جست‌وجوی استاد و پژوهشگر

Frédéric Meunierمشاهده پروفایل

استاد

• Combinatorics
• Operations Research
• Optimization
• +۳ مورد دیگر

Frédéric Meunier is a Professor at École Nationale des Ponts et Chaussées and a researcher at CERMICS (Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique). He holds an associate professorship at École Polytechnique. His research focuses on combinatorics, operations research, and topological combinatorics. He has contributed extensively to areas such as graph coloring, fair division, and optimization problems in logistics and transportation. Education: PhD in Applied Mathematics (2006), École Nationale des Ponts et Chaussées, advised by András Sebő Habilitation in Applied Mathematics (2012) Engineer degree from École Polytechnique (2001), specializing in Theoretical Physics Master in Optimization, Games, and Modelling in Economics (2002) Research Interests: Combinatorial optimization, topological methods in graph theory, fair division problems, scheduling algorithms, and applications to transportation systems. His work bridges discrete mathematics and real-world optimization challenges. Teaching: Offers courses in optimization, discrete mathematics, and topological combinatorics at École Nationale des Ponts et Chaussées, École Polytechnique, and other institutions. Recent courses include 'Discrete Optimization' and 'Topological Combinatorics.' Labs/Teams: Member of the Optimization team at CERMICS and collaborates with the Combinatorics and Optimization group at Institut de Mathématiques de Jussieu-Paris Rive Gauche. He organizes conferences like the 'SPO Séminaire Parisien d'Optimisation' and workshops on topological combinatorics.

Marc-Antoine Weisserمشاهده پروفایل

پژوهشگر

• Network Optimization
• Algorithm Design
• Graph Theory
• +۳ مورد دیگر

Marc-Antoine Weisser is a researcher with a focus on network optimization, algorithm design, and graph theory. His work spans telecommunications, electrical networks, and computational complexity. He has contributed to studies on inter-domain network hierarchies, optical network optimization, and combinatorial problems such as Steiner trees and bin packing. Weisser's research often involves developing polynomial and approximation algorithms for real-world network challenges. Key research areas: Network topology analysis, algorithmic design for resource allocation, and optimization in electrical/optical networks His publications highlight contributions to congestion avoidance mechanisms, optical ring networks, and inter-domain routing architectures. Weisser collaborates frequently with institutions like the University of ... [university name missing in source text].

Ilias Zadikمشاهده پروفایل

استادیار

• High-Dimensional Statistics
• Machine Learning Theory
• Computational-Statistical Trade-offs
• +۵ مورد دیگر

Ilias Zadik is an Assistant Professor at Yale University's Department of Statistics and Data Science. His research focuses on computational-statistical trade-offs in modern machine learning, high-dimensional statistics, and probability theory. He has held postdoctoral positions at MIT (2021-2023) and NYU (2019-2021), and earned his PhD from MIT (2019), advised by David Gamarnik. He teaches courses like Stochastic Processes and has contributed to numerous conferences and workshops. His awards include MIT's Best Student Paper Honorable Mention (2017) and scholarships from Trinity College and the Onassis Foundation. Education: PhD in Operations Research, MIT (2019) MASt in Mathematics (Part III), University of Cambridge (2014) BA in Mathematics, University of Athens (2013) Internship at Microsoft Research New England (2017) Research Interests: Computational-statistical trade-offs, phase transitions in inference (e.g., All-or-Nothing phenomena), cryptographic methods in statistics, privacy-cost analysis, and algorithmic lower bounds. Awards: MIT Operations Research Best Student Paper Honorable Mention (2017) Trinity College Senior Scholarship (2014) Onassis Foundation Scholarship (2013-2014) SEEMOUS Gold Medal (2011), IMC First Prize (2011) Teaching & Service: Instructor for Yale's S&DS 351 (Stochastic Processes) and advanced courses on computational-statistical trade-offs. Co-organized the MaD+ seminar during the pandemic. Served on program committees for COLT, NeurIPS, and FOCS. Labs/Teams: Active in MIT's NSF/Simons Collaboration on Theoretical Foundations of Deep Learning and NYU's Math and Data group.

Georg Lohoمشاهده پروفایل

استاد

• Discrete Geometry
• Topological Combinatorics
• Optimization
• +۴ مورد دیگر

Georg Loho is a Professor at Freie Universität Berlin (FU Berlin), acting head of the Discrete Geometry and Topological Combinatorics Group. Previously, he held an assistant professorship at the University of Twente (on leave since 2023). He specializes in discrete geometry, optimization, and algebraic combinatorics, with notable contributions to tropical geometry and machine learning. His research integrates geometric and combinatorial methods with applications in optimization and data science. Education: PhD in Mathematics (2017) from TU Berlin Diploma in Mathematics (2012) from Universität Würzburg Research Interests: Focuses on tropical geometry, discrete optimization, and their applications in machine learning. Explores geometric structures like oriented matroids, polytopes, and their connections to neural networks and algorithm design. Advocates for sustainability in research and education. Teaching: Leads courses on discrete geometry, mathematics & sustainability, and optimization. Active in educational innovation, including free open-source course materials and the MatchTheNet educational game on polytopes. Grants & Collaborations: Participated in the HIM Trimester Program (Bonn, 2021), substitute professorships (Kassel, 2020–2021), and multiple ERC-funded projects. Collaborates with institutions like the London School of Economics (LSE) and EPFL. Labs/Teams: Coordinates the Discrete Geometry and Topological Combinatorics research group at FU Berlin, fostering interdisciplinary projects in geometry, combinatorics, and optimization.

Ryan Williamsمشاهده پروفایل

استاد

• Algorithm Design
• Computational Complexity
• Theoretical Computer Science
• +۴ مورد دیگر

Ryan Williams is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science. Previously, he held a faculty position at Stanford University from 2011 to 2016. He obtained his PhD in Computer Science from Carnegie Mellon University under Manuel Blum and completed his undergraduate studies at Cornell University. His research focuses on computational complexity theory, exploring the boundaries of efficient computation and connections between algorithm design and complexity lower bounds. He teaches advanced courses such as Automata, Computability, and Complexity Theory at MIT. Education: PhD in Computer Science, Carnegie Mellon University (Advisor: Manuel Blum) Bachelor's Degree in Computer Science, Cornell University Research Interests: His work addresses fundamental questions in theoretical computer science, including the P vs. PSPACE problem, circuit lower bounds, and the development of algorithms with provable efficiency. He investigates connections between algorithmic techniques and complexity-theoretic limitations, aiming to establish barriers to solving computational problems efficiently. Publications and Trends: Ryan Williams' recent work spans topics like space-bounded computation, probabilistic polynomial sparsity, circuit lower bounds, and algorithms for compression and graph problems. His research often bridges theoretical insights with practical algorithm design, emphasizing the interplay between computational models and their limitations. Advising and Grants: Current advisees include Rahul Ilango, Ce Jin, and Ted Pyne. He has mentored numerous PhD students who have contributed to areas like fine-grained complexity and circuit analysis. While specific grants are not detailed, his research aligns with foundational studies in theoretical computer science. Labs and Teams: Williams is affiliated with MIT CSAIL, where he collaborates on projects exploring computational complexity and algorithmic foundations.

Eric Rosenbergمشاهده پروفایل

استادیار

• Optimization
• Fractals
• Design and Analysis of Large Hierarchical Networks
• +۴ مورد دیگر

Eric Rosenberg is an Assistant Professor of Computing and Decision Sciences at Seton Hall University's Stillman School of Business. Previously, he held roles at Bell Labs, AT&T Labs, Rutgers University (visiting professor), and Georgian Court University. He specializes in optimization, fractals, and network design, with a focus on mathematical modeling of real-world systems. Education: B.A. in Mathematics, Oberlin College (1975) Ph.D. in Operations Research, Stanford University (1979) Research Interests: Dr. Rosenberg’s work spans optimization algorithms, fractal network analysis, and hierarchical routing systems. His recent studies explore applications in biological networks (e.g., leaf venation) and supply chain risk modeling. He has authored influential books on multicast routing and fractal dimensions in networks. Publications Trends: His articles often blend theoretical frameworks with practical network challenges, addressing topics like fractal geometry in complex systems, hierarchical network efficiency, and Bayesian risk analysis in supply chains. Awards: AT&T Labs President’s Excellence Award (2012) Distinguished Member of Technical Staff Award, AT&T Bell Labs (1987) Teaching & Intellectual Property: Teaches courses in data analytics and business statistics. Holds multiple patents on virtual network resource management and hierarchical routing optimizations.

Gregory Valiantمشاهده پروفایل

دانشیار

• Algorithms
• Machine Learning
• Statistics
• +۳ مورد دیگر

Gregory Valiant is an Associate Professor of Computer Science at Stanford University , specializing in Algorithms, Machine Learning, Statistics, and Information Theory. He holds a PhD from UC Berkeley and a BA in Mathematics from Harvard University. His research focuses on designing efficient algorithms for inferring information from limited data, addressing challenges in computation, memory, communication, and data quality. He advises multiple PhD students and collaborates with institutions like Microsoft Research New England. Educations: PhD, Computer Science, UC Berkeley (2012) BA, Mathematics, Harvard University (2006) Research Interests: Gregory’s work centers on the interplay between algorithms and statistical inference, particularly in high-dimensional settings. His lab explores topics like distribution learning, sample amplification, and adversarial robustness. Recent projects include developing algorithms for trace reconstruction, matrix completion, and transformer-based in-context learning. His methodologies often combine theoretical rigor with practical applications in machine learning and data science. Key Article Trends: His publications span theoretical foundations (e.g., sample amplification, convex optimization complexity) and applied machine learning (e.g., transformer capabilities, adversarial testing). Notable work includes contributions to distribution testing, statistical estimation under constraints, and algorithmic lower bounds. Advising: Current advisees include Annie Marsden, Steven Cao, and Chirag Pabbaraju. Former students have pursued roles at institutions like Harvard, Berkeley, and companies like Google, Waymo, and Facebook.

Bruce Hajekمشاهده پروفایل

استاد

• Communication networks
• Stochastic analysis
• Game theory
• +۷ مورد دیگر

Bruce Hajek is the Leonard C. and Mary Lou Hoeft Endowed Chair in Engineering and Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Coordinated Science Laboratory. He holds a PhD in Electrical Engineering from the University of California, Berkeley (1979). His research spans communication networks, stochastic processes, game theory, wireless systems, and data science. He has authored influential works, including the textbook Random Processes for Engineers (2015). Hajek's roles include Department Head of ECE (2019–2024) and membership in prestigious organizations like the National Academy of Engineering (since 1999) and IEEE. He has received accolades such as the ACM SIGMETRICS Achievement Award (2015), Guggenheim Fellowship (1992), and IEEE Fellow distinction (1989). His research interests emphasize stochastic analysis, network dynamics, and algorithmic game theory. Notable contributions include foundational work in random graph matching, blockchain protocols, and statistical inference. Teaching excellence awards span decades at UIUC, and his service includes leadership roles in the IEEE Information Theory Society. Hajek's interdisciplinary expertise bridges theory and applications, impacting fields from wireless communications to machine learning. His work on semidefinite programming for community detection and auction mechanisms exemplifies his innovative problem-solving approach.

Ayfer Ozgurمشاهده پروفایل

دانشیار

• Information Theory
• Wireless Communication
• Networks
• +۷ مورد دیگر

Ayfer Ozgur is an Associate Professor of Electrical Engineering at Stanford University and a Chambers Faculty Scholar in the School of Engineering. She holds a Ph.D. from EPFL (2009) and B.S./M.S. degrees from Middle East Technical University. Her research focuses on information theory, wireless communication, distributed estimation, and machine learning, with notable contributions to federated learning, privacy-aware systems, and energy-efficient networks. Education: Ph.D., EPFL (2009); M.S., Middle East Technical University (2005); B.S. in Electrical Engineering and Physics (2001). Research Interests: Information Theory and Geometry Wireless Networks and Communication Federated Learning and Privacy Energy Harvesting Systems Distributed Estimation and Learning Publications Trends: Ozgur's work emphasizes theoretical foundations of decentralized data science, privacy-utility trade-offs, and geometric approaches to information theory. Recent articles explore over-the-air statistical estimation, federated learning with secure aggregation, and optimal compression under privacy constraints. Awards: Recipient of the IEEE CTTC Early Achievement Award (2018), Okawa Foundation Award (2018), and NSF Career Award (2013). Advising & Grants: Supervises doctoral and master's students in distributed systems and machine learning. Current NSF projects include 'Foundations of Decentralized Data Science' and 'Geometric Network Information Theory.' Labs/Teams: Leads a research group with active members in theoretical and applied communication systems, collaborating with industry partners like Google and META.

Christos Papadimitriouمشاهده پروفایل

استاد

• Theoretical Computer Science
• Algorithms
• Computational Complexity
• +۸ مورد دیگر

Christos Papadimitriou is the Donovan Family Professor of Computer Science and Provost's Senior Faculty Teaching Scholar at Columbia University. He previously held the C. Lester Hogan Professorship at UC Berkeley (1996–2017) and taught at Harvard, MIT, Stanford, UC San Diego, and the National Technical University of Athens. His research bridges theory and practice, focusing on algorithms, complexity, computational biology, AI/ML, game theory, and neuroscience. He pioneered algorithmic game theory and explores brain-mind interfaces using neuronal assembly models. Education: BS in EE (Athens Polytechnic, 1972), PhD in EECS (Princeton, 1976) Awards: IEEE John von Neumann Medal (2016), Gödel Prize (2012), National Academy of Sciences/Engineering membership, 9 honorary doctorates Books: Elements of the Theory of Computation , Computational Complexity , Algorithms , and novels like Logicomix His work applies computational lenses to biology, economics, and language, emphasizing formal models for emergent cognition. Recent research includes neuronal assembly-based computation, fairness in ML, and game-theoretic dynamics.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

• Automata Theory
• Logic in Computer Science
• Infinite-State Systems
• +۷ مورد دیگر

Wojciech Czerwiński is an Associate Professor at the Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw. His research lies at the intersection of automata theory, logic, and computational complexity, with a focus on infinite-state systems such as vector addition systems (VASS) and Petri nets. His research interests include: Automata and logic Infinite-state systems Reachability and separability problems Complexity of computational models Formal verification and concurrency theory His recent work has significantly advanced the understanding of the complexity of reachability in VASS, proving it to be Ackermann-complete. This line of research has been published in top venues including FOCS, STOC, LICS, and CONCUR, where several papers received Best Paper Awards. His publications reflect a strong trend toward resolving long-standing open problems in decidability, complexity, and logical definability in infinite-state models. Scientific awards include: Best Paper Award, CONCUR 2022 Best Paper Award, STOC 2019 He leads an ERC-funded project and is actively recruiting PhD students and post-docs. He organizes the Friday Afternoon Seminar and co-organized the Infinite Automata Workshop (2024). He has also contributed to public understanding of science as an editor and author for the journal Delta from 2013 to 2022. His work involves collaboration with leading researchers such as Sławomir Lasota, Jérôme Leroux, Georg Zetzsche, and others. He has made foundational contributions to problems like bisimilarity, language equivalence, and regular separability in various automata models.

• Approximation Algorithms
• Probabilistically Checkable Proofs
• Hardness of Approximation
• +۳ مورد دیگر

Amey Bhangale is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, Riverside. Prior to this, he held positions as a post-doctoral fellow at the Weizmann Institute of Science under Irit Dinur and a research fellowship at the Simons Institute. His research focuses on Approximation Algorithms , Probabilistically Checkable Proofs , Hardness of Approximation , and Analysis of Boolean Functions . Research Trends His recent work explores inapproximability bounds for constraint satisfaction problems, parallel repetition theorems, and additive combinatorics in finite fields. Notable collaborations include Subhash Khot, Dor Minzer, and Yang P. Liu. Teaching CS219: Advanced Algorithms (2025) CS141: Intermediate Data Structures and Algorithms (2024) CS218: Design and Analysis of Algorithms (2023) CS215: Theory of Computations (2021-2023)

Brian Lavalleeمشاهده پروفایل

مدرس

• Computer Science
• Algorithms
• Graph Theory
• +۴ مورد دیگر

Brian Lavallee is a Lecturer in the Department of Computer Science at the University of Vermont , joining in 2024. His research bridges structural graph theory , parameterized complexity , and approximation algorithms to solve real-world computational problems. At UVM, he teaches foundational courses in programming, automata theory, and computational complexity. Education : Ph.D. in Computing (University of Utah, 2023), B.S. in Computer Science (Duke University, 2017) Brian's research focuses on developing efficient algorithms for NP-hard problems through structural graph analysis and complexity-based approaches. His work on structural rounding and vertex cover approximation has advanced scalable solutions for graphs near tractable classes. Recent publications highlight his expertise in hypergraph clustering, gerrymandering complexity, and generalized coloring hardness. In teaching, he emphasizes core computer science principles through courses like CS 1210 - Computer Programming I and CS 2250 - Computability and Complexity . His office is located in Innovation Hall, Room E322, at UVM's Burlington campus.

Gregor Böhlمشاهده پروفایل

پژوهشگر

• Dynamic Macroeconomics
• Macroeconometrics
• Econometrics
• +۱۲ مورد دیگر

Dr. Gregor Böhl is a Principal Investigator at the Institute for Macroeconomics and Econometrics within the Department of Economics at the University of Bonn. His research is fully funded by the German Research Foundation (DFG) under project 441540692, focusing on dynamic macroeconomics, econometrics, and public economics. He is also affiliated with the CRC TR 224 EPoS and contributes to the OSE initiative for teaching and research infrastructure. His research interests lie at the intersection of Dynamic Macroeconomics , Heterogeneous Agent Models (HANK) , DSGE modeling , and computational economics . He investigates how household heterogeneity shapes macroeconomic outcomes, particularly in the context of inequality , climate policy , monetary financing , and financial frictions . His methodological expertise includes nonlinear solution techniques, Bayesian estimation under occasionally binding constraints, and high-performance computational tools. The recent trend in his publications reveals a strong focus on the empirical and structural analysis of monetary policy , especially at the zero lower bound, the macroeconomic implications of climate policies , and the development of robust computational methods for solving complex economic models. His work combines theoretical modeling with advanced numerical techniques and open-source software development. 2017 Student Prize of the Society for Computational Economics Dr. Böhl has secured competitive research funding from the DFG and the OSE initiative. He actively mentors researchers and collaborates with leading economists such as Cars Hommes, Felix Strobel, and Gavin Goy. His academic contributions extend beyond publications to the development of widely used open-source software packages like pydsge , econpizza , and dime_sampler , which support estimation, simulation, and inference in macroeconomic models. He previously held a postdoctoral position at IMFS, Goethe University Frankfurt, with collaborations at the Hoover Institution, Stanford University. He leads a research group focused on computational macroeconomics and maintains a strong commitment to open science, reproducibility, and the use of free and open-source software (FOSS) in economic research. His team develops and maintains several GitHub repositories that provide tools for nonlinear filtering, Bayesian inference, and HANK model solutions.