جست‌وجوی استاد و پژوهشگر

• Algebra
• Commutative Algebra
• Coding Theory
• +۴ مورد دیگر

Emanuela De Negri is a Full Professor at the University of Genoa's Department of Mathematics (DIMA), serving on both the Academic Senate and Department Board. Her teaching responsibilities span undergraduate and graduate programs including Computer Science (Algebra), Mathematics (Linear Algebra and Analytic Geometry, Coding Theory), and Biomedical Engineering (Geometry). Her research centers on Commutative Algebra and Algebraic Geometry, with emphasis on structural properties of ideals and varieties. Key areas include initial ideals, radical ideals, semiregular sequences, and secant varieties, often exploring combinatorial and computational aspects with applications in coding theory and cryptography. Recent work reveals consistent focus on multigraded structures and characteristic-free methods. Publications from 2020-2022 demonstrate collaborative research patterns, particularly with Conca and Gorla, addressing foundational questions in ideal theory and tensor decompositions. The work shows strong interdisciplinary connections between abstract algebra and information theory, notably in encryption and error-correcting codes.

Tom McCoyمشاهده پروفایل

استادیار

• Computational Linguistics
• Cognitive Science
• Artificial Intelligence
• +۵ مورد دیگر

Tom McCoy is an Assistant Professor in the Department of Linguistics at Yale University, with additional affiliations in the Department of Computer Science and the Wu Tsai Institute. His interdisciplinary research bridges computational linguistics, cognitive science, and artificial intelligence to understand the computational principles underlying human language. Dr. McCoy completed his PhD in Cognitive Science at Johns Hopkins University in 2021, where his graduate training emphasized interdisciplinary approaches to studying language acquisition from multiple theoretical and methodological perspectives. McCoy's research centers on two primary strands: language learning mechanisms investigating how humans acquire language from minimal data and how these capabilities can be replicated in machines, and linguistic representations exploring what computational architectures can effectively model language structure. His work frequently employs neural network language models while maintaining strong connections to linguistic theory and cognitive science principles. He has published influential work on hierarchical inductive biases in sequence-to-sequence networks and generalization capabilities of neural networks with respect to center embeddings. His recent publications reveal a research trajectory increasingly focused on the intersection of neural networks and cognitive science, examining inductive biases, Bayesian approaches to language learning, and the symbolic-subsymbolic debate in AI. His work appears in top venues including Nature Communications, Proceedings of the National Academy of Sciences, and leading AI conferences. McCoy actively mentors the next generation of researchers, currently considering PhD applicants for Fall 2026 and postdoctoral researchers for Fall 2026. He maintains a selective research group focused on computational linguistics and cognitive AI, prioritizing quality mentorship and methodological rigor. Though specific lab names aren't publicized, McCoy's research operates at the intersection of multiple departments at Yale, suggesting a collaborative environment where linguists, computer scientists, and cognitive scientists work together to tackle fundamental questions about language processing in both humans and machines.

• Computational Complexity Theory
• Machine Learning
• Parallel Computation
• +۵ مورد دیگر

Leslie Valiant serves as the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University's School of Engineering and Applied Sciences, where he has been faculty since 1982. Previously, he held positions at Carnegie Mellon University, Leeds University, and the University of Edinburgh. His academic journey began with education at King's College Cambridge, Imperial College London, and Warwick University, where he earned his PhD in computer science in 1974. Valiant's research spans theoretical computer science with primary focus areas including computational complexity theory, machine learning foundations, parallel computation systems, computational neuroscience, and evolutionary computation. His work bridges artificial and natural computational phenomena, addressing fundamental limitations in both engineered systems and biological processes. Key contributions include the development of the PAC (Probably Approximately Correct) learning model, holographic algorithms, robust logics for reconciling reasoning and learning, and theoretical frameworks for understanding cortical computation and evolvability. His publication record demonstrates sustained impact across decades, with recent work focusing on cortical computation primitives, multi-core algorithm design, and evolutionary dynamics with drifting targets. These publications reveal a consistent trajectory toward understanding computational principles in both artificial systems and biological cognition. Nevanlinna Prize (1986) Knuth Award (1997) EATCS Award (2008) A.M. Turing Award (2010) Fellow of the Royal Society Member of the National Academy of Sciences Valiant's research program integrates theoretical rigor with profound questions about natural computation, maintaining active engagement with both computer systems design and fundamental neuroscience questions. His work continues to influence multiple disciplines through formal frameworks that address computational limitations in learning, evolution, and neural processing.

• Information Theory
• Network Security
• Coding Theory
• +۷ مورد دیگر

Venkat Anantharam is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley . His research spans Information Theory , Network Security , Coding Theory , and Stochastic Processes , with a focus on theoretical foundations and applications in communication systems, game theory, and data compression. He has supervised numerous PhD and Master’s students , including Soham Phade, Payam Delgosha, and Sudeep Kamath, and hosted postdoctoral fellows such as Lei Yu and Charles Bordenave. His recent publications address advanced topics like hypercontractivity in Boolean functions, universal compression of graphical data, and game-theoretic models for security. Articles from 2019-2021 highlight work on entropy power inequalities, error bounds for Markov chains, and distributed compression techniques. Venkat's research often bridges theoretical insights with practical applications, including LDPC decoders, network coding, and risk-sensitive control.

Xavier Goaocمشاهده پروفایل

استاد

• Computational Geometry
• Discrete Mathematics
• Combinatorial Convexity
• +۵ مورد دیگر

Xavier Goaoc is a Professor of Computer Science at Université de Lorraine, affiliated with the Department of Computer Science & Engineering at École des Mines de Nancy and the Gamble research team (joint between LORIA and INRIA). His research focuses on algorithms and discrete mathematics, particularly discrete and computational geometry, including convex hulls, intersection patterns, topological generalizations, and geometric transversal theory. University: Université de Lorraine School: School of Engineering Department: Department of Computer Science & Engineering Research Team: Gamble (LORIA/INRIA) Emails: xavier.goaoc@loria.fr , xavier.goaoc@univ-lorraine.fr His research spans computational geometry, combinatorial convexity, geometric transversal theory, and random geometric structures. Key topics include homological minors, order types of point sets, and geometric optimization. His 15 most recent publications highlight advancements in computational geometry algorithms, structural complexity, and topological constraints. Notably, his work has received Best Paper Awards at SoCG 2020, 2018, 2016, and 2012. Administrative roles include heading the computer science & engineering department at Mines Nancy and co-chairing the computer science department of the IAEM doctoral school. He is also a member of the Université de Lorraine's ‘pôle AM2I’ council. Teaching activities encompass courses in algorithms, computer architecture, blockchains, and geometric models for vision, with publications and grants reflecting his interdisciplinary impact in computer science and mathematics.

• Algorithms
• Combinatorics
• Complexity Theory
• +۳ مورد دیگر

Sundar Vishwanathan is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay (IIT Bombay), where he has established himself as a leading researcher in theoretical computer science. His academic career spans over three decades with consistent contributions to algorithms, combinatorics, and complexity theory. His research interests form a cohesive body of work focused on theoretical foundations of computing: Algorithms (particularly approximation algorithms, online algorithms, and randomized algorithms) Combinatorics and extremal set theory Complexity theory and circuit lower bounds Graph theory and graph algorithms Combinatorial optimization problems Vishwanathan's publication record demonstrates remarkable consistency and depth, with publications spanning from 1990 to 2024. His recent work focuses on graph algorithms (particularly maximum matching problems), approximation algorithms for combinatorial optimization, and circuit complexity. He has developed innovative techniques using linear algebra, combinatorial methods, and probabilistic analysis to solve challenging theoretical problems. His approach often bridges theoretical insights with practical algorithmic considerations. His collaborative work spans multiple research groups, with frequent co-authorships with researchers such as Ashish Chiplunkar, Sumedh Tirodkar, and Sreyash Kenkre. His research has evolved from foundational work in online graph coloring in the 1990s to more specialized topics in combinatorics and recently to circuit lower bounds, showing both depth in core areas and adaptability to evolving research landscapes.

• Combinatorial Optimization
• Quadratic Programming
• Binary Optimization
• +۵ مورد دیگر

Cédric Elloumi is a Professor at the CEDRIC Laboratory within Conservatoire National des Arts et Métiers (CNAM), specializing in combinatorial optimization and mathematical programming. With a continuous publication record since 1992, he has established himself as a leading researcher in quadratic programming, binary optimization, and facility location problems. His research interests focus on developing exact and approximate methods for discrete optimization problems, particularly through convex reformulation techniques. Elloumi has made significant contributions to the p-center and p-median problems, quadratic assignment problems, and more recently, quantum-inspired optimization methods. His work bridges theoretical advancements with practical applications in network design, energy systems, and telecommunications. Analysis of his recent publications (2022-2025) reveals a continued focus on facility location problems, with increasing attention to robust optimization under uncertainty and emerging applications in quantum computing. His research demonstrates consistent methodological innovation, particularly in reformulation techniques that transform difficult non-convex problems into tractable forms. Throughout his career, Elloumi has maintained extensive collaborations with researchers including Billionnet, Lambert, Alès, and Plateau, resulting in numerous publications in top-tier optimization journals such as Journal of Global Optimization, Computers and Operations Research, and Mathematical Programming.

Michael Margaliotمشاهده پروفایل

استاد

• Dynamical Systems
• Control Theory
• Systems Biology
• +۳ مورد دیگر

Michael Margaliot is a Professor of Electrical Engineering and incumbent of the Systems and Control Chair at Tel Aviv University's School of Electrical and Computer Engineering. His research focuses on dynamical systems, control theory, and systems biology, with specialized interests in stability analysis of switched systems, Boolean networks, compound matrix applications, and ribosome flow modeling. His work bridges theoretical control concepts with biological applications, particularly in mRNA translation dynamics. Recent publications demonstrate consistent focus on: Extensions of contraction theory (k-contraction) Cooperative dynamical systems Matrix compound methodologies Ribosome flow modeling Networked control systems He maintains active collaborations with researchers globally and will teach a specialized course on The Ribosome Flow Model in 2025.

• Partial Differential Equations
• Dynamic Systems
• Applied Analysis
• +۳ مورد دیگر

Stefanopoulos Vangelis is a Professor at the University of Patras, affiliated with the Division of Applications and Foundations of Computer Science. His research spans mathematics and computer science, focusing on theoretical and applied problems. University: University of Patras Department: Division of Applications and Foundations of Computer Science Academic Rank: Professor Research Interests: His work centers on Partial Differential Equations, Dynamic Systems, and Applied Analysis, with applications in mathematical physics, algorithmic modeling, and computational theory. Key areas include eigenvalue problems, universal series, and stability analysis. Email: vstefan@ceid.upatras.gr Office Hours: Tuesday (9:00-11:00, 10:00-11:00 when no meetings), Friday (12:00-14:00) or by appointment.

Lakhdar Saisمشاهده پروفایل

استاد

• Artificial Intelligence
• Propositional satisfiability
• Quantified boolean formula
• +۴ مورد دیگر

Lakhdar Sais is a Professor of Computer Science at the Centre de Recherche en Informatique de Lens (CRIL), CNRS UMR 8188, at Université d'Artois, Faculty of Jean Perrin Sciences in Lens, France. His research focuses on search and representation problems in Artificial Intelligence, including propositional satisfiability, quantified boolean formulas, constraint programming, knowledge representation and reasoning, data mining, and AI applications in Social and Human Sciences. He has supervised numerous PhD students throughout his career, with recent students including David ING (2021-present) working on migration data knowledge extraction, and previously Ikram NEKKACHE (2021), Sofiane TOUATI (2021), and Kahina BOUCHAMA (2020). His research has been recognized with multiple awards including best paper awards at SAT'11 and ICTAI'2009, and first place in the International SAT 2009 competition. His current research projects include the ANR project HYCI (2023-2026) on Hyper-places, Crises, Migrations and Inequalities, Project ERA (2022-2025) on producing new knowledge in juvenile justice and mental health, and ANR project POSTCRYPTUM (2021-2023) on algebraic cryptanalysis for post-quantum cryptography. He has also edited the Handbook of Parallel Constraint Reasoning (Springer, 2018). Scientific Awards: Best paper award at SAT'11 for 'On freezing and reactivating learnt clauses' Best paper award at ICTAI'2009 for 'Learning for Subsumption' ManySAT - First rank at International SAT 2009 competition (Parallel Track) LySAT - Two bronze medals at International SAT 2009 competition (Sequential Track) ManySAT - First rank at SAT Race 2008 competition Professor Sais has taught numerous courses including Artificial Intelligence, Constraint Programming, Knowledge Representation and Reasoning, Expert Systems, Complexity Theory, Advanced Data Structures, Algorithmics, and Functional Programming. He has served as leader of the inference and decision process research group at CRIL (2002-2013) and as Delegate Director of the CRIL laboratory (2013-2018).

Nitin Saurabhمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Complexity Theory
• Analysis of Boolean Functions
• +۲ مورد دیگر

Nitin Saurabh is an Assistant Professor in the Department of Computer Science & Engineering at the Indian Institute of Technology Hyderabad. His research focuses on foundational areas of theoretical computer science, including complexity theory, analysis of Boolean functions, circuit complexity, and algorithm design. Education: Ph.D. from Institute of Mathematical Sciences (IMSc), Chennai Research Interests: Theoretical Computer Science Complexity Theory Analysis of Boolean Functions Circuit Complexity Algorithms

• Algorithmic Game Theory
• Theory of Computation
• Artificial Intelligence
• +۹ مورد دیگر

Simina Branzei is an Associate Professor of Computer Science at Purdue University, where she joined in Spring 2018. Her research spans multiple areas at the intersection of theoretical computer science, game theory, and economics, with a focus on developing rigorous theoretical frameworks for understanding complex systems. Dr. Branzei's research interests include algorithmic game theory, theory of computation, artificial intelligence, algorithms, learning, computational complexity, and their interfaces with dynamical systems and optimization. Specific topics she has worked on include fair division, markets and auctions, games, learning dynamics, and local search processes. Her research is supported by an NSF CAREER Award, a prestigious grant recognizing early-career faculty who demonstrate exceptional potential for leadership in their field. Analysis of her publication record reveals consistent contributions to understanding computational aspects of economic and social systems. Her work shows particular expertise in computational complexity of fixed point problems, fair allocation mechanisms, market dynamics, and game theoretic models. She has developed novel theoretical frameworks for analyzing cake cutting protocols, market equilibria, and strategic behavior in multi-agent systems. NSF CAREER Award IBM Ph.D. Fellowship Google Anita Borg Memorial Scholarship Dr. Branzei actively mentors graduate students in theoretical computer science, with current Ph.D. student Reed C. Phillips. Her past mentees include Nicholas J. Recker (Ph.D.) and Nithish Kumar (Master's). She has been instrumental in organizing academic workshops including the ACM EC 2024 Mentoring Workshop, Mini-Symposium on Games and Learning at CanaDAM 2023, and Workshop on Fair Division at FOCS 2022, demonstrating leadership in building research communities. She teaches graduate courses including Algorithm Design, Theory of Computation, and Algorithmic Game Theory, as well as undergraduate courses in algorithms and theory of computation, shaping the next generation of computer scientists with strong theoretical foundations.

Wei Zhangمشاهده پروفایل

استاد

• Cancer genomics
• Precision oncology
• Glioma biology
• +۷ مورد دیگر

Wei Zhang, PhD is Professor of Cancer Biology and Professor of Biostatistics and Data Science (Public Health Sciences) at Wake Forest University School of Medicine . He directs the Wei Zhang Lab , where his team conducts pioneering research in cancer genomics, precision oncology, and systems biology, with a special focus on glioma and other solid tumors. Education: BS, 1985 MS, University of Texas MD Anderson Cancer Center, 1990 PhD, University of Texas MD Anderson Cancer Center, 1992 Research interests span genomic profiling , discovery of oncogenes and tumor suppressor genes (e.g., MIIP), miRNA regulatory networks , and mathematical modeling of gene-regulatory systems using Probabilistic Boolean Networks and MIRACLE algorithms. His group integrates wet-lab cancer biology with advanced bioinformatics to identify actionable biomarkers and therapeutic targets. Recent publications (2011–2018) reveal a consistent emphasis on integrative analyses of large cancer genomics datasets (notably TCGA), uncovering germline and somatic drivers of tumorigenesis, elucidating miRNA-mediated control of epithelial-to-mesenchymal transition (EMT) and DNA-damage response, and translating these findings into predictive markers for chemotherapy response and patient survival. Scientific contributions include: Discovery and functional characterization of the tumor suppressor gene MIIP Identification of miR-506 as a key EMT-inhibitory miRNA in ovarian cancer Demonstration of BRCA2 mutation impact on ovarian cancer prognosis Elucidation of POLE mutation and PD-L1 co-occurrence in lung adenocarcinoma Dr. Zhang is actively involved in graduate education through the Cancer Biology PhD Program and Biomedical Engineering PhD Program , mentoring trainees at the intersection of cancer biology, genomics, and computational science. His laboratory is supported by the National Foundation for Cancer Research Center for Cancer Systems Informatics and multiple NIH and foundation grants. The Wei Zhang Lab is located within the Wake Forest Comprehensive Cancer Center ecosystem, providing access to cutting-edge genomics cores, bioinformatics resources, and collaborative clinical networks.

• Algebraic Cryptanalysis
• Post-Quantum Cryptography
• Symmetric Cryptography

Morten Øygarden is a Postdoctoral Fellow in the Department of Cryptography at Simula UiB, a joint research initiative between Simula Research Laboratory and the University of Bergen. His work bridges theoretical cryptography and practical security analysis within Norway's premier cryptographic research environment. His educational background culminates in a 2021 PhD from the University of Bergen with the thesis "Algebraic Cryptanalysis of Cryptographic Schemes with Extension Field Structure", establishing his expertise in algebraic methods for cryptanalysis. Øygarden's research focuses on Algebraic Cryptanalysis , Post-Quantum Cryptography , and Symmetric Cryptography , specializing in vulnerabilities of multivariate systems, code-based schemes, and ring-based primitives. He develops novel Gröbner basis techniques to break cryptographic assumptions underlying quantum-resistant candidates, with direct implications for NIST standardization processes. Analysis of his 2020-2024 publications reveals consistent innovation in algebraic attack methodologies, particularly against arithmetization-oriented primitives for zero-knowledge proofs and MPC-friendly ciphers. His work demonstrates exceptional depth in exploiting mathematical structures of symmetric primitives while advancing post-quantum security evaluations of multivariate and code-based systems. His publication record in top venues including CRYPTO, EUROCRYPT, and the Journal of Cryptology reflects significant contributions to cryptographic science, though specific awards or grant details are not documented in available sources.

• Cryptography
• Security analysis of cryptographic algorithms
• Fully Homomorphic Encryption
• +۱ مورد دیگر

Håvard Raddum serves as Chief Research Scientist in the Department of Cryptography at Simula UiB, a joint research entity of Simula Research Laboratory and the University of Bergen. His primary focus involves advancing cryptographic security through algorithm analysis, Fully Homomorphic Encryption applications, and hardware-level attack mitigation. Raddum's research spans cryptography with specialization in security analysis of cryptographic algorithms, practical deployment of Fully Homomorphic Encryption, and physical attacks on hardware implementations. His work addresses vulnerabilities in symmetric ciphers, multivariate schemes, and lattice-based systems through algebraic cryptanalysis, side-channel techniques, and theoretical security proofs. Analysis of his 2019-2025 publications reveals concentrated expertise in cryptanalysis across diverse primitives. Key trends include algebraic attacks on arithmetization-oriented cryptography, side-channel analysis of lightweight ciphers, error detection in lattice computations, and security evaluations of Fully Homomorphic Encryption. His research consistently bridges theoretical frameworks with practical implementation vulnerabilities.