جست‌وجوی استاد و پژوهشگر

• Graph Algorithms
• Convex Optimization
• Fine-Grained Complexity Theory
• +۳ مورد دیگر

Rasmus Kyng is an Assistant Professor at the Department of Computer Science, ETH Zurich, since Fall 2019. He focuses on developing fast algorithms for graph problems, convex optimization, and structured linear equations, with a specialization in fine-grained complexity theory and dynamic graph algorithms. Assistant Professor, Department of Computer Science, ETH Zurich (2019–present) Postdoc, Theory of Computation Group, Harvard (2018–2019) Research Fellow, Simons Institute, UC Berkeley (Fall 2017) His research explores the intersection of graph algorithms, optimization, and complexity, particularly through dynamic graph data structures, sparsification, and applications in machine learning. Recent work includes breakthroughs in almost-linear time algorithms for network flow problems. Key trends in his publications include: Advancements in dynamic graph algorithms and data structures Applications of convex optimization to graph theory Connections between matrix theory and algorithmic efficiency Derandomization techniques for algorithmic speed Expander graph-based oblivious routing Sparsification and low-congestion vertex sparsifiers Scientific recognition includes the FOCS Best Paper Award (2022), ICBS Frontiers of Science Award (2022), and the Machtey Award for Best Student Paper (2017). His group mentors PhD candidates and postdocs, including Ming Ding , Federico Soldà , Simon Meierhans , Aurelio Sulser , and Wuwei Yuan . Research is supported by Swiss National Science Foundation grants (project no. 200021 204787, starting grant no. TMSGI2 218022).

• Computational Complexity
• Circuit Complexity
• Randomness in Computation
• +۲ مورد دیگر

Dr. Zhenjian Lu is an Assistant Professor in the Department of Computer Science at the University of Victoria, Canada. He previously held postdoctoral positions at the University of Warwick and the University of Oxford, and served as a Research Fellow for the Meta-Complexity program at the Simons Institute. Ph.D. in Computer Science from Simon Fraser University (advised by Valentine Kabanets and Andrei Bulatov) B.Sc. in Computer Science from the University of British Columbia His research focuses on computational complexity theory, including circuit complexity, randomness in computation, meta-complexity, and the interplay between lower bounds and algorithms. Recent work explores one-way functions, Kolmogorov complexity, comparator circuits, and pseudodeterministic constructions. Selected publications highlight advancements in time-bounded Kolmogorov complexity, coding theorems, and derandomization techniques. His research often bridges theoretical foundations with practical algorithmic applications. Contact: zhenjianlu@uvic.ca | Office: ECS 626 | Phone: 250-472-5731

Termeh Koushaمشاهده پروفایل

استاد مدعو

• Environmental Health
• Public Health
• Biostatistics
• +۳ مورد دیگر

Termeh Kousha is an Adjunct Professor in the Department of Mathematics and Statistics at the University of Ottawa’s Faculty of Science. Their research focuses on the intersection of environmental health and statistical modeling, particularly examining the impact of air pollution on emergency healthcare utilization. They specialize in applying advanced statistical methods to analyze environmental health risks and public health outcomes. Key research areas include air pollution exposure effects on mental health, respiratory diseases, skin conditions, and cardiovascular health, often employing case-crossover study designs. Their work bridges mathematical rigor—such as convex geometry and algorithmic optimization—with real-world public health challenges. Publications highlight trends in linking air quality indices (e.g., AQHI) to emergency department visits for conditions like asthma, otitis media, and hypertension. Methodological contributions include derandomization of computational algorithms and factorization techniques for environmental pollutant source analysis. No scientific awards or student advisees are explicitly listed. Research has been applied in Canadian cities such as Montreal, Edmonton, Calgary, and Windsor, emphasizing regional health disparities and urban environmental challenges.

• Theoretical Computer Science
• Machine Learning
• Algorithms
• +۴ مورد دیگر

Nicholas J. A. Harvey is a Professor of Computer Science at the University of British Columbia. His research spans multiple areas of theoretical computer science with significant contributions to algorithms, machine learning theory, and network coding. Harvey's research focuses on the theoretical foundations of computer science with particular emphasis on: Theory and analysis of randomized algorithms Machine learning theory, including online learning and neural networks Network coding and information theory Combinatorial optimization and graph algorithms High-dimensional probability and statistics His work combines deep mathematical insights with practical applications in computer systems and networking. Analysis of Harvey's recent publications (2022-2025) reveals a strong focus on theoretical aspects of machine learning and optimization. His work spans continuous prediction with experts' advice, tight analyses of optimization algorithms like subgradient descent, and fundamental questions in randomized algorithms. A significant portion of his recent research addresses challenges in high-dimensional statistics, privacy-preserving estimation, and graph neural networks. His publications demonstrate a consistent pattern of addressing foundational theoretical questions with implications for practical machine learning systems. Harvey has maintained a strong publication record with an h-index of 35 and over 6,000 citations according to Google Scholar metrics. His influential papers include 'SkipNet: A Scalable Overlay Network with Practical Locality Properties' (2003), 'On the capacity of information networks' (2006), and 'Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks' (2019). Harvey has supervised numerous graduate students and postdoctoral researchers, contributing to the development of the next generation of theoretical computer scientists. His research has been supported by various grants from funding agencies including the Natural Sciences and Engineering Research Council of Canada (NSERC) and collaborations with industry partners. While specific details about laboratory facilities are not available in the provided information, Professor Harvey appears to be part of the theoretical computer science research group at the University of British Columbia, collaborating with researchers across multiple institutions on fundamental problems in algorithms and machine learning theory.

Guy Evenمشاهده پروفایل

پژوهشگر

• Computer Science
• Mathematics
• Approximation Algorithms
• +۴ مورد دیگر

Guy Even is a prominent researcher in Computer Science and Mathematics, with significant contributions to approximation algorithms, graph theory, and network design. His work spans theoretical and applied domains, including algorithmic complexity, Steiner tree problems, and IEEE floating-point arithmetic optimization. Research Interests: His research focuses on combinatorial optimization, generalized connectivity in graphs, and efficient algorithm design. He has advanced methodologies for divide-and-conquer strategies, feedback set approximation, and spreading metrics in undirected and directed graph problems. Key Article Trends: His publications from 1992 to 2013 highlight advancements in approximation algorithms for NP-hard problems, with a particular emphasis on Steiner networks (undirected/directed), Boolean network observability, and hardware-efficient rounding techniques for floating-point arithmetic. These works bridge theoretical insights with practical applications in computing. Collaborations: Guy has collaborated with notable researchers such as Chandra Chekuri, Anupam Gupta, and Danny Segev. His co-authorship network includes Moti Medina (71 publications), Nissim Halabi (14 publications), and other leading scholars in algorithmic and computational fields. Notable Affiliations: While specific institutions are not explicitly stated in the provided text, his work is cited in prestigious venues like ACM, IEEE, and Springer, indicating strong ties to academia and research communities.

• 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.

Jeffrey Negreaمشاهده پروفایل

استادیار

• Machine Learning
• Statistics
• Artificial Intelligence
• +۳ مورد دیگر

Jeffrey Negrea is an Assistant Professor at the University of Waterloo. His research focuses on foundational aspects of machine learning, statistics, and optimization, with a particular emphasis on stochastic algorithms, Markov chain theory, and adversarial analysis. His work bridges theoretical computer science and applied statistics, addressing challenges in algorithmic convergence, generalization bounds, and scalable inference methods. Key research directions include developing robust methods for online learning, analyzing stochastic iterative algorithms through scaling limits, and exploring the interplay between information theory and machine learning generalization. He has contributed to advancements in Thompson Sampling for adversarial environments, minimax regret optimization, and the theoretical underpinnings of MCMC and approximate inference techniques. His publications span topics from adversarial analysis of Thompson Sampling to quantitative error bounds for stochastic processes. While no formal awards or grants are explicitly listed in the provided data, his active publication record reflects ongoing contributions to computational statistics and machine learning theory. No student advising or lab affiliations are mentioned in the available information.

Jeffrey S. Rosenthalمشاهده پروفایل

استاد

• Probability theory
• Stochastic processes
• Markov chain Monte Carlo
• +۴ مورد دیگر

Jeffrey S. Rosenthal is a Professor in the Department of Statistical Sciences at the University of Toronto, Faculty of Arts and Science. He holds a PhD in Mathematics from Harvard University and a BSc from the University of Toronto. PhD, Mathematics, Harvard University BSc, University of Toronto His research centers on probability theory , stochastic processes , and statistical computation , with a particular focus on Markov chain Monte Carlo (MCMC) algorithms . His work spans theoretical foundations and practical applications, including random walks on groups and interdisciplinary modeling. He is also known for his public engagement in statistics through bestselling books and media appearances. The recent publications reflect a sustained focus on the theoretical underpinnings and convergence properties of MCMC methods, including adaptive and non-reversible algorithms. His work also extends into data science applications, such as analyzing streaks in online chess and the long-term impact of the COVID-19 pandemic on mortality. The keywords span probability, statistics, computational mathematics, and machine learning, indicating a blend of theoretical rigor and applied relevance. Scientific Awards and Honors: CRM-SSC Prize in Statistics COPSS Presidents' Award SSC Gold Medal Fellow of the Royal Society of Canada Fellow of the Institute of Mathematical Statistics Alumnus of Influence, University College Pierre Robillard Award SSC Student Research Presentation Award Savage Award Finalist Academic Supervision and Grants: Professor Rosenthal has supervised a large and diverse group of students, including numerous PhD candidates, MSc students, and post-doctoral fellows, many of whom have gone on to successful academic careers. His research is supported by ongoing publications and collaborations, indicating active grant funding and a vibrant research program. He maintains a well-documented research team and provides extensive resources for students and collaborators. Research Teams and Labs: He leads an active research group in probability and computational statistics, with a documented team of current and past post-doctoral fellows, PhD students, and research assistants. The group maintains a collaborative environment, as evidenced by joint publications and team photos, and focuses on advanced topics in MCMC theory and applications.

• Theoretical Computer Science
• Algorithms
• Parameterized Complexity
• +۵ مورد دیگر

Yang Liu is a computer scientist specializing in theoretical computer science, particularly in the design and analysis of parameterized and exact algorithms. He has been affiliated with institutions such as Texas A&M University and has collaborated extensively with researchers like Jianer Chen and Songjian Lu. His work primarily focuses on NP-hard graph problems, including feedback vertex set, multiway cut, matching, and packing, where he has contributed improved fixed-parameter tractable algorithms and kernelization techniques. His research lies at the intersection of algorithms, complexity theory, and combinatorics. Key areas include: Fixed-parameter tractability (FPT) Kernelization and measure-and-conquer methods Graph partitioning and structural graph theory Randomized and deterministic exact algorithms Algebraic methods in polynomial testing His publications in top journals such as Journal of the ACM , Algorithmica , and Theoretical Computer Science demonstrate a consistent contribution to foundational algorithmic research. The article trends show a strong focus on solving hard combinatorial problems through novel algorithmic frameworks, often improving time complexity or kernel bounds. Notable scientific contributions include: A landmark 2008 JACM paper proving that the Directed Feedback Vertex Set problem is fixed-parameter tractable. Improvements in kernel sizes for feedback vertex and packing problems. Applications of color-coding and iterative expansion in 3D-matching. While no explicit information about advisees or grants is available, his long-term collaboration network suggests a role in mentoring and team-based research. He has not been associated with any lab or research center in the provided data.

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.

• Distributed Algorithms
• Quantum Computing
• Graph Theory
• +۲ مورد دیگر

Henrik Lievonen is a Doctoral Researcher at Aalto University's Department of Computer Science. His work focuses on distributed algorithms and their connections to mathematics, computer science theory, and quantum computing. University: Aalto University Role: Researcher in distributed algorithms and quantum computing Research interests center on distributed systems, quantum advantage analysis, and algorithmic complexity in graph problems. Key areas include LOCAL model constraints, quantum derandomization, and theoretical limits of distributed quantum computing. Scientific Awards: Nokia Scholarship 2024 (Nokia Foundation)

William Hozaمشاهده پروفایل

استادیار

• Computational Complexity Theory
• Pseudorandom Generators
• Derandomization of Algorithms
• +۳ مورد دیگر

William Hoza is an Assistant Professor in the Department of Computer Science at the University of Chicago, affiliated with the UChicago CS Theory Group. His research focuses on computational complexity theory, particularly pseudorandom generators, derandomization of space-bounded computation, and circuit complexity. He earned a Ph.D. in Computer Science from the University of Texas at Austin (2021) and a B.S. from the California Institute of Technology (2016). Hoza advises two PhD students, including Zelin Lv (co-advised with Aaron Potechin). He has taught courses such as Introduction to Complexity Theory, Pseudorandomness, and Circuit Complexity. His academic journey includes a postdoctoral fellowship at UC Berkeley's Simons Institute. Hoza's work bridges theoretical computer science and practical algorithm design, with contributions to derandomization techniques and pseudorandom generator constructions. Key contributions include research on hitting sets, branching programs, and the limitations of pseudorandom generators. His publications span venues like FOCS, STOC, and ITCS, addressing foundational questions in computational complexity.

Laurent Feuilloleyمشاهده پروفایل

پژوهشگر

• Distributed Computing
• Graph Theory
• Algorithms
• +۴ مورد دیگر

Laurent Feuilloley is a junior CNRS researcher (Chargé de recherche) at LIRIS, Université de Lyon 1 since 2022. He previously held postdoctoral positions at LIRIS, Universidad de Chile, and LIP6 at Sorbonne Université. His research focuses on Distributed Computing , Graph Theory , and Local Certification , with applications in Fault-Tolerant Systems and Algorithm Engineering . He collaborates extensively with researchers like Nicolas Bousquet, Franck Petit, and José Correa. Education: PhD in Computer Science (2018) at IRIF, Université Paris Diderot, supervised by Pierre Fraigniaud. Research: Works on graph algorithms, certification, and robustness in dynamic networks, with a focus on maximal independent sets and self-stabilizing systems. Scientific Contributions: His publications include lower bounds for local certification, robustness refinements in dynamic networks, and space complexity for leader election algorithms. He has received multiple Best Paper Awards at SAND 2023 and SSS 2022 Best Student Paper at OPODIS 2021 Best Reviewer Award at DISC 2020 Advising: Currently supervising Sébastien Zeitoun's PhD (co-advised with Nicolas Bousquet and Éric Duchêne) and Antonin Kiladjian's M1 internship (with Théo Pierron). He has also advised Guillermo Dinamarca's master thesis in 2019-2020.

Shuichi Hiraharaمشاهده پروفایل

دانشیار

• Computational Complexity Theory
• Meta-complexity
• Average-case Complexity
• +۴ مورد دیگر

Shuichi Hirahara is an Associate Professor at the National Institute of Informatics (NII), Japan, within the Principles of Informatics Research Division. He holds a Ph.D. from the University of Tokyo and has held research positions at the University of Warwick and Nagoya University. His work lies at the intersection of theoretical computer science and cryptography, focusing on foundational questions in computational complexity. Educational Background: Ph.D. in Computer Science, Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo (2016–2019) His research centers on meta-complexity , a field probing the complexity of problems about computational complexity itself, such as the Minimum Circuit Size Problem (MCSP). He investigates average-case complexity , Kolmogorov complexity , and circuit minimization , aiming to understand the limits of efficient computation and to establish secure cryptographic foundations. His work often reveals unexpected hardness results and bridges worst-case and average-case complexity. The recent publications highlight a strong trend toward using meta-complexity to understand cryptographic primitives such as one-way functions and to unify hardness conjectures like the Planted Clique problem. His results have significant implications for proving the security of cryptography based on average-case hardness. Scientific Awards: Young Scientists' Award from MEXT (2024) Yamato Scientific Award (2024) Microsoft Informatics Research Award (2024) Complexity Result of the Year 2022 Funai Research Encouragement Award (2022) Machtey Award (FOCS 2018) Academic Encouragement Award (IEICE, 2019) Hirahara has been actively involved in research funding and academic service. He is the principal investigator of a JSPS Challenging Research (Pioneering) grant on average-time NP-completeness and has participated in several JST and JSPS projects on complexity theory and quantum algorithms. He serves on the program committees of major conferences including STOC, FOCS, and CCC. He is a frequent invited speaker at international workshops and seminars, contributing to the global discourse on computational complexity and cryptography. While no formal advisees are listed, his leadership in research projects suggests an active mentoring role. Labs and Research Teams: He is affiliated with the Informatics Principles Research Division at NII, a leading center for theoretical informatics in Japan. His work is highly collaborative, involving researchers from institutions like JST, University of Tokyo, and international partners.

Danny Huckeمشاهده پروفایل

پژوهشگر

• Data Compression
• Grammar-Based Compression
• Empirical Entropy
• +۳ مورد دیگر

Danny Hucke is a researcher affiliated with the University of Siegen, Department of Electrical Engineering and Computer Science. His work focuses on advanced data compression techniques, algorithmic complexity, and formal verification of streaming systems. Education: PhD in Grammar-based compression for strings and trees (University of Siegen, 2019) Research Interests: Driven by challenges in grammar-based compression, empirical entropy metrics, and formal language processing in streaming environments, his research bridges theoretical computer science and practical algorithm design. Key areas include: Data Compression for Trees and Strings Sliding-Window Algorithms Circuit Complexity Algorithmic Entropy Analysis Scientific Contributions: His work has been recognized with a Best Paper Award at SPIRE 2016. Publications span top-tier venues like IEEE Transactions on Information Theory , ACM TOCT , and conferences including ICALP, STACS, and LATIN. Contact: Department of Electrical Engineering and Computer Science, University of Siegen, Hölderlinstrasse 3, D-57076 Siegen. Email: hucke@eti.uni-siegen.de . Phone: +49-271-740-3415. Office: Room H-A 7104. Collaborations: Conducted research within the group of Prof. Markus Lohrey, collaborating extensively with Moses Ganardi, Louisa Seelbach, and Eric Nöth.