جست‌وجوی استاد و پژوهشگر

Cezary Kaliszykمشاهده پروفایل

استاد

• Automated Reasoning
• Formal Methods
• Formalized Mathematics
• +۷ مورد دیگر

Cezary Kaliszyk is a Professor in Theoretical Computer Science at the University of Melbourne, previously affiliated with the University of Innsbruck. He is actively involved in research and leadership in formal methods, automated reasoning, and machine learning for theorem proving. Research Interests: Automated Reasoning and Interactive Theorem Proving Formalized Mathematics and Proof Automation Machine Learning for Logic and Theorem Proving Integration of AI with Proof Assistants (Coq, Isabelle) Dependent Type Theory and Higher-Order Logic His recent publications (2023–2025) span topics in dependently-typed logic, learning for proof guidance, formalization of surreal numbers, and blockchain-based formal methods. The works consistently bridge formal logic with machine learning, emphasizing automation, explainability, and cross-system integration. Scientific Leadership and Projects: Principal Investigator, ERC project FormalWeb3 Lead Developer, CoqHammer , Tactician , ProofWeb WG5 Leader, COST Action EuroProofNet (until 2024) Contributor to HOL(y)Hammer , Isabelle Enigma He supervises multiple PhD students and has mentored several graduates in formal methods and AI. He teaches courses in theoretical computer science, logic, and machine learning. There are no listed awards in the provided data, but his extensive publication record and project leadership indicate significant recognition in the field. Labs and Research Groups: He leads a research group focused on formal methods and learning-based reasoning, collaborating internationally on projects involving proof automation, formal libraries, and semantic technologies.

• Computational Logic
• Symbolic Machine Learning
• Neuro-symbolic AI
• +۳ مورد دیگر

Professor Alessandra Russo leads the Structured and Probabilistic Knowledge Engineering (SPIKE) research group at Imperial College London's Department of Computing. With expertise spanning computational logic, symbolic machine learning, and neuro-symbolic AI, she develops foundational AI techniques applied to security, network management, healthcare, and adaptive systems. Professor Russo holds a PhD in Computing from Imperial College London and an MSc in Computer Science from Ionian University. Her research pioneers logic-based learning systems for intelligent adaptive technologies, with projects including declarative networking for security management, privacy-preserving federated learning, and hybrid neuro-symbolic approaches for robust reasoning. Her current work focuses on developing interpretable AI systems through neuro-symbolic integration, creating frameworks that combine neural networks with symbolic reasoning for explainable decision-making. Recent publications explore rule learning from knowledge graphs, transformer-based world models, and formal methods for representation learning. Professor Russo teaches courses on Logic-Based Learning and AI Applications, and has received the Google PhD Fellowship for her research contributions. She mentors numerous PhD students in areas spanning theoretical foundations and practical applications of computational logic and machine learning.

Vasileios Mavroeidisمشاهده پروفایل

دانشیار

• Cybersecurity
• Security Automation and Orchestration (SOAR)
• Cyber Threat Intelligence (CTI)
• +۶ مورد دیگر

Vasileios Mavroeidis is an Associate Professor in Digital Security at the Department of Informatics, University of Oslo (UiO). He specializes in security automation and orchestration (SOAR) and cyber threat intelligence (CTI) representation, reasoning, and sharing. He actively contributes to European cybersecurity initiatives, including Horizon Europe, Connecting Europe Facility, and the European Defense Fund, and serves as the primary representative of UiO at the OASIS standards development organization since 2017. Role : Associate Professor Department : Digital Security (SEC), University of Oslo Standardization Involvement : Chairman of OASIS Threat Actor Context (TAC), Leading Contributor to CACAO and OpenC2 Projects : Concordia, CyberHunt, JCOP (Joint Cyber Security Operations Platform), Oslo Analytics, P4C (Partnership for Cybersecurity) His research focuses on cyber threat intelligence (CTI), exploring its taxonomies, sharing standards (STIX, CACAO), and ontologies, with contributions to the European Union Agency for Cybersecurity (ENISA) Cybersecurity Playbooks task force. He analyzes quantum computing's impact on cryptography, develops automated threat detection systems using machine learning (e.g., recurrent neural networks for malware-generated domains), and investigates privacy issues under GDPR. Recent publications highlight his work on LLMs for code stylometry , neurosymbolic AI for cyber defense , and knowledge management systems for CACAO playbooks . His articles span 2017–2025, emphasizing formal verification, biometric data protection, and incident response automation. He collaborates with organizations like OASIS (Threat Actor Context, CACAO, OpenC2) and FIRST (Traffic Light Protocol), and participates in European research projects. His work includes standardization efforts in cybersecurity playbooks , MITRE ATT&CK representation, and quantum-resistant cryptography .

Liangming Panمشاهده پروفایل

استادیار

• Natural Language Processing
• Large Language Models
• Machine Learning
• +۳ مورد دیگر

Liangming Pan is an Assistant Professor at the University of Arizona's College of Information Science. His research focuses on building trustworthy large language models (LLMs) with an emphasis on logical reasoning, truthfulness, and safety. He holds a PhD in Computer Science from the National University of Singapore (2022), a Master's from Tsinghua University, and a Bachelor's from Beihang University. Education : PhD in Computer Science, National University of Singapore (2022) Master of Engineering in Computer Science, Tsinghua University (2017) Bachelor of Engineering in Computer Science, Beihang University (2014) Research Interests : Dr. Pan's work centers on enhancing LLMs' reliability through: Logical reasoning mechanisms to ensure faithful deductions Truthfulness verification to combat misinformation Safety protocols to mitigate societal harm Key Contributions : Developed TART, an open-source framework for explainable table-based reasoning Created benchmarks like SCITAB and FactCheck-Bench for evaluating LLMs Advanced techniques for knowledge editing and causal reasoning Awards : Best Paper Runner-Up at NeurIPS Table Representation Workshop (2024) Area Chair Award for Question Answering (IJCNLP-AACL 2023) Service & Outreach : He serves as an Area Chair for EMNLP (2024), COLING (2025), and ACL (2024). He has delivered invited talks at Tsinghua University, Peking University, and other institutions.

Sebastian Rudolphمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Description Logics
• +۷ مورد دیگر

Prof. Sebastian Rudolph is a Professor of Computational Logic at the Institute for Artificial Intelligence , Faculty of Computer Science , TU Dresden. Since 2021, he has been an Affiliate Member of the Faculty of Mathematics. His research spans theoretical and applied artificial intelligence, focusing on Knowledge Representation and Reasoning through formalisms like Description Logics, Existential Rules, and Formal Concept Analysis, with applications in Semantic Technologies. 2017 : ERC Consolidator Grant for decidability principles in logic-based knowledge representation 2006-2013 : Postdoctoral researcher, project leader, and Privatdozent at KIT's Institute AIFB 2011 : Habilitation at KIT Earlier : PhD in Algebra and teaching qualification in mathematics, physics, and computer science at TU Dresden His recent publications address decidability of logical reasoning, non-monotonic extensions in formal concept analysis, standpoint logics, and multiagent systems. He supervises the DeciGUT and KIMEDS projects, and is involved in the SECAI and ScaDS.AI centers. Teaching activities include courses on Theoretical Computer Science, Existential Rules, and Formal Concept Analysis.

• Differential Privacy
• Machine Learning
• Privacy-Preserving Data Analysis
• +۳ مورد دیگر

Daniel Kifer is a Professor in the Computer Science and Engineering department at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His work bridges computer science, privacy-preserving machine learning, and geoscience applications. With over 10,000 citations and a high h-index, he focuses on methods to unify theoretical and applied research. Research Interests: Differential Privacy, Privacy-Preserving Machine Learning, Physics-Informed Neural Networks, Landslide Prediction, and Formal Verification of Privacy Systems. Recent projects include grants from the National Science Foundation: SaTC: CORE: Small (2024): privacy-preserving user data embedding in machine learning pipelines. SaTC: CORE: Medium (2017-2023): formal methods for differential privacy and accuracy optimization. His research outputs span domains like geoscience, database systems, and policy analysis, emphasizing precision and scalability of privacy-preserving algorithms.

• Artificial Intelligence
• Reinforcement Learning
• Large Language Models
• +۳ مورد دیگر

Dr. Meng Fang is a researcher specializing in Artificial Intelligence with a focus on Reinforcement Learning, Large Language Models, and their applications in medical QA, game theory, and causal inference. Their work combines technical innovation with practical problem-solving in safety-critical and domain-specific contexts. Key research areas: Social bias in AI, data augmentation, embodied agents, and model-based reinforcement learning Teaching: Coordinated module COMP532 - Machine Learning and BioInspired Optimisation (2024-25). Recent publications address challenges in offline RL robustness, vision-based safe reinforcement learning, and strategic game generalization.

Hector Geffnerمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Automated Planning
• +۵ مورد دیگر

Hector Geffner is an Alexander von Humboldt Professor at RWTH Aachen University, leading the Chair of Machine Learning and Reasoning. He specializes in automated planning, machine learning, and reasoning, with a focus on representation learning for acting and planning. His work bridges symbolic and model-based AI, emphasizing general policies and subgoal decomposition. Education & Background : PhD from UCLA (1989), prior roles at IBM Watson Research Center and Universidad Simón Bolívar. Former ICREA researcher and professor at Universitat Pompeu Fabra (2001–2022). Research Interests : Classical and probabilistic planning, reinforcement learning, knowledge representation, and applications in robotics. His ERC-funded RLeap project explores learning generalized policies and symbolic representations for effective decision-making. Teaching : Courses include 'Actions and Planning in AI' and 'Social and Technological Change', emphasizing interdisciplinary AI applications. Awards & Recognition : Alexander von Humboldt Professorship (2023), AAAI/EurAI Fellowships, and editor of influential works on Judea Pearl’s contributions to AI. Grants & Projects : Advanced ERC grant (2020–2025), Humboldt Foundation support, and RWTH funding for research on planning and reasoning. Labs & Teams : Heads the Chair of Machine Learning and Reasoning at RWTH, focusing on interdisciplinary research in AI, robotics, and planning algorithms.

• Computational Linguistics
• Natural Language Processing (NLP)
• Dependency Parsing
• +۵ مورد دیگر

Joakim Nivre is a Professor at Uppsala University's Department of Linguistics and Philology. He is a leading researcher in computational linguistics, with a focus on dependency parsing, Universal Dependencies (UD) framework development, and multilingual NLP applications. His recent work explores LLMs in climate change discourse analysis, pharmacovigilance explainability, and historical text processing. Key research areas: Dependency parsing theory, Universal Dependencies standardization, LLM evaluation Collaborations: SweSAT-1.0 benchmark development, ClimateEval project, PARSEME integration His 2025-2023 publications demonstrate expertise in explainable AI for healthcare, synthetic data generation for idioms, and multilingual benchmark design. Notably, he co-developed SweSAT-1.0 to evaluate Swedish LLMs and contributed to typology-informed UD revisions. Despite extensive work in NLP, no scientific awards are mentioned in available texts.

• Computational Psychopathology
• Learning Theory
• Cognitive Neuroscience
• +۳ مورد دیگر

Robin A. Murphy is a Professor of Experimental Psychology at the University of Oxford and a Fellow and Tutor for Admissions at Corpus Christi College. He holds a PhD from McGill University (Canada) and has contributed over 50 scientific publications on associative learning mechanisms, their role in mental disorders like depression and psychopathy, and translational neuroscientific applications. His research bridges animal and human learning, with a focus on computational psychopathology and neurochemical modulation (e.g., serotonin). Education: Undergraduate degree, Queen's University MA and PhD in Psychology, McGill University Research Interests: Dr. Murphy investigates how associative learning processes underpin mental disorders, particularly through neurochemical systems like serotonin. His work explores causal reasoning biases in depression, psychopathy's learning deficits, and translational models linking animal studies to human cognition. Key themes include agency perception, contingency judgment, and the neural substrates of prejudice. Recent projects involve peptide research in aging and computational modeling of multi-agent systems in depression. Grants and Funding: Supported by MRC, BBSRC, Wellcome Trust, and Japan Society for the Promotion of Science. He has advised UK Research Councils and NSERC (Canada). Labs and Teams: Leads a lab focusing on associative learning mechanisms, collaborating with institutions globally on computational psychopathology and translational neuroscience.

Cesla Amarelleمشاهده پروفایل

استاد

• Migration Law
• Human Rights
• Constitutional Law
• +۵ مورد دیگر

Cesla Amarelle serves as Full Professor and Chairholder of Public Law and Migration Law at the University of Neuchâtel's Faculty of Law. Her academic leadership extends to co-directing the Swiss Center for Migration Law (CDM) and contributing to national research initiatives including the National Center of Competence in Research (NCCR) 'On the Move'. She concurrently holds significant public appointments as Chair of the Institute of European Law at the University of Fribourg and Federal Commissioner for Women's Issues, with recent expert designation to the Federal Round Table on signature collection integrity for 2024. Her academic foundation includes a law degree from the University of Lausanne (1996), LL.M. in European and International Economic Law from the University of Fribourg, and doctoral thesis from the University of Lausanne (2004). Her career progression at the University of Neuchâtel spans assistant professor (2009-2014), extraordinary professor (2015-2016), and full professor positions since 2016, complemented by earlier research roles at Swiss National Science Foundation and University of Fribourg. Professor Amarelle's research centers on migration law frameworks, human rights protections in asylum systems, and constitutional dimensions of European integration. Her work critically examines the intersection of migration governance with digital technologies, climate change impacts, and gender equality. Current projects analyze temporary protection regimes, non-refoulement principles in technological contexts, and the evolving relationship between Swiss migration law and European legal frameworks. Her scholarship consistently bridges theoretical legal analysis with practical policy implementation challenges. Her extensive publication record demonstrates evolving focus from foundational migration law codification toward contemporary challenges including digital discrimination, child rights in emergency contexts, and sustainable finance frameworks. Recent scholarship reflects increasing engagement with technological impacts on migration governance and the humanitarian dimensions of temporary protection systems, while maintaining rigorous analysis of constitutional and European law interfaces. Professor Amarelle actively contributes to academic and public discourse through leadership in the Swiss Center for Migration Law and participation in the NCCR 'On the Move' consortium. Her public service includes significant political roles as Vaud State Councilor for Education (2017-2022) and National Councilor (2011-2017), where she chaired key committees on political institutions and finance while representing Swiss higher education interests nationally. She maintains active engagement with professional associations including the Swiss Conference of Higher Education Institutions and Conference of Directors of Public Education.

• Graph Representation Learning
• Graph Neural Networks
• Deep Generative Models
• +۵ مورد دیگر

Jian Tang is an Assistant Professor at HEC Montréal and a core member of the Montreal Institute for Learning Algorithms (MILA). His research focuses on graph representation learning, generative models, and their applications in drug discovery and material science. Prior to this, he was a postdoctoral researcher at the University of Michigan and Carnegie Mellon University, and a researcher at Microsoft Research Asia (2014-2016). He has received several prestigious recognitions, including the Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) and best paper nominations at WWW’16. His work on LINE (WWW’15) was recognized as the most cited paper in its year. Tang’s research spans theoretical foundations and practical systems, such as GraphVite for scalable graph embedding and TorchDrug for drug discovery. Key research interests include geometric deep learning for molecular structures, generative models for protein design, and neural-symbolic reasoning for knowledge graphs. He actively collaborates with leading biological labs and leverages industry partnerships for GPU resources. Recent publications emphasize molecular property prediction, 3D conformation generation, and algorithmic reasoning frameworks. He has secured grants from IBM/MILA, Amazon, and the National Research Council Canada, supporting projects like molecular pretraining and geometric representation learning. Tang teaches courses on graph representation learning and deep learning, and mentors a vibrant team of PhD and master’s students. His lab has developed impactful software tools like LINE, PTE, and LargeVis, widely used in the research community.

Jerome Busemeyerمشاهده پروفایل

استاد

• Cognitive Science
• Quantum Cognition
• Decision Making
• +۷ مورد دیگر

Jerome Busemeyer is a Distinguished Professor and Provost Professor in the Department of Psychological and Brain Sciences at Indiana University Bloomington, with a joint affiliation in the Cognitive Science Program. He is a Fellow of the American Academy of Arts and Sciences, reflecting his significant contributions to the science of decision-making and cognitive modeling. His educational background includes a Ph.D. in Psychology from the University of South Carolina (1979), an M.A. from the same institution (1976), and a B.A. cum laude from the University of Cincinnati (1973). He completed postdoctoral training in Quantitative Methods at the University of Illinois in 1980. Busemeyer's research focuses on mathematical and computational models of human judgment and decision-making , with pioneering work in quantum cognition , dynamic models , and neural network modeling . He developed Decision Field Theory , a dynamic cognitive model of decision processes, and has led the application of quantum probability to explain cognitive 'errors' such as order effects and violations of rational decision rules. His recent publications reveal a consistent trend toward integrating quantum-theoretic frameworks with empirical decision research, exploring topics such as pricing behavior, confidence judgments, multi-attribute decisions, and medical decision-making. These works appear in top journals including Psychological Review , Trends in Cognitive Sciences , and Proceedings of the National Academy of Sciences , indicating sustained high-impact contributions. Fellow, American Academy of Arts and Sciences Distinguished Professor, Indiana University Provost Professor, Indiana University Busemeyer has advised numerous graduate students and postdoctoral researchers, many of whom have become active collaborators and co-authors. His lab, the Judgment and Decision-Making Laboratory (Decision Research Laboratory), supports interdisciplinary research in cognitive science and receives ongoing funding for theoretical and experimental work. He has authored influential books, including Quantum Models of Cognition and Decision and Cognitive Modeling . The lab, located in Room A300G of the Psychology Building, fosters collaborative research on decision-making, quantum cognition, and computational modeling, with active projects involving fMRI, behavioral experiments, and theoretical development.

Sebastijan Dumancicمشاهده پروفایل

استادیار

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +۲ مورد دیگر

Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

Seth Freyمشاهده پروفایل

دانشیار

• Computational Social Science
• Governance of Commons
• Complex Systems
• +۵ مورد دیگر

Seth Frey is an Associate Professor in the Department of Communication at the University of California, Davis, with affiliate status at Indiana University's Ostrom Workshop and as Research Director at Metagov. His research focuses on computational social science approaches to understanding self-governance in complex social systems, particularly through the lens of online communities as model institutions. Education: Ph.D. in Cognitive Science and Informatics (complex systems), Indiana University, 2013 B.A. in Cognitive Science, UC Berkeley, 2004 Research Interests: Frey specializes in computational approaches to institutional analysis and the cognitive science of strategic behavior . His work examines how communities design governance systems to overcome collective action problems, with emphasis on: Emergent institutional structures in digital commons Policy-as-data through NLP and institutional grammar frameworks Cognitive mechanisms underlying cooperative behavior Design principles for participatory change in online platforms His methodology integrates large-scale data analysis, web-based experiments, and computational modeling across diverse contexts including Minecraft, Reddit, and professional sports ecosystems. Publication Trends: Recent publications (2023-2025) demonstrate a cohesive trajectory toward computational institutional analysis, with increasing focus on NLP-driven policy analysis (e.g., NLP4Gov), decentralized governance architectures (DAOs, multi-level platform governance), and the cognitive foundations of collective action. His work consistently bridges theoretical institutional analysis with practical applications in digital community design, showing particular growth in translating Ostrom's design principles into computational frameworks. Awards: Honorable Mention Award for Best Paper at ACM CSCW 2019 Advising and Grants: Frey mentors students interested in data science applications at the intersection of communication, cognition, and complex systems, emphasizing resourcefulness and intellectual curiosity. His research has secured substantial funding from: National Science Foundation (NSF) NASA Ford Foundation Google Open Source Foundation He actively encourages aspiring graduate students with strong self-directed research skills to explore computational approaches to social phenomena. Labs and Teams: He leads the Computational Communication Lab at UC Davis and co-directs the Institutional Grammar Research Initiative. Through Metagov, he develops the 'Governance API' framework for modular community governance. His past affiliations include Disney Research (Walt Disney Imagineering) where he applied complexity science to theme park systems, and the New England Complex Systems Institute (NECSI). Current collaborations span Ethereum governance, Minecraft server ecosystems, and Colorado's cannabis monitoring infrastructure.