Dr. Lisa Kohl is a tenured Researcher in the Cryptology Group at CWI Amsterdam since October 2020. Her work focuses on secure computation and practical post-quantum secure protocols . Prior to CWI, she was a postdoctoral researcher at Technion with Yuval Ishai and completed her PhD at Karlsruhe Institute of Technology under Dennis Hofheinz in 2019. She also spent eight months at the FACT center, IDC Herzliya during her PhD and wrote her master’s thesis at CWI Cryptology Group as a visiting student in 2015. PhD in Cryptology (2019, Karlsruhe Institute of Technology) Postdoctoral Researcher (Technion, 2019-2020) Research Visit Fellow (FACT Center, 2015-2019) Visiting Student (CWI Cryptology Group, 2015) Her research spans secure multi-party computation , homomorphic secret sharing , post-quantum cryptography , and pseudorandom correlation generation . Articles demonstrate expertise in optimizing oblivious transfer , improving Σ-protocol efficiency , and constructing cryptographic primitives from lattice problems and LPN assumptions . Contact: Lisa.Kohl@cwi.nl
Ioan Todinca is a Professor of Computer Science at the University of Orléans, France, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic career spans over two decades, with significant contributions to theoretical computer science, particularly in graph algorithms and distributed computing. Faculty of Science, University of Orléans LIFO Research Laboratory Member of Institut thématique pluridisciplinaire Modélisation, Systèmes, Langages (since 2014) Former director of MIPTIS doctoral school (2012-2014) Former head of Computer Science degree program (2007-2011) Former leader of LIFO Graphs, Algorithms and Computational Models team (2008-2012) Todinca's research focuses primarily on graph algorithms, with expertise in exact algorithms (moderately exponential), parameterized algorithms, and algorithms for specific graph classes. He has made significant contributions to techniques involving tree decompositions, treewidth, minimal separators, and potential maximal cliques. More recently, his work has expanded into distributed algorithms, especially in communication-constrained models like the broadcast congested clique. His research bridges theoretical foundations with practical algorithmic approaches for NP-hard problems. The analysis of his recent publications reveals a strong trend toward distributed computing problems, particularly in congested network models. His work spans from fundamental graph theory problems (cycle detection, graph modification) to applications in quantum computing and model checking. The consistent focus on communication complexity, verification, and efficient algorithms across different computational models demonstrates his ability to adapt theoretical computer science principles to emerging computational paradigms. Todinca has supervised numerous PhD students and has been actively involved in the theoretical computer science community through conference organization and editorial work. His publications appear consistently in top-tier venues including SIAM Journal on Computing, Algorithmica, and proceedings of major conferences like STACS, WG, and DISC. Teaching responsibilities include algorithms, graph theory, and discrete structures for undergraduate and graduate students, with previous experience teaching databases, programming, and software engineering. His educational materials are hosted on the university's Celene platform.
Stefan Rass is a Professor at the Institute of Networks and Security within the Faculty of Engineering & Natural Sciences at Johannes Kepler University Linz (JKU), where he leads the LIT Secure and Correct Systems Lab. As Principal Investigator for FFG-funded projects including reSilienz (digital supply chain resilience, 2023–2025) and ITPUK (AI signature verification, 2022–2024), he bridges theoretical game theory with practical cybersecurity solutions for critical infrastructures and robotics systems. His research spans game-theoretic security models (patrolling games, defense-in-depth strategies), quantum cryptography (QKD network architectures), and cyber deception frameworks like Honeyquest for measuring honeypot effectiveness. Recent work addresses robotics security benchmarking (RobotPerf), cryptographic instruction chaining for control flow protection, and risk assessment methodologies for interdependent infrastructures. His mathematical decision-making approach integrates bounded rationality and stochastic modeling to solve real-world security challenges. Professor Rass actively shapes the field through program committee roles (ARES 2023), peer reviews, and invited talks on security transparency. His current projects focus on cost-benefit-aware monitoring for cyber-physical systems and quantum key distribution standardization, reflecting Austria’s strategic priorities in digital resilience. The LIT Secure and Correct Systems Lab under his direction develops foundational theories while deploying tools for industrial applications, particularly in critical infrastructure protection and secure robotics workflows.
Eyal Lubetzky is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University, and an affiliate Associate Professor at the University of Washington's Mathematics Department since 2008. He holds a PhD from Tel-Aviv University (2007), supervised by Noga Alon, and completed postdoctoral research at Microsoft Research. His research focuses on Probability Theory, Combinatorics, and their applications to statistical physics and theoretical computer science. Key areas of research include phase transitions in statistical mechanics, mixing times of Markov chains, and probabilistic combinatorics. He has received prestigious awards such as the AMS Centennial Fellowship (2016–2017), Rollo Davidson Prize (2013), and fellowships from the American Mathematical Society and Institute of Mathematical Statistics. Lubetzky has mentored numerous interns and serves on editorial boards of journals like Probability and Mathematical Physics and Communications in Pure and Applied Mathematics . His teaching includes advanced courses on probability theory, stochastic processes, and spin glass dynamics. Collaborative work spans interdisciplinary topics, including percolation, random graphs, and the interplay between combinatorial structures and probabilistic models. His research outputs address foundational questions in statistical mechanics, such as cutoff phenomena in Glauber dynamics, interface fluctuations in Ising/Potts models, and the geometry of SOS surfaces. He has also contributed to understanding phase transitions in random media and the dynamics of complex systems.
Oshani Seneviratne is an Assistant Professor of Computer Science at Rensselaer Polytechnic Institute (RPI), leading the BRAINS Lab. She holds a Ph.D. and S.M. from MIT (Computer Science) and a B.Sc. (Hons) from the University of Moratuwa, Sri Lanka. Her research focuses on decentralized systems, blockchain, health informatics, and federated learning. She previously directed RPI's Health Data Research division. Education Ph.D. in Computer Science, MIT S.M. in Computer Science, MIT B.Sc. (Hons) in Computer Science and Engineering, University of Moratuwa Research Interests Her work bridges decentralized technologies with clinical and financial applications. Key areas include blockchain-based frameworks for federated learning, explainable AI in healthcare, and semantic web technologies for data interoperability. She emphasizes ethical AI, data provenance, and scalable systems for real-world challenges. Recent Work Trends Recent publications highlight advancements in smart contract auditing, LLM applications in financial systems, and blockchain-enhanced data provenance. Her work often combines technical innovation with societal impact, such as improving health data sharing and combating misinformation. Awards & Grants No awards explicitly listed, but her research has been supported by grants related to blockchain, health informatics, and decentralized systems. Advising & Labs Leads the BRAINS Lab, focusing on resilient, intelligent networked systems. Collaborates on projects like the Punya platform for clinical apps and BlockIoT for health data integration. Future Work Expanding into AI explainability for clinical decision support, decentralized mental health monitoring, and blockchain interoperability solutions.
Christina Büsing is a Professor in Combinatorial Optimization at RWTH Aachen University. She leads the Teaching and Research Group on Combinatorial Optimization and is a principal investigator in the UnRAVeL Graduate College. Her research focuses on optimization under uncertainty , robust optimization , and combinatorial optimization , with applications to healthcare , energy systems , and transportation logistics . Alumni of TU Berlin, WWU Münster, and Universidad Complutense de Madrid Junior Professor for Robust Planning in Medical Care at RWTH Aachen (2016-2021) Her methodological expertise spans exact algorithms , heuristics , and complexity theory , with recent publications addressing network flows, facility location, and healthcare scheduling. She has received awards for teaching excellence and gender equality advocacy. 2022 Brigitte Gilles Award for women in science 2019 FAMOS Award for family-friendly leadership 2018 Best Teaching Award at RWTH Aachen Her work combines theoretical rigor with practical applications in urban railway traffic management , solar power systems , and agent-based pandemic response modeling .
Erik Henning Thiede is an Assistant Professor of Chemistry at Cornell University, affiliated with the Department of Chemistry and Chemical Biology. He joined the faculty in Summer 2023 and leads the Thiede Lab, which focuses on understanding protein motion and function through computational tools that integrate machine learning, molecular simulation, and chemical physics. PhD in Chemistry from the University of Chicago (advised by Profs. Aaron Dinner and Jonathan Weare) Postdoctoral Research at Flatiron Institute CCM (collaborating with Prof. Risi Kondor, Dr. Pilar Cossio, and Dr. Sonya Hanson) The lab's research spans several key areas: Developing algorithms to extract free energies from cryo-EM data Creating permutation-equivariant machine learning models for chemical systems Improving error estimation in molecular simulation frameworks like MBAR Applying Wasserstein flows and probabilistic methods to cryo-EM analysis Expanding cryo-EM capabilities for disordered protein regions Integration of experimental data with computational simulations His lab's publications highlight trends in computational chemistry, including the application of graph neural networks, Bayesian inference, and advanced statistical methods to molecular dynamics. These works bridge chemical physics, machine learning, and structural biology. As an academic advisor, Thiede mentors a diverse group of graduate students and postdoctoral researchers. His lab actively collaborates with interdisciplinary teams across Cornell and other institutions, with current projects involving chemical engineering, applied mathematics, and biophysics. The Thiede Lab at Cornell is committed to open and inclusive science. Values include curiosity, openness to new ideas, mutual support in research, and active inclusion of diverse backgrounds. The lab occupies space at 214 Baker Lab and maintains a research website at thiedelab.github.io .
Michael M. Zavlanos is the Yoh Family Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University's Pratt School of Engineering. He also holds secondary appointments in the Department of Computer Science and the Department of Electrical and Computer Engineering. Currently serving as the Director of the Healthcare Systems Optimization program with Duke AI Health and as an Amazon Scholar with Amazon Robotics, his academic career spans control theory, optimization, and artificial intelligence with applications across multiple domains. Dr. Zavlanos received his educational foundation from prestigious institutions: Diploma in Mechanical Engineering from the National Technical University of Athens (NTUA), Greece (2002) M.S.E. in Electrical and Systems Engineering from the University of Pennsylvania (2005) Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2008) His research program spans multiple interconnected domains, with a strong foundation in control theory, optimization, and learning methodologies . This theoretical work directly enables applications in robotics and autonomous systems , where his team develops algorithms for multi-robot coordination, motion planning under complex constraints, and network connectivity maintenance. A significant portion of his work addresses networked and distributed control systems , focusing on how multiple agents can coordinate effectively with limited communication. More recently, he has expanded his research into cyber-physical systems with healthcare applications, leveraging his expertise to optimize healthcare delivery systems through the Duke AI Health initiative. Dr. Zavlanos' work demonstrates a consistent trajectory from theoretical foundations to real-world applications. His early work established fundamental principles for maintaining connectivity in mobile robot networks, which evolved into more sophisticated approaches for temporal task planning and risk-averse decision making in uncertain environments. The most recent phase of his research integrates machine learning with traditional control theory to address complex healthcare system optimization problems. His significant contributions to the field have been recognized through prestigious awards: Office of Naval Research Young Investigator Program (YIP) Award (2014) National Science Foundation Faculty Early Career Development (CAREER) Award (2012) National Science Foundation Faculty Early Career Development (CAREER) Award (2011) Duke University Distinguished Faculty Rank (2019) Duke University Distinguished Professor designation (2018) As an educator, Dr. Zavlanos has taught courses including ME 627: Linear System Theory, ME 592: Research Independent Study, ECE 391/291: Projects in Electrical and Computer Engineering, and CEE 627: Linear System Theory. His research program has been supported by multiple grants from the National Science Foundation and the Office of Naval Research, enabling him to mentor numerous graduate students and postdoctoral researchers in the development of cutting-edge control and optimization algorithms. Dr. Zavlanos leads research efforts at the intersection of control theory, optimization, and artificial intelligence, with particular focus on translating theoretical advances into practical applications. His recent work with Duke AI Health represents a strategic expansion of his research portfolio into healthcare systems optimization, where he applies his expertise in algorithmic decision making to improve patient scheduling, resource allocation, and operational efficiency in medical settings. Through his Amazon Scholar role, he also contributes to advancing robotics technologies for real-world applications.
Evangelia (Eva) Kalyvianaki is a Senior Lecturer (equivalent to Associate Professor) in the Department of Computer Science and Technology at the University of Cambridge , where she is also a member of the Systems Research Group / netos group . Previously she held faculty positions as Lecturer at City University London and as post-doctoral researcher at Imperial College London. Education Ph.D. in Computer Science, Computer Laboratory (SRG/netos group), University of Cambridge M.Sc. in Computer Science, University of Crete, Greece B.Sc. in Computer Science, University of Crete, Greece Research Interests Her research spans the broad areas of Cloud Computing , Big Data Processing , Autonomic Computing , and Distributed Systems . A central theme is the design and management of next-generation, large-scale cloud applications, with an emphasis on applying mathematical reasoning—particularly control-theoretic techniques such as Kalman and H-infinity filtering—to address the complexity and uncertainty inherent in modern distributed infrastructures. Topics of active investigation include adaptive CPU and resource provisioning for virtualized servers, fairness and overload management in federated stream-processing systems, explicit state management for big-data frameworks, and distributed optimization algorithms for large-scale networked systems. Publications & Research Impact Across more than thirty peer-reviewed papers, her work demonstrates a consistent trajectory toward bridging rigorous control theory with practical systems challenges in the cloud. Signature contributions include the THEMIS framework for fair federated stream processing, dynamic block-sizing algorithms for data-stream engines, and robust resource-provisioning schemes based on advanced filtering techniques. Recent publications extend these ideas to fully distributed, finite-time coordination protocols that operate under quantized communications and time-varying delays, reflecting an expanding scope toward large-scale networked control systems. Scientific Awards No specific awards or fellowships are listed in the provided material. Advising & Funding While individual student names are not disclosed, her extensive publication record with numerous co-authors indicates active supervision of doctoral and master’s researchers. Funding acknowledgements in papers suggest support from UK research councils, EU projects, and industrial partnerships, although explicit grant details are not provided. Labs & Teams She is affiliated with the Systems Research Group (netos) within the Cambridge Computer Laboratory, a leading collective focused on networked and operating systems research, providing a collaborative environment for experimental cloud and distributed-systems work.
Stanislaw Radziszowski is a Professor in the Department of Computer Science at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. He has been at RIT since 1984, after working at the National Autonomous University of Mexico from 1980-1984. His academic career includes multiple visiting positions at the Australian National University in the 1990s and ongoing collaborations with institutions in Poland. Radziszowski earned his Ph.D. in Mathematics and Computer Science from the University of Warsaw in 1980, advised by Antoni Kreczmar and Andrzej Salwicki. His educational background includes BS and MS degrees from the same institution. Dr. Radziszowski's primary research focuses on combinatorial computing, with special emphasis on Ramsey theory and computational design theory. His "Small Ramsey Numbers" survey has become a standard reference in the field. More recently, he has expanded into applied cryptography, particularly post-quantum cryptography, leading to collaborations with the Computer Engineering Department. His work often combines theoretical insights with practical computational approaches to solve classical problems in combinatorics and graph theory. His recent publications demonstrate a growing focus on cryptographic applications, including post-quantum cryptography education, analysis of the MK-3 Authenticated Encryption Algorithm, and homomorphic encryption implementations. These works reflect his ability to bridge theoretical mathematics with practical security applications, particularly in hardware implementations and side-channel resistance. Dr. Radziszowski has made significant contributions to Ramsey theory, most notably computing R(4,5)=25 with Brendan McKay and improving bounds for R(5,5) and R(4,6). His survey "Small Ramsey Numbers" is a regularly updated reference in the field. He has also contributed to computational design theory, proving nonexistence of certain block designs. As an educator, Radziszowski teaches theory-oriented courses including Introduction to Cryptography, Foundations of Cryptography, and Quantum-Resistant Cryptography. He has developed specialized courses on combinatorial computing and has supervised numerous MS theses and projects. His recent work on post-quantum cryptography has led to educational initiatives in this emerging field. Dr. Radziszowski maintains active research collaborations, particularly with Xiaodong Xu on Ramsey and Folkman problems, and with Marcin Lukowiak on cryptographic implementations. His work often bridges the gap between theoretical mathematics and practical computing applications.
Madhur Tulsiani is a Professor at the University of Chicago's Department of Computer Science and a researcher at the Toyota Technological Institute at Chicago (TTIC). His research focuses on theoretical computer science, particularly complexity theory and algorithm design, with applications in coding theory and information theory. He has been supported by NSF grants 1254044, 1816372, and 2326685. Education: Bachelor’s in Computer Science, IIT Kanpur (2001-2005) Ph.D. in Computer Science, UC Berkeley (2005-2009), advised by Luca Trevisan Postdoctoral fellowships at the Institute for Advanced Study (IAS) and Princeton University Research Interests: Mathematical foundations of computation Complexity theory and algorithm design Coding theory and error-correcting codes Sum-of-Squares hierarchies and approximation algorithms Recent Contributions: Pioneering work on list decodable codes and expander-based constructions Advances in approximation algorithms for high-dimensional expanders Lower bounds for Sum-of-Squares algorithms using high-dimensional expanders Teaching: Information and Coding Theory Mathematical Toolkit (linear algebra/probability) Summer REU programs in theoretical computer science Students: Advised PhD students including Fernando Granha Jeronimo, Goutham Rajendran, and Shashank Srivastava Co-advised students with Sasha Razborov, Janos Simon, and others Labs/Groups: Member of the Theoretical Computer Science Group at TTIC and UChicago, contributing to cross-disciplinary research in algorithms and complexity.
Kasra Rafi is a Professor at the Department of Mathematics, University of Toronto, based at the Bahen Centre (Room 6236). His research focuses on Geometry and Dynamical Properties of Teichmuller Space, with significant contributions to graph theory, hyperbolic geometry, and the connectivity of trivalent graphs. His work involves collaborations with Roi Docampo, Jing Tao, and Ci Zhang, utilizing computational tools like Sage to study graph stratification by girth and properties such as 3-covered cycles. He also explores eigenvalues and bottlenecks in graph structures, addressing questions related to expander families and graph connectivity. His teaching includes specialized graduate courses such as Randomness in Groups (Winter 2025) and projects like Graph of Graphs , which visualizes the geometry of trivalent graphs through Whitehead moves. He has mentored students in programs like the Math Mentorship Program at the University of Toronto and Work Study initiatives. Rafi’s research intersects mathematical theory with computational analysis, emphasizing geometric stratification and automorphism properties in distance-transitive graphs.
Ramin Javadi is an Associate Professor of Mathematics at Isfahan University of Technology and a Visiting Professor at the Laboratoire de l'Informatique du Parallélisme (LIP) at ENS Lyon from June to July 2025. His research focuses on graph theory and combinatorics, with notable contributions to structural graph theory and algorithm design. He holds a Ph.D. in Mathematics from Sharif University of Technology (thesis: 'Isoperimetric problems on graphs'), and an M.Sc. from Isfahan University of Technology (thesis: 'Chip-firing game and b-coloring of graphs'). His collaboration with LIP aims to strengthen scientific exchange in structural graph theory and expand institutional ties. Key research areas include parameterized complexity, Ramsey numbers, and graph coloring. Despite no explicit mention of awards, his extensive publication record reflects impactful contributions to discrete mathematics. He has advised multiple students from Isfahan University of Technology, though specific names are not listed. His work bridges theoretical computer science and pure mathematics, particularly through interdisciplinary studies in graph algorithms and combinatorial optimization.
Amin Jalali is an Associate Professor of Computer and Systems Sciences at Stockholm University, specializing in business process modeling, analysis, and management. He is affiliated with the Department of Computer and Systems Sciences within the Faculty of Social Sciences, where he serves as a board member and manages three graduate courses: Business Process Design and Intelligence, Business Process and Case Management, and Data Warehousing. Institution: Stockholm University Department: Department of Computer and Systems Sciences (DSV) Research Groups: Natural Language Processing Research Group and PRECIS (Process, Requirements, Enterprise, Capability, Information Systems modelling) His research focuses on business process analysis through model-based and data-driven techniques, with particular emphasis on process simulation, process mining, event knowledge graphs, and object-centric process mining. He has led numerous research projects across healthcare, education, and finance domains. His work includes significant contributions to the development of NLP methods involving large language models, with focus on privacy, explainability, and domain adaptation. Jalali's research output shows a strong trend toward practical applications of process mining techniques, particularly in healthcare contexts like drug-drug interaction analysis and elderly care. More recently, his work has expanded into blockchain applications for fraud detection, motor imagery signal classification, and advanced object-centric process mining approaches. His publications span both theoretical contributions to business process management frameworks and practical implementations in real-world settings. He has extensive industry experience in designing and implementing Business Intelligence and Big Data Analytics solutions, which informs his academic work and teaching approach. His research has been published consistently from 2012 through 2024, demonstrating sustained scholarly productivity in his field. Dr. Jalali has contributed significantly to the development of tools and libraries for process mining, including the dfgcompare library for process variant analysis and implementations for object-centric process mining. His work bridges academic research with practical applications, particularly evident in healthcare projects like the DDIs-Graph system for identifying drug-drug interactions.
Jim Renshaw is an Associate Professor in the Mathematics department at the University of Southampton, where he conducts research in algebraic semigroup theory with particular emphasis on actions of semigroups and monoids on sets and ordered structures. Renshaw's primary research interests include: Algebraic semigroup theory and its applications Actions of semigroups and monoids on sets and ordered structures Semigroup amalgams and homological classification theory of monoids Flat covers and pre-covers of monoid acts Structure and actions of E-dense and E-inversive semigroups Discrete log problem and its connections with semigroup theory His publication record shows a clear progression in his research focus, beginning with foundational work on semigroup amalgams and flat covers of monoid acts, then expanding to investigate inverse semigroup actions on graphs and trees, and most recently exploring connections between E-dense semigroups and the discrete log problem. His 2024 paper 'Semilattices of stratified extensions' represents the current direction of his research in ordered algebraic structures. Renshaw is actively involved in doctoral supervision, currently mentoring William Lee Warhurst who is pursuing an iPhD in Mathematical Sciences. He has indicated that he is accepting applications from prospective PhD students. His teaching interests are concentrated in algebra and number theory, contributing to the mathematics curriculum at the University of Southampton.
