جست‌وجوی استاد و پژوهشگر

Joshua Agterbergمشاهده پروفایل

استادیار

• High-Dimensional Statistics
• Matrix and Tensor Analysis
• Nonconvex Optimization
• +۴ مورد دیگر

Joshua Agterberg is an Assistant Professor in the Department of Statistics at the University of Illinois, College of Liberal Arts & Sciences. His research focuses on high-dimensional statistics, matrix and tensor analysis, nonconvex optimization, and statistical network analysis. He holds a B.S. in Actuarial Science and Mathematics from the University of Wisconsin-Madison, followed by M.S. and Ph.D. degrees in Statistics from Johns Hopkins University. After a postdoctoral fellowship at the University of Pennsylvania, he returned to the Midwest to join UIUC. Education: Bachelor of Science in Actuarial Science and Mathematics, University of Wisconsin-Madison Master of Science in Statistics, Johns Hopkins University Doctor of Philosophy in Statistics, Johns Hopkins University Research Interests: His work bridges theoretical foundations and practical applications, addressing problems in nonconvex matrix sensing, multilayer network analysis, and statistical-computational gaps. He aims to develop methodologies for analyzing complex data structures, particularly in neuroscience, sociology, and engineering. Recent projects include advancing spectral methods, nonconvex optimization, and hypothesis testing frameworks for networks. Teaching Philosophy: Agterberg emphasizes statistical intuition over formulaic approaches, encouraging students to tackle problems from first principles. He teaches courses on theoretical statistics and hopes to develop a high-dimensional statistics curriculum for PhD students. Future Goals: He seeks to create a principled pipeline for network data analysis and to enhance understanding of machine learning algorithms' theoretical underpinnings. Collaborations include work on dynamic networks and statistical methodologies for connectomics.

Ari Karchmerمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Artificial Intelligence
• Theoretical Computer Science
• +۳ مورد دیگر

Ari Karchmer is a Research Fellow at Harvard University, participating in the SAFR AI Lab led by Seth Neel and Salil Vadhan. He completed his PhD in Computer Science at Boston University under Ran Canetti, graduating in Spring 2024. His research focuses on theoretical computer science, machine learning, and cryptography, with particular emphasis on model extraction defenses, covert learning, and privacy-preserving techniques. Education: PhD in Computer Science, Boston University (2024) BSc in Computer Science, Brandeis University (2018, Dean’s List) Research Interests: Ari's work bridges machine learning theory and cryptography. He explores foundational questions such as understanding why multimodal data benefits ML models, developing interpretable methods for data attribution, and analyzing computational limits of model extraction defenses. His research also addresses practical challenges like mechanistic interpretability and model editing. Recent Article Trends: His publications emphasize theoretical guarantees for observational defenses, computational separations in ML, and PAC-learning connections. Notable contributions include uncovering vulnerabilities in model extraction defenses and advancing component attribution techniques through linear models. Awards: Best Student Paper Award at ITCS 2024 Advising & Grants: Ari has no listed students but contributed to teaching fellowships at Boston University, including courses on Network Security, Algebraic Algorithms, and Responsible AI. His work has been supported by grants from the SAFR AI Lab and related initiatives. Labs & Teams: He is part of the SAFR AI Lab at Harvard, focusing on advancing AI safety and theoretical foundations. His collaborations include Seth Neel, Martin Pawelczyk, and Ran Canetti.

Anish Agarwalمشاهده پروفایل

استادیار

• Causal Inference
• Econometrics
• High-Dimensional Statistics
• +۴ مورد دیگر

Anish Agarwal is an Assistant Professor in the Department of Industrial Engineering and Operations Research at Columbia University's School of Engineering and Applied Science. He is an affiliated member of the Data Science Institute (DSI), Financial and Business Analytics, Foundations of Data Science, and Smart Cities centers. His research focuses on causal inference, econometrics, and high-dimensional statistics, with applications to data-driven decision-making in engineering and social systems. Agarwal holds a PhD in Electrical Engineering and Computer Science (EECS) from MIT, advised by Alberto Abadie, Munther Dahleh, and Devavrat Shah. Prior to Columbia, he was a postdoctoral scientist at Amazon's Core AI team and the Simons Institute at UC Berkeley. He has consulted for TauRx Therapeutics, Uber Technologies, and Boston Consulting Group on topics like experiment design and causal inference. His research interests include designing causal frameworks for complex systems, integrating econometric methods with machine learning, and addressing challenges in high-dimensional data analysis. Notable contributions include synthetic control methodologies, data auctions with externalities, and frameworks for combinatorial interventions. Awards: INFORMS George B. Dantzig Best Thesis Award (2nd Place) ACM SIGMETRICS Outstanding Thesis Award (2nd Place) Best Paper Awards at NSDI’23 and the American Statistical Association Grants & Collaborations: Work with industry partners on experiment design and causal inference frameworks. Agarwal's work bridges theoretical advancements with practical applications in healthcare, economics, and engineering systems, emphasizing scalable and robust methods for decision-making under uncertainty.

Larry Wassermanمشاهده پروفایل

استاد

• nonparametric inference
• causal inference
• machine learning
• +۶ مورد دیگر

Larry Wasserman is a UPMC University Professor at Carnegie Mellon University, jointly appointed in the Department of Statistics and Data Science and the Machine Learning Department. He received his Ph.D. from the University of Toronto in 1988 and is recognized as one of the leading statisticians of his generation. His research spans theoretical and applied statistics, with core interests in: Foundational inference : Nonparametric methods, asymptotic theory, causal frameworks Modern applications : Machine learning, high-dimensional statistics, astrostatistics Interdisciplinary domains : Bioinformatics, genomics, physical sciences via the STAMPS group His recent publications demonstrate strong emphasis on causal methodology, optimal transport, and robust inference, with applications ranging from particle physics to genomic analysis. Articles frequently develop novel nonparametric techniques with minimax optimality guarantees. Award highlights include: COPSS Presidents' Award (1999) - Top honor for statisticians under 40 CRM-SSC Prize (2002) - Landmark contributions to statistics Fellowships: American Statistical Association, Institute of Mathematical Statistics, AAAS He leads the Statistical Machine Learning Theory Group and founded STAMPS (Statistical Methods for Physical Sciences). His textbooks All of Statistics and All of Nonparametric Statistics are widely used in graduate programs globally.

Nisansala Yatapanageمشاهده پروفایل

مدرس ارشد

• Formal Methods
• Concurrency Verification
• Safety-Critical Systems
• +۷ مورد دیگر

Nisansala Yatapanage is a Senior Lecturer at the Australian National University (ANU) since January 2025, and served as a Lecturer from 2022 to 2024. She holds the position of Interim Associate Director of Engagement and Impact in the School of Computing within the ANU College of Systems & Society. Prior to ANU, she was a Senior Lecturer/Lecturer at De Montfort University, Leicester, U.K. (2017-2019), and worked with Prof. Cliff Jones at Newcastle University, U.K. (2013-2017) on the "Taming Concurrency" project. Her educational background includes a PhD from Griffith University (2012), supervised by Dr. Kirsten Winter. Before her PhD, she was a researcher in the ARC Centre for Complex Systems and Griffith University (2004-2007), working on specification and verification for safety-critical systems using Behavior Tree language and model checking. Nisansala's research focuses on the application of formal methods for various applications, particularly in concurrency, safety-critical systems, and security. She is currently investigating concurrency verification using rely/guarantee, separation logics, and temporal logic. Her work also explores the verification of security protocols and the foundations of concurrency, with recent research examining the links between rely/guarantee reasoning and linearisability for non-blocking algorithms. She has developed techniques for reducing Behavior Tree models prior to model checking and created a novel form of branching bisimulation called "Next-preserving Branching Bisimulation." Her publications demonstrate a consistent focus on formal verification methods, with particular emphasis on concurrency and safety-critical systems. Over the years, her work has evolved from Behavior Tree specifications and model checking to more advanced techniques involving rely/guarantee reasoning and separation logic. Recent publications show increasing focus on security protocols and the theoretical foundations of concurrent systems. Fellow of AdvanceHE Commended in Australian Photographer of the Year Competition 2020 (Travel category) Nisansala has extensive teaching experience across multiple institutions. At ANU, she has served as Course Convenor for multiple courses including COMP4011/8011 Special Topics in Formal Methods and Programming Languages, COMP1600 Foundations of Computing, and COMP1100/1130 Programming as Problem Solving. Previously at De Montfort University, she was Deputy Programme Leader of the BSc Computer Science & BSc Software Engineering degrees and Course Convenor for Data Structures & Algorithms and Concurrent & Parallel Algorithms. She has also taught at the ANU Logic Summer School. She has been involved in several significant research projects including "Taming Concurrency" at Newcastle University (2013-2017), a Model-Based Safety Analysis of Air Traffic Control Operational Procedures at UQ (2011-2012), and her PhD project on Slicing Behavior Trees for Verification of Large Systems at Griffith University (2007-2011).

Vincent Vuمشاهده پروفایل

دانشیار

• High-Dimensional Data Analysis
• Statistical Learning
• Statistical Computing
• +۳ مورد دیگر

Vincent Vu is an Associate Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics. He holds a PhD from the University of California, Berkeley (2009). His research focuses on high-dimensional statistics, statistical machine learning, and their applications in neuroimaging, particularly fMRI. He previously worked as a software engineer in Silicon Valley for over six years, blending computational and statistical expertise. His expertise includes statistical computing, multivariate analysis, and computational sufficiency. He has been funded by the NSF and received the 2012 AISTATS Best Paper Award. He serves as an associate editor for the Journal of the American Statistical Association and The American Statistician. His work bridges statistical theory and computation, addressing challenges in high-dimensional data analysis. Collaborations involve neuroscientific applications, combining algorithmic innovation with real-world problem-solving.

Ayse Sena Eruguzمشاهده پروفایل

استادیار

• Operations Research
• Supply Chain Management
• Inventory Control
• +۴ مورد دیگر

Ayse Sena Eruguz is an Assistant Professor at the Department of Operations Analytics, School of Business and Economics, Vrije Universiteit Amsterdam. She previously held positions as an Assistant Professor at Erasmus University Rotterdam (2020-2023) and a postdoctoral researcher at Eindhoven University of Technology (2017-2020). She earned her Ph.D. in Industrial Engineering from CentraleSupélec (France) in 2016. Her research focuses on operations research applied to supply chains, inventory control, sustainability, and maintenance systems, with collaborations in high-tech and maritime industries. Her teaching includes courses on E-Commerce Supply Chain Management, Procurement and Supply Management, and Sustainable Operations. She leads the 'Startersbeurs - Eruguz' research project (2023-2027), investigating maintenance optimization strategies for multi-component systems. She contributed to datasets such as the 'Maintenance Optimization for Multi-Component Systems with a Single Sensor' (2024). Research trends emphasize sustainability integration, e-commerce logistics challenges, and food waste reduction through inventory strategies. Her work bridges theory and practice, addressing real-world issues in global supply chains and maintenance systems.

Denny Wuمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Neural Networks
• High-Dimensional Statistics
• +۲ مورد دیگر

Denny Wu is a Faculty Fellow at the New York University Center for Data Science and affiliated with the Flatiron Institute 's Center for Computational Mathematics. He completed his PhD in Computer Science at the University of Toronto and the Vector Institute for Artificial Intelligence , advised by Assistant Professors Jimmy Ba and Murat A. Erdogdu . Prior, he earned an undergraduate degree in Computational Biology from Carnegie Mellon University as a research assistant under Ruslan Salakhutdinov . His research focuses on Theoretical Machine Learning , particularly Neural Network Optimization , Generalization Performance , and High-Dimensional Statistics . His work has been presented at top conferences like NeurIPS , ICML , and AISTATS , alongside publications in journals such as Nature Protocols and Journal of Statistical Mechanics: Theory and Experiment . Collaborative efforts include partnerships with RIKEN AIP 's Deep Learning Theory Team and Microsoft's Deep Learning Group. Borealis AI Fellowship 2023 UChicago Rising Star in Data Science Denny actively collaborates with institutions like the Vector Institute , RIKEN AIP , and Flatiron Institute , focusing on mathematical frameworks for understanding deep learning systems.