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Freya BehrensView profile

Researcher

Swiss Federal Institute of Technology in LausanneSwitzerland

Freya Behrens is a Researcher at the Statistical Physics of Computation Laboratory (SB) within the École Polytechnique Fédérale de Lausanne (EPFL). Her work bridges statistical physics and computational systems, focusing on topics like neural networks, cellular automata, and AI applications in education. She investigates phase transitions in attention mechanisms and the theoretical underpinnings of deep learning. Recent publications explore AI's impact on higher education and the interplay between Hessian matrices and decision boundaries. Her research integrates interdisciplinary methods from physics and computer science, addressing questions such as how memorization occurs in neural networks and the dynamics of spin glasses. Freya’s contributions span theoretical frameworks (e.g., dynamical cavity methods) and applied AI systems (e.g., knowledge graph exploration). While no specific grants or awards are listed, her work demonstrates engagement with cutting-edge challenges in machine learning and computational modeling. She is affiliated with EPFL’s School of Basic Sciences, reflecting her foundational research in physics and mathematics-driven computation.

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Richard StockerView profile

Senior Lecturer

University of ChesterUnited Kingdom

Richard Stocker is a Senior Lecturer in the Department of Computer and Engineering Sciences at The University of Chester, affiliated with the Faculty of Science, Business and Enterprise. He previously worked as a grade II Computer Scientist at NASA Ames (2013–2015), focusing on human-agent-robot interactions and air traffic control simulations. Before joining Chester in 2017, he taught at Middlesex University (2015–2017). His current roles include designing the new Data Science programme and overseeing the Year in Industry module, where students gain industry experience. Education: BSc in Computer Science (2005), University of Liverpool MSc in Advanced Computer Science (2008), University of Liverpool PhD in Formal Verification of Human-Agent-Robot Teamwork (2013), University of Liverpool Richard’s research interests span Formal Methods , Human-Agent-Robot Simulation , and Energy-Efficient Computing , with recent work emphasizing Quantum-Dot Cellular Automata (QCA) circuits for ultra-low power dissipation. His projects combine QCA design innovations with practical applications in nanochip integration, robotics, and affective computing. Prior work includes formal verification of astronaut-rover teams for planetary exploration and stigmergy-based swarm robotics coordination. His publications reflect a strong focus on reversible computing and nanotechnology , alongside contributions to audio emotion recognition and multi-agent systems. Energy efficiency and physical-level quantum mechanics modeling are recurring themes in his QCA research. Advising & Grants: Mentored student Mohammed Alharbi Former Programme Leader for Software Engineering Designed the Data Science programme at the University of Chester He is affiliated with the Health and Wellbeing Research and Knowledge Exchange Institute , contributing to interdisciplinary projects at the faculty level.

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Narad RampersadView profile

Professor

The University of WinnipegCanada

Narad Rampersad is a Professor of Mathematics and Chair (until June 2025) at the Department of Math/Stats, University of Winnipeg. He holds a Ph.D. and M.Math. in Computer Science from the University of Waterloo and a B.Sc. in Computer Science from the University of Ottawa. His research focuses on combinatorics on words, automata theory, numeration systems, and related areas, supported by an NSERC Discovery Grant. He is internationally recognized for contributions to Dejean's Conjecture and has over 50 journal publications. He actively organizes the One World Combinatorics on Words Seminar with global collaborators. Education: B.Sc. (Computer Science, University of Ottawa), M.Math. (Computer Science, University of Waterloo), Ph.D. (Computer Science, University of Waterloo). His Erdős number is 2 via Jeffrey Shallit. He has supervised 7 undergraduate research projects and co-supervised 2 postdocs, with outputs in refereed journals. Awards include the Erica and Arnold Rogers Award (2017) and multiple University of Winnipeg Merit Awards (2011–2014). Research trends in his articles emphasize automata properties, repetition thresholds, and pattern avoidance in sequences, with applications to number theory and formal languages. He collaborates widely, including with leading institutions like University of Liege and the Fields Institute.

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Gerald Jay SussmanView profile

Professor

Massachusetts Institute of TechnologyUnited States

Gerald Jay Sussman is the Panasonic Professor of Electrical Engineering at the Massachusetts Institute of Technology (MIT). He received his S.B. (1968) and Ph.D. (1973) in mathematics from MIT and has been conducting artificial intelligence research there since 1964. His primary research focuses on understanding problem-solving strategies used by scientists and engineers, with dual goals of automating these processes and formalizing educational methodologies. He co-directs the Sussman Lab at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). His research spans artificial intelligence, computer languages, VLSI design, computational classical mechanics, synthetic biology, and telescope engineering. Notable contributions include co-creating the Scheme programming language, developing AI-based CAD tools for VLSI, designing the Digital Orrery for orbital mechanics simulations, and pioneering computational approaches to teaching classical mechanics. His current work includes developing explainable AI systems for autonomous vehicles. Professor Sussman's publications demonstrate broad interdisciplinary impact, with recent works spanning computer science education, software design, computational physics, and synthetic biology. His research consistently bridges theoretical computer science with practical engineering applications and educational innovation. Scientific Awards & Honors: Karl Karlstrom Outstanding Educator Award (ACM, 1990) Amar G. Bose Award for Teaching (MIT, 1992) IEEE EAB Major Education Innovation Award (2023) Taylor L. Booth Education Award (IEEE, 2024) National Academy of Engineering Member Fellow: IEEE, AAAI, ACM, AAAS, American Academy of Arts and Sciences He has supervised 46 PhD students spanning five decades, with dissertations covering AI, computer architecture, computational biology, and physical system modeling. His Sussman Lab develops computational tools for science education and engineering design, including contributions to the Magellan telescopes in Chile. Current projects involve explainable AI systems and computational mechanics frameworks.

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Junjie HuView profile

Assistant Professor

University of Wisconsin-MadisonUnited States

Junjie Hu is an Assistant Professor at the University of Wisconsin-Madison, holding appointments in the Department of Biostatistics and Medical Informatics (School of Medicine and Public Health) and the Department of Computer Science (College of Letters and Science). He leads the Hulab research group focusing on natural language processing (NLP) and machine learning, with a particular emphasis on multilingual NLP, multimodal learning, and large language models (LLMs). His work aims to build robust AI systems for healthcare and communication applications. Affiliations: Biostatistics & Medical Informatics, Computer Science, Data Science Institute Education: PhD in Computer Science from Carnegie Mellon University (advisors: Jaime Carbonell & Graham Neubig) His research spans multilingual NLP, transfer learning, LLM alignment, and cultural-aware AI. Key interests include efficient multimodal models for high-stakes domains and cognitive language agents for complex reasoning. Recent projects explore LLM interpretability and societal impact through benchmarks like COMMA and cultural alignment studies. Dr. Hu teaches Advanced Natural Language Processing (CS 769) and has supervised over 20 graduate and undergraduate students. He has received awards including the CMU Dissertation Writing Grant and NAACL Best Demo Nomination. Current research focuses on safe and inclusive AI development through alignment techniques and ethical evaluation frameworks. Labs/Teams: Hulab (Human-Centered Language Agents Lab), collaborating with interdisciplinary teams on NLP and medical informatics applications.

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Lisa HellersteinView profile

Professor

New York UniversityUnited States

Lisa Hellerstein is a Professor in the Department of Computer Science and Engineering at the NYU Tandon School of Engineering. She holds an A.B. from Harvard University (1984) and a Ph.D. from the University of California, Berkeley (1989). Her research focuses on Approximation Algorithms, Stochastic Combinatorial Optimization, Machine Learning Theory, Boolean Functions, and Computational Complexity. Her work spans theoretical computer science with contributions to algorithmic efficiency, stochastic decision-making, and discrete mathematics. Notable publications include advancements in min-sum ordering problems, stochastic score classification, and approximation algorithms for Boolean function evaluation. She is affiliated with the Algorithms and Foundations Group at NYU Tandon, contributing to projects like 'Minimum Cost Strategies for Sequential Search and Evaluation' and 'Robust Uncertain Data Management.' Her research has appeared in top venues such as ITCS, ESA, SODA, and SIAM Journal on Computing, with a focus on theoretical guarantees and practical algorithmic solutions. She has advised numerous graduate students and holds grants related to computational learning and optimization frameworks.

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Shuaiwen SongView profile

Associate Professor

University of SydneyAustralia

Shuaiwen Song is a SOAR Associate Professor (tenured) at the School of Computer Science , University of Sydney, and directs the Future System Architecture (FSA) Lab . He holds affiliated professor positions at the University of Washington's Electrical Engineering department and serves as a Visiting Professor at Microsoft. Key research areas: High Performance Computing (HPC), Hardware-Software Co-design, Emerging Architectures (heterogeneous, quantum), and System ML Current projects: Large-Scale Sparse Model Design (Google), Tiered Memory Systems (Google), Compiler Optimizations for Heterogeneous Computing (Microsoft/Alibaba), Planet-Scale XR Systems (Meta), Quantum System Architecture (Australian Research Council) His work bridges system software and hardware, focusing on holistic design for complex many-accelerator systems and futuristic architectures like VR/AR and quantum accelerators. Recent publications highlight advancements in temporal graph processing, VR rendering, and ReRAM-based CNN training. He has received prestigious awards including IEEE Mid-Career Award for Scalable Computing , Alibaba AIR Faculty Award , and Australia's Most Innovative Engineers recognition.

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Jean-Baptiste JeanninView profile

Assistant Professor

University of Michigan-Ann ArborUnited States

Jean-Baptiste Jeannin is an Assistant Professor in the Department of Aerospace Engineering at the University of Michigan — Ann Arbor, leading the MARVL group. He is also affiliated with the departments of Computer Science and Engineering and Robotics. His research focuses on formal verification of cyber-physical systems, logics and semantics of programming languages, and coinductive programming types. He holds a Ph.D. in Computer Science from Cornell University (2013) and a Diplôme d'Ingénieur from École polytechnique (2007), with prior roles at Samsung Research America and Carnegie Mellon University. His research interests include verification of aerospace applications, program logics, and formal methods applied to robotics and distributed systems. Notable work includes formally verified algorithms for collision avoidance, network consensus protocols, and numerical methods. He teaches courses such as AERO 552 (Aerospace Information Systems) and EECS 590 (Advanced Programming Languages). Jeannin's recent publications emphasize formal verification in hybrid systems, rounding error analysis, and hardware-software co-design. His work bridges theoretical foundations with practical applications in autonomous systems, cybersecurity, and computational physics. Collaborations include projects on certified approximations of mathematical functions and verified distributed system protocols.

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Elena ShermanView profile

Associate Professor

Boise State UniversityUnited States

Elena Sherman is an Associate Professor in the Department of Computer Science at Boise State University. She earned her Ph.D. and Master's degrees in Computer Science from the University of Nebraska-Lincoln in 2012 and earlier. Her research focuses on program analysis, static analysis, formal verification, and symbolic execution. She actively contributes to the software engineering community through organizing workshops like the Java Pathfinder Workshop and serving on conference program committees and journal review boards. Her work emphasizes tool development, such as PAClab, and advancing techniques in abstract domains and data-flow analysis. Education: Ph.D. in Computer Science, University of Nebraska-Lincoln, 2012 Master's in Computer Science, University of Nebraska-Lincoln Research Interests: Program Analysis Formal Verification Symbolic Execution Data-Flow Analysis Static Analysis Recent Research Trends: Focus on abstract domains (e.g., zonotope, zone domains) Development of benchmarks like muRelBench Integration of Monte Carlo techniques for neural network verification Notable Achievements: Organizer of the Java Pathfinder Workshop series Co-developer of PAClab, a program analysis collaboratory Labs/Teams: PAClab (Program Analysis Collaboratory)

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Michael HahnView profile

Professor

Saarland UniversityGermany

Michael Hahn is a Tenure-Track Professor (W2) at Saarland University's Saarland Informatics Campus, directing the Language, Computation, and Cognition Lab (LaCoCo). He holds dual affiliations with the Department of Language Science and Technology and the Department of Computer Science. Hahn earned his PhD from Stanford University in 2022, advised by Judith Degen and Dan Jurafsky. Education: PhD in Computational Linguistics, Stanford University (2022) Research Interests: His work bridges machine learning and cognitive science, focusing on information processing in machines and the brain. Key areas include understanding transformer architectures, computational cognition, and the theoretical foundations of neural networks. He investigates how models like GPT-2 simulate human cognitive limitations, explores biases in perception, and develops methods for mechanistic interpretability. Recent Achievements: Best Paper Award at ACL 2024 for 'Why are Sensitive Functions Hard for Transformers?' Outstanding Area Chair at EMNLP 2024 Recipient of the Second Prize at ICML 2024's Mechanistic Interpretability Workshop Lab & Collaborations: The LaCoCo Lab focuses on advancing theoretical and applied research in NLP and machine learning, with recent work on chain-of-thought reasoning limits, state space models, and evolutionary pressures in word order.

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Rolf SocherView profile

Professor

Brandenburg University of TechnologyGermany

Prof. Rolf Socher is a Professor at the Department of Computer Science and Media, Brandenburg University of Technology. His expertise lies in Mathematics for Computer Scientists, with a focus on theoretical foundations, algebraic structures, and artificial intelligence. He is a member of the German Mathematical Society (DMV) and the German Informatics Society (GI). Teaching responsibilities include foundational mathematics courses for the Online Media Informatics program. His textbook publications span core areas of computer science education, with notable works on algebraic methods, theoretical computer science, and AI fundamentals. Recent works include an updated 2021 edition of Mathematik für Informatiker . Research interests bridge mathematics and computer science, emphasizing practical applications in algorithm design, formal methods, and computational theory. No specific grants or lab affiliations are mentioned in the provided materials.

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Tom StojsavljevicView profile

Assistant Professor

Beloit CollegeUnited States

Tom Stojsavljevic is an Assistant Professor of Mathematics and Computer Science at Beloit College, and Co-chair of Data Science and Data Analytics. He holds a PhD and BS in Mathematics from the University of Wisconsin-Milwaukee. His research bridges applied mathematics, biology, and chemistry, focusing on complex biological systems modeling. Current projects include epidemiological modeling with students, while prior work explored freshwater ecosystems and computational neuroscience applications in Parkinson’s disease treatment. Education: PhD in Mathematics, University of Wisconsin-Milwaukee BS in Pure Math, University of Wisconsin-Milwaukee Research Interests: Mathematical biology (agent-based models, cellular automata), computational neuroscience (deep brain stimulation modeling), and statistical methods like parameter estimation and global sensitivity analysis. Active in Math Circle initiatives promoting math education outreach. Teaching: Courses include Calculus, Vector Calculus, Probability & Statistics, Differential Equations, and Mathematical Modeling. Currently teaching Math 205, 290, 383, and 385 in Fall 2025. Collaborations: Works with interdisciplinary teams on ecological modeling (Lake Michigan bacteria-virus dynamics) and biomedical applications (Parkinson's disease biophysical networks). Engages students in epidemiological modeling projects.

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Paulo FernandesView profile

Professor

Merrimack CollegeUnited States

Paulo Fernandes is a Program Director and Professor of Computer Science at Merrimack College. He holds a Ph.D. in Computer Science from INP Grenoble (France), an M.Sc. and B.Sc. from UFRGS (Brazil). His research focuses on stochastic models, natural language processing, and interdisciplinary applications of computer science. He teaches graduate courses in algorithms, discrete structures, and software systems. Education: Ph.D. (INP Grenoble), M.Sc. & B.Sc. (UFRGS) Teaching: Master’s courses in Algorithms, Systems & Languages, and Discrete Structures His work spans computational linguistics (e.g., PortiLexicon-UD lexical resource), stochastic modeling for software projects, and Markovian analysis in geology and engineering. He has published extensively on topics like HMM fitting, term relevance metrics, and distributed team performance. Key research contributions include: language-independent POS tagging systems, stochastic frameworks for global software development, and dimensionality reduction techniques for event forecasting. He has collaborated on tools such as PEPS and SANGE for stochastic modeling.

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Deborah WilsonView profile

Assistant Professor

Mount Vernon Nazarene UniversityUnited States

Dr. Deborah Wilson is an Assistant Professor of Computer Science at Muskingum Valley Nazarene University (MVNU), within the School of Natural and Social Sciences. She previously served as a Visiting Assistant Professor of Computer Science at Ashland University (2019-2021). Her research focuses on interdisciplinary applications of computer science in bioinformatics, including chaos theory, protein sequence analysis, and computational biology. Education: Ph.D. in Computer Science, Kent State University, 2016 M.S. in Computer Science, Kent State University, 2005 B.S. in Biology and Computer Science, Ashland University, 2001 Research interests include chaos automata modeling, neural networks for protein disorder prediction, and sequence alignment algorithms for structural bioinformatics. Her work bridges theoretical computer science with practical applications in biomolecular analysis and toxicology. Publications span topics from chaos theory in automata (2016 Ph.D. thesis) to computational studies in xenopus embryo toxicity (2000). Her recent work emphasizes bioinformatics tools for protein secondary structure analysis and algorithmic comparisons in sequence alignment (2004-2008). No scientific awards are explicitly listed. Advising/grants section remains unpopulated as no specific details are provided. Affiliations include MVNU and Ashland University.

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Paulo H. AcioliView profile

Professor

Northeastern Illinois UniversityUnited States

Paulo H. Acioli is Professor in the Physics and Astronomy department at Northeastern Illinois University. His research integrates theoretical and computational approaches across condensed matter physics, nanocluster science, and physics education research. Primary research areas include computational studies of metal clusters using density functional theory and quantum Monte Carlo methods, electronic structure analysis via photoelectron spectroscopy simulations, and pattern formation in complex systems. Additional work focuses on developing educational models for physics concepts through computational approaches. Current NSF-funded projects explore experiential learning models and peer mentoring in STEM education. Publications demonstrate consistent innovation in computational method development and physics pedagogy.

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