جست‌وجوی استاد و پژوهشگر

Ariel Pakesمشاهده پروفایل

استاد

• Econometrics
• Industrial Organization
• Economics of Technological Change
• +۳ مورد دیگر

Ariel Pakes is the Thomas Professor of Economics at Harvard University, part of the Faculty of Arts and Sciences and the Department of Economics. He previously held the Charles and Dorothea Dilley Professorship at Yale University and tenured roles at institutions including the University of Wisconsin and the Hebrew University of Jerusalem. His research focuses on industrial organization (I.O.), the economics of technological change, and econometric theory. Pakes has advised notable students who now lead economics departments globally and has received prestigious awards including the Frisch Medal (1986) and election to the American Academy of Arts and Sciences (2002). Education: Pakes earned his Ph.D. in Economics from Harvard University in 1980. His academic journey includes roles as a lecturer at Harvard and positions at the University of Jerusalem. Research Interests: Pakes has pioneered methodologies for analyzing I.O. models, including demand and cost system estimation. Recent work examines AI’s impact on pricing strategies, antitrust policy implications of market dynamics, and healthcare cost structures. His theoretical contributions include simulation estimators and frameworks for dynamic oligopolies. Awards: Frisch Medal (Econometric Society, 1986) Fellow of the Econometric Society (1988) Fellow of the American Academy of Arts and Sciences (2002) Best Graduate Student Advisor Award (Yale University, 1996) Advisory and Grants: Pakes has served on NSF panels, including the Economics Advisory Panel and Presidential Fellow Advisory Board. His consulting work spans government agencies and large firms. He edited the RAND Journal of Economics and co-chaired the AEA Census Advisory Panel. Labs/Teams: Collaborates with NBER as a Research Associate and contributes to interdisciplinary projects like the PNAS issue on 'Science, Technology and the Economy.'

Jiatao Guمشاهده پروفایل

استادیار

• Machine Learning
• Generative Models
• 3D Generation
• +۳ مورد دیگر

Jiatao Gu is an Assistant Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, with a part-time role as Staff Research Scientist at Apple (MLR). He holds a Ph.D. in Electrical and Electronic Engineering from the University of Hong Kong (2018) and a B.Eng. in Electronic Engineering from Tsinghua University (2014). His research focuses on generative machine learning and AI agent interaction with the physical world, emphasizing multi-modal systems spanning language, images, videos, and 3D. Key themes include efficient modeling , flexible architecture design , and scalable decision-making frameworks . 2025: ICLR paper on DART framework 2024: TMLR work on GFlowNet alignment 2023: NeurIPS research on diffusion stability 2022: ACL papers on speech translation Recent publications explore diffusion models for text-to-image synthesis, 3D reconstruction, and efficient sampling techniques. His work addresses fundamental challenges in attention mechanisms, entropy collapse, and multi-stage distillation while advancing non-autoregressive translation and vision-language reasoning . Prospective students can apply through his recruitment process at UPenn. Prior affiliations include Meta AI (FAIR Labs) and academic collaborations with institutions like New York University's CILVR Lab.

Erniel Bayhon Barriosمشاهده پروفایل

استاد

• Computational Statistics
• Nonparametric Methods
• Data Science/Big Data Analytics
• +۵ مورد دیگر

Erniel Bayhon Barrios is a Professor at the Malaysia School of Business, Monash University. Formerly a professor at the University of the Philippines Diliman and a visiting scholar at Karlstad University (Sweden) and the Asian Development Bank Institute (Japan). He holds a PhD in Statistics (1990) from the University of the Philippines Diliman. His research focuses on computational statistics, nonparametric methods, data science, and computational econometrics, with applications in spatiotemporal modeling, time series, and financial markets. He has contributed to high-dimensional data analysis, volatility modeling, and robust statistical techniques. Key projects include the National Mental Health Survey and Well-Being (2019–2021) and building a data-driven organization for the Habib Group (2025–2026). He is an elected member of the International Statistical Institute (2012), associate editor of Communications in Statistical Applications and Methods , and served on the board of the International Association of Statistical Computing (2022–2025). He advises PhD students on topics like stochastic frontier models, data assignment in big data, and high-frequency time series analysis. His work aligns with UN Sustainable Development Goals related to education and economic growth.

Thomas Hartmanمشاهده پروفایل

استاد

• Quantum Gravity
• Quantum Field Theory
• String Theory
• +۴ مورد دیگر

Thomas Hartman is a Professor of Physics in the College of Arts and Sciences at Cornell University. He received his A.B. in Physics from Princeton University in 2004 and his Ph.D. in Physics from Harvard University in 2010. His professional journey includes being a Member of the School of Natural Sciences at the Institute for Advanced Study (2010-2013), Research Associate at the Kavli Institute for Theoretical Physics, UCSB (2013-2014), Assistant Professor at Cornell University (2014-2020), Associate Professor at Cornell University (2020-2022), and Professor at Cornell University (2022-present). Hartman's research focuses on theoretical aspects of quantum gravity and quantum field theory, with particular emphasis on black hole information and strongly interacting quantum fields. His work explores four major interconnected areas: gauge/gravity duality (examining how quantum field theory degrees of freedom organize into fluctuating spacetime), black hole information paradox (investigating the relationship between classical black hole solutions and quantum statistical systems), new approaches to quantum field theory using dualities and entanglement dynamics, and the physics of de Sitter space with implications for early universe cosmology. His research employs techniques from string theory, holographic duality, general relativity, and quantum information theory. Analysis of Hartman's publication record reveals a strong focus on resolving fundamental questions in quantum gravity, particularly through the development of replica wormhole techniques that address the black hole information paradox. His work spans both highly mathematical approaches to quantum gravity and connections to potentially observable phenomena, with increasing emphasis on connections between quantum information science and gravitational physics in recent years. Member, School of Natural Sciences, Institute for Advanced Study, 2010-2013 Hartman has advised graduate students including Jeevan Chandra Namburi and Wan Zhen Chua, contributing to the next generation of theoretical physicists. His research group actively investigates the emergence of spacetime from quantum information principles and develops new mathematical frameworks for understanding quantum gravity. The group maintains strong connections with other leading institutions through collaborative projects and participates in major theoretical physics initiatives including Snowmass planning for future research directions in high energy physics. Hartman's research program represents a vital bridge between abstract theoretical concepts in quantum gravity and potential experimental tests, working to develop frameworks that could ultimately connect quantum gravity to observable phenomena in both high-energy physics and cosmological observations.

Mohsen Ghaffariمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Distributed Algorithms
• Parallel Algorithms
• +۳ مورد دیگر

Mohsen Ghaffari is an Associate Professor at MIT's Department of Electrical Engineering and Computer Science (EECS), holding the Steven and Renee Finn Chair. His research focuses on theoretical computer science, particularly distributed and parallel algorithms, graph theory, and network optimization. Formerly, he was a tenured CS faculty member at ETH Zurich until 2022. PhD in Computer Science from MIT (2016) His research interests include distributed algorithms, parallel computing, graph decomposition, and network congestion management. His recent work addresses coreness decomposition, spanner construction, and Euclidean k-center optimization in massive parallel computation frameworks. Notable scientific awards include the ACM Doctoral Dissertation Award (Honorable Mention), ACM-EATCS Doctoral Dissertation Award, and multiple best paper awards at FOCS, PODC, and SODA. He has advised numerous PhD and Master's students, many of whom have transitioned to academic and industry roles. He has taught courses at MIT and ETH Zurich on distributed algorithms, advanced algorithms, and massively parallel computation. His professional activities include serving on program committees for SODA, FOCS, STOC, and organizing workshops like Highlights of Algorithms (HALG) and Workshop on Local Algorithms (WOLA).

John Duchiمشاهده پروفایل

دانشیار

• Statistical Learning
• Optimization
• Privacy in Machine Learning
• +۳ مورد دیگر

John Duchi is an Associate Professor at Stanford University, holding positions in the Department of Statistics and the Department of Electrical Engineering (with a courtesy appointment in Computer Science). He is affiliated with the School of Engineering. His research focuses on statistical learning, optimization, information theory, and computation, with an emphasis on balancing computational efficiency, privacy, and robustness. Key interests include developing algorithms for large-scale optimization, privacy-preserving techniques, and tools for evaluating machine learning systems' validity. Education: BS and MS in Computer Science (Stanford University, 2007–2008), MA in Statistics (UC Berkeley, 2012), and PhD in EECS (UC Berkeley, 2014). Research Interests: His work addresses three core areas: (1) optimizing trade-offs between computational resources and statistical performance, (2) creating scalable optimization methods, and (3) quantifying confidence in machine learning systems. Recent publications highlight contributions to privacy in federated learning, robust statistical validation, and uncertainty quantification. Articles: His recent work explores topics like distribution-free M-estimation, private federated learning, and instance-optimal mechanisms for statistical estimation. These studies emphasize privacy, robustness, and scalable solutions for modern data challenges. Advising & Grants: While no advisees are listed, his research has been supported by grants focused on optimization, privacy, and statistical theory.

Ellen Riloffمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Sentiment Analysis
• +۳ مورد دیگر

Ellen Riloff serves as Department Head and Professor in the Department of Computer Science at the University of Arizona, where she leads research at the intersection of natural language processing (NLP) and artificial intelligence. Her work bridges theoretical advancements with real-world applications in social computing, planetary science, and crisis response systems. Education: Ph.D. in Computer Science, University of Massachusetts at Amherst (1994) Research Focus: Dr. Riloff specializes in affective computing and information extraction , developing techniques to recognize emotion, social cues, and embodied expressions in text. Her methodologies frequently employ bootstrapping, stacked learning, and semantic lexicon induction. Recent projects address crisis informatics (e.g., social cue recognition in emergencies) and interdisciplinary applications like the Mars Target Encyclopedia for planetary science data extraction. Publication Trends: Analysis of her 15 most recent publications (2021–2025) reveals three dominant trajectories: (1) affective event modeling in social contexts with applications to crisis response; (2) domain-specific NLP for planetary science and food systems; and (3) advanced language model techniques including retrieval-augmented generation and multi-view prompting. Her work increasingly integrates deep learning with traditional linguistic features. Grants and Leadership: Dr. Riloff has directed multiple NSF-funded projects, including RI: Small: Recognizing Implicit Personal States in Natural Language (2016) and RI: Small: Acquiring Domain Knowledge from Text through Cooperative Bootstrapping (2010). These initiatives pioneered bootstrapping frameworks for affective event recognition and information extraction. She also co-organized the Workshop on Pattern-based Approaches to NLP (2023), highlighting her leadership in advancing hybrid NLP methodologies. Collaborative Infrastructure: She co-developed the Mars Target Encyclopedia—a large-scale information extraction system that processes planetary science literature to create structured databases of Mars surface targets. This project demonstrates her commitment to building reusable scientific infrastructure through NLP.

Simone Giombiمشاهده پروفایل

استاد

• High Energy Theoretical Physics
• Quantum Field Theory
• String Theory
• +۳ مورد دیگر

Simone Giombi is a Professor of Physics at Princeton University and currently serves as Associate Chair and Director of Graduate Studies. He holds a B.Sc. in Theoretical Physics from the University of Bologna, Italy, and a Ph.D. in Physics and Astronomy from Stony Brook University (2007). His research focuses on high-energy theoretical physics, quantum field theory, string theory, and their interconnections, particularly exploring higher-spin gravity and holographic dualities. He has held postdoctoral positions at Harvard University and the Perimeter Institute for Theoretical Physics. Giombi's work includes groundbreaking contributions to AdS/CFT correspondence, Wilson loop defects, and quantum M2 branes. He has been recognized with prestigious awards, including the New Horizons in Physics Prize (2017) and the SIGRAV Prize (2014). His recent articles (2022–2025) emphasize non-planar corrections in ABJM theory, boundary reparametrizations in AdS2, and RG interfaces from double-trace deformations. His research also engages with fermionic CFTs, line defects, and quantum fluctuations in Wilson loops. Awards: New Horizons in Physics Prize (2017), SIGRAV Prize (2014) Advising: Students include Yagmur Erhan and Jieru Shan Labs/Teams: Active in Princeton's High Energy Theory Group

Silviu Pufuمشاهده پروفایل

استاد

• Quantum Field Theory
• String Theory
• Gravity
• +۳ مورد دیگر

Silviu Pufu is a Professor of Physics at Princeton University, where he earned both his A.B. (2007) and Ph.D. (2011) in Physics. Prior to his faculty position, he was a Pappalardo Postdoctoral Fellow at MIT (2011–2013). His research focuses on quantum field theory, string theory, and gravity, with emphasis on conformal field theory, gauge/gravity duality, and lattice gauge theory. He has received the Alfred P. Sloan Research Fellowship (2017) and led the Simons Collaboration for Nonperturbative Bootstrap (2016–2023). His work explores advanced topics such as AdS/CFT correspondence, M-theory corrections, and non-perturbative bootstrap methods. Pufu advises three graduate students: Ross Dempsey, Debaditya Pramanik, and Benjamin Søgaard. His research outputs span theoretical frameworks like super-Yang-Mills theories, M-theory orbifolds, and lattice Hamiltonian formulations of QCD. His 2020–2025 publications highlight contributions to bootstrap techniques, holographic calculations, and precision studies of strongly coupled systems.

Yiping Luمشاهده پروفایل

استادیار

• Machine Learning
• Scientific Machine Learning (AI4Science)
• Numerical Algorithms
• +۷ مورد دیگر

Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford

Claire Donnatمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Graph Neural Networks (GNNs)
• +۷ مورد دیگر

Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago, specializing in statistical and machine learning methods for graph-structured and high-dimensional data. Her work bridges theoretical innovation with applications in biomedical research, environmental science, and public health. Education: B.S. and M.S. in Applied Mathematics from Ecole Polytechnique; Ph.D. in Statistics from Stanford University (2020). Her research focuses on three methodological directions: (1) statistical foundations for graph neural networks (GNNs), (2) structured estimation with graph constraints, and (3) multimodal data integration with uncertainty quantification. Key applications include thermotolerance in photosynthetic microbes, family network analysis for child welfare, and spatial transcriptomics. The 15 most recent publications highlight her work in GNNs, CCA, tensor modeling, and epidemic analysis, with keywords spanning statistics, machine learning, and network science. Her methodological contributions address challenges in sparsity, graph topology, and heterogeneous data fusion. Scientific Awards: Facebook Research Award (2021), C3.AI COVID Grand Challenge winner (2020), Lumiata hackathon winner (2020), Stanford Centennial Award (2019), and others. Claire's research group actively recruits postdocs and students for projects involving graph-based modeling, data integration, and biomedical applications. She also provides research consulting in statistical methodology and graph modeling for life sciences.

Xiaofeng Shaoمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Change-Point Analysis
• +۳ مورد دیگر

Xiaofeng Shao is a Professor of Statistics & Data Science at Washington University in St. Louis, with a joint appointment in the Department of Economics. He holds a PhD from the University of Chicago and previously served at the University of Illinois at Urbana-Champaign for 18 years. He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. His research focuses on econometrics, time series analysis, change-point detection, high-dimensional statistics, nonparametric methods, and functional data analysis. Recent work emphasizes object-valued time series modeling and machine learning applications in high-dimensional and imaging data. Notable contributions include the dependent wild bootstrap method and self-normalization techniques for time series inference. Key awards include Fellowships from leading statistical societies. His publications span over 20 years, addressing topics like change-point detection in climate projections, statistical methods for COVID-19 infection trends, and high-dimensional dependence testing.

Axel Gandyمشاهده پروفایل

استاد

• Computational Statistics
• Statistics in Finance
• Survival Analysis
• +۳ مورد دیگر

Axel Gandy is a Professor of Statistics at the Department of Mathematics, Imperial College London. He serves as Director of the EPSRC CDT in Modern Statistics and Statistical Machine Learning , overseeing PhD supervision and advanced statistical training.

• Econometric Theory
• Machine Learning/Nonparametrics
• Robust Inference for Clustered & Network Data
• +۶ مورد دیگر

Harold D. Chiang is an Assistant Professor in the Department of Economics at the University of Wisconsin-Madison. His research focuses on econometric theory and methods, particularly robust inference techniques for clustered and network data, machine learning applications, and causal inference frameworks like regression discontinuity/kink designs. He employs computational statistics and asymptotic theory to address methodological challenges in high-dimensional and complex datasets.

Rudi Zagstمشاهده پروفایل

استاد

• Financial Engineering
• Risk Management
• Asset Management
• +۳ مورد دیگر

Prof. Dr. Rudi Zagst is a Professor of Mathematical Finance at the Technical University of Munich (TUM), where he serves as Head of the Department of Mathematical Finance within the TUM School of Computation, Information and Technology. He has held this position since 2001 and is actively involved in teaching, research, and academic leadership. In 2003, he was appointed as a second member of the Faculty of Economics, and since 2004, he has served as Deputy Chairman of the joint elite degree program 'Finance & Information Management' of the University of Augsburg and TUM. Prof. Zagst earned his doctorate in business mathematics from the University of Ulm, where he later completed his habilitation in 2000. His academic journey began with a professional career at HypoVereinsbank AG, where he served as Head of Product Development in Institutional Investment Management before becoming Managing Director of RiskLab GmbH in 1997. His research focuses primarily on financial engineering, risk management, and asset management, with particular emphasis on portfolio optimization, mathematical finance, and quantitative risk management. His work bridges theoretical finance with practical applications, often incorporating advanced mathematical techniques to solve complex financial problems. Recent publications demonstrate his continued interest in GARCH models, portfolio optimization under various constraints, and the application of machine learning techniques to financial problems. Analysis of his recent publications (2024-2025) reveals a strong focus on portfolio optimization under complex market conditions, particularly using GARCH models to capture volatility dynamics. His work increasingly incorporates machine learning techniques (as seen in the credit spread analysis paper) while maintaining rigorous mathematical foundations. Many papers explore the intersection of theoretical finance with practical investment strategies, reflecting his commitment to bridging academic research with real-world financial applications. Professor of the Year 2007 (awarded by Unicum Profession magazine) Prof. Zagst has supervised numerous bachelor's, master's, and doctoral theses through TUM's Finance and Actuarial Science research group. His collaborative work with industry partners through the TUM CAIR Labs and RiskFactory demonstrates strong connections between academic research and practical financial applications. He has received research funding through various industry partnerships with major financial institutions including Allianz, Munich Re, and ERGO Group AG. Prof. Zagst leads the Research Group Finance and Actuarial Science at TUM, which includes Professors Matthias Scherer, Aleksey Min, and Christoph Knochenhauer. The group maintains strong industry connections through the TUM CAIR Labs initiative, collaborating with over 25 financial institutions including Allianz, Munich Re, Deloitte, PwC, and KPMG. Their RiskFactory laboratory serves as a bridge between academic research and practical financial risk management applications in the industry.