جست‌وجوی استاد و پژوهشگر

• Software Engineering
• Requirements Engineering
• Natural Language Processing
• +۵ مورد دیگر

Liping Zhao is a Senior Lecturer in Information Management. His expertise spans Software Engineering (SE), Requirements Engineering (RE), Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL). He holds a PhD and MPhil from the University of Leeds. He is a founding member of the Asian Pacific Conference on Pattern Languages of Programs and led the UK’s first multi-disciplinary service science network (2007–2010). He has received IBM Faculty Awards (2004, 2005, 2008) and a Best Tool Demo Award for TRAM (Transforming Textual Requirements into Analysis Models). He serves as an Associate Editor for Springer’s Requirements Engineering and Wiley’s Expert Systems , and guest-edited special issues on topics like Knowledge Intensive Service Systems and Problem Orientation. His research focuses on applying NLP/ML to software engineering tasks, blockchain systems, and service-oriented computing. Key contributions include a theory of pattern-based software design and datasets like FN-REQ (frame-annotated requirements) and TRAM. His work addresses challenges in requirements classification, blockchain architectures, and business problem validation through ML. Recent projects include process mining for blockchain applications, hierarchical classification of software requirements, and developing taxonomies for blockchain systems. His datasets and tools are publicly available via Zenodo and Mendeley, supporting reproducible research and industry collaboration.

• Computer Science
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Hong Liu is a Professor at Shandong University's School of Computer Science and Technology, with an extensive publication record spanning multiple disciplines within computer science and engineering. His research spans computer vision, machine learning, medical imaging, robotics, and signal processing, with over 500 publications listed in the dblp database from 1993 to 2025. Dr. Liu's research interests focus on the intersection of artificial intelligence and practical applications across various domains. His recent work demonstrates expertise in deep learning architectures, medical image analysis, robotics navigation, and computer vision systems. He has made significant contributions to medical imaging techniques, particularly in segmentation methods using quaternion mathematics and self-attention mechanisms, as well as applications in liver disease diagnosis and pediatric fracture detection. The publication trends show increasing activity in recent years, with substantial contributions in 2023-2025 across IEEE journals and conferences. His work frequently appears in high-impact venues including IEEE Transactions on Medical Imaging, IEEE Access, and Pattern Recognition, demonstrating both theoretical rigor and practical applications. His research bridges theoretical computer science with real-world engineering and medical challenges, particularly in China's healthcare and technology sectors. Dr. Liu has collaborated extensively with researchers across Chinese institutions including Shandong University, Harbin Institute of Technology, and Huazhong University of Science and Technology, as well as international partners. His work shows strong interdisciplinary connections between computer science, electrical engineering, and medical applications.

Lindsey J. Heagyمشاهده پروفایل

استادیار

• Geophysics
• Data Science
• Inverse Theory
• +۴ مورد دیگر

Lindsey J. Heagy is an Assistant Professor in the Department of Earth, Ocean, and Atmospheric Sciences at the University of British Columbia. Her research focuses on geophysics, data science, and inverse theory, with applications in resource exploration, groundwater, and environmental studies. She leads a research group developing machine learning and inversion methods for geophysical data analysis. Key affiliations: University of British Columbia (Department of Earth, Ocean, and Atmospheric Sciences) Her work emphasizes open science, contributing to projects like SimPEG (an open-source Python package for geophysical simulations) and GeoSci.xyz (collaborative educational resources for geophysics). She advocates for reproducible workflows and open-source software in the geosciences. Research interests include electromagnetic methods, inverse problem solutions, and interdisciplinary applications of data science. Notable contributions include advancements in 3D electromagnetic modeling, UXO detection via machine learning, and open-source tools for airborne and ground-based geophysical data analysis. Publications span peer-reviewed journals like Geophysical Journal International and Exploration Geophysics , focusing on numerical methods, inversion techniques, and open-source software development. Her work bridges computational geophysics with real-world challenges in environmental monitoring and resource management. She collaborates on projects like SimPEG and GeoSci.xyz, fostering community-driven advancements in geophysical education and research. Her efforts in capacity building include geophysical training in regions like Myanmar to improve water security.

Marijn Heuleمشاهده پروفایل

دانشیار

• Satisfiability (SAT) Solving
• Formal Verification
• Combinatorial Mathematics
• +۵ مورد دیگر

Marijn Heule is an Associate Professor in the Computer Science Department at Carnegie Mellon University. He received his PhD from Delft University of Technology in the Netherlands. His academic career has been focused on advancing the theory and practice of satisfiability (SAT) solving and its applications. Dr. Heule's research primarily centers on solving hard combinatorial problems in formal verification, number theory, and extremal combinatorics. His work has made significant contributions to both theoretical foundations and practical implementations of SAT solvers. He has developed award-winning SAT solvers, and his preprocessing techniques are incorporated into state-of-the-art SAT solvers used worldwide. His current research focuses on two major challenges: exploiting high-performance computing for SAT solving and validating results from SAT solvers and related tools. Dr. Heule pioneered the "cube-and-conquer" paradigm, a novel parallel SAT solving approach that enables linear time speedups on many hard problems. His first publication on this topic won the best paper award at HVC 2011. He also designed a new proof format with a corresponding fast proof checker for SAT and QBF solvers. This proof logging format has been mandatory for SAT Competitions since 2013, significantly increasing confidence in tool correctness. His work on the Boolean Pythagorean Triples problem, which produced a 200 TB proof that was validated, demonstrated that proof logging and verification is feasible even for the most complex problems. Dr. Heule is one of the editors of the 900+ page "Handbook of Satisfiability," which has become a standard reference in the SAT community. He serves as an Associate Editor of the Journal on Satisfiability, Boolean Modeling and Computation and previously co-chaired the SAT 2015 conference in Austin. Best paper award at HVC 2011 for cube-and-conquer publication Multiple best paper awards/nominations at major conferences including IJCAR, SAT, TACAS Work featured in New York Times, Quanta Magazine, Nature, and other major media outlets Dr. Heule has advised several PhD students including Emre Yolcu, Joseph Reeves, Bernardo Subercaseaux, Cayden Codel, James Gallicchio, and Amar Shah. He has developed important software tools including DRAT-trim (a proof checking utility), QRAT-trim, and implementations of the cube-and-conquer paradigm that have become standard in the SAT solving community.

Michael S. Floaterمشاهده پروفایل

استاد

• Approximation Theory
• Numerical Analysis
• Geometric Modelling
• +۲ مورد دیگر

Professor Michael S. Floater is affiliated with the Department of Mathematics at the University of Oslo , specializing in Differential Equations and Computational Mathematics . His research spans approximation theory, numerical analysis, and geometric modeling, with a focus on polynomial and spline-based data approximation, recursive subdivision techniques, and optimal function spaces. Fields of Interest: Approximation Theory, Numerical Analysis, Geometric Modelling, Computational Mathematics, Differential Equations Recent publications address B-spline properties, supersmoothness in Alfeld splits, and advanced polynomial interpolation methods. His work often intersects with applications in computer-aided geometric design (CAGD) and numerical solutions to partial differential equations. He serves on the editorial boards of Computer Aided Geometric Design (CAGD) and BIT Numerical Mathematics , and has supervised technical reports and collaborative projects in computational methods.

Austin J. Strommeمشاهده پروفایل

استادیار

• Optimal Transport
• High-Dimensional Statistics
• Geometry
• +۲ مورد دیگر

Austin J. Stromme is an Assistant Professor in the Statistics Department at ENSAE Paris, where he serves as a permanent CREST member. His research bridges optimization, geometry, and probability with a focus on statistical aspects of optimal transport, positioning him at the forefront of theoretical machine learning and high-dimensional statistics. Stromme earned his PhD in Electrical Engineering and Computer Science from MIT under Guy Bresler and Philippe Rigollet. His academic journey reflects deep engagement with mathematical foundations of data science, emphasizing rigorous theoretical frameworks for complex statistical problems. His primary research spans entropic optimal transport, high-dimensional inference, and geometric methods in machine learning. Stromme investigates fundamental questions about statistical efficiency, computational complexity, and probabilistic structures in large-scale data analysis. His work reveals critical connections between functional inequalities, sampling algorithms, and manifold geometry, with applications spanning trajectory reconstruction, covariance estimation, and non-convex optimization. Recent publications (2023-2025) demonstrate accelerating impact in theoretical statistics, particularly in establishing sample complexity bounds and convergence rates for entropic optimal transport. This body of work shows increasing sophistication in handling high-dimensional settings while maintaining computational feasibility, reflecting broader trends toward unifying geometric intuition with statistical rigor in modern machine learning. Stromme received the Best Paper Award at the 2024 International Conference on Soft Methods in Probability and Statistics for his breakthrough on intrinsic dimension scaling in entropic optimal transport. This recognition highlights his contributions to advancing theoretical understanding of dimensionality challenges in statistical learning. As an emerging faculty leader, Stromme co-organizes an influential online seminar series bridging statistics and geometry, fostering interdisciplinary collaboration. While no current PhD students are listed, his active seminar leadership and recent faculty appointment indicate growing mentorship responsibilities. His research is supported through CREST's institutional framework, which facilitates collaborations across mathematics, economics, and data science. Stromme operates within CREST's vibrant research ecosystem at ENSAE Paris, contributing to a community that integrates advanced statistical theory with real-world economic applications. His work exemplifies the center's commitment to methodological innovation in quantitative social sciences and data-driven decision making.

John Rhodesمشاهده پروفایل

استاد

• Mathematical Biology
• Phylogenetics
• Algebraic Geometry
• +۵ مورد دیگر

John Rhodes is a Professor of Mathematics at the University of Alaska, with a PhD from Massachusetts Institute of Technology (1986). His research spans Mathematical Biology, focusing on Phylogenetics, where he develops methods to infer evolutionary relationships from biological sequences using probability, discrete mathematics, and computational algebra tools. PhD in Mathematics, Massachusetts Institute of Technology, 1986 His work addresses complex problems such as phylogenetic networks, horizontal gene transfer, and coalescent modeling. Recent publications highlight quartet methods, species tree inference, and statistical frameworks for genomic data analysis. Advising contributions include mentoring UAF graduate students and postdocs like H.D. Banos, J.D. Mitchell, and C. Long, as noted in co-authored studies. For correspondence, Dr. Rhodes can be reached at j.rhodes@alaska.edu.

Sinho Chewiمشاهده پروفایل

استادیار

• Optimal Transport
• Log-concave Sampling
• Machine Learning Theory
• +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.

Amin Coja-Oghlanمشاهده پروفایل

استاد

• Probabilistic Combinatorics
• Mathematical Physics
• Disordered Systems
• +۲ مورد دیگر

Amin Coja-Oghlan is Professor of Efficient Algorithms and Complexity Theory at TU Dortmund University's Department of Computer Science. His research integrates probabilistic combinatorics, information theory, and statistical physics to solve fundamental problems in theoretical computer science. Education includes a doctorate in Mathematics (University of Hamburg, 2002) and habilitation in Computer Science (Humboldt University Berlin, 2005). Research advances understanding of phase transitions in constraint satisfaction problems, optimization landscapes, and random structures. Recent publications analyze SAT thresholds, group testing, and sparse matrix properties. Academic appointments include professorships at Goethe University Frankfurt and lectureships at Edinburgh and Warwick. Research contributions bridge discrete mathematics with computational complexity.

• Algebraic Combinatorics
• Enumerative Combinatorics
• Polynomials with only real roots
• +۳ مورد دیگر

Mirkó Visontai is a postdoctoral researcher in the Department of Mathematics at the Royal Institute of Technology (KTH) in Stockholm, Sweden. His mentor is Petter Brändén. His research focuses on algebraic and enumerative combinatorics, particularly polynomials with only real roots, and their applications in discrete mathematics. Research interests include Eulerian polynomials, multivariate polynomial stability, combinatorial enumeration, and the study of real-rootedness in algebraic structures. His work bridges algebraic methods with combinatorial problems, often involving symmetric functions, permutation statistics, and extremal set theory. Key contributions include studies on W-Eulerian polynomials, type D Eulerian polynomials, and monotone column permanent conjectures. His publications span journals like the Transactions of the American Mathematical Society, Journal of Combinatorial Theory Series A, and Discrete Mathematics & Theoretical Computer Science.

Marina Halacمشاهده پروفایل

استاد

• Economic Theory
• Fiscal Policy
• Monetary Policy
• +۴ مورد دیگر

Marina Halac is the Stanley B. Resor Professor of Economics at Yale University, affiliated with the Cowles Foundation for Research in Economics (Director 2020–2023). She holds editorial roles at Econometrica and Theoretical Economics . Her research focuses on game theory, fiscal/monetary policy, and mechanism design, addressing topics like fiscal responsibility regimes, central bank incentives, and organizational monitoring. She earned her Ph.D. in Economics from UC Berkeley (2009). Education: Ph.D. in Economics, UC Berkeley (2009) Her work explores strategic interactions in fiscal policy, optimal contractual frameworks for innovation and experimentation, and the dynamics of managerial attention. Key contributions include models of fiscal crisis transitions, team incentive structures, and central bank accountability under political pressures. Awards include the Elaine Bennett Research Prize (AEA) and fellowships from the Econometric Society and阿根廷国家经济科学院. Her research integrates political economy, mechanism design, and empirical policy analysis to address macroeconomic governance challenges. Labs/Teams: Cowles Foundation, Yale Economics Department research groups

Luc Devroyeمشاهده پروفایل

استاد

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

Luc Devroye is a Professor in the School of Computer Science at McGill University, Montreal. He joined McGill in 1977 after completing his PhD at the University of Texas in 1976. His research spans probabilistic analysis of algorithms, random structures, and typography. Notable contributions include work on random trees, data structures, and simulation methods. Affiliations : McGill University since 1977, School of Computer Science. Education : PhD in Computer Science from the University of Texas (1976), studies at the University of Leuven and Osaka University. Research Interests : Algorithms, probability theory, random graphs, data structures, and typography. Focus on average-case analysis, random tree structures, and simulation techniques. His work emphasizes theoretical foundations with practical applications, including contributions to random number generation and algorithmic efficiency. He advises students in theoretical computer science and maintains an active research agenda with over 500 publications. Teaching : Courses like COMP 252 (Algorithms), COMP 690 (Probabilistic Analysis of Algorithms). Students : Mentored numerous PhD and MSc students, many now in academia and industry.

Michal Walickiمشاهده پروفایل

دانشیار

• Mathematical Logic
• Philosophical Logic
• Graph Theory
• +۱۳ مورد دیگر

Michal Walicki is an Associate Professor in the Department of Informatics at the University of Bergen, Norway. His research lies at the intersection of mathematical logic, theoretical computer science, and philosophy of logic. He specializes in graph-theoretic methods for analyzing logical paradoxes, paraconsistency, and the algebraic semantics of nondeterminism. His primary research interests include self-reference, semantic paradoxes, digraph kernels, paraconsistent logic, multialgebras, and bounded reasoning in epistemic logic. He has developed graph-based semantics for logical theories, where paradoxes correspond to graph cycles and consistent theories relate to digraph kernels. His recent work focuses on the Poison Game for infinite digraphs and a Logic of Sentential Operators (LSO) that handles unrestricted sentential quantification without explosion. His publications span journals such as the Journal of Philosophical Logic, Journal of Symbolic Logic, Synthese, and Discrete Mathematics. He has authored the textbook Introduction to Mathematical Logic and maintains comprehensive lecture notes for courses like INF-227. His work shows a strong trend in using combinatorial and algebraic structures to model logical and computational phenomena, especially in handling inconsistency and self-reference. Strongly complete axiomatizations of finite syntactic epistemic states Complete calculus for multialgebraic semantics of nondeterminism Graph Normal Form for first-order logic Paraconsistent resolution with local kernels Applications of digraph kernels to SAT solving Michal Walicki has supervised several PhD students, including Sjur Dyrkolbotn, Thomas Ågotnes, and Yngve Lamo. He has also received research funding for projects in logic and algebra, though specific grants are not detailed. He leads a research group focused on logical foundations of computation and collaborates with colleagues in logic, computer science, and philosophy. His work continues to explore the deep connections between logic, graphs, and algebraic structures.

Leonid Gurvitsمشاهده پروفایل

استاد

• Mathematics
• Computer Science
• Quantum Computing
• +۵ مورد دیگر

Leonid Gurvits is a Professor of Computer Science at The City College of New York, part of the City University of New York (CUNY) system. His career spans institutions like Los Alamos National Laboratory (lanl.gov) and collaborations with leading researchers in mathematics and computer science, including Ankit Garg and Rafael Oliveira. He has held visiting scientist roles at programs such as the Simons Institute's Geometry of Polynomials (Spring 2019) and Bridging Continuous and Discrete Optimization (Fall 2017). Fields of Interest: Quantum computing, hyperbolic polynomials, operator scaling, and computational complexity. Key Contributions: Pioneering deterministic polynomial-time algorithms, solving problems in mixed discriminants, and advancing understanding of quantum entanglement and matrix stability. His work is deeply rooted in the interplay between mathematics and theoretical computer science, with applications to optimization, control theory, and quantum information. Notably, he resolved conjectures related to the permanent of matrices and developed capacity-preserving operators for combinatorial applications. Publications include groundbreaking articles on the complexity of quantum entanglement (2003), stability of switched linear systems (2007), and operator scaling for Brascamp-Lieb inequalities (2017). These works have been cited extensively, reflecting their impact on theoretical computer science and mathematical optimization.

• Algorithms
• Counting and Sampling
• Random Structures
• +۷ مورد دیگر

Mary Cryan is a Senior Lecturer in the School of Informatics at the University of Edinburgh, where she has been a faculty member since July 2003. She is affiliated with the Laboratory for Foundations of Computer Science (LFCS) and conducts research in theoretical computer science, with a focus on algorithms, counting and sampling, random structures, learning theory, and pseudorandom generators. Her educational background includes a joint undergraduate degree in Computer Science and Mathematics from University College Dublin (1993), an MSc by research at UCD (1995), and a PhD from the University of Warwick (awarded 2000) under the supervision of Leslie Ann Goldberg. She held postdoctoral positions at BRICS, University of Aarhus (1999–2001), and the University of Leeds (2001–2003) working with Martin Dyer. PhD, University of Warwick (2000) MSc, University College Dublin (1995) BSc (Joint Hons), Computer Science and Mathematics, University College Dublin (1993) Her research interests lie in the design and analysis of algorithms, particularly randomized algorithms for counting and sampling combinatorial structures. She has made significant contributions to the understanding of Markov chains for sampling matchings, contingency tables, Euler tours, and phylogenetic trees. Her work often bridges theoretical computer science with probability and discrete mathematics. The trend in her publications shows a sustained focus on fundamental algorithmic problems involving random structures and probabilistic methods. Her work spans exact and approximate counting, mixing times of Markov chains, and learning models in phylogenetics. Recent work includes analyzing log-Sobolev inequalities for log-concave distributions and exact counting on bounded-treewidth graphs. She has supervised several PhD students, including Veselin Blagoev ("Counting and Sampling Acyclic Orientations", 2018), Páidí Creed ("Counting and Sampling problems on Eulerian graphs", 2010), and James Matthews (co-supervised, "Markov chains for sampling matchings", 2008). She has also been active in academic service, serving as Deputy Director of the Informatics Graduate School since August 2020. Her research has been published in top-tier venues such as FOCS, STOC, SIAM Journal on Computing, and Journal of the ACM. She regularly lectures courses such as "Informatics 2 - Introduction to Algorithms and Data Structures".