جست‌وجوی استاد و پژوهشگر

Koen Tielsمشاهده پروفایل

استادیار

• System Identification
• Machine Learning
• Nonlinear Dynamical Systems
• +۳ مورد دیگر

Koen Tiels is an Assistant Professor in the Control Systems Technology (CST) group at the Department of Mechanical Engineering of Eindhoven University of Technology (TU/e). His research focuses on the identification of nonlinear dynamical systems, combining techniques from system identification and machine learning. Education Master's in Electromechanical Engineering, Vrije Universiteit Brussel (2010) PhD in Electromechanical Engineering, Vrije Universiteit Brussel (2015) His work contributes to areas such as magnetic hysteresis modeling using physics-aware neural networks, optimal control design with low computational cost, and kernel-based identification methods for sampled data. He has collaborated with institutions like Uppsala University and TU/e's EAISI (Eindhoven Artificial Intelligence Systems Institute) on high-tech systems and mechatronics. Recent publications emphasize applications in recurrent neural networks for magnetic hysteresis, sparse modeling of piezoelectric systems, and robust experiment design for MIMO systems. His research aligns with UN Sustainable Development Goals related to clean energy and advanced technologies.

Ning Xieمشاهده پروفایل

استاد

• Algorithms
• Computational Complexity
• Property Testing
• +۳ مورد دیگر

Ning Xie is a Professor in the Department of Computer Science at Florida International University's School of Computing and Information Sciences. He has been actively teaching graduate and undergraduate algorithm courses since at least 2013, including COT-6405 Analysis of Algorithms, COP-4534 Algorithm Techniques, and COT-6446 Randomized Algorithms. Dr. Xie's research focuses on theoretical computer science, particularly in the areas of sub-linear algorithms, property testing, local computation algorithms, and Fourier analysis of Boolean functions. He was advised by Professor Ronitt Rubinfeld during his doctoral studies. His work bridges theoretical foundations with practical algorithmic applications, contributing significantly to our understanding of computational complexity and efficient algorithm design. His research has resulted in numerous publications in top theoretical computer science venues including FOCS, STOC, SODA, and various prestigious journals. Dr. Xie mentors several PhD students, currently supervising Yekun Xu and Xiaolong Zhu, and has successfully graduated students including Shokoufeh Mokhtari (now at Microsoft) and Shuai Xu (now at Case Western Reserve University). His teaching portfolio demonstrates a strong commitment to algorithm education at both undergraduate and graduate levels, with detailed course materials covering dynamic programming, graph algorithms, NP-completeness, and other advanced topics.

Henry Towsnerمشاهده پروفایل

استاد

• Mathematical Logic
• Proof Theory
• Combinatorics
• +۴ مورد دیگر

Henry Towsner is a Professor of Mathematics and Undergraduate Chair in the Department of Mathematics at the University of Pennsylvania. His research focuses on mathematical logic, proof theory, combinatorics, and their applications. He holds an office at 4N51 DRL and can be contacted via email or phone. His work bridges foundational mathematics with combinatorial and analytical methods, particularly in hypergraph regularity, reverse mathematics, and epsilon substitution. Research Interests: Towsner’s research spans proof theory, reverse mathematics, combinatorics (including hypergraph regularity and Ramsey theory), and applications of logic to analysis. He explores foundational questions in logic while developing tools for extracting computational content from proofs. His work on ultraproducts and exchangeable structures connects model theory with probability theory. Recent Trends in Articles: His publications from 2023-2017 highlight advancements in hypergraph regularity, Borel combinatorics, and proof-theoretic methods. Key themes include algorithmic extraction from proofs, nonalgorithmic combinatorial proofs, and applications of logic to functional analysis. His work often intersects with computability theory and effective bounds in algebraic structures. Advising and Grants: While specific grant details are not listed, his active research program suggests involvement in NSF or institutional grants typical for a senior faculty member. He advises graduate students in logic and combinatorics, though specific names are not provided in the text. Labs/Teams: No dedicated lab is mentioned, but his collaborations span pure mathematics disciplines, including work with researchers in combinatorics, functional analysis, and set theory. His research group likely engages in interdisciplinary projects within the mathematics department.

Amin Saberiمشاهده پروفایل

استاد

• Algorithms
• Social Networks
• Market Design
• +۴ مورد دیگر

Amin Saberi is a Professor of Management Science and Engineering at Stanford University and Director of the Stanford Center for Computational Market Design. He earned his BSc from Sharif University of Technology and PhD in Computer Science from Georgia Institute of Technology. Research Interests : Design and analysis of algorithms, social networks, market design, and applications in economics and optimization. Key Contributions : His work spans online algorithms, stochastic optimization, and matching theory, with applications in ride-hailing, social learning platforms, and resource allocation. Recent Article Trends : Saberi’s recent research focuses on adaptive approximation schemes for matching queues, prophet inequalities in stochastic matching, and locality-aware graph rewiring in GNNs. His work bridges theoretical insights with practical applications in market design and computational economics. Scientific Awards : Terman Fellowship, Sloan Fellowship, Best Paper Awards at FOCS (2009), SODA (2010), WINE (2017), and Test of Time Award at ACM EC (2021). Advising & Collaborations : Saberi has advised numerous graduate students and postdocs, many of whom hold faculty or industry leadership roles. He co-founded NovoEd, a social learning platform, and has organized programs at mathematical optimization institutes.

• Graph Algorithms
• Distributed Algorithms
• Parallel Algorithms
• +۳ مورد دیگر

Dr. Peter Davies-Peck is an Assistant Professor in the Department of Computer Science at Durham University. His research focuses on distributed algorithms, graph theory, and parallel computing. He has held roles on programme committees for major conferences like PODC and ICDCS, and has been an invited speaker at workshops associated with DISC. His research interests include Graph Algorithms, Distributed Algorithms, Randomised Algorithms, and Communications Networks. Notable work involves applying the Lovász Local Lemma to distributed computing challenges and developing efficient message-passing protocols in noisy environments. Recent contributions include advancements in parallel derandomization for graph coloring, optimal message-passing in radio networks, and distributed mean estimation techniques. His work bridges theoretical algorithm design with practical distributed system challenges, emphasizing scalability and resilience. Esteem indicators include Programme Committee membership for PODC 2023, ALGOSENSORS 2022, and ICDCS 2021. His publications span top venues like STOC, SODA, and the Journal of the ACM, reflecting impactful contributions to theoretical computer science and distributed systems.

• Theory of Machine Learning
• Randomized Algorithms
• Convex Optimization
• +۴ مورد دیگر

Nicholas Harvey is a Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. He holds affiliations as an Associate Member in the Department of Mathematics and serves as Director of the Mathematics of Information, Learning, and Data (MILD) research cluster. His research focuses on randomized algorithms, convex optimization, and machine learning theory, with notable contributions to online learning, submodular functions, and algorithmic aspects of discrepancy theory. Harvey's academic journey includes significant work on problems such as prediction with expert advice, learning mixtures of Gaussians, and graph algorithms. He has supervised numerous graduate students, including notable researchers like Huang Fang and Victor Sanches Portella. His work has been recognized with awards such as the Best Paper Award at NeurIPS 2018 and the Excellence in Teaching Award in 2024. His research outputs span theoretical computer science, optimization, and machine learning, with key contributions in algorithms for submodular functions, online convex optimization, and probabilistic methods. He has also contributed to practical applications, such as algorithms for network coding and efficient failure notification systems in distributed networks. Harvey is actively involved in teaching, including pioneering courses on randomized algorithms and machine learning theory. His research group collaborates on projects related to the MILD cluster, focusing on interdisciplinary applications of data science and mathematics.

Russell Impagliazzoمشاهده پروفایل

استاد

• Computational Complexity
• Cryptography
• Randomness in Computation
• +۵ مورد دیگر

Russell Impagliazzo is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD). He has held positions as Assistant Professor, Associate Professor, and Professor at UCSD since 1991 and was a Visiting Professor at the Institute for Advanced Study (Princeton) from 2007 to 2012. His academic journey includes a BA in Mathematics from Wesleyan University and a PhD in Mathematics from UC Berkeley. His research focuses on computational complexity theory , with key contributions to: Randomness in computation Cryptography (e.g., pseudorandom generators) Circuit lower bounds Proof complexity (e.g., polynomial calculus, resolution) Structural complexity (e.g., average-case hardness) Optimization heuristics (e.g., local search) The trends in his publications include foundational work on derandomization, hardness amplification, and algebraic proof systems. His papers often bridge theoretical computer science with mathematics, particularly in analyzing the limits of computational models. Scientific awards and honors include: NSF Young Investigator Sloan Fellow Fulbright Scholar Guggenheim Fellow Simons Investigator Best Paper Award (Computational Complexity Conference) Best Paper Award (STOC) Outstanding Paper Award (SIAM) He actively advises students and has contributed to grants and programs such as the Simons Institute’s Fine-Grained Complexity and Algorithms and the Meta-Complexity program at the Simons Lab in Spring 2023.

Hongjin Liangمشاهده پروفایل

دانشیار

• Programming Languages
• Formal Verification
• Concurrency
• +۳ مورد دیگر

Hongjin Liang is an Associate Professor at the School of Computer Science , Nanjing University , where he researches programming languages and formal verification as part of the PLaX group. His office is located at Room 404, Building of Computer Science and Technology, Nanjing University (Xianlin Campus). His research spans concurrency , compiler verification , distributed systems , and theorem proving , with a focus on developing formal methods for verifying program correctness under complex execution models (e.g., randomized concurrency, CRDTs). His publications emphasize verification techniques for concurrent and distributed systems, program logics, and compiler optimizations. Recent work explores probabilistic program verification and algorithmic foundations of distributed computing. Awards: PLDI'19 Distinguished Paper Award He teaches courses including Formal Semantics of Programming Languages and Concurrency: Algorithms and Theories . He actively serves on program committees for PLDI, POPL, ESOP, and related conferences.

Nick Harveyمشاهده پروفایل

استاد

• Theory of Machine Learning
• Randomized Algorithms
• Convex Optimization
• +۳ مورد دیگر

Dr. Nick Harvey is a Full Professor in the Department of Computer Science at the University of British Columbia (UBC), with an affiliation to the Department of Mathematics. He leads the Mathematics of Information, Learning and Data (MILD) research cluster. His research focuses on randomized algorithms, machine learning theory, and convex optimization, with notable contributions to algorithm design and analysis. Harvey has taught courses such as Randomized Algorithms (CPSC 436R/536N), Theory of Computing (CPSC 421), and Advanced Algorithms (CPSC 420). He has received prestigious awards including the NeurIPS 2018 Best Paper Award and the CS-Can/Info-Can Outstanding Young Research Prize (2014). His work emphasizes probabilistic techniques in algorithms, including applications in graph theory, streaming algorithms, and privacy-preserving methods. Harvey supervises graduate and undergraduate students, contributing to their academic and professional development. His research group explores cutting-edge topics in algorithmic theory and machine learning, with ongoing projects in algorithmic fairness, optimization, and data science. Key achievements include advancing the Lovász Local Lemma, developing efficient graph sparsification techniques, and pioneering work on the complexity of matrix completion. Harvey's teaching philosophy prioritizes clarity and rigor, reflected in his award-winning pedagogy. He actively engages in academic service, including curriculum development and conference organization.

Vladimir Kolmogorovمشاهده پروفایل

استاد

• Discrete Optimization
• Combinatorial Optimization
• Global Optimization
• +۳ مورد دیگر

Vladimir Kolmogorov is a Professor at the Institute of Science and Technology Austria (IST Austria), leading the Kolmogorov Group focused on Discrete Optimization. He previously held positions as Assistant Professor at IST Austria (2011–2014), Lecturer at University College London (2005–2011), and Associate Researcher at Microsoft Research (2003–2005). His research spans algorithm development for graphical models, combinatorial optimization, and applications in computer vision. Education: M.S. in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology, and Ph.D. in Computer Science from Cornell University (2003). Research interests include complexity classifications, algorithm design for discrete optimization problems (e.g., max-flow, min-cost matching), and applications in computer vision. Notable contributions include the 'Boykov-Kolmogorov' max-flow algorithm and 'Blossom V' for minimum cost perfect matching. Honors include the ERC Consolidator Grant (2014–2019), Koenderink Prize (2012), and best paper awards at CVPR and ECCV. His work on optimization algorithms and theoretical complexity has significantly impacted computer vision and combinatorial optimization fields. Advising and grants: Supervised multiple PhD students and postdocs, with current advisees including Martin Dvořák and Pavel Arkhipov. His team explores inference in graphical models, combinatorial optimization, and discrete optimization theory.

Jan Vondrakمشاهده پروفایل

استاد

• Discrete optimization
• Matroids and submodular functions
• Approximation algorithms
• +۲ مورد دیگر

Jan Vondrak is a Professor of Mathematics at Stanford University. He earned his PhD in Applied Mathematics from MIT in 2005, followed by postdoctoral positions at Microsoft Research and Princeton University. He worked as a Research Staff Member at IBM Almaden Research Lab (2009–2015) before joining Stanford in 2016. His roles include teaching courses such as Linear Algebra, Combinatorics, and advanced topics in submodular optimization. Department: Mathematics University: Stanford University Academic Rank: Professor Research interests include discrete optimization, submodular functions, approximation algorithms, algorithmic game theory, and probabilistic combinatorics. His work bridges theoretical mathematics and algorithmic applications, with a focus on non-constructive proofs and polynomial-based methods in combinatorics. Recent publications highlight advancements in Nash Social Welfare approximation, content resolution schemes, prophet inequalities, and submodular function analysis. His work intersects computer science, economics, and probability theory, often applying algebraic and combinatorial techniques to optimization problems. Scientific awards include recognition for the paper Computational complexity of truthful mechanisms for combinatorial auctions (EC 2012, Top 10% paper). Advising PhD students like Pranav Nuti (Mathematics, 2024), Wenzheng Li (Computer Science, 2024), and Paul Liu (Computer Science, 2022. He has also organized academic programs on data structures, optimization, and the geometry of polynomials.

Isaac Goldbringمشاهده پروفایل

استادیار

• Model Theory
• Nonstandard Analysis
• Operator Algebras
• +۵ مورد دیگر

Isaac Goldbring is an Assistant Professor in the Department of Mathematics, Statistics, and Computer Science at the University of Illinois at Chicago (UIC). His research focuses on model theory, nonstandard analysis, and their applications to operator algebras, geometric group theory, and combinatorial number theory. He holds an NSF CAREER Grant for his work on Model Theory and Operator Algebras. Goldbring co-organized the 2016 Notre Dame Model Theory Program and actively leads seminars on model theory and related topics. His collaborations span mathematicians like Lou van den Dries and Bradd Hart. He has published extensively in journals such as the Annals of Mathematics and Journal of Functional Analysis, addressing foundational problems in logic, operator algebras, and combinatorics. Goldbring's academic journey includes teaching at UCLA and UIC, covering advanced topics like Nonstandard Analysis and Model Theory. His research notably tackles Hilbert's Fifth Problem, Erdős sumset conjectures, and structural properties of II₁ factors. He has contributed to bridging model theory with operator algebras, including reformulations of the Connes Embedding Problem using computability theory. His work frequently employs nonstandard methods to analyze metric structures and topological dynamics. Key scientific awards include the NSF CAREER Grant, reflecting his impactful contributions. His articles explore intersections between pure logic and applied mathematics, such as hyperfinite approximations of graph structures and the model-theoretic analysis of tracial von Neumann algebras. Goldbring's interdisciplinary approach connects foundational mathematical logic with modern algebraic and combinatorial challenges.

• Quantum Computation
• Quantum Programming
• Formal Methods
• +۶ مورد دیگر

Professor Mingsheng Ying (University of Technology Sydney) is a Distinguished Professor specializing in quantum programming , quantum verification , and the foundations of artificial intelligence . He leads the Centre for Quantum Software and Information and co-founded the Quantum Lab . His work bridges quantum computation with formal methods and reasoning under uncertainty. Education : Mathematics, Fuzhou Teachers College (1981) Research Interests His research spans: Quantum programming languages and verification techniques Model checking quantum systems and cryptographic protocols Quantum machine learning robustness Entanglement theory and distributed quantum computation Recent Publications Key contributions include: Quantum error correction verification frameworks Quantum register machine architecture Hamiltonian simulation parallelization Symbolic execution for quantum debugging Robustness tools like VeriQR Awards & Editorial Roles NSF China Distinguished Young Scholar Award (1997) China National Science Award (2008) Co-Editor-in-Chief, ACM Transactions on Quantum Computing Vice President, International Fuzzy Systems Association (2005) Leadership & Grants He oversees 14 active grants (2010–2029) from: Australian Research Council (ARC Discovery Projects) National Natural Science Foundation of China Sydney Quantum Academy Baidu Contract Research His grants fund research in quantum program verification, entanglement classification, and distributed quantum protocols.

Jonathan Noelمشاهده پروفایل

استادیار

• Extremal Combinatorics
• Probabilistic Combinatorics
• Combinatorial Limits
• +۷ مورد دیگر

Jonathan Noel is an Assistant Professor in the Department of Mathematics and Statistics at the University of Victoria. His research focuses on extremal combinatorics, probabilistic combinatorics, and combinatorial limits, with applications to quasirandomness, Ramsey theory, and graph theory. He holds a DPhil (PhD) from the University of Oxford and teaches courses such as MATH 122 (Logic and Foundations) and MATH 529 (Topics in Discrete Mathematics). His research interests include extremal set theory, graph colouring, graph homomorphisms, bootstrap percolation, and computational complexity. He explores how local patterns in discrete structures influence global properties, such as the interplay between edge densities and cycle counts in graphs. Recent work emphasizes quasirandomness through permutation patterns and entropy methods, alongside bootstrap percolation dynamics in hypercubes and cellular automata. His articles often bridge combinatorial limits with analytic approaches, such as the Szemerédi Regularity Lemma. No awards are listed for Jonathan Noel in the provided text. He advises no formally listed students but contributes to academic supervision through course instruction.

Dimitris Achlioptasمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithms
• Randomized Algorithms
• +۵ مورد دیگر

Dimitris Achlioptas is a Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His research focuses on theoretical computer science, algorithms, and probabilistic methods. He has contributed extensively to areas such as satisfiability problems, random constraint satisfaction, combinatorial optimization, and phase transitions in random structures. His work bridges algorithm design with probabilistic analysis, often leveraging tools from discrete mathematics and statistical physics. Key research themes include the Lovász Local Lemma, random k-SAT, and the interplay between computational complexity and statistical properties of problems. Notable contributions address algorithmic barriers from phase transitions, solution space geometry of random CSPs, and efficient sampling techniques for combinatorial structures. His recent work explores applications of probabilistic methods in machine learning and optimization, including scalable algorithms for high-dimensional problems.