Tomáš Helikar is a researcher at the Department of Mathematical and Statistical Sciences within the College of Arts and Sciences at the University of Nebraska at Omaha. His work focuses on computational modeling of biological processes, with a particular emphasis on dynamical systems and qualitative modeling frameworks. Helikar co-developed Bio-Logic Builder , a web-based tool for creating Boolean rule-based models of biological regulatory mechanisms The Cell Collective , an open platform for systems biology collaboration His research spans applications in signal transduction , cell cycle modeling , and viral replication (notably influenza A), combining mathematical formalism with biomedical inquiry. While no formal awards or student advisement information appears in the available text, Helikar's publications demonstrate strong technical expertise in discrete formalism and non-technical model construction for laboratory scientists. His work enables qualitative data conversion into Boolean expressions for simulating complex biological networks.
Nutan Limaye is a Professor at the Department of Theoretical Computer Science , IT University of Copenhagen , specializing in Algorithms , Computational Complexity , and Algebraic Circuits . She actively contributes to research on polynomial complexity, quantum computation, and lower bound techniques. Key Research Areas : Algebraic Circuit Complexity, Polynomial Computation, Graph Isomorphism, Boolean Satisfiability Current Projects : FLows : Formula complexity and lower bounds (2024-2026) DIREC: OnlineAlgo : Digital research initiatives (2022-2025) BARC2 : Basic Algorithms Research Copenhagen (2024-2029) Scientific Recognition includes the FOCS Best Paper Award (2022) . Her work frequently appears in top conferences like CCC , FSTTCS , and SIGACT News , with recent collaborations in Denmark and international institutions. She contributes to public understanding through media appearances on topics like basic computer science research and BARC's initiatives .
Konstantin Korovin is a Reader at the Department of Computer Science, The University of Manchester. He has held various academic roles including Senior Lecturer (2015-2023), Royal Society University Research Fellow (2007-2015), and Research Associate (2004-2007). Current research focuses on automated theorem proving , machine learning integration , and verification of hardware/software . His work includes developing systems like iProver , iProver-ML , and SMLP , which combine formal methods with ML techniques. Key contributions span non-linear constraint solving , quantified Boolean logic , and DNA computing . He has won over 20 international awards, including SMT-COMP and CASC categories. Scientific Awards : Ackermann Award, Best Thesis Prize, Best Paper at FroCoS'19, CASC and SMT-COMP prizes. He supervises PhD and postdoc researchers, with alumni working at Intel, Google, and MathWorks. His tools are applied in industry, notably by Intel for hardware optimization.
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 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.
Sebastian Dalleiger is an Assistant Professor at the Division of Theoretical Computer Science, Kungliga Tekniska Högskolan (KTH Royal Institute of Technology). His research focuses on theoretical foundations of machine learning, data mining, and graph theory, with particular expertise in matrix factorization, pattern discovery, and hypergraph analysis. Current affiliation: KTH Royal Institute of Technology Department: Theoretical Computer Science Email: sdall@kth.se His recent work explores federated learning architectures, non-negative matrix factorization, and structural analysis of stochastic block models across multiple graphs. He develops algorithms combining proximal optimization with privacy-preserving techniques, addressing challenges in distributed data analysis. Publications demonstrate interdisciplinary applications in network science, information theory, and computational geometry. Key contributions include novel frameworks for Ollivier-Ricci curvature in hypergraphs and sequential false discovery control for pattern mining.
Sylvie Coste-Marquis is a Lecturer at the Institute of Technology of Lens, part of the University of Artois. Her research focuses on knowledge representation, argumentation frameworks, and computational logic, particularly in artificial intelligence applications. PhD in Computer Science (1994) from Henri Poincaré University of Nancy Co-supervised three PhD theses on argumentation systems, QBF, and speech recognition Administrative roles: Vice-President for Digital Affairs (2016-), Project Manager (2012-2016), and Head of IT Department (2010-2013) Her work explores abstract argumentation, belief revision, and quantified Boolean formulae, with recent contributions to enforcing extensions via optimization and translating argumentation frameworks. She actively participates in program committees for top-tier conferences like IJCAI and COMMA. She has contributed to ANR projects on multi-agent argumentation, configurable product recommendation, and preference handling in combinatorial domains. Her teaching includes modules on computer systems, object-oriented design, and AI, with recognized innovations in educational methods.
Rolf Drechsler is a Full Professor and Head of the Group of Computer Architecture at the University of Bremen's Institute of Computer Science since 2001, and Director of the Cyber-Physical Systems Group at DFKI Bremen since 2011. He holds an adjunct professorship at the Indian Statistical Institute and has been affiliated with Duke University. Education: Diploma (1992) and Dr. phil. nat. (1995) in Computer Science from Goethe University Frankfurt Academic Leadership: Dean of Mathematics and Computer Science Faculty (2018-2025), Vice Rector for Research (2008-2013) His research focuses on formal verification , RISC-V architectures , and quantum/in-memory computing . Recent work explores LLM integration in hardware testing and polynomial-based verification techniques. Publications from 2024-2025 span IEEE Transactions , DATE , and DAC , emphasizing automated verification , quantum circuit mapping , and LLM-driven testbench generation . Scientific Awards IEEE/ACM Best Paper Awards (2013, 2018) Berninghausen-Preis for Innovative Teaching (2018) IEEE Fellow (2015) Founder Award for Solvertec (2013) He has served on program committees for DAC, ICCAD, DATE, and founded graduate schools in Embedded Systems and System Design under Germany's Excellence Initiative.
Giuseppe Sanfilippo is a Full Professor of Probability (MAT/06) in the Department of Mathematics and Computer Science at the University of Palermo, Italy. He holds the position of FULL PROFESSOR (MATH-03/B) and maintains office hours on Thursdays from 9:00 to 11:00 at DMI, Via Archirafi 34, second floor, Room 213. His academic appointments include teaching positions across multiple schools at the University of Palermo: School of Basic and Applied Sciences (Mathematics program) School of Basic and Applied Sciences (Artificial Intelligence program) Polytechnic School (Statistics for Data Analysis program) School of Basic and Applied Sciences (Computer Science program) Professor Sanfilippo's research focuses on the theoretical foundations of probability theory, particularly exploring the intersection between probability, mathematical logic, and conditional reasoning. His work centers on conditional events, coherence principles, trivalent logics, and connexive logic. He has made significant contributions to understanding the probabilistic interpretation of Aristotelian syllogisms, entropy and extropy measures, and the mathematical structures underlying compound conditionals. His research has important applications in artificial intelligence, uncertainty management, and decision theory, with over two decades of publications showing consistent development of these themes. Professor Sanfilippo has been actively involved in the academic community, organizing and participating in numerous international conferences including SUM (Scalable Uncertainty Management), ECSQARU (European Conferences on Symbolic and Quantitative Approaches to Reasoning with Uncertainty), and specialized workshops on connexive logic and probabilistic reasoning. His work bridges theoretical developments with practical applications in knowledge representation and reasoning under uncertainty, as evidenced by his extensive conference participation from 2014-2024 across Europe. He mentors students through various academic programs and has supervised numerous theses in probability theory and its applications. His teaching portfolio includes core courses such as 'Calculation of Probabilities' across Mathematics, Artificial Intelligence, Statistics for Data Analysis, and Computer Science programs, as well as specialized courses like 'Uncertain Reasoning and Probability,' reflecting his commitment to both foundational education and advanced research training. Professor Sanfilippo maintains an active research laboratory focused on probabilistic reasoning, where interdisciplinary teams explore the mathematical foundations of uncertainty and their applications in artificial intelligence and decision systems. His current research agenda includes extending coherence principles to complex conditional structures and developing scalable methods for uncertainty management in AI systems, as demonstrated by his upcoming conference chair position for SUM 2024 in Palermo.
Leonard Harris is an Assistant Professor in the Department of Biomedical Engineering within the College of Engineering at the University of Arkansas. His research focuses on cancer systems biology, with expertise in computational modeling and simulation of complex intracellular signaling pathways and cell-cell interactions in tumors. Working closely with experimental collaborators, his lab develops comprehensive, mechanistic models of molecular pathways underlying non-genetic mechanisms of drug resistance in cancer cells. Education: Postdoctoral Research Fellow, Biochemistry, Vanderbilt University School of Medicine Postdoctoral Associate, Computational & Systems Biology, University of Pittsburgh School of Medicine Ph.D., Chemical and Biomolecular Engineering, Cornell University B.S., Chemical Engineering, University of Colorado, Boulder Dr. Harris's research centers on cancer systems biology with a focus on phenotypic plasticity and non-genetic heterogeneity in anticancer drug response. His lab develops multiscale models of intracellular signaling pathways and cell-cell interactions to understand tumor heterogeneity. A key aspect of his work examines intrinsic stochasticity in cell fate decision making and its role in therapeutic resistance. His computational approaches aim to create in silico platforms for virtual anticancer drug screens to identify novel molecular targets and improve cancer treatment outcomes. Analysis of Dr. Harris's recent publications reveals a consistent focus on computational approaches to understand tumor heterogeneity and drug resistance mechanisms. His work integrates mathematical modeling with experimental data to dissect genetic, epigenetic, and stochastic sources of variability in cancer cell populations. His research spans multiple cancer types, with particular emphasis on melanoma and lung cancer, examining how non-genetic mechanisms contribute to therapeutic resistance. The publications demonstrate progression from foundational computational methods to increasingly sophisticated models of tumor dynamics and drug response. Scientific Awards: NIH/NCI Transition Career Development Award to Promote Diversity (K22) National Library of Medicine Biomedical Informatics Postdoctoral Fellowship Semiconductor Research Corporation Graduate Fellowship Dr. Harris serves as principal investigator for multiple research grants focused on cancer systems biology and computational oncology. His lab receives funding from the National Institutes of Health, including the NCI K22 award supporting his transition to independence. His research integrates experimental and computational approaches through collaborations with wet-lab researchers at the University of Arkansas and other institutions. Current projects examine tumor-induced bone disease, phenotypic plasticity in small cell lung cancer, and mechanisms of drug tolerance in melanoma. Dr. Harris leads a computational cancer biology lab that develops and applies advanced modeling techniques to understand tumor heterogeneity and drug resistance. His team works at the intersection of computational biology, systems pharmacology, and cancer biology, creating models that bridge molecular, cellular, and population scales. The lab collaborates extensively with experimental groups to validate model predictions and generate new hypotheses about cancer progression and therapeutic response.
Simone Severini is a Professor of Physics of Information at the University College London , affiliated with the Department of Computer Science . He is a Royal Society University Research Fellow and contributes to multidisciplinary groups including Intelligent Systems , UCL CS Quantum , UCL Quantum Science and Technology Institute , and CoMPLEX . Research Interests: His work bridges Quantum computing Machine learning Graph theory Quantum information theory Computational biology with a focus on quantum algorithms, classical simulation of quantum systems, and mathematical frameworks for physical correlations. Scientific Contributions: Recent publications span quantum state learning, non-Markovian dynamics, adversarial quantum learning, and graph isomorphism. His projects include Quantum Computing, Information, and Algebras of Operators and the Distributed Information initiative . Awards: Royal Society University Research Fellowship Best Paper Award at FCT2017
Gheorghe Craciun is a Professor in the Department of Mathematics and Department of Biomolecular Chemistry at the University of Wisconsin-Madison . His research focuses on Mathematical and Computational Methods in Biology and Medicine , particularly in analyzing chemical reaction networks, dynamical systems, and algebraic geometry applications. He has taught advanced courses like Math 703 and organized workshops, including the Moshe Mendelson Memorial Lecture and the Moshe Mendelson Workshop on Mathematics of Reaction Networks . His recent publications explore topics such as Toric Differential Inclusions , Complex Balanced Equilibrium , and Reaction Network Stability . His work involves collaborations with researchers like Casian Pantea , Polly Yu , and Minh Binh Tran . He also contributes to interdisciplinary areas, including biochemistry , neuroscience , and genomics , applying mathematical frameworks to biological systems.
Professor Andrew Schumann is a faculty member at the University of Information Technology and Management in Rzeszow, Poland. With 202 publications and 1,143 citations, he has established himself as a significant interdisciplinary researcher working at the intersection of philosophy, logic, history, and unconventional computing. His academic expertise spans: Analytical Philosophy Philosophy of Language Ontology and Ancient Philosophy Philosophy of Religion Artificial Intelligence and Unconventional Computing History of Logic across civilizations Professor Schumann's research focuses on examining logical structures across different civilizations and historical periods, from ancient Mesopotamian divination practices to Judaic hermeneutics and Buddhist logic. He is particularly known for his work on unconventional computing models inspired by biological systems, especially slime mold (Physarum polycephalum), which he studies as a natural computing substrate capable of implementing logical operations and solving complex problems. His recent publications (2023-2025) demonstrate a continued exploration of ancient logical systems and their relevance to modern computational paradigms. His work spans diverse areas including: Historical analysis of logical traditions (Judaic, Mesopotamian, Buddhist, Greek) Unconventional computing models based on biological systems Cultural diffusion of philosophical and religious ideas Comparative studies of logical systems across civilizations Applications of ancient logical structures to modern computational problems Professor Schumann has received significant scholarly attention with 58,434 reads of his publications, indicating broad interest in his interdisciplinary approach. His work bridges humanities and computational sciences in innovative ways that challenge traditional disciplinary boundaries. His international collaborations include researchers from institutions worldwide, reflecting the global relevance of his research topics. With publications spanning multiple languages and cultural contexts, Professor Schumann contributes to a truly cross-cultural understanding of logic and its applications.
