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Chunlei LiView profile

Professor

• Cryptography
• Coding Theory
• Wireless Communications
• +4 more

Chunlei Li is a Professor in the Department of Informatics at the University of Bergen, specializing in Coding Theory and Cryptography. His research focuses on signal design, error-correcting codes, cryptographic primitives, and blockchain technology. He holds a PhD from the University of Bergen (2014) and has held postdoctoral positions at the University of Stavanger and the Selmer Center. He teaches courses in algebraic coding, cybersecurity, and cryptography. His research spans waveform design for wireless communications, feedback shift register sequences, rank metric codes, homomorphic encryption, and discrete mathematics. He leads projects such as 'Sequences and Their Applications' and contributes to conferences like SETA and NISK. Supervised students include those in symmetric cryptography, sequence design, and code-based cryptography. His work emphasizes cryptographic strength, with contributions to APN functions, permutation polynomials, and decoding algorithms for rank metric codes. He has authored numerous papers on topics like differential properties of functions and blockchain-based security systems.

Gregory ProvanView profile

Professor

• Complex Systems
• Artificial Intelligence
• Diagnostics
• +3 more

Professor Gregory Provan is a Full Professor and Programme Coordinator for BSc Data Science and Analytics at University College Cork's School of Computer Science and Information Technology. He directs the Complex Systems Laboratory and holds a PhD from the University of Oxford, with prior academic appointments at the University of Pennsylvania. His educational journey includes: BSE from Princeton University MSc from Stanford University DPhil from University of Oxford This foundation supports his interdisciplinary research approach. Professor Provan's research spans: complex systems design and analysis with applications in: Embedded diagnostics algorithms for large-scale systems Stochastic model-based diagnosis frameworks Automated benchmark model generation Sustainable energy system optimization Co-design of embedded systems His work bridges artificial intelligence, data analytics, and network architectures to solve real-world engineering challenges. Analysis of his extensive publication record reveals core themes: Advancements in model-based diagnosis and control Computational methods for complex systems Applications in sustainable energy and building automation Innovations in stochastic optimization and machine learning His recent work increasingly focuses on cybersecurity for cyber-physical systems and explainable AI. Professor Provan has secured significant research funding including: Science Foundation Ireland grants totaling over €1.2M Industry partnerships with Tyndall National Institute Enterprise Ireland support for competence centers His research group addresses fundamental challenges in complex systems engineering. He has supervised numerous doctoral candidates to completion, including: Jun Wang (Modeling real-world complex systems) Alie El-Din Mady (Networked control systems) Margarita Razgon (Constraint satisfaction problems) Ma Ji (Uncertainty visualization) Current doctoral researchers continue work on wireless sensor networks and analog system diagnosis. Honors include the 2008 Best Paper Award from the IEEE Prognostics and Health Management Conference for his work on approximation techniques in model-based diagnostics. Professor Provan leads the Complex Systems Laboratory which focuses on: Modeling and analysis of sustainable energy systems Diagnostics and control of large-scale networks Embedded system co-design The lab maintains collaborations with international research institutions and industry partners.

• Deductive Verification
• Interactive Theorem Proving
• Formal Methods
• +7 more

Yong Kiam Tan is a research scientist at the Institute for Infocomm Research (I²R), A*STAR, Singapore, and holds a joint appointment as a Nanyang Assistant Professor at the College of Computing and Data Science, Nanyang Technological University (NTU), Singapore. He earned his PhD in Computer Science (Pure and Applied Logic) from Carnegie Mellon University, advised by Prof. André Platzer, under the A*STAR National Science Scholarship. His research lies at the intersection of formal methods, interactive theorem proving, and automated reasoning, with applications in hybrid systems, compiler verification, and cybersecurity. He has made significant contributions to the CakeML and KeYmaera X projects, focusing on verified compilation, deductive verification of differential equations, and formalized mathematics. The 15 most recent publications reflect a strong trend in formally verified automated reasoning, certified algorithms, and applications in both classical and emerging domains such as neural networks and cryptography. His work consistently appears in top-tier venues like POPL, PLDI, CAV, and ITP, emphasizing end-to-end verification from logic to executable code. Distinguished Paper Award at CAV 2024 Best Paper and Best Repeatability Evaluation Award at HSCC 2022 Best Tool Paper Award at FM 2019 Peter Landin Prize at IFL 2015 CMU SCS Distinguished Dissertation Award 2022 Singapore NRF Fellowship Class of 2024 He actively advises students and researchers at NTU and A*STAR, collaborates internationally with experts such as Magnus Myreen, André Platzer, and Jakob Nordström, and has secured competitive funding including the NRF Fellowship. He has served on program committees for CPP, ITP, TACAS, and PLDI Artifact Evaluation, demonstrating active engagement in the research community. He leads a research team working on verified tools for automated reasoning and hybrid systems, with current members including Wei-Lin Wu, Joe Watt, Shuhan He, and several student researchers. His group maintains verified proof checkers in CakeML and contributes extensively to the Archive of Formal Proofs (AFP) in Isabelle/HOL.

Aukosh JagannathView profile

Associate Professor

• Probability theory
• Mathematical foundations of data science
• Mathematical physics
• +3 more

Aukosh Jagannath is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, with cross-appointments in the Department of Applied Mathematics and the Cheriton School of Computer Science. He is also an affiliate of the Perimeter Institute and Waterloo.AI. His research focuses on probability theory, mathematical foundations of data science, and mathematical physics, particularly in areas like high-dimensional learning, statistical-computational tradeoffs, and spin glasses. Before joining Waterloo, he held postdoctoral positions at Harvard University and the University of Toronto, and completed his PhD in mathematics at NYU’s Courant Institute under Gerard Ben Arous, alongside an undergraduate degree in math and physics from NYU. His work has been supported by grants from NSERC, the Canada Research Chairs program, the Canadian Foundation for Innovation, and the Ontario Research Fund. He has contributed to groundbreaking research on topics such as stochastic gradient descent dynamics, spectral alignment in neural networks, and the theoretical underpinnings of deep learning. Jagannath has advised numerous graduate and postdoctoral researchers, including current PhD students Taj Jones-McCormick and Varnan Sarangian, and former advisees like Aseem Baranwal (now at XTY Labs) and Yiming Xu (now at the University of Kentucky). His awards include an Outstanding Paper Award at NeurIPS 2022 for work on high-dimensional SGD dynamics.

• Theoretical Computer Science
• Computational Complexity
• Randomness Extraction
• +13 more

Eshan Chattopadhyay is a prominent researcher in theoretical computer science, focusing on computational complexity, pseudorandomness, and cryptography. His work centers on the explicit construction of randomness extractors, condensers, pseudorandom generators, and non-malleable codes, often improving entropy requirements and error bounds. He has made significant contributions to derandomization, space-bounded computation, and tamper-resilient cryptography. His research is published extensively in the Electronic Colloquium on Computational Complexity (ECCC), indicating deep engagement with foundational aspects of computer science. Research Interests: Eshan's research spans randomness extraction from weak sources, including sumset sources, polynomial sources, and adversarial models. He investigates pseudorandomness for branching programs, linear threshold functions, and Fourier-based constructions. His work in cryptography includes non-malleable codes, leakage resilience, and secret sharing under bounded collusion. He also contributes to combinatorics through extremal hypergraphs and designs, and to complexity theory via lower bounds and derandomization techniques. The recent articles (2021–2025) show a continued focus on improving extractor and condenser constructions under challenging models such as number-on-forehead protocols, online adversaries, and interleaved or adversarial sources. There is a strong trend toward handling sources with very low entropy, achieving near-optimal parameters, and extending results to two-sided and unbalanced settings in expander graphs. His collaborations with leading researchers like Xin Li, David Zuckerman, and Jesse Goodman reflect his central role in the community. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While no formal students or grants are listed, the volume and depth of publications suggest active mentorship and likely grant funding in theoretical computer science. His work often involves junior collaborators, indicating a role in guiding emerging researchers. Labs and Teams: No specific lab or team affiliations are mentioned in the scraped content. However, his frequent co-authorship with researchers from institutions like UT Austin, CMU, and others implies participation in collaborative research networks focused on complexity and cryptography.

Daniel HrozencikView profile

Professor

• Mathematical Biology
• Mathematical Modeling
• Gene Regulatory Networks
• +4 more

Daniel Hrozencik is a Professor in the Department of Mathematics at Chicago State University, part of the College of Arts & Sciences. He holds a Ph.D. in Mathematics from the University of Notre Dame (1988) and has been actively contributing to interdisciplinary research at the intersection of mathematics and biology. Education: Ph.D., University of Notre Dame, 1988 M.S., University of Notre Dame, 1985 B.A., Knox College, 1983 His research interests center on mathematical biology and mathematical modeling , particularly in gene regulatory networks , epidemiological modeling (including recent work on COVID-19 mortality and patient frailty ), and interdisciplinary education reform . He employs computational methods such as artificial neural networks , particle swarm optimization , and self-organizing maps to analyze complex biological systems. The recent publications show a strong trend toward integrating machine learning with traditional mathematical modeling in biology, especially in understanding gene regulation and pandemic dynamics. His work often bridges theoretical modeling with practical applications in public health and education. Professional Memberships: Intercollegiate Biomathematics Alliance American Mathematical Society Society for Mathematical Biology Phi Beta Kappa Daniel Hrozencik has advised undergraduate and graduate researchers in biomathematics projects, particularly through collaborative symposia such as BEER. While specific grants are not listed, his involvement in national education reform initiatives suggests participation in funded interdisciplinary efforts. He contributes significantly to curriculum development and professional society leadership in biomathematics. He is also involved in academic outreach and education reform, advocating for improved integration of mathematics and biology in undergraduate programs. No dedicated lab or research team is explicitly mentioned, but his collaborative work indicates active engagement in research networks.

Yuval PeresView profile

Professor

• Probability Theory
• Analysis
• Theoretical Computer Science
• +6 more

Yuval Peres is a Professor at the Beijing Institute of Mathematical Sciences and Applications (BIMSA). His research spans Probability Theory , Analysis , Theoretical Computer Science , and Game Theory . He has made foundational contributions to topics such as Random Walks , Markov Chains , Fractals , and Brownian Motion , with applications in network science and mathematical physics. Research Interests : Peres’ work often intersects probability and analysis, focusing on problems like phase transitions , mixing times , Gaussian analytic functions , and homogenization . His recent papers address homogenization problems, cutoff phenomena in Markov chains, and boundary value problems via stochastic methods. Advising and Mentorship : Peres has mentored over 20 PhD students and postdoctoral scholars, many of whom hold prominent positions at institutions like MIT, UC Berkeley, and Yale. His collaborations span mathematics, computer science, and physics. Awards : Linked to the David P. Robbins Prize (specific paper not named in text)

Panagiotis NastouView profile

Assistant Professor

• Secure Discrete Systems Modeling
• Graph Theory
• Cryptography
• +5 more

Panagiotis Nastou is an Assistant Professor at the Department of Mathematics , University of the Aegean , with research interests in Secure Discrete Systems Modeling, Graph Theory, Cryptography, Design and Analysis of Algorithms , and Complexity Theory . He has been active in teaching courses such as Discrete Mathematics and Cryptography since 2007 and serves as Director of the Applied Mathematics and Mathematical Modeling Laboratory since 2019. Education : Ph.D., MSc., and BSc. in Computer Engineering and Informatics from Patras University. Membership : Center for Applied Optimization (University of Florida), GDPR TEAM, IEEE, ACM, and Hellenic Technical Chamber. His research bridges theoretical computer science and applied security, focusing on cryptographic protocols, algorithm design, and the application of discrete mathematics to real-world problems. He has contributed to the development of secure systems for critical infrastructure and privacy-preserving frameworks in smart cities. Recent publications (2021-2025) emphasize malware evasion , quantum cryptanalysis , and privacy in IoT environments , utilizing formal methods and combinatorial structures. His work also extends to deep learning for document analysis and consensus algorithms in social networks . He has supervised numerous Master's and undergraduate theses in areas like Zero-Knowledge Proofs , Neural Networks , and Cryptographic Boolean Functions . Administrative roles include representation in the ERASMUS+ program and participation in EU-funded project evaluations.

• Formal Verification
• Model Checking
• Automated Reasoning
• +9 more

Georg Weissenbacher is a Full Professor of Computer Science at Vienna University of Technology (TU Wien), working in the Institute of Logic and Computation within the Faculty of Informatics. He leads the Formal Methods in Systems Engineering research group and has established himself as a leading researcher in formal verification and automated reasoning. His educational background includes a DPhil from Oxford University (2008-2010), research at ETH Zürich (2005-2010), and a Master's degree from TU Graz (completed by 2003). He completed his Habilitation at TU Wien in 2016 with a thesis on Logical Methods in Automated Hardware and Software Verification. Weissenbacher's research focuses on developing automated tools for software and hardware verification, with particular emphasis on detecting and explaining bugs in complex systems. He is renowned for his work on heisenbugs - bugs that disappear when analyzed, which are particularly challenging in concurrent and multi-core systems. His research bridges theoretical foundations in logic with practical applications in software engineering, with significant contributions to interpolation-based verification techniques and SAT/SMT solving applications. His recent publications show a clear progression from traditional software verification toward emerging challenges in AI security, neural network verification, and sophisticated concurrency models. The research trajectory demonstrates increasing sophistication in handling complex systems properties, with a growing emphasis on probabilistic guarantees and formal methods applied to modern computing challenges. OOPSLA'18 Distinguished Paper Award for 'Randomized Testing of Distributed Systems with Probabilistic Guarantees' Weissenbacher has advised numerous PhD students and postdocs, including current researchers Mai AL-Zu'bi, Katalin Fazekas, and Sarah Sallinger. His research has been supported by significant grants including the Vienna Research Groups for Young Investigators (2011), the National Research Network 'Rigorous Systems Engineering' funded by FWF, and a Microsoft Research PhD Scholarship (2016). He actively contributes to the academic community through his service as co-chair for major conferences including CAV 2018 and FMCAD 2017. As leader of the FORSYTE research group at TU Wien, Weissenbacher oversees a vibrant research team working at the intersection of logic, verification, and practical software engineering challenges. The group is currently involved in the Doctoral College on Automated Reasoning, funded by FWF, which aims to train the next generation of researchers in this critical field.

• Cryptography
• Computational Complexity
• Pseudorandomness
• +5 more

Andrej Bogdanov is a Professor in the Department of Computer Science at the Weizmann Institute of Science's Faculty of Mathematics and Computer Science. With a prolific publication record spanning over two decades from 2002 to 2025, he has established himself as a leading researcher in theoretical computer science and cryptography. His research interests span multiple areas of theoretical computer science, with a particular focus on cryptography, computational complexity, pseudorandomness, and secret sharing. His work often bridges theoretical foundations with practical cryptographic applications, exploring the mathematical underpinnings of secure computation and cryptographic primitives. His research has evolved to address contemporary challenges in quantum computing security and machine learning evaluation. Bogdanov's publication record shows consistent contributions to top-tier conferences including FOCS, STOC, CRYPTO, TCC, and ITCS. His work demonstrates deep theoretical insights while maintaining relevance to practical cryptographic applications. Recent publications indicate expanding interests into quantum computing security and machine learning evaluation frameworks. Bogdanov has collaborated extensively with leading researchers in theoretical computer science, most notably with Alon Rosen (31 joint publications), as well as Siyao Guo, Yuval Ishai, and Chin Ho Lee. His collaborative work spans multiple institutions and reflects the interdisciplinary nature of modern theoretical computer science research. His academic contributions include foundational work on pseudorandom generators, secret sharing schemes, hardness amplification, and more recently, contributions to post-quantum cryptography and quantum security. His research has been supported by multiple grants that have enabled his team to explore the theoretical boundaries of cryptographic security.

Matthias WalterView profile

Assistant Professor

• Operations Research
• Optimization
• Mathematical Programming
• +5 more

Matthias Walter is an Assistant Professor in the Department of Mathematics of Operations Research. His research focuses on optimization, mathematical programming, and algorithm design, with applications in transportation, combinatorial optimization, and logistics. He contributes to the SCIP Optimization Suite and has developed the Combinatorial Matrix Recognition Library. His work spans topics such as linear programming relaxations, pseudo-Boolean solving, and multilinear optimization. Key research interests include operations research, integer programming, and algorithmic solutions for complex optimization problems. He has collaborated on projects like optimizing parcel transportation and analyzing congestion games. His recent publications address challenges in flow shop scheduling, graph realization, and pseudo-Boolean functions. Walter actively participates in academic activities, organizing events like the Mathematical Olympiad in Saxony-Anhalt and presenting invited talks on topics such as McCormick relaxations. His contributions to computational tools and theoretical advancements highlight his role in advancing optimization methodologies.

Lulu QianView profile

Professor

• Biological Engineering
• Molecular Programming
• DNA Nanotechnology
• +6 more

Lulu Qian is a Professor of Bioengineering at the California Institute of Technology (Caltech), leading the Qian Lab. She holds a B.E. from Southeast University (2002) and a Ph.D. from Shanghai Jiao Tong University (2007). Prior to her current role, she served as an Assistant Professor at Caltech (2013–2018). Her research focuses on designing molecular systems with programmable behaviors, including biochemical circuits, DNA neural networks, and molecular robots. These systems aim to mimic biological intelligence and perform tasks like pattern recognition, decision-making, and spatial organization at the molecular level. Her lab’s key projects include developing DNA strand displacement cascades for scalable computation, creating DNA-based neural networks capable of associative memory, and engineering molecular robots that perform collective tasks like cargo sorting. Notable achievements include the world’s smallest DNA-based Tic-Tac-Toe game, fractal assembly of DNA origami arrays, and a DNA robot capable of sorting molecules. Qian’s work bridges molecular engineering, computer science, and synthetic biology to advance nanotechnology and programmable matter. Her lab actively collaborates with Caltech’s graduate programs in Bioengineering, Computer Science, and Chemical Engineering. Current and past students have contributed to projects ranging from programmable DNA circuits to bio-inspired robotics. The lab emphasizes interdisciplinary training, preparing students for careers in academic research and industry innovation.

• Computational Complexity
• Cryptography
• Theoretical Computer Science
• +3 more

Manuel Blum is the Bruce Nelson Professor of Computer Science at Carnegie Mellon University's School of Computer Science, Department of Computer Science. He is renowned for foundational contributions to computational complexity, cryptography, and theoretical computer science. His work includes pioneering research on CAPTCHAs, human computation, and self-testing programs. Blum has collaborated extensively with figures like Luis von Ahn, contributing to projects like Peekaboom and Verbosity. His research also extends to philosophical inquiries into consciousness, blending computer science with neuroscience and philosophy. Blum's teaching includes courses on theoretical cryptography (15-503) and undergraduate complexity theory (15-455). He has advised students such as Brendan Juba and Matt Humphrey, whose work explores computational complexity and related fields. His research spans grants on machine learning, game theory, and computational complexity, including an NSF proposal on understanding concept understanding. Blum's contributions to security, randomness, and algorithm design have had lasting impacts on computer science.

David D. GamarnikView profile

Professor

• Discrete Probability
• Optimization and Algorithms
• Quantum Computing
• +3 more

David D. Gamarnik is a Professor of Operations Research at the MIT Sloan School of Management, affiliated with the Operations Research and Statistics Group. He holds a BA in Mathematics from New York University (1993) and a PhD in Operations Research from MIT (1998). Prior to joining MIT in 2005, he was a Research Staff Member at IBM T.J. Watson Research Center. His research focuses on discrete probability, optimization, quantum computing, machine learning, and stochastic processes. He serves as an Area Editor for Mathematics of Operations Research and has held editorial roles in journals such as Operations Research and Annals of Applied Probability . Education: Bachelor of Arts in Mathematics, New York University (1993) PhD in Operations Research, MIT (1998) His honors include the Erlang Prize, Best Publication Award from INFORMS’ Applied Probability Society, and recognition as a finalist for the Franz Edelman Prize. He is a Fellow of the Institute for Mathematical Statistics, Institute for Operations Research and Management Science, and the American Mathematical Society. His academic contributions span theoretical computing, algorithm design, and applications in healthcare (e.g., kidney exchange models). Recent work explores algorithmic barriers in optimization, quantum computing, and high-dimensional statistics.

Christine ZargesView profile

Senior Lecturer

• Artificial Intelligence
• Evolutionary Algorithms
• Optimization
• +3 more

Christine Zarges is a Senior Lecturer in the Department of Computer Science at Aberystwyth University. Her research focuses on evolutionary algorithms, artificial immune systems, and optimization techniques, with applications in data preprocessing, anomaly detection, and bioinformatics. She has contributed significantly to the theoretical foundations of immune-inspired randomized search heuristics and has explored their practical implementations in various domains. Her work often addresses challenges in combinatorial optimization, such as improving variable orderings in OBDDs and developing efficient algorithms for Lyndon factorization of biosequences. Recent projects include applications of adversarial diffusion for video anomaly detection and the use of rough set theory for big data preprocessing. She has also been involved in initiatives to address bias in physical science and engineering research. Christine has led and collaborated on externally funded projects, including the Horizon 2020-funded RoSTBiDFramework project and EPSRC grants focused on bias reduction in STEM fields. She has supervised graduate students, such as Yumna Zahid, whose work contributes to video anomaly detection. She actively contributes to the academic community by organizing conferences like EvoCOP and the Genetic and Evolutionary Computation Conference (GECCO), and serves on editorial boards of journals including Evolutionary Computation and IEEE Transactions on Evolutionary Computation .