Eleonora Bilotta is a Full Professor of General Psychology at the Department of Physics, University of Calabria, Italy. She is the coordinator of the Ph.D. Course in Psychology of Programming and Artificial Intelligence and vice-coordinator of the Ph.D. Course in Science, Engineering of Environment, Construction, and Energy. Her research spans interdisciplinary fields such as cognitive psychology, educational technologies, artificial life, and mathematical modeling of behavior. Prof. Bilotta has over 150 publications and has contributed to journals like Complexity and International Journal of Bifurcation and Chaos . She led initiatives like the COMSON project (EU-funded) for e-learning platforms and directed the Evolutionary Systems Group (ESG). She also organized conferences such as the International Conference on Simulation of Adaptive Behavior and the Workshop on Artificial Life. Her work bridges science and art, leveraging chaos theory for creative design and educational tools. She collaborates globally, including with institutions like Bergische Universität Wuppertal and Chapman University. Her labs focus on cognitive science, modeling, and simulation, emphasizing innovation in education and technology.
Luc Segoufin is a Research Professor at INRIA (French National Institute for Research in Computer Science and Automation), affiliated with the Department of Computer Science at École Normale Supérieure (ENS) in Paris. He leads the VALDA research team, focusing on theoretical computer science foundations. His research spans: Database theory: Query answering, consistency, and enumeration complexity Logic and automata: Finite model theory, automata over data structures Computational complexity: Fine-grained analysis and lower bounds Formal methods: Verification and logic-based modeling His recent publications (2022–2024) concentrate on: Dichotomy theorems for query answering under constraints Constant-delay enumeration algorithms for structured data Decidability in logic fragments over trees and graphs Connections between automata, algebra, and complexity No scientific awards are mentioned in available sources. He collaborates extensively within the VALDA team and international researchers on projects involving database theory, logic, and automata. No student advising details are provided.
Rajarshi Roy is a Professor at the Institute for Physical Science and Technology (IPST) at the University of Maryland. His research focuses on nonlinear dynamics, chaos theory, and their applications in optical systems. He explores phenomena such as synchronization patterns, machine learning-driven network analysis, and quantum-optical systems. His experimental work includes studies on optoelectronic oscillators, delay-coupled systems, and photonic random number generation. His research interests span nonlinear dynamics , chaos theory , and optical systems . Key areas include synchronization of coupled oscillators, chimera states, and machine learning applications in network inference. He also investigates noise effects in photonics and quantum technologies, such as entanglement quality estimation in fiber systems. His recent work emphasizes combining machine learning with nonlinear dynamics to analyze complex systems. For instance, his studies on delayed dynamical systems and neuromorphic computing showcase innovations in network inference and reservoir computing. Experimental validations using optoelectronic setups highlight his interdisciplinary approach. Roy’s articles explore cutting-edge topics like entropy harvesting in photon-counting systems, topological control of synchronization, and suppression of optical scattering via chaos. His contributions bridge fundamental nonlinear science with technological applications in photonics and information systems.
Michael Saks is a Distinguished Professor of Mathematics at Rutgers, The State University of New Jersey . He is affiliated with both the Department of Mathematics and the Computer Science Department as a graduate faculty member. His research focuses on theory of computation and discrete algorithms , with applications in computational complexity, combinatorics, and algorithmic analysis. His recent publications include work on edit distance approximations , randomized algorithms , discrepancy of random matrices , and Boolean function analysis , reflecting a strong emphasis on theoretical foundations. He has contributed to journals such as Combinatorica , Journal of Graph Theory , and Discrete Applied Mathematics , and served on editorial boards and conference program committees, including the 2014 IEEE Conference on Computational Complexity . Michael Saks maintains active research and teaching pages , including guidance for graduate applicants and links to seminars like the Discrete Mathematics/Theory of Computing Seminar . He also participates in initiatives such as the Center for Computational Intractability and DIMACS .
Petteri Kaski is an Associate Professor at the Department of Computer Science, School of Science, Aalto University , and a member of the Helsinki Institute for Information Technology (HIIT) . His research focuses on theoretical computer science, particularly in algorithm design, exact and parameterized algorithms, algebraic algorithms, and combinatorics. Doctoral Degree in Engineering and Technology, Helsinki University of Technology (2005) Licentiate Degree in Engineering and Technology, Helsinki University of Technology (2002) Master's Degree in Engineering and Technology, Helsinki University of Technology (2001) His recent work explores tensor scaling, Johnson-Lindenstrauss transforms, Hamiltonian cycles, and computational complexity, with contributions to polynomial-time algorithms, finite field computations, and combinatorial optimization. Notable awards include the Best Paper Award at ICALP 2017 , an ERC Starting Grant (2014) , and the Kirkman Medal (2007) . He has served on scientific committees for conferences like STACS 2025 and ICALP 2024 , and collaborated with institutions such as the IT University of Copenhagen and Universität Regensburg . Key Research Areas : Theoretical Computer Science, Algorithm Design, Exact Algorithms, Algebraic Computation, Graph Theory, Combinatorics
Dr. Kalikinkar Mandal is an Associate Professor in the Faculty of Computer Science at the University of New Brunswick (UNB), Fredericton, Canada. He holds the prestigious NB Power Cybersecurity Research Chair for smart grid security and privacy, a position supported by $500,000 in funding from NB Power for a five-year term. Dr. Mandal is also a member of the Canadian Institute for Cybersecurity (CIC), an ACM member, and a member of the International Association for Cryptologic Research (IACR). Education: PhD in Electrical and Computer Engineering from the University of Waterloo (2013) MTech in Computer Science from the Indian Statistical Institute, Kolkata (2009) Additional Master's degree in Mathematics Dr. Mandal's research broadly focuses on cryptography, cybersecurity, and privacy, with specific expertise in lightweight cryptography, privacy-preserving computation, security and privacy in smart grids and Internet of Things (IoT), trusted computing, and high-speed cryptography. His work addresses critical challenges in securing emerging technologies, particularly in energy infrastructure where cybersecurity threats can have severe consequences for essential services. His research bridges theoretical cryptography with practical applications in real-world systems. Analysis of Dr. Mandal's recent publications reveals a consistent focus on cryptographic techniques for resource-constrained environments, particularly for smart grid and IoT applications. His work spans theoretical foundations of cryptographic primitives, practical implementations of lightweight ciphers, and innovative privacy-preserving protocols for emerging technologies. A notable trend in his research is the development of efficient cryptographic solutions that balance security requirements with performance constraints in critical infrastructure systems. Scientific Awards: NB Power Cybersecurity Research Chair ($500,000 funding) Contributor to multiple cryptographic algorithms (ACE, SPIX, SpoC, WAGE) that reached Round 2 of NIST Lightweight Cryptography standardization Dr. Mandal actively mentors graduate students in cybersecurity research, currently supervising three students working on cryptographic protocols for cyber-physical systems, cybersecurity in advanced metering infrastructure, and privacy for electric vehicles. His NB Power Cybersecurity Research Chair supports research that provides training opportunities for both graduate and undergraduate students, preparing them as future cybersecurity leaders. Through direct applied research, knowledge dissemination, and student training, his work addresses critical challenges in power and security infrastructure. Dr. Mandal is actively involved in several research initiatives related to lightweight cryptography. He is part of the development teams for ACE, SPIX, SpoC, and WAGE - all of which were Round 2 candidates in the NIST Lightweight Cryptography standardization project. His GitHub repository (comsec-lwc) contains reference and optimized implementations of these cryptographic algorithms. He also contributes to the Canadian Institute for Cybersecurity at UNB, focusing on practical applications of cryptographic techniques in critical infrastructure security.
Benjamin Rossman is an Associate Professor in the Computer Science and Mathematics Departments at Duke University , affiliated with the Theory Group. His research focuses on computational complexity, particularly circuit complexity and finite model theory, with notable contributions to lower bounds for subgraph isomorphism problems and logical definability. Rossman earned his Ph.D. from MIT under Madhu Sudan's supervision. He has held faculty positions at the University of Toronto and postdoctoral roles at the Tokyo Institute of Technology and National Institute of Informatics in Japan. He has received prestigious awards including the Alfred P. Sloan Fellowship and NSERC grants. His teaching includes courses on computational complexity, mathematical logic, and discrete mathematics. Recent publications explore homomorphism preservation theorems, formula complexity, and treedepth in subgraph isomorphism. Rossman advises graduate students in theoretical computer science and has led research on circuit lower bounds, algorithmic logic, and combinatorial complexity. His work bridges foundational theory with practical algorithmic challenges, contributing to the understanding of computational limits in combinatorial problems.
Maris Ozols is an Assistant Professor at the University of Amsterdam and a researcher at QuSoft, affiliated with the Algorithms and Complexity department at Centrum Wiskunde & Informatica (CWI). His primary research focuses on quantum algorithms and quantum information theory, with significant contributions to quantum complexity, quantum cryptography, and quantum state discrimination. His research interests span quantum algorithms, quantum information theory, quantum cryptography, quantum complexity, and theoretical computer science. Ozols has developed fundamental techniques in quantum query complexity, quantum state discrimination, and quantum cryptographic security models. His work often bridges theoretical computer science with quantum information physics, demonstrating practical implications for quantum computing architectures. His publication record shows consistent output in top venues including Communications in Mathematical Physics, Leibniz International Proceedings in Informatics, and Quantum journal. Recent work (2022-2025) focuses on quantum state discrimination, quantum circuit optimization, quantum machine learning, and cryptographic applications of quantum algorithms. His research demonstrates strong theoretical foundations with practical implications for quantum computing development. Leverhulme Early Career Fellow (University of Cambridge) Ozols has secured research funding through multiple Netherlands Organisation for Scientific Research (NWO) grants including the Quantum Software Consortium (QSC) and Quantum Computation with Bounded Space projects. His collaborative work spans international institutions including the University of Waterloo (where he earned his PhD), University of Cambridge, IBM Research, and various European quantum computing groups. He maintains active research groups in quantum algorithms at both QuSoft and CWI, with recent focus on quantum machine learning applications and quantum cryptographic protocols.
Nicola Galesi serves as an Associate Professor in the Department of Computer, Control and Management Engineering (DIAG) at Sapienza University of Rome since 2022, following 17 years in Sapienza's Department of Computer Science (2005-2022). His academic journey began with an Associate Professorship at Universitat Politecnica de Catalunya (2001-2005) after postdoctoral positions at the Institute for Advanced Studies in Princeton (2000-2001) and University of Toronto (2002-2003) under Stephen Cook and Toni Pitassi. His educational background features a PhD from Universitat Politecnica de Catalunya supervised by Maria Luisa Bonet, complemented by dual Italian habilitations as full professor in Mathematical Logic (2012) and Computer Science. These qualifications underpin his rigorous theoretical approach across research domains. Galesi's research program centers on Computational Complexity and Logic in Computer Science , with specialized expertise in Proof Complexity (investigating resolution refinements and algebraic proof systems), SAT-Solving , Optimization , and applied domains like Group Testing and Network Tomography . His seminal work on space complexity in algebraic proof systems (JACM 2015) established foundational frameworks, while recent network tomography research develops mathematical models for node failure identification in communication networks using Boolean algebra and graph connectivity principles. Analysis of his 15 most recent publications (2022-2025) reveals a cohesive research trajectory bridging theoretical proof complexity and practical network analysis. Key trends include depth lower bounds in stabbing planes for combinatorial principles, vertex-connectivity metrics for failure localization, and algebraic investigations of vanishing sums in polynomial calculus. His work consistently applies combinatorial principles to derive tight bounds across graph structures while advancing the theoretical understanding of proof systems through tensor isomorphism and roots of unity analyses. His scientific recognition includes: ACM Computing Review Most Notable paper in Theory of Computing for 2012 Galesi mentors the next generation through PhD supervision of Massimo Lauria (2009), Ilario Bonacina (2015), and Fariba Ranjbar (2021), while hosting postdocs including Alan Skelley, Olaf Beyersdorff, and Massimo Lauria. His research is amplified through prestigious visiting positions at the Simons Institute for Theory of Computing (2015, 2021) and Tokyo Institute for Technology (2015), building on his foundational work at IAS Princeton and Toronto. He actively shapes the theoretical CS landscape as organizer of the Sapienza LOC3 (Logic, Complexity, Combinatorics, Computability) seminar series and founder of the RaTLoCC workshops (Ramsey Theory in Logic, Complexity and Combinatorics). His editorial role for Logical Methods in Computer Science (LMCS) and program committee service for CIAC, IJCAI, and FSTTCS conferences demonstrate sustained community leadership beyond his core research and teaching responsibilities in Calculus, Mathematical Logic, and Computational Complexity.
Ronald de Wolf is a part-time Full Professor at the Institute for Logic, Language and Computation (ILLC), University of Amsterdam, and a researcher/group leader at the Algorithms and Complexity group of CWI (Dutch Centre for Mathematics and Computer Science). He is an active member of QuSoft and the Amsterdam Theoretical Computer Science ecosystem. His PhD was completed at CWI and ILLC, followed by postdoctoral research at UC Berkeley. Research Focus: De Wolf specializes in quantum computing, complexity theory, and algorithm design. His work explores quantum advantages in computation, communication, and learning, with applications in optimization, machine learning, and information theory. Recent investigations include quantum algorithms for linear algebra, error correction, and communication complexity. Publication Trends: His recent articles (2020-2025) predominantly focus on quantum algorithmic advantages, complexity bounds, and practical applications in machine learning and optimization. Key themes include quantum speedups for linear algebra, error-resilient quantum protocols, and theoretical limits of quantum computation. Awards & Honors: ERCIM Cor Baayen Award (2003) STOC Best Paper Award (2012) STOC Test-of-Time Award (2022) Gödel Prize (2023) Academic Leadership: He currently advises PhD student Lynn Engelberts and has graduated 10 doctoral students. As coordinator of the NWO Gravitation program Quantum Software Consortium , he oversees major research initiatives. He secured participation in EU projects (QAIP, RESQ, QAP, QCS, QALGO, QuantAlgo) and leads research teams at CWI and QuSoft.
Anup Rao is a Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, specializing in the Theory & Models of Computation. His research focuses on algorithms, complexity theory, and communication complexity, with significant contributions to understanding fundamental limits of computation and communication. Rao has established himself as a leading researcher in theoretical computer science through his extensive publication record in top venues including FOCS, STOC, and CCC. Rao's research interests span a broad spectrum of theoretical computer science. He has made significant contributions to communication complexity, developing new techniques for analyzing information flow in distributed computation. His work on sunflowers in combinatorics has provided novel connections between extremal set theory and computational complexity. In information theory, Rao has advanced our understanding of error-correcting codes, Boolean function analysis, and the Fourier spectrum of bounded functions. His research often bridges multiple areas, revealing deep connections between seemingly disparate fields of theoretical computer science. An analysis of Rao's recent publications reveals a strong focus on fundamental questions in communication complexity and combinatorics. His work frequently explores the boundaries of what can be efficiently computed or communicated, often developing novel mathematical techniques in the process. There's a clear progression from foundational work on direct sum theorems and information complexity to more recent breakthroughs on sunflower lemmas and XOR lemmas. His research demonstrates consistent innovation in theoretical methods while maintaining relevance to practical computational problems. NSF Career Award Sloan Research Fellowship BSF 2010089 NSF CCF-1420268 NSF CCF-1524251 NSF CCF-1016565 Rao has successfully advised several doctoral students to completion, including Makrand Sinha (PhD 2018) and Sivaramakrishnan Ramamoorthy (PhD 2020), with current advisees Siddharth Iyer and Oscar Sprumont continuing his research program. His research has been generously supported by multiple NSF grants, a Sloan Research Fellowship, and international funding through the Binational Science Foundation. Rao's funding record demonstrates sustained recognition of the importance and quality of his research program by major funding agencies. As a faculty member in the Theory group at the Paul G. Allen School, Rao contributes to a vibrant research community focused on foundational aspects of computer science. His work intersects with multiple research centers at the University of Washington, particularly those focused on theoretical foundations and algorithms. Rao's research group actively collaborates with other theoretical computer scientists both within UW and across the global research community, maintaining a strong presence in major theory conferences and workshops.
Yuncong Hu is an Assistant Professor at Shanghai Jiao Tong University specializing in applied cryptography, decentralized systems, and zero-knowledge proofs. Previously, he completed his Ph.D. at UC Berkeley's RISE Lab under Prof. Raluca Ada Popa and Prof. Alessandro Chiesa, and earned his Bachelor's degree from Shanghai Jiao Tong University in 2017 as a member of the ACM Honored Class. His educational background includes: Ph.D. in Computer Science, UC Berkeley (RISE Lab), advised by Prof. Raluca Ada Popa and Prof. Alessandro Chiesa Bachelor's degree in Computer Science, Shanghai Jiao Tong University, 2017 (ACM Honored Class) Dr. Hu's research focuses on practical cryptographic systems, particularly zero-knowledge proofs (zkSNARKs), with emphasis on efficiency, versatility, and real-world deployment. His work bridges theoretical cryptography with system implementation, addressing challenges in decentralized trust, secure computation, and privacy-preserving protocols. Key contributions include foundational work on preprocessing zkSNARKs, transparency log systems, and cryptographic primitives for emerging applications. Analysis of his 15 most recent publications (2020-2025) reveals a dominant focus on zkSNARKs optimization and novel applications, with significant contributions to vector commitments, range proofs, and federated learning security. His research trajectory shows increasing diversification into AI security (LLM fingerprinting) and hardware-aware cryptographic implementations while maintaining core expertise in proof systems. Publications consistently appear in top venues including Eurocrypt, S&P, and NeurIPS, demonstrating both theoretical rigor and practical impact. No scientific awards are mentioned in the provided information. Dr. Hu has not listed current advisees or major grants in the provided materials, but his active open-source contributions (arkworks, Merkle^2, Gemini) and program committee service for Asiacrypt 2023 and USENIX Security 2024 indicate ongoing research leadership. His work shows strong industry relevance through implementations targeting real-world constraints in IoT and decentralized systems. During his doctoral work at UC Berkeley's RISE Lab, he contributed to security-focused systems research. At Shanghai Jiao Tong University, he leads research advancing cryptographic protocols for next-generation applications, with particular emphasis on making zero-knowledge proofs accessible across diverse computing environments through projects like the arkworks ecosystem.
Anil Ada is an Associate Teaching Professor at the Computer Science Department of Carnegie Mellon University . He was born in Istanbul and completed his B.Sc. (Hon.), M.Sc., and Ph.D. in Mathematics and Computer Science at McGill University , advised by Denis Thérien and Hamed Hatami. Research Focus : Theoretical computer science with an emphasis on communication complexity, circuit complexity, and the intersection of structure vs randomness in computation. Education : B.Sc. (Hon.) in Mathematics and Computer Science from McGill University M.Sc. in Computer Science from McGill University Ph.D. in Computer Science from McGill University Academic Contributions : His research has explored foundational questions in computational complexity, including pseudorandomness, Boolean function analysis, and additive combinatorics. He has published in venues such as Computational Complexity , ICALP , and International Journal of Foundations of Computer Science . Teaching Innovation : He developed Panda Notes , a web application combining document collaboration with AI-assisted educational tools. He teaches courses like 15-251: Great Ideas in Theoretical Computer Science and 15-155: The Computational Lens , with prior teaching roles at McGill University.
Vardges Melkonian is an Associate Professor in the Department of Mathematics at Ohio University, part of the College of Arts and Sciences. His academic work bridges theoretical and applied mathematics, with a strong emphasis on optimization and algorithmic problem-solving. Research Interests: Dr. Melkonian specializes in combinatorial optimization, network design, approximation algorithms, and applications of operations research. His research integrates mathematical programming and discrete modeling to solve complex real-world scheduling and allocation problems. These interests are reflected in his extensive publication record spanning sports leagues, hybrid work, exercise routines, and social partitioning. Publication Trends: Over the past decade, his scholarly output has consistently focused on developing integer programming and optimization models for diverse domains—from recreational puzzles like KenKen to large-scale logistical challenges in manufacturing and public policy. His work demonstrates a unifying theme: transforming practical problems into formal mathematical frameworks for efficient solution. Scientific Awards: No awards are mentioned in the provided text. Advising and Grants: The available information does not list any students or grant funding. However, his research contributions suggest active engagement in academic mentorship and potential involvement in funded projects, though specifics are not disclosed. Labs and Research Teams: There is no mention of specific labs, research groups, or collaborative teams associated with Dr. Melkonian in the provided content.
Ondřej Čepek is an Associate Professor at the Department of Theoretical Computer Science and Mathematical Logic, Faculty of Mathematics and Physics, Charles University in Prague. His academic career spans several decades with numerous publications demonstrating his expertise in theoretical computer science, particularly in Boolean logic, computational complexity, and operations research. Čepek's research primarily focuses on Boolean functions, Horn formulas, and computational complexity. His work explores structural properties of logical constructs, minimization techniques, and applications in knowledge representation. He has made significant contributions to understanding satisfiability testing complexity, CNF minimization, and Boolean function representations using interval structures. His research also extends to scheduling problems, particularly just-in-time scheduling with periodic time slots, where he has developed efficient algorithms for multislot scheduling on identical parallel machines and nonpreemptive flowshop scheduling with machine dominance. Analysis of his recent publications reveals a consistent research trajectory from theoretical foundations to practical applications. His work demonstrates expertise in tractable classes of Boolean formulas, knowledge compilation techniques, and the relationship between computational complexity and logical representations. The recurring themes include efficient representations for logical formulas, characterization of tractable problem classes, and development of optimization algorithms for discrete structures. His collaborations with researchers like Petr Kučera, Roman Barták, and Endre Boros have produced influential work in constraint programming and artificial intelligence. Distinguished Paper Award at CP2004 for 'Unary resource constraint with optional activities' While specific details about his advising activities are not provided in available sources, his extensive publication record spanning from 1989 to 2017 suggests substantial involvement in academic mentoring. His numerous collaborations both within Charles University and internationally indicate an active role in the academic community. His research has practical applications in knowledge-based systems, constraint satisfaction problems, and artificial intelligence. Čepek maintains an active research profile with publications continuing through 2017. His recent work has focused on knowledge compilation techniques, recognition of tractable DNFs, and the complexity of CNF minimization. He has also explored applications of Boolean techniques to DNA microarray data analysis, demonstrating the interdisciplinary nature of his research.
