minors in Linguistics and German, University of British Columbia (2004–2008) Research Interests: Jackie Cheung's research lies at the intersection of natural language processing, machine learning, and cognitive science. He focuses on natural language generation , automatic summarization , commonsense and pragmatic reasoning , and the evaluation of NLP systems . His work aims to build language models that reflect real-world structure and reasoning, with applications in health, education, and language revitalization. He is particularly interested in how implicit meaning is processed in context, a core concern in linguistic pragmatics. Publication Trends: His recent publications (2024–2025) show a strong emphasis on evaluation methodologies (e.g., COSMIC, ECBD), hallucination and factuality in language models, coreference and reasoning , and long-context modeling . He frequently collaborates across institutions and integrates insights from linguistics and psychology into NLP system design and analysis. Scientific Awards: Best Paper Award, ACL 2018 Outstanding Paper Award, NAACL 2018 Student Research Workshop SAC Award, ACL 2024 (for COSMIC) Best Poster Award, CMDO AI-Health Symposium 2024 Advising and Grants: He advises a large and diverse group of graduate students, including PhD and Master’s candidates, often in co-supervision with other faculty. His group has received support from major AI and health research initiatives, including CIFAR and Mila. He has trained alumni who have gone on to faculty and industry research positions. He has served in leadership roles in top NLP conferences, including as Senior Area Chair, Workshop Chair, and Program Chair of Canadian AI 2018. Labs and Teams: He co-directs the Reasoning and Learning Lab at McGill and is deeply involved with Mila – Quebec AI Institute . He founded the NLP Reading Group at McGill, which brings together researchers from computer science, linguistics, and information studies to discuss theoretical and applied NLP topics.

Meredith Tammingaمشاهده پروفایل

دانشیار

• Sociolinguistics
• Psycholinguistics
• Language Change
• +۴ مورد دیگر

Meredith Tamminga is an Associate Professor of Linguistics at the University of Pennsylvania, where she directs the Language Variation and Cognition Lab . Her work bridges sociolinguistics , psycholinguistics , and theoretical linguistics , focusing on how linguistic variability is represented in mental grammar and how social context influences speech perception and production. She co-leads the Philadelphia Signs Project , exploring sign language sociolinguistics with collaborators at Penn and Gallaudet University. PhD in Linguistics (2014), University of Pennsylvania BA in Linguistics (2009), McGill University Her research integrates experimental methods with naturalistic speech analysis to study individual and group-level language change. Keywords include phonetics-phonology mapping , intraspeaker variation , and quantitative modeling . Notable grants include NSF awards and SAS Dean’s Mentorship Award . Key affiliations include MindCORE , Penn’s hub for integrative mind sciences, and collaborative ties to the Phonetics Lab , Child Language Lab , and Cultural Evolution of Language Lab . Her lab emphasizes cross-departmental research and welcomes undergraduate RAs and PhD students.

Daniel Cooneyمشاهده پروفایل

استادیار

• Mathematical Biology
• Applied Dynamical Systems
• Differential Equations
• +۴ مورد دیگر

Daniel Cooney is an Assistant Professor in the Department of Mathematics at the University of Illinois Urbana-Champaign. He holds additional affiliations as an Affiliate at the Carl R. Woese Institute for Genomic Biology. His research focuses on applying partial differential equations (PDEs), dynamical systems, and stochastic processes to study evolutionary dynamics, particularly in biological and social systems. Key themes include multilevel selection, evolutionary game theory, and the emergence of cooperative behavior. Cooney earned his PhD in Applied and Computational Mathematics from Princeton University, advised by Simon Levin, and completed a postdoc as a Simons Fellow in Mathematical Biology at the University of Pennsylvania. His work bridges theoretical mathematics with applications in ecology, epidemiology, and social sciences. Recent publications explore topics like altruistic punishment in cultural systems, classroom-turnover dynamics, and protocell evolution. He actively participates in academic outreach, co-organizing conferences such as the AMS Special Session on Mathematics of Infectious Disease and the SIAM Minisymposium on Social-Ecological Systems. His research has been published in high-impact journals including *Proceedings of the National Academy of Sciences* and *Bulletin of Mathematical Biology*.

David Trendمشاهده پروفایل

استاد

• Cultural Studies
• Media Analysis
• Critical Pedagogy
• +۳ مورد دیگر

David Trend is a Professor and former Chair of the Department of Art at the Claire Trevor School of the Arts, University of California, Irvine. He previously served as Dean of Creative Arts at De Anza College and Graduate Program Coordinator at San Francisco State University's Inter-Arts Center. Trend specializes in visual culture, cultural studies, and critical pedagogy, teaching courses like Language and Vision in Everyday Life and Media, Art, and Technology . His research bridges media analysis, education policy, and radical democracy. He has authored over 200 articles and 16 books, including Elsewhere in America and Reading Digital Culture . As a Getty Scholar, his work explores cultural belonging, digital media impacts, and educational innovation. He advises on state arts councils and the National Endowment for the Arts. His academic contributions span visual arts education, media literacy, and the sociopolitical role of art. Education: - MFA in Photography, Visual Studies Workshop Program, SUNY Buffalo - PhD in Curriculum Theory, Miami University Research Interests: Trend's work examines cultural crises of meaning, digital media's societal impact, and the intersection of art with education and activism. He critiques gentrification's artistic drivers, explores identity formation in digital spaces, and advocates for culturally democratic pedagogical frameworks. Publications Trends: Recent works focus on gentrification's artistic dimensions ( 2022 ), creativity crises ( 2020 ), and belonging in fragmented cultures ( 2016 ). Earlier work addressed media violence myths ( 2007 ), digital literacy ( 2001 ), and radical democracy ( 1996 ). His writing spans academic critiques to practical pedagogical solutions. Awards: Getty Scholar Grants & Consulting: Extensive NEA collaborations, foundation advising, and state arts council consultations. His work emphasizes bridging educational institutions with Silicon Valley corporations through multimedia partnerships. Labs/Teams: Affiliated with UCI's University Art Galleries, Institute and Museum of California Art, and Electronic Art + Design programs.

Kevin D. Ashleyمشاهده پروفایل

استاد

• Computational Social Science
• Artificial Intelligence
• Algorithms
• +۷ مورد دیگر

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.

Christopher Pottsمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Semantics
• +۵ مورد دیگر

Christopher Potts is Professor and Chair of Linguistics at Stanford University, with a courtesy appointment as Professor of Computer Science. He serves as Director Emeritus of the Stanford Center for the Study of Language and Information (CSLI) and leads the Pragmatic Enrichment & Contextual Interface Lab. His work bridges theoretical linguistics and computational approaches to language understanding. Education: B.A. in Linguistics from New York University (1999) Ph.D. in Linguistics from University of California, Santa Cruz (2003) Potts' research focuses on how computational methods can illuminate linguistic phenomena, particularly in the areas of semantics, pragmatics, and sentiment analysis. His work explores how emotion is expressed in language and how linguistic production and interpretation are influenced by context. He has made significant contributions to understanding conventional implicatures, sentiment analysis frameworks, and the application of neural networks to linguistic problems. His recent work has increasingly focused on the interpretability of large language models and the development of frameworks like DSPy for building reliable AI systems. An analysis of Potts' recent publications reveals a strong trend toward the intersection of linguistic theory and practical AI applications. His work spans theoretical linguistics (e.g., compositionality, preposing constructions), neural network interpretability, and practical NLP systems (e.g., ColBERT, DSPy). The research demonstrates consistent focus on making language models more transparent, controllable, and linguistically informed, with particular attention to how context shapes meaning. Scientific Awards: Best Paper Award at 2024 ACL for 'Mission: Impossible Language Models' Outstanding Paper Award at 2024 ACL for 'CausalGym' ACL Test of Time Award 2023 Dean's Award for Distinguished Teaching (2015-2016) Best New Data Set or Resource Award at 2015 EMNLP Potts has secured numerous research grants as PI or Co-PI from major organizations including Google, Amazon, NSF, Office of Naval Research, and Stanford's HAI institute. His current projects focus on evaluation of retrieval-augmented generation systems, LLM-mediated communication in organizations, interpretability techniques for language models, and frameworks like DSPy for building next-generation AI systems. He has mentored numerous researchers who have gone on to make significant contributions in NLP and computational linguistics. As Director of CSLI (2013-2020) and current Chair of Linguistics at Stanford, Potts has played a key leadership role in shaping interdisciplinary research at the intersection of language, computation, and cognition. His Pragmatic Enrichment & Contextual Interface Lab continues to be a hub for innovative research combining formal linguistic theory with cutting-edge computational methods.

• Psycholinguistics
• Cognitive Psychology
• Speech Prosody
• +۴ مورد دیگر

Andrés Buxó-Lugo serves as an Assistant Professor of Psychology at the University at Buffalo, where he directs the Language Processing and Computation Lab. His research investigates the cognitive mechanisms underlying language production, comprehension, and acquisition with a specialized focus on speech prosody—the rhythm, intonation, and intensity patterns in speech—and their role in human communication. His primary research interests include psycholinguistics, cognitive psychology, speech prosody, language production, language comprehension, language acquisition, and computational linguistics. He examines how listeners integrate diverse linguistic cues during speech processing, how individuals learn unfamiliar constructions like non-native pronunciations or novel prosodic patterns, and the cognitive basis of durational changes in speech. His work also explores how communicative context shapes prosodic production and how higher-level linguistic information aids prosodic structure parsing. Analysis of his 15 most recent publications (2019-2025) reveals consistent interdisciplinary work bridging cognitive science, linguistics, and computational modeling. Key trends include phonological representation studies, speech planning mechanisms, intonation adaptation across talkers, lexical representation structures, and the integration of input expectations in syntactic parsing. His research demonstrates significant methodological diversity, incorporating experimental paradigms, computational modeling, and acoustic analysis to unravel language processing complexities. As director of the Language Processing and Computation Lab at the University at Buffalo, Buxó-Lugo leads research initiatives focused on developing computational models of language processing while investigating the cognitive foundations of speech and prosody through empirical experimentation and theoretical innovation.

Aleksandar Jeremicمشاهده پروفایل

دانشیار

• Biomedical Signal Processing
• Statistical Signal Processing
• Biometrics
• +۷ مورد دیگر

Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.

Jelani Nelsonمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithms
• Data Streams
• +۸ مورد دیگر

Jelani Nelson is a Professor and Department Chair in the UC Berkeley EECS Department (College of Engineering). His work focuses on theoretical computer science , particularly algorithms , data streams , dimensionality reduction , and privacy-preserving computation . Advising : Current students include Ishaq Aden-Ali, Xin Lyu, Mihir Singhal, and Hongxun Wu (co-advised with leading researchers). Education : PhD from MIT (George M. Sprowls Award), M.Eng from MIT. Research Highlights : Developed foundational results in Johnson-Lindenstrauss dimensionality reduction (optimality, sparse embeddings). Advancements in differential privacy (lower bounds, private mean estimation, threshold learning). Pioneering work on streaming algorithms for heavy hitters, norm estimation, and graph problems. Innovations in compressed sensing and oblivious subspace embeddings . Scientific Awards : PODS Best Paper Award (2011, 2022) IBM Pat Goldberg Memorial Best Paper Award (2011) George M. Sprowls Award for MIT doctoral thesis (2009) NeurIPS 2020 Spotlight Presentation

• Causal Inference
• Representation Learning
• Generative Models
• +۵ مورد دیگر

Arthur Gretton is a Professor at University College London (UCL), leading the Gatsby Computational Neuroscience Unit and serving as director of the Centre for Computational Statistics and Machine Learning. He also works as a Research Scientist at Google DeepMind. His research focuses on causal inference, representation learning, and nonparametric hypothesis testing with applications in machine learning and computational neuroscience. Academic Affiliations Gatsby Computational Neuroscience Unit, UCL Centre for Computational Statistics and Machine Learning, UCL Google DeepMind Arthur's research spans several key areas in modern machine learning, including: Kernel methods for causal effect estimation and two-sample testing Deep learning architectures for proxy causal learning with complex confounding Adaptive gradient flows for generative modeling Nonparametric statistical tests with theoretical guarantees Applications in distributional reinforcement learning and Bayesian inference His work addresses both methodological advancements and practical implementations, particularly for high-dimensional data settings. Recent publications demonstrate his focus on causal inference with hidden confounders (AISTATS 2025), deep learning adaptivity in instrumental variable regression (ICLR 2025), and novel approaches to two-sample testing with adaptive kernel selection (NeurIPS 2024). He has also contributed extensively to distributional reinforcement learning and hypothesis testing frameworks. As an advisor, he supervises multiple PhD students including Jakub Wornbard, Zikai (Steve) Shen, and Zonghao (Hudson) Chen, while co-supervising others. His methodological contributions are implemented in various software packages available at the Gatsby Unit, including tools for kernel-based covariate shift correction, independence testing, and maximum mean discrepancy calculations.

David A. Hsiehمشاهده پروفایل

استاد

• Financial Risk Management
• Hedge Funds
• Asset Pricing
• +۳ مورد دیگر

David A. Hsieh is the Bank of America Professor of Finance at the Fuqua School of Business, Duke University, where he has been a faculty member since 1993. Previously, he served as Associate Professor and Assistant Professor at the University of Chicago's Graduate School of Business from 1981-1989. His extensive research has significantly contributed to the understanding of hedge funds, financial risk management, and nonlinear dynamics in financial markets. Massachusetts Institute of Technology, Ph.D. in Economics, 1981 Yale University, B.S. in Economics and Mathematics, 1976 (Summa Cum Laude, Phi Beta Kappa) Phillips Academy, Andover, 1972 (Cum Laude) Dr. Hsieh's research primarily focuses on the dynamics of asset prices and their implications for financial risk management. He has made significant contributions to understanding risk and return characteristics in hedge funds and commodity funds, pioneering work on nonlinear dynamics applications to financial markets. His research has evolved from early work on exchange rates and volatility modeling to more recent comprehensive analyses of hedge fund strategies, performance measurement, and industry structure. Hsieh's publication history reveals a clear progression from foundational work on nonlinear dynamics in financial markets to increasingly sophisticated analyses of hedge fund strategies and risk characteristics. His recent work, often in collaboration with William Fung and other prominent finance researchers, has focused on mega hedge fund firms, franchise value in the industry, and the evolution of hedge fund strategies toward more index-like products. The research consistently combines rigorous theoretical frameworks with robust empirical analyses across diverse market conditions. CAIA Award for Excellence in Alternative Investment Research (2015) CFA Institute Graham and Dodd Award of Excellence (2004) Bank of America Faculty Award (2002) Duke Cross-Continent Executive MBA Teaching Excellence Award (2002) Fischer Black Memorial Foundation Robert J. Schwartz Memorial Prize (1999) Smith Breeden First Prize (1990) Yale Science and Engineering Association High Scholarship Award (1976) Russell Henry Chittenden Prize (1976) Dr. Hsieh has served as a consultant for the International Monetary Fund (2007-2016) and the Bank for International Settlements (1998), and as a Visiting Scholar at both the International Monetary Fund and the Board of Governors of the Federal Reserve System. His editorial service includes Finance Editor for Management Science (2003-2009) and Associate Editor roles for several leading finance journals. He has developed extensive research resources including a Hedge Fund Data Library that has become widely used in academic and industry research.