جست‌وجوی استاد و پژوهشگر

Daniel Hathawayمشاهده پروفایل

مدرس ارشد

• Mathematics
• Logic
• Set Theory
• +۳ مورد دیگر

Daniel Hathaway is a Senior Lecturer in the Department of Mathematics and Statistics at the University of Vermont (UVM). He holds a PhD in Mathematics from the University of Michigan (2015) and an MS/BS in Mathematics and Computer Science from Rensselaer Polytechnic Institute (2010). Prior to joining UVM in 2018, he was a Visiting Assistant Professor at the University of Denver (2015–2018). His research focuses on set theory, logic, and their applications, including topics like forcing, Ramsey theory, and descriptive set theory. Hathaway teaches courses ranging from foundational calculus to advanced topics such as real analysis and discrete structures. His educational background includes: PhD in Mathematics, University of Michigan, 2015 MS in Mathematics, Rensselaer Polytechnic Institute, 2010 BS in Mathematics and Computer Science, Rensselaer Polytechnic Institute, 2010 Research Interests: Daniel’s work explores foundational questions in set theory and mathematical logic, with a particular emphasis on forcing techniques, Ramsey-type theorems, and the structure of Borel equivalence relations. He has contributed to areas such as generalized Baire spaces, measurable cardinals, and combinatorial set theory. His teaching portfolio includes calculus, linear algebra, real analysis, and discrete mathematics. Publications: Hathaway has published widely in leading journals such as The Annals of Pure and Applied Logic , Journal of Symbolic Logic , and Topology and Its Applications , with a focus on advancing theoretical frameworks in set theory and logic. Advising and Grants: While no specific advising roles or grants are detailed in the provided text, his academic roles indicate involvement in undergraduate and graduate education at UVM. Labs/Teams: No specific lab affiliations or collaborative teams are mentioned in the text.

Flemming Stassenمشاهده پروفایل

دانشیار

• Integrated Circuits
• Design for Test
• Fault-Tolerant Circuits
• +۵ مورد دیگر

Flemming Stassen is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). He specializes in integrated systems design with a focus on test methodologies and fault-tolerant circuits. Since 1999, he has served on the appeal board for patents and trademarks under Denmark's Ministry of Business and Growth. His research interests span CMOS technology (including 90nm and 0.25 micron processes), high-speed low-power design, and curriculum development for engineering programs. He has contributed to CDIO-based curricula at DTU and authored textbooks on semiconductor design principles. Recent work emphasizes merging interdisciplinary educational programs and advancing low-power circuit techniques. His publications reflect a 40-year career in electronics innovation, from transistor history to modern nanometer-scale design. Projects: SecClo Master's Program in Security and Cloud Computing (2021–2026), UAV Ground Control System (2015–2016) Awards: DTU International Faculty of the Year (2001) Professional Activities: Peer reviewer for SecClo Network Meetings, organizer of computer arithmetic conferences He leads educational initiatives in embedded systems engineering and collaborates on security-focused graduate programs.

Fabio Papacchiniمشاهده پروفایل

مدرس ارشد

• Modal Logic
• Automated Planning
• Formal Verification
• +۵ مورد دیگر

Dr. Fabio Papacchini is a Senior Lecturer in Computer Science at Lancaster University. His research focuses on modal logic, automated planning, formal verification, agent systems, and ontology-mediated querying. He contributes to theoretical foundations of logic and their applications in artificial intelligence and software engineering. Papacchini is actively involved in the Security Lancaster research groups, specifically in Software Security and Systems Security domains. His recent work includes advancements in modal logic reduction techniques, efficient model construction, and formal verification of autonomous systems. He has published extensively in top-tier conferences and journals, with notable contributions to automated planning, BDI agents, and runtime verification frameworks. Current projects include fostering professional development through collaborative academic initiatives. Awards and recognitions are not explicitly listed in the provided texts. Papacchini’s research has been supported by grants and collaborations, though specific funding details are not detailed here. He is affiliated with the Department of Computer Science at Lancaster University and maintains an active presence in international academic communities.

Ana Ozakiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Knowledge Representation
• Machine Learning Theory
• +۳ مورد دیگر

Ana Ozaki is an Associate Professor at the University of Oslo (full-time) and holds a part-time position at the University of Bergen. Her research focuses on Artificial Intelligence, particularly knowledge representation, machine learning theory, and algorithms for learning logical theories in description logic. Her primary research interests include the formalization of learning phenomena to investigate questions of learnability, complexity, and reducibility. Ozaki specializes in algorithms for learning logical theories using description logic and related formalisms. Her work bridges theoretical foundations with practical applications in knowledge representation. Ozaki's publications predominantly explore themes in computational logic, knowledge representation, and machine learning theory. Recent works focus on ethical AI applications, hybrid logic systems, and knowledge extraction techniques from language models, demonstrating consistent innovation at the intersection of formal methods and practical AI systems. She has led significant research projects including 'Learning Description Logic Ontologies' (funded by RCN) and 'Apprendimento PAC di Ontologie in Logica Descrittiva' (PACO), and has collaborated internationally on graph-based computation models. Ozaki serves the AI community through editorial roles at the Journal of Machine Learning Research and Journal of Web Semantics, and has chaired program committees for major conferences including the International Joint Conference on Rules and Reasoning and the International Description Logic Workshop.

• Formal Methods
• Automata Theory
• Temporal Logic
• +۵ مورد دیگر

Daniel Neider is a Professor at TU Dortmund University, Germany. He holds former affiliations with Carl von Ossietzky University Oldenburg and the Max Planck Institute for Software Systems (Kaiserslautern). His research focuses on formal methods, automata theory, temporal logic, and their applications in verification, AI, and machine learning. Neider earned his PhD in 2014 from RWTH Aachen University and a second PhD in 2022 from Kaiserslautern University. His work bridges theoretical foundations with practical tools, such as developing algorithms for automata learning, robust temporal logics (rLTL), and neuro-symbolic verification frameworks for deep neural networks. Key research areas include: (1) learning-based formal methods for system verification, (2) robust temporal logics for handling noisy environments, (3) reinforcement learning with temporal logic constraints, and (4) automated specification mining from data. He has contributed to tools like libALF (Automata Learning Framework) and Sorcar for property-driven invariant synthesis. His recent work explores: (a) scalable LTL learning from noisy data using MaxSAT, (b) neuro-symbolic verification techniques, and (c) applying large language models to automate reinforcement learning with reward machines. He has published over 135 papers in top venues like AAAI, TACAS, and FMCAD, focusing on formal methods intersections with AI and systems verification. Current projects involve: (i) robustness-aware synthesis for reactive systems, (ii) temporal logic inference from positive examples, and (iii) developing interpretable verification tools for complex operational domains. His lab collaborates on applying formal methods to real-world systems like smart contracts and neural networks.

Fabio Cozmanمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Probabilistic Reasoning
• +۷ مورد دیگر

Fabio Cozman is a Full Professor at the School of Engineering, University of São Paulo (USP), and Director of the Center for Artificial Intelligence at USP. He holds a PhD from Carnegie Mellon University (1996) and completed his engineering degree at Poli-USP. His research spans artificial intelligence, machine learning, and probabilistic reasoning, with significant contributions to knowledge representation under uncertainty, credal networks, and physics-informed AI. Current research focuses on AI interpretability , AI and societal impact , and ocean hazard prediction . Key publications cover probabilistic logic programming , credal networks , and Bayesian network extensions . He is a founding member and former president of the Society for Imprecise Probability Theory and Applications (SIPTA) and has served as Associate Editor for multiple AI journals. His work integrates theoretical advancements in probabilistic modeling with practical applications in robotics, computer vision, and environmental systems, particularly focused on the Brazilian maritime territory through projects like the Blue Amazon Brain (BLAB) architecture.

James Trippمشاهده پروفایل

پژوهشگر

• Cognitive Science
• Data Visualization
• Statistical Analysis
• +۳ مورد دیگر

James Tripp is a Senior Research Software Engineer at the University of Warwick's Centre for Interdisciplinary Methodologies (CIM). He holds a BSc(Hons) and PhD in Psychology from the University of Warwick. His work spans statistical analysis, software development, and technical support for research and teaching infrastructure at CIM. He specializes in tools like R, Python, and geospatial analysis, and leads workshops on digital methods and visualization. Tripp has developed software packages such as Backfillz (for MCMC visualization), LE-CAT (lexicon-based analysis tool), and contributed to projects like WAYS (Turing-funded). His technical roles include managing servers hosting Jupyter Notebooks, ShinyProxy, and data capture systems. He collaborates with academic partners like the Media of Cooperation Group at the University of Siegen. His research interests bridge cognitive science and computational methods, with a focus on interdisciplinary digital methodologies.

Adam Chlipalaمشاهده پروفایل

استاد

• Programming Languages
• Formal Verification
• Compiler Verification
• +۲ مورد دیگر

Adam Chlipala is the Arthur J. Conner Professor of Computer Science at MIT, with appointments in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science. His research integrates programming languages and formal methods to develop verified computer systems. Education: PhD in Computer Science, University of California, Berkeley MS in Computer Science, University of California, Berkeley BS in Computer Science, Carnegie Mellon University Research Focus: Dr. Chlipala's work spans: Verified compilers and programming tools Cryptographic protocol implementation Hardware description languages Proof automation in theorem provers High-performance parallel computing His current projects focus on rethinking abstractions for parallel systems with end-to-end formal verification. Teaching: He has created and teaches courses on verified software engineering (6.S057), programming fundamentals (6.1010), and formal reasoning about programs (6.5120). His educational materials include textbooks on Certified Programming with Dependent Types and Formal Reasoning About Programs. Awards and Honors: His research has been recognized with the German IT Security Award, Humies Gold Award for evolutionary computation, and multiple teaching prizes including the Jamieson Prize. He is an ACM Distinguished Member and NSF CAREER awardee. Research Teams: Dr. Chlipala leads the Programming Languages & Verification Group at MIT CSAIL. His startup Nectry develops no-code enterprise software based on the Ur/Web language and UPO proof system.

• Artificial Intelligence
• Machine Learning
• Inductive Logic Programming
• +۳ مورد دیگر

Andrew Cropper is a Research Fellow in the Department of Computer Science at the University of Oxford. His research focuses on the intersection of logic and machine learning, particularly inductive logic programming (ILP) and program synthesis. He leads the Logic and Learning research group and the 'The Automatic Computer Scientist' project, which explores automated program generation and symbolic learning methods. His work emphasizes developing efficient algorithms for learning logical programs and improving the scalability of inductive logic programming. Recent publications highlight advancements in combining small logical rules into larger structures, discovering higher-order abstractions in logic programs, and applying symbolic metaprogramming to enhance learning efficiency. His research also addresses challenges in handling noisy data and integrating numerical reasoning into relational program synthesis. Collaborations with cognitive scientists explore parallels between human rule-learning processes and machine learning algorithms. Cropper has advised students including Rolf Morel (past researcher) and collaborates with researchers like Céline Hocquette and Matti Järvisalo. His contributions span over 30 peer-reviewed articles in top conferences (IJCAI, AAAI) and journals (Nature Communications, Machine Learning). He maintains active roles in the ILP community, co-authoring comprehensive reviews of the field's 30-year evolution and advancing its applications in program induction and general game playing. Research highlights include developing the Metagol system for ILP, pioneering predicate invention techniques, and introducing constraint-driven methods to improve program synthesis efficiency. His work bridges symbolic AI with modern machine learning, aiming to create interpretable and generalizable algorithms for complex problem-solving.

Anton Freundمشاهده پروفایل

استاد

• Mathematical Logic
• Proof Theory
• Reverse Mathematics
• +۳ مورد دیگر

Prof. Dr. Anton Freund is a tenure-track professor and Emmy Noether group leader at the University of Würzburg’s Department of Mathematics (Mathematical Logic). He holds the Chair of Mathematics III and specializes in proof theory, reverse mathematics, and ordinal analysis. His research focuses on well-ordering principles, dilators, and the logical strength of combinatorial principles. Education & Career: Bachelor/Master (2008–2014): LMU Munich, with a gap year in Paris PhD (2014–2018): University of Leeds under Michael Rathjen Postdoc (2018–2021): TU Darmstadt with Ulrich Kohlenbach Emmy Noether Group Leader (2021–2023): TU Darmstadt Current: Tenure-track professor & Emmy Noether Group Leader at Würzburg Research Interests: Freund investigates foundational aspects of mathematics via proof-theoretic methods. Key themes include ordinal analysis of subsystems of arithmetic, combinatorial principles (e.g., Kruskal’s theorem, Higman’s lemma), and the interplay between computability and set-theoretic reflection principles. His work bridges constructive mathematics and impredicative systems, often employing dilators and ordinal notation systems. Publications: Freund’s recent work explores topics like provable dilators, Bachmann-Howard fixed points, and the logical strength of minimal bad arrays. These contributions advance understanding of reverse mathematics and computational aspects of proof theory. Awards & Grants: He leads an Emmy Noether Research Group funded by the German Research Foundation (DFG), reflecting his role in advancing foundational mathematical research. Lab & Collaboration: His team includes Alexandra Fick and Davide Manca. Collaborations span institutions like Leeds, Darmstadt, and international journals in mathematical logic.

Maryam Daryalalمشاهده پروفایل

استادیار

• Sequential decision making under uncertainty
• Stochastic programming
• Robust optimization
• +۵ مورد دیگر

Dr. Maryam Daryalal is an Assistant Professor in the Department of Decision Sciences at HEC Montréal, affiliated with the University of Montréal. She holds secondary memberships in CIRRELT (Centre Interuniversitaire de Recherche sur les Réseaux d'Entreprise) and GERAD (Groupe pour la Recherche en Analyse des Décisions). Her research focuses on sequential decision-making under uncertainty, stochastic/robust optimization, and their applications in telecommunications, energy systems, and supply chain management. Education: Ph.D. in Industrial Engineering, University of Toronto (2022) M.Sc. in Computer Science, Concordia University (2016) M.Sc. in Industrial Engineering, Amirkabir University of Technology (2013) B.Sc. in Industrial Engineering & Systems Analysis, Amirkabir University of Technology (2011) Research Interests: Her work develops novel methodologies for multistage stochastic programming and robust optimization, addressing challenges in telecommunications network migration, energy systems planning, and large-scale decision-making. She bridges theoretical advances with practical applications through innovative decomposition techniques like hybrid logic-based Benders decomposition. Awards: 2023 INFORMS Telecom Best Paper Award Judith Liebman Award (2021) NSERC Discovery Grant (2024-2029) Advising & Grants: Supervises PhD/MSc students in optimization and telecommunication projects. Leads IVADO grants (e.g., $40k R3AI project) and co-leads a $100k IVADO project on net-zero power grids. Active in Mitacs industry collaborations. Professional Engagement: Serves on INFORMS committees, organizes international conferences (e.g., CORS, INFORMS Computing Society), and reviews for top journals like Operations Research and Mathematical Programming.

• Knowledge Representation
• Artificial Intelligence
• Computer Science
• +۶ مورد دیگر

François De Bertrand De Beuvron is a Lecturer at INSA Strasbourg, teaching modules such as JAVA Programming, UML Modeling, Databases, Graphic Interfaces, and Logic and AI. He is affiliated with the Shared Knowledge Department and contributes to the Theoretical Bioinformatics, Data Mining and Stochastic Optimization (BFO) team at the Laboratory of Image Sciences, Computer Science and Remote Sensing (LSiiT), University of Strasbourg (UdS). His research focuses on hybrid knowledge representation combining description logics with real-world applications in design, configuration, diagnosis, and classification. Key projects include semantic interpretation of satellite imagery through geographic ontologies and applying TRIZ theory to innovative design via CAD semantic modeling. He emphasizes lifecycle analysis of products, integrating functional, structural, behavioral aspects like maintainability and recyclability. Publications are listed on the LSiiT laboratory's website. No specific awards, grants, or advising activities are detailed in the provided text.

• Philosophy of Mind
• Philosophy of Language
• Philosophical Logic
• +۳ مورد دیگر

Dr. Tom Williams is an Assistant Professor in Philosophy and Associate Director for Enrolment Management in Humanities at Northeastern University London. He holds a PhD (2022), MPhilStud, and BA from UCL's philosophy program. His research focuses on philosophy of mind, language, and logic, with recent work on singular thought, semantic relationism, and AI ethics. He contributes to Northeastern's AI and Information Ethics research cluster and the Internet Democracy Initiative. Teaching specialties include philosophy of mind, language, logic, and technology ethics. He previously taught at UCL and Birkbeck, leading modules like logic courses and seminars in metaphysics/epistemology. His academic background includes roles as module leader and research assistant for interdisciplinary initiatives. Awards: Recipient of the AHRC doctoral award supporting his PhD research on acquaintance and singular thought. Active in academic administration through enrolment management roles.

Forrest Sheng Baoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Natural Language Processing
• Electronic Design Automation (EDA)
• +۳ مورد دیگر

Forrest Sheng Bao is an Assistant Professor of Computer Science at Iowa State University (ranked 63rd in U.S. CS departments). His research focuses on AI, NLP, EDA, and medical data analytics. He holds a Ph.D. in Computer Science (with Electrical Engineering minor) from Texas Tech University (2012), followed by a postdoc at Stony Brook University. Previously, he served as an Assistant Professor at the University of Akron (2013–2017). His work is funded by NSF, FAA, AFRL, Microsoft, and others, with media coverage in MIT Technology Review and Lancet Neurology. Education: Ph.D., Computer Science (minor in Electrical Engineering), Texas Tech University, 2012 Postdoc, Stony Brook University (2012–2013) Research Interests: AI/ML, NLP (NLG metrics, review analysis), EDA (circuit routing, HDLs), medical signal/image processing (EEG/MRI). His publications span top conferences like ACL, NAACL, EMNLP, DAC, and DATE, with notable work in EDA using reinforcement learning and NLP metrics. He advises students on NLP and EDA projects, co-founding startups Funix.io and Codepod.io. His lab focuses on applying ML to PCB/IC design and biomedical applications. Awards: Best Paper Award at HPCC 2019, NSF grants totaling $462k. Teaching: Courses in NLP, Machine Learning, and Python. Actively mentors students through research and TA/RA opportunities. He engages in entrepreneurship (AI and hardware startups) and maintains active GitHub repositories for educational projects like PyEEG and Mercury.

Fred Maierمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Agriculture Technology
• +۴ مورد دیگر

Fred Maier serves as Associate Director of the Artificial Intelligence Institute and Assistant Research Scientist at the University of Georgia, with a Courtesy Professor appointment in Computer Science. His interdisciplinary research bridges artificial intelligence with practical applications across agriculture, environmental science, and healthcare. Research interests focus on machine learning applications in diverse domains: Agricultural technology : Crop yield prediction, biomass estimation, and precision farming using computer vision and data synthesis Human activity recognition : Wearable sensor data analysis for health monitoring and behavior classification Environmental forecasting : Solar radiation prediction and forest management optimization Semantic technologies : Knowledge representation and ontology engineering for intelligent systems Publication trends reveal consistent focus on practical AI applications, with recent emphasis on agricultural analytics (alfalfa yield prediction, cotton boll counting) and human activity recognition techniques. Earlier work demonstrates foundations in semantic web technologies and logic-based AI systems.