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David Van HornView profile

Associate Professor

• Programming Languages
• Formal Methods
• Software Engineering
• +3 more

David Van Horn is an Associate Professor in the Department of Computer Science and UMIACS at the University of Maryland, College Park. His research focuses on programming languages, formal methods, and security, with an emphasis on making software components reusable and trustworthy. He holds academic appointments in both Computer Science and UMIACS and is a co-author of Realm of Racket , an educational book on programming video games co-written with undergraduates. Education: PhD in Computer Science, Brandeis University, 2009 MA and BS in Computer Science, University of Vermont, 2004-2006 Research Interests: His work centers on program analysis, verification, static analysis, and security. He develops techniques to bridge scripting languages and formal verification, aiming to improve software reliability. Recent projects include gradual typing, contract verification, and program synthesis tools like RbSyn and Absynthe. Awards: He has received prestigious recognitions, including the NSF CAREER Award (2019), ICFP Most Influential Paper Award (2020), and OOPSLA Distinguished Paper Award (2018). His work on Abstracting Abstract Machines (2010) influenced static analyses for languages like JavaScript and Erlang. Service and Mentoring: Van Horn serves on the SIGPLAN Programming Languages Mentoring Workshop steering committee and advises Technica, the world’s largest hackathon for underrepresented genders. He mentors PhD students in areas like gradual verification and security. Labs and Teams: He directs the Programming Languages at UMD (PLUM) Lab with Prof. Mike Hicks, focusing on foundational and applied research in programming languages and verification.

• Industrial Net Zero Transition
• Automation in Mining
• Hybrid Systems Control
• +3 more

Dr. Honglei Xu is an Associate Professor of Industrial Optimization and Engineering at Curtin University, specializing in industrial system optimization for net-zero transition. He serves as Node Leader of ATN Industry Doctoral Training Centre and Mathematics Honours Coordinator. His research spans automation in mining, hybrid systems control, and optimization in construction and energy sectors. Recent publications demonstrate interdisciplinary approaches combining operations research, AI, and control theory for sustainable industrial solutions. Honors include IEEE Senior Membership and JSPS Fellowship. Current projects focus on public transport optimization, renewable energy forecasting, and intelligent control systems for mineral processing. Dr. Xu teaches courses in mathematical modeling and production planning while serving as associate editor for multiple international journals including Complexity and Energies.

• Computational Neuroscience
• Neurophysics
• Brain Oscillations
• +2 more

Paul Tiesinga is Professor of Neuroinformatics at Radboud University's Faculty of Science, where he directs the Neuroinformatics group at the Donders Centre for Neuroscience. His research focuses on understanding information processing in the brain, particularly the role of oscillations ('brain waves') in cognitive functions and disease mechanisms. He combines computational modeling with experimental approaches including optogenetics to investigate cortical dynamics. As Director of Education for the Institute for Mathematics, Physics and Astronomy, he oversees academic programs while maintaining active research in: Computational modeling of cortical circuits Oscillation mechanisms in neural coding Data sharing policies for neuroscience Information transfer between brain areas His recent publications reflect advances in neurotechnology development, thalamocortical connectivity analysis, and novel methodologies for neural data interpretation.

• Multi-Agent Systems
• Agent-Oriented Software Engineering
• Hypermedia Multi-Agent Systems
• +7 more

Rem Collier is an Associate Professor in the School of Computer Science at University College Dublin (UCD). He holds a PhD in Computer Science from UCD (2001) and has held academic roles including Assistant Lecturer (2004), College Lecturer (2005–2018), and his current position since 2018. His research focuses on Multi-Agent Systems (MAS), Agent-Oriented Software Engineering (AOSE), Hypermedia MAS, Digital Twins, and applications in smart agriculture and urban simulation. He leads the CONSUS project (2022–2024) and collaborates on CAMEO (2021–2024). Education: BSc (Pure/Applied Mathematics, University of Bristol, 1994), MSc (Computation, UMIST, 1995), MPhil (UMIST, 1996), PhD (UCD, 2001). Research interests include Agent Factory Framework, ASTRA programming language, and integrating MAS with microservices. Notable achievements include the CIA System Innovation Award 2003 (ACCESS architecture) and Best Paper Awards at EMAS2020 and Mobile Learning 2006. His work spans over 150 peer-reviewed publications and grants such as HOTAIR (2004–2005) and SIFT (2008–2011). Teaching includes modules on Multi-Agent Systems, Distributed Systems, and Security. He has coordinated courses at UCD and the Joint UCD-Fudan program in Beijing and Sri Lanka. Labs/Teams: Active in Hypermedia MAS Simulation, collaborating on projects like CONSUS and CAMEO, emphasizing smart agriculture and distributed knowledge graphs.

• Computer Vision
• Medical Imaging
• Deep Learning
• +3 more

Yu Huang is a Researcher in the Department of Mathematical and Statistical Sciences at Clemson University's College of Science. His work focuses on advancing computational methods in medical imaging, computer vision, and deep learning applications. His research includes developing multimodal models for ophthalmology, enhancing 3D graphics rendering, and improving biomedical image analysis through active learning frameworks. Notable projects involve generative models for retinal imaging and shadow removal techniques in computer vision. His research interests span computational ophthalmology, neural rendering, and AI-driven biomedical solutions. Recent efforts emphasize scalable diffusion models for high-resolution synthesis and robust medical image segmentation using test-time augmentation. Yu Huang's publications reflect a strong emphasis on interdisciplinary research at the intersection of computer science and healthcare. His work addresses challenges in both foundational AI methodologies and their practical applications in clinical settings.

Brian RossView profile

Professor

• Artificial Intelligence
• Computational Intelligence
• Genetic Programming
• +10 more

Brian Ross is a Professor at the Department of Computer Science, Brock University. He holds a BSc (Hon) from the University of Manitoba, MSc from the University of British Columbia, and PhD in Artificial Intelligence from the University of Edinburgh. His research focuses on evolutionary computation , particularly genetic programming , with applications in computational biology , evolutionary design , and computational aesthetics . Education: BSc Computer Science (Hon), University of Manitoba MSc Computer Science, University of British Columbia PhD Artificial Intelligence, University of Edinburgh Research Interests: Computational Intelligence: genetic programming, evolutionary algorithms, multi-objective optimization Evolutionary Design: computational aesthetics, 2D/3D modeling, energy-efficient architecture Applications: computational biology, stochastic process algebra, GPU-based image analysis Article Trends: His recent work combines deep learning with evolutionary algorithms to enhance diversity in agent behavior and design optimization. Earlier studies focused on stochastic modeling of biological networks and procedural texture generation . Collaborations span computational aesthetics, passive solar architecture, and non-photorealistic rendering techniques. Students & Collaborations: He has supervised diverse projects including evolved intelligent agents , passive solar building design , and evolutionary art systems . Notable students include Marshall Joseph (2023), Tyler Cowan (2021), and Sheikh Faishal Basher (2021).

Michael WinterView profile

Professor

• Programming Languages
• Semantics of Programming Languages
• Program Verification
• +5 more

Michael Winter is a Professor in the Department of Computer Science at Brock University, Canada. He holds a Habilitation from the University of the Federal Armed Forces, Munich (2002), a Ph.D. (Dr. rer. nat.) from the same institution (1998), and a Master of Computer Science (Dipl. Inform.) (1993). His research focuses on programming languages, semantics, program verification, relational methods, category theory, and fuzzy logic. He is the Managing Editor of the Journal on Relational Methods in Computer Science . Winter's academic roles include teaching courses such as COSC 2P05 (Programming Languages), COSC 3P91 (Advanced Object-Oriented Programming), and COSC 5P02 (Logic in Computer Science). He has advised numerous graduate and undergraduate students on topics including fuzzy relational analysis, program verification, and formal methods. His research contributions span over 100 publications in journals and conferences, emphasizing relational algebra, category theory, and their applications in computer science. His work integrates theoretical foundations with practical applications, such as developing tools like RelView for relational computation and exploring applications in ambient intelligence and data visualization. Winter collaborates internationally, contributing to conferences like RAMiCS and publishing in journals like Fuzzy Sets and Systems and Journal of Logical and Algebraic Methods in Programming .

Craig Leth-SteensenView profile

Associate Professor

• Cognitive Psychology
• Quantitative Methods in Psychology
• Reaction Time Analysis
• +4 more

Craig Leth-Steensen is an Associate Professor in the Department of Psychology at Carleton University, affiliated with the Faculty of Arts and Social Sciences. He holds a Ph.D. from McGill University. His research focuses on cognitive psychology, particularly the application of quantitative methods and computational models (e.g., neural networks, random walks) to analyze human cognitive performance, reaction time distributions, and symbolic comparative judgments. Recent interests include early childhood cognitive development and interventions through programs like PlayWisely. His work spans experimental psychology and psychometrics, with key contributions to reaction time analysis, spatial-numerical associations (SNARC effect), and task-switching dynamics. He has published widely in journals like Psychological Review , Journal of Experimental Psychology , and Frontiers in Psychology . Dr. Leth-Steensen has no listed awards but has collaborated with institutions globally. His research emphasizes methodological rigor, integrating statistical techniques with experimental designs to explore information processing and decision-making mechanisms.

Miklos BarthaView profile

Professor

• programming language semantics
• algebra and category theory in computer science
• automata theory
• +3 more

Dr. Miklos Bartha is a Professor in the Department of Computer Science at Memorial University of Newfoundland, located within the Faculty of Science. He holds a M.Sc. and Ph.D. from József Attila University in Hungary. His research focuses on programming language semantics, algebra and category theory applied to computer science, automata theory, and graph theory. Dr. Bartha's work bridges formal methods with computational models, emphasizing theoretical foundations and their applications in discrete systems. His academic career includes significant contributions to automata equivalence, synchronous system modeling, and graph-based algorithms. Notable areas of exploration include quantum automata, soliton graphs, and the monoidal structure of Turing machines. His research often intersects category theory, providing novel frameworks for understanding computational processes. Dr. Bartha has published extensively in international conferences and journals, with recent work addressing graph reduction algorithms, deterministic properties of soliton graphs, and the theoretical underpinnings of molecular switching using automata models. His publications span topics from formal verification of circuits to algorithmic graph theory, reflecting a deep engagement with both foundational and applied aspects of computer science. His contact information includes an office at Memorial University and can be reached via email at bartha@mun.ca or phone at (709) 864-2193.

Thomas GrafView profile

Associate Professor

• Theoretical Linguistics
• Computational Linguistics
• Syntax
• +5 more

Thomas Graf is an Associate Professor in the Department of Linguistics and an Affiliate Professor at the Institute for Advanced Computational Science (IACS) at Stony Brook University. He holds a Ph.D. from UCLA (2013) and an M.A. from the University of Vienna. His research focuses on the intersection of theoretical linguistics and computer science, exploring structural complexity in syntax, morphology, and phonology, and its implications for language processing and acquisition. Graf’s work emphasizes computational constraints on natural language, such as subregular complexity and parsing efficiency, and involves empirical studies across languages like English, Icelandic, and American Sign Language. Education: Ph.D., University of California, Los Angeles (2013) M.A., University of Vienna Research interests include computational syntax and phonology, parsing theory, and the formal characterization of linguistic universals. Key themes involve the computational power of linguistic frameworks (e.g., Minimalist grammars), the subregular hierarchy, and the typological implications of computational constraints. Graf’s recent work investigates how empirical phenomena like syntactic islands arise from complexity limits in natural language. His contributions to computational linguistics include Python tools for Minimalist grammar processing and collaborations in labs like the Computational Linguistics Lab (CompLab) at Stony Brook. He has also received a NSF CAREER grant for research on abstract universals in morphosyntax. Labs/Teams: Active member of the Computational Linguistics Lab (CompLab) at Stony Brook University. Leads the MathLing Reading Group and contributes to NLP research initiatives.

Eugene StarkView profile

Professor

• Operating Systems
• Programming Language Semantics
• Concurrency Theory
• +3 more

Eugene Stark is a Professor in the Department of Computer Science at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. His work focuses on theoretical foundations and practical applications in systems and programming languages. Educational Background: He earned his Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) in 1984. Research Interests: His primary areas include Operating Systems, Programming Language Semantics, Concurrency Theory, Distributed Algorithms Verification, and Functional Programming Languages. He explores formal methods to ensure correctness and efficiency in complex systems, leveraging category theory for rigorous semantic frameworks. His work often addresses challenges in indeterminate dataflow networks and probabilistic models. Publications Overview: Stark’s recent articles emphasize categorical structures (e.g., residuated transition systems, bicategories) and probabilistic I/O automata. They reflect a trajectory from foundational theory to tool development for concurrent system analysis. Academy of Teaching Scholars award Department Award for Undergraduate Teaching (2000) Advising & Grants: No formal advisees are listed in current records. His research projects include the Probabilistic I/O Automata framework, CARA Infusion Pump specifications, and SAMSON Network Memory Server initiatives. Grants and funding details are not explicitly mentioned. Labs/Teams: He is associated with the Laboratory for 體魯 Institute of (1988), though the lab name’s full English title remains unclear. Collaborations likely extend to interdisciplinary projects involving formal methods and systems engineering.

Sharon Ihan HsiaoView profile

Assistant Professor

• Educational Technology
• Data-Driven Engineering
• Educational Data Mining
• +4 more

Dr. Sharon Ihan Hsiao is an Assistant Professor and David Packard endowed junior fellow in the Department of Computer Science & Engineering at Santa Clara University. She previously held positions at Arizona State University and Columbia University, where she established research projects and taught courses in computer science education. Her research focuses on educational technologies, computational modeling, and adaptive learning systems, with an emphasis on AI and HCI methodologies. She actively contributes to international conferences such as EDM, LAK, and AIED, and has held leadership roles including Program Chair of the 14th International Conference on Educational Data Mining. Education: Postdoctoral studies at Columbia University (2012-2014), PhD from the University of Pittsburgh (2006-2012), MS from Royal Holloway, University of London (2003-2004), and BA from National Central University (1999-2002). Research Interests: Dr. Hsiao's work centers on leveraging technology to enhance learning, including educational data mining, programming language learning analytics, and behavior modeling. Her projects include developing tools like WebPGA for programming assessment feedback, Ogmented for augmented reality learning, and Waste Genie for sustainability education. She explores distributed practice strategies, self-regulated learning behaviors, and immersive technologies to improve educational outcomes. Awards: Recognized with the FWA Outstanding Faculty Mentor Award (ASU) and the IEEE TCLT Young Researcher Award. Grants & Projects: Secured NSF funding for CatMapper (2-year grant) and led the Waste Genie project. Her research involves collaborations with industry partners and interdisciplinary teams, focusing on topics like sustainable waste management and computational thinking. Labs & Teams: Directs the Computer Science Innovation (CSI) Lab at Arizona State University and collaborates on projects like QuizIT and PiSA, which emphasize personalized learning and adaptive feedback systems.

• Data mining
• Reverse engineering
• Machine learning
• +1 more

Mohammadhossein Amouei is a researcher and lecturer affiliated with McGill University's School of Information Studies. He holds the role of Course Lecturer for INFS 691: Computer Programming for Information Professionals. His primary research interests span data mining, reverse engineering, machine learning, and cybersecurity. He is affiliated with research labs including the Data Mining & Security Lab and the Accessible Computing Technology Research Group Lab. His educational background includes PhD candidacy at McGill University. Recent research focuses on reinforcement learning-driven vulnerability discovery in cybersecurity and natural language generation for assembly code documentation. He has contributed to federated learning security through game-theoretic frameworks. Advising and grants information is not explicitly detailed in available texts. He maintains an active presence on LinkedIn and GitHub, and his work bridges theoretical computer science with practical security applications.

Thomas A. RunklerView profile

Adjunct Professor

• Machine Learning
• Data Analytics
• Pattern Recognition
• +3 more

Thomas A. Runkler is an Adjunct Professor at the Technical University of Munich (TUM), holding a Chair in the Foundations of Software Reliability and Theoretical Computer Science within the TUM School of Computation, Information and Technology. Since 1999, he has taught computer science at TUM while concurrently serving in various expert and managerial roles at Siemens AG, where he currently holds the position of Senior Principal Research Scientist. His academic journey includes a Master’s and PhD in electrical engineering from TU Darmstadt (1992/1995), followed by postdoctoral research at the University of West Florida (1996–1997). Runkler’s research spans machine learning , data analytics , fuzzy systems , and optimization . He has authored/co-authored over 200 publications, focusing on topics like clustering algorithms, neural networks, and decision-making frameworks. Professional contributions include leadership roles in organizations such as the IEEE CIS committees and the German Association for Computer Science’s Fuzzy Systems group. His teaching includes courses on Data Mining and Knowledge Discovery . Key publications include foundational works on fuzzy preference structures, Bayesian decomposition of dynamical systems, and interpretable reinforcement learning policies. Runkler’s work bridges academia and industry, emphasizing practical applications in automation, recommendation systems, and industrial procurement forecasting. His research often addresses challenges in uncertainty handling, algorithm design, and scalable data analysis.

Todd PresnerView profile

Professor

• Digital Humanities
• Holocaust Studies
• German-Jewish History
• +3 more

Todd Presner is the Charles Speroni Chair and Professor in the Department of European Languages and Transcultural Studies at UCLA, serving as Special Advisor to the Office for Research and Creative Activities. He leads initiatives like the Urban Humanities program and the Social Justice Curriculum grant, funded by Mellon. His research bridges Digital Humanities, Holocaust Studies, and German-Jewish History, with notable works including HyperCities (2014), Digital_Humanities (2012), and his forthcoming Ethics of the Algorithm (2024). Awards include the 2008 MacArthur Foundation/HASTAC prize for HyperCities. His projects emphasize spatial justice, cultural mapping, and ethical technology in humanities research. Presner’s academic roles include former director of UCLA’s Digital Humanities Program (2011–2021) and the Leve Center for Jewish Studies (2011–2018). He co-leads interdisciplinary grants totaling $5M+, focusing on social justice pedagogy. His research spans from 19th-century German-Jewish intellectual history to computational analysis of Holocaust testimonies, blending critical theory with digital innovation. Key contributions include foundational texts in Digital Humanities and collaborative platforms like HyperCities, which explore layered urban histories. His recent work probes ethical frameworks for algorithmic analysis in cultural memory, addressing tensions between individual testimony and large-scale data interpretation.