جستجوی استاد راهنمای دانشگاهی

Alexandra Silva is a Professor of Computer Science in the Department of Computer Science at Cornell University's College of Engineering. She joined Cornell as faculty in 2021 after previously serving as a Royal Society Wolfson Fellow and Professor of Algebra, Semantics, and Computation at University College London. Her research spans programming languages, formal verification, and theoretical computer science, with particular focus on Kleene Algebra with Tests (KAT), probabilistic programming, and automata theory. She has held numerous leadership roles in major programming languages conferences including POPL, PLDI, and ICFP. Dr. Silva completed her PhD at Centrum Wiskunde & Informatica (CWI) in Amsterdam under the supervision of Jan Rutten and Marcello Bonsangue, with her thesis entitled "Kleene coalgebra" defended in December 2010. Prior to her PhD, she was an undergraduate student at University of Minho in Portugal, where she completed a 5-year Mathematics and Computer Science degree in May 2006. Her research focuses on the modular development of specification languages and algorithms for models of computations, often from the unifying perspective offered by coalgebra. She has made significant contributions to Kleene Algebra with Tests, probabilistic programming semantics, network verification, and automata learning. Her work bridges theoretical foundations with practical verification tools, particularly in the domain of Software-Defined Networking where her NetKAT framework has gained significant attention. Analysis of her recent publications reveals a strong trend toward unifying frameworks for program verification, particularly through her development of Outcome Logic which provides foundations for both correctness and incorrectness reasoning. Her work increasingly integrates probabilistic and concurrent aspects of programming languages, with applications to network verification and security. The NetKAT ecosystem remains a central theme, with extensions to infinite state verification, symbolic execution, and learning-based approaches. Distinguished paper award for Guarded Kleene algebra with tests: verification of uninterpreted programs in nearly linear time (POPL 2020) Dr. Silva advises a large research group with numerous PhD students, postdocs, and undergraduate researchers. Her group has produced significant work in programming languages theory, verification, and applications to networking. She has secured substantial research funding through various grants that support her work on formal methods for network verification and probabilistic programming. Her mentoring approach emphasizes both theoretical depth and practical impact, with many of her students moving to prestigious academic and industry positions. Her research group, spanning both Cornell University and University College London, focuses on developing theoretical foundations for programming languages with practical applications in network verification, probabilistic systems, and program analysis. The group maintains active collaborations with researchers at CWI, University of Oxford, and other leading institutions in programming languages and formal methods.

• Programming Languages
• Formal Verification
• Kleene Algebra with Tests
• +۶ مورد دیگر

Matija Bucic serves as an Assistant Professor in the Department of Mathematics at the University of Vienna, based at Oskar-Morgenstern-Platz 1, 1090 Wien (Room 11.143). His contact details include email matija.bucic@univie.ac.at and phone +43-1-4277-55756, with his profile last updated on 2025-09-11. His research spans core mathematical disciplines, with primary emphasis on Discrete Mathematics and Combinatorics , extending to specialized areas including Graph Theory , Extremal Combinatorics , and Probabilistic Methods . These interests align with foundational theoretical research in discrete structures and algorithmic applications.

• Mathematics
• Discrete Mathematics
• Combinatorics
• +۳ مورد دیگر

Sinho Chewi is an Assistant Professor of Statistics and Data Science at Yale University, where he conducts research at the intersection of mathematics, statistics, and machine learning. His work focuses on theoretical aspects of computational statistics, particularly leveraging optimal transport theory for solving complex problems in sampling and inference. Chewi earned his B.S. in Engineering Mathematics and Statistics from the University of California, Berkeley in 2018, followed by a PhD in Mathematics and Statistics from the Massachusetts Institute of Technology in 2023 under the supervision of Philippe Rigollet. Prior to joining Yale, he was a postdoctoral researcher at the Institute for Advanced Study during the 2023-2024 academic year. His research interests span optimal transport theory , log-concave sampling , variational inference , and theoretical foundations of machine learning . Chewi is currently authoring a comprehensive book on the complexity of log-concave sampling, building on his extensive publication record. His work bridges theoretical mathematics with practical applications in statistical computing and artificial intelligence. Analysis of Chewi's publication record reveals a strong focus on developing rigorous mathematical frameworks for sampling algorithms, with particular emphasis on complexity analysis, convergence guarantees, and connections between functional inequalities and optimization. His recent work extends into diffusion models, neural network theory, and parallel sampling algorithms, demonstrating both depth in core statistical theory and breadth across machine learning applications. ICLR 2023 (Notable Top 5%) ALT 2023 (Best Student Paper) NeurIPS 2021 (Spotlight) ICLR 2025 DeLTA Workshop (Best Short Paper Award) Chewi has received significant research support through his postdoctoral position at the Institute for Advanced Study and collaborations with leading researchers at institutions including MIT, NYU, and Microsoft Research. His teaching portfolio includes advanced courses in optimization techniques and sampling methods, reflecting his expertise in theoretical foundations of machine learning algorithms.

• Optimal Transport
• Log-concave Sampling
• Machine Learning Theory
• +۳ مورد دیگر