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Jiang Kan is a Lecturer at the Department of Computer Science, National University of Singapore. He earned his Ph.D. in Computer Science from NUS in 2023, following an MComp (2017) and B.Sc. (1994) from NUS and Shanghai Jiao Tong University respectively. His teaching portfolio includes courses like Introduction to Computing , Introduction to Programming , Database Systems and Management , and Systems Programming . Education: Ph.D., Computer Science, National University of Singapore, 2023 M.Comp., National University of Singapore, 2017 B.Sc., Shanghai Jiao Tong University, 1994 Research Focus: Jiang Kan specializes in sports analytics, integrating computer vision and probabilistic modeling to analyze sports strategies, player dynamics, and broadcasting data. His work includes tennis and soccer strategy analysis , event recognition in sports videos , and injury prediction models . Publication Trends: Recent articles (2023-2025) explore hybrid approaches combining deep learning with probabilistic model checking for sports analytics. Key contributions include automated court detection , fine-grained event analysis , and dynamic team strategy modeling in tennis and soccer. Teaching: He teaches foundational and advanced courses in programming, computer organization, software engineering, and databases to both full-time and part-time students.

• Computer Science
• Artificial Intelligence
• Sports Analytics
• +4 more

Abhinav Bhatele is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park, and directs the Parallel Software and Systems Group. He holds affiliate roles in the AIM Institute and AMSC Program. His research focuses on high-performance computing, systems, networking, and AI, particularly in parallel computing, distributed AI, and machine learning applications. He has contributed to frameworks like AxoNN and Hatchet, and his work spans performance modeling, network simulation, and scalable deep learning. Education: Ph.D. (2010), M.S. (2007), and B.Tech. (2005) in Computer Science from the University of Illinois at Urbana-Champaign and IIT Kanpur, respectively. Prior to UMD, he was at Lawrence Livermore National Laboratory (2011–2019). Research interests include parallel systems, network design, performance analysis tools, and applying machine learning to optimize HPC workflows. He has received notable awards, including the IEEE TCSC Award (2023), NSF CAREER (2021), and UIUC Early Career Alumni Award (2024). Key achievements include developing open-source tools like Hatchet and AxoNN, optimizing large-scale LLM training, and advancing GPU-based supercomputing. He advises over 10 students and has secured grants totaling millions, focusing on HPC software ecosystems and AI integration. Professional service includes roles at SC, ISC conferences, and editorial work for IEEE TPDS. His lab collaborates on projects like the Exascale Computing Project and DOE INCITE allocations.

• High Performance Computing
• Systems
• Networking
• +4 more

James Marcum is a Professor of Philosophy at Baylor University’s College of Arts & Sciences. He holds dual doctorates in Philosophy (Boston College) and Physiology (University of Cincinnati Medical College), alongside a Master’s in Theological Studies from Gordon-Conwell Theological Seminary. Prior to Baylor, he served at Harvard Medical School and MIT for nearly two decades, with a postdoctoral fellowship at Harvard Medical School. His research focuses on the philosophy and history of science and medicine, systems biology, and medical ethics. He has secured grants from NIH, NSF, and the American Heart Association, and frequently delivers invited lectures globally. Notable contributions include works on Thomas Kuhn’s philosophy of science, systems medicine, and the integration of person-centered healthcare principles. Education: Ph.D. in Philosophy, Boston College Ph.D. in Physiology, University of Cincinnati Medical College M.A. in Theological Studies, Gordon-Conwell Theological Seminary Research Interests: Philosophy of science and medicine History of medical concepts (e.g., vitalism, Harvey’s work) Ethical dimensions of clinical practice Systems approaches to biology and healthcare Thomas Kuhn’s revolutionary and evolutionary frameworks Grants & Awards: Funded by NIH, NSF, and the American Heart Association Recipient of international speaking invitations and conference engagements Labs/Teams: While no specific lab is mentioned, his work intersects with interdisciplinary teams in medical ethics, systems biology, and philosophy of science.

• Philosophy of Science
• History of Medicine
• Medical Ethics
• +3 more

John Clements is a Lecturer in the Department of Computer Science at California Polytechnic State University. He teaches undergraduate and graduate courses including CSC 430 (Programming Languages) and CSC 202 (Data Structures). His research focuses on programming languages, functional programming, and educational technology, with contributions to the Racket language ecosystem and tools like DrRacket. He emphasizes ethical computing and pedagogical innovation in introductory courses. Clements is actively involved in curriculum design, particularly in integrating test-driven development and socially responsible computing principles. His work spans educational research, compiler design, and IDE development, with a focus on making programming more accessible and inclusive. Education: While specific academic credentials are not explicitly detailed, his extensive contributions to Racket and functional programming suggest advanced training in computer science. He has been affiliated with Cal Poly since at least 2008, contributing to courses and research initiatives. Research Interests: Functional programming, language design, educational computing, test-driven development, and ethics in technology. His projects include Racket's Typed Racket extension, spectral analysis tools for music, and password generation systems using Markov models. He advocates for diverse and inclusive learning environments. Publications: Over 20 papers on topics ranging from notional machines for runtime stacks to ethics-first computing education. Recent work emphasizes integrating social responsibility into CS curricula and improving student retention through interest-based CS0 courses. Labs/Teams: Active in the Racket community, contributing to open-source projects like rust-antlr, RSound, and molis-hai. Collaborates with researchers on pedagogy and programming language theory.

• Computer Science
• Programming Languages
• Functional Programming
• +5 more

Chad Lee-Stronach is an Assistant Professor of Philosophy at Northeastern University's College of Social Sciences and Humanities (CSSH), specializing in Information Ethics. He holds a PhD in Philosophy from the Australian National University (2019) and has held interdisciplinary roles including a postdoctoral fellowship at Stanford University's Center for Ethics in Society and Apple University (2019-20). Currently, he is an Affiliate at the Center for Law, Innovation, and Creativity at Northeastern's School of Law. His research focuses on ethical principles governing uncertainty, privacy, algorithmic transparency, and misinformation. Key themes include moral reasoning under uncertainty, the ethics of automated decision-making, and societal impacts of technological systems. Recent work examines how liberal democratic values interact with algorithmic systems and the implications of interdependent social conventions for ethical frameworks. His publications span top journals like Philosophical Quarterly and Canadian Journal of Philosophy . Research trends emphasize interdisciplinary approaches bridging philosophy, computer science, and legal studies. He has no listed scientific awards but demonstrates active engagement in academic leadership through postdoctoral fellowships and institutional affiliations. His advising and grant activities remain unspecified in the provided data. Lee-Stronach's work contributes to Northeastern's CSSH mission to explore societal ethics in a rapidly changing technological landscape.

• Information Ethics
• Moral Philosophy
• Privacy Ethics
• +3 more

Vasanth Sarathy is a Research Assistant Professor of Computer Science at Tufts University, specializing in the intersection of Artificial Intelligence and Natural Language Processing. His work combines neuro-symbolic machine learning techniques with social and cognitive sciences to develop socially-competent and safe AI systems. Education: Ph.D. in Computer Science and Cognitive Science, Tufts University (2020) Juris Doctor (J.D.), Boston University School of Law (2010) M.S. in Electrical Engineering and Computer Science, MIT (2005) B.S. in Electrical Engineering, University of Arkansas (2003) His research spans three interconnected themes: Social NLP, Reasoning with Social Norms, and Sense-making and Problem-Solving. Through Social NLP, he develops computational methods to uncover patterns in human social behavior using qualitative texts. His work on Reasoning with Social Norms focuses on building AI architectures that can understand and apply social norms in reasoning and language interpretation. His Sense-making research explores how humans and AI systems simplify complex worlds for efficient problem-solving. His recent publications demonstrate a growing trend toward integrating Large Language Models with symbolic reasoning for more trustworthy AI. His work spans applications including automated writing assistance, intelligent tutoring, fact-checking, social media content moderation, and embodied social robots. His research shows particular strength in developing methods that combine neural and symbolic approaches to address complex social reasoning tasks that neither approach could solve alone. Previously, Dr. Sarathy worked as a Senior Researcher of AI at Smart Information Flow Technologies, collaborating with the U.S. Department of Defense and Intelligence Community. Before his AI career, he practiced intellectual property law at Ropes and Gray for nearly a decade. His unique interdisciplinary background bridges technology, law, and cognitive science. He advises students in AI, NLP, and cognitive systems, with research supported by grants from DARPA and other government agencies. His work has applications in human-robot interaction, social media analysis, and educational technologies. Outside academia, he creates single-panel cartoons, practices martial arts, plays chess, and designs puzzle video games. His interdisciplinary journey from law to AI has been featured in a BU Law article.

• Artificial Intelligence
• Natural Language Processing
• Neuro-symbolic Machine Learning
• +7 more

May Haidar is an Assistant Professor in the Department of Electrical Engineering & Computer Science at York University. Her work bridges theoretical and applied domains, focusing on software engineering, web applications, and formal verification techniques. Education: Ph.D. in Computer Science (Université de Montréal, 2008), M.S. (Concordia University), B.S. (American University of Beirut) Current Role: Teaching Object-Oriented Programming, Advanced Programming, and Web Development courses Her research explores formal methods in software engineering, particularly temporal logics and run-time verification for web applications. She has contributed to hybrid analysis techniques for composite web services and anomaly detection frameworks. Publications span formal verification, web application security, and embedded systems design, with a focus on integrating temporal logic extensions into validation processes. While no explicit awards are listed, her work has been presented at global conferences, including events in Dubai, Cambridge, and Las Vegas.

• Software Engineering
• Web Applications
• Formal Methods
• +3 more

Selmer Bringsjord is a Professor of Cognitive Science, Computer Science, Logic & Philosophy, and Management & Technology at Rensselaer Polytechnic Institute (RPI). He serves as Director of the Rensselaer AI & Reasoning (RAIR) Lab and Graduate Program Director in Cognitive Science. His research spans AI, cognitive science, formal logic, and ethics, with a focus on machine ethics, computational creativity, and conscious AI. Bringsjord has authored 6 books, over 260 refereed papers, and secured $28M+ in sponsored research. He has supervised 20 PhD and 19 MS students. Notable contributions include founding machine ethics, developing Psychometric AI, and the Λ measure for cognitive consciousness. His work bridges philosophy, computer science, and robotics, with media appearances across global platforms. Education: PhD in Philosophy from Brown University (1987), BA in Philosophy from the University of Pennsylvania (1981). Administrative roles include Department Chair of Cognitive Science (2002–2016) and Director of RAIR Lab since 2002. Awards include patents, grants, and a Best Paper Award at AGI 2024. Teaching spans 40 years in formal logic and AI. Ongoing projects include ethical AI frameworks, AGI, and cognitive consciousness models.

• Artificial Intelligence
• Cognitive Science
• Computational Logic
• +9 more

Eric Koskinen is the Charles Berendsen Chair Associate Professor in the Department of Computer Science at Stevens Institute of Technology. His research focuses on improving concurrent software development for multi-core and distributed systems through advances in programming languages, verification techniques, and formal methods. He holds a PhD from the University of Cambridge and has held roles at Yale, NYU, and industry positions at IBM, Microsoft, and Amazon. Education: PhD (2013) University of Cambridge, MS (2008) Brown University, BS (2001) William & Mary Research Interests: Concurrent programming, formal verification, programming language design, and distributed systems Recipient of over $5M in grants from NSF/ONR/DARPA. Active in conference organization (POPL, PLDI) and has developed verification tools like CityProver and Veracity. Advises PhD students in concurrency research and teaches courses on programming languages, discrete structures, and software verification.

• Concurrent Programming
• Formal Verification
• Programming Languages
• +3 more

Christopher Lynch is a Professor in the Department of Computer Science at Clarkson University, part of the Coulter School of Engineering & Applied Sciences. His research focuses on Automated Deduction, including theorem proving, algorithm efficiency, and cryptographic protocol analysis. He has contributed to the development of efficient algorithms and tools for verification in hardware/software systems. His research interests include automated deduction, automated reasoning, and theorem proving, with a particular emphasis on improving algorithm efficiency for verifying specifications in hardware and software. He has developed new algorithms and modified existing ones to enhance their performance. Additionally, his work extends to cryptographic protocol analysis, focusing on symbolic methods and tools like CryptoSolve to ensure system security. His recent articles explore advancements in satisfiability modulo theories (SMT), unification algorithms (e.g., XOR unification and asymmetric unification), and the application of formal methods to cryptographic systems. Key contributions include improving theorem proving efficiency and developing tools for protocol verification. Christopher Lynch has been involved in collaborative research grants, including the "Unification Laboratory" projects aimed at enhancing cryptographic protocol analysis tools. No specific grants or advising details beyond this are provided in the text.

• Automated Deduction
• Automated Reasoning
• Theorem Proving
• +4 more

Professor Jixin Ma is Director of the PhD/MPhil Programme and Professor of Computer Science (Artificial Intelligence) at the University of Greenwich's School of Computing and Mathematical Sciences. He leads the Artificial Intelligence Research Group and has published over 200 research papers in top-tier journals and conferences. His research spans artificial intelligence, data science, temporal reasoning, information security, machine learning, and case-based reasoning. Research interests focus on: Temporal representation and reasoning Secure computation and encrypted search Machine learning applications in security Computer vision and pattern recognition He has received multiple awards including 6 Best Paper awards and has secured research grants exceeding £330,000.

• Artificial Intelligence
• Data Science
• Temporal Representation
• +3 more

Andrei Ciortea is an Assistant Professor in Interaction and Communication based Systems at the University of St. Gallen, affiliated with the ICS-HSG (Interaction and Communication Systems) department. His research focuses on Internet-scale multi-agent systems, hypermedia systems, the Web of Things, and socio-technical systems. He explores topics such as autonomous agents, hypermedia environments, and the integration of digital twins in industrial automation. Key research interests include the design of adaptive coordination mechanisms, affordance-driven interaction models, and semantic hypermedia search. His work emphasizes practical applications in manufacturing, energy systems, and sustainable design. Notable contributions include the HyperBrain project leveraging large language models for human-inspired guidance and the Solid-based Pody framework for agent embodiment in web systems. He received the Best Paper Award at the 6th International Conference on the Internet of Things (IoT 2016). His publications span topics like digital twin ecosystems, circular design strategies, and governance frameworks for autonomous agents on the web. Current research trends highlight the interplay between agent autonomy, hypermedia architectures, and socio-technical interactions in smart environments. Dr. Ciortea collaborates on interdisciplinary projects involving AI ethics, transparent machine intelligence, and real-time collaboration in linked data systems. His work bridges theoretical agent models with real-world industrial applications, emphasizing scalable, interoperable solutions for distributed systems.

• Multi-Agent Systems
• Hypermedia Systems
• Web of Things
• +3 more

Christian Meilicke is a Researcher at the Data and Web Science Group (DWS) within the School of Business Informatics and Mathematics at the University of Mannheim. His work focuses on artificial intelligence, ontology matching, and knowledge graph completion, with recent contributions to rule-based methods and their applications in business process modeling. He is heavily involved in teaching, coordinating courses such as 'Modeling Business Processes' and 'Artificial Intelligence.' His research interests include the integration of open and structured knowledge, probabilistic reasoning frameworks, and improving the efficiency of knowledge base systems. He has explored topics like inductive logic programming, automated debugging of ontologies, and the use of Markov Logic Networks for root cause analysis in IT systems. In terms of trends, his recent publications emphasize combining symbolic rule-based approaches with machine learning for knowledge graph tasks, such as activity recommendation and link prediction. He also investigates explainability in embeddings and temporal forecasting in knowledge graphs. His work often bridges theoretical advancements with practical applications in business informatics and data integration. No scientific awards have been explicitly mentioned. Christian has advised no formal students listed here but has contributed to teaching and mentoring through his courses and tutorials. His research and teaching are closely tied to the DWS Group, which focuses on data-centric AI and semantic technologies.

• Artificial Intelligence
• Knowledge Graphs
• Ontology Matching
• +5 more

Alan Hu is a Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. His research focuses on formal verification, algorithms, computer architecture, and electronic design automation. He teaches courses such as Intermediate Algorithm Design and Analysis (CPSC 320) and Introduction to Formal Verification and Analysis (CPSC 513). Dr. Hu has received notable awards, including the IEEE Council on Electronic Design Automation Outstanding Service Award and the IBM Faculty Award. His work emphasizes scalable verification techniques, SAT-based algorithms, and optimization in cloud computing and hardware systems. His research spans formal methods for hardware/software systems, including verification of embedded software, cache coherence, and network function virtualization. Notable contributions include advancements in SAT modulo theories, data race detection in heterogeneous systems, and cloud resource allocation frameworks like Cospot. Dr. Hu has been actively involved in teaching and curriculum development, consistently offering courses on algorithms, formal verification, and software design since 2000. His publications reflect a blend of theoretical foundations and practical applications in electronic design, cloud infrastructure, and verification tools. His scientific achievements include innovations in post-silicon validation, emulation-based coverage reduction, and formal analysis for debug trace optimization. He also contributes to the academic community through conference organization and editorial roles in formal verification and computer-aided design.

• Formal Verification
• Algorithms
• Computer Architecture
• +3 more

Tim French is an Associate Professor in the Department of Computer Science and Software Engineering at the University of Western Australia's School of Physics, Maths and Computing. He is affiliated with the UWA Oceans Institute and serves as Regional Contest Director for the South Pacific Programming Contest and Programme Chair for the Australasian Conference on Artificial Intelligence 2022. His research focuses on logic, artificial intelligence, knowledge representation, and reasoning about uncertainty in multi-agent systems, probabilistic reasoning in games, and industrial applications like automated planning and machine learning for complex processes. French holds a PhD in Computer Science (2007) and BSc in Computer and Mathematical Sciences (1999), both from UWA. His expertise spans algorithms, automated reasoning, formal methods in software, and temporal logic verification systems. He has led or contributed to 6 major research projects including the ARC Research Hub for Transforming Energy Infrastructure and the ARC Training Centre for Transforming Maintenance through Data Science. His research outputs include 118 publications covering topics like aleatoric logic for probabilistic reasoning, semantic knowledge extraction from industrial maintenance systems, and deep learning applications in wastewater treatment. He has developed novel methods for knowledge graph construction, state estimation in complex systems, and user interface design informed by work characteristics models. Key grants: 6 active/finished projects totaling $M+ funding Leadership roles: Programming contest director, conference chair Interdisciplinary focus: Combines formal logic with industrial automation challenges French's work contributes to UN Sustainable Development Goals through education and innovation in sustainable industrial processes and environmental systems modeling. His team has developed practical solutions for maintenance procedure digitization, wastewater plant optimization, and robust agent-based systems for uncertain environments.

• Logic
• Artificial Intelligence
• Knowledge Representation and Reasoning
• +5 more