جستجوی استاد راهنمای دانشگاهی

David Christensenمشاهده پروفایل

استاد

• Epistemology
• Philosophy of Science
• Formal Logic
• +۳ مورد دیگر

David Christensen is a Professor of Philosophy and Director of Undergraduate Studies at Brown University. He holds a B.A. from Hampshire College and a Ph.D. from UCLA. Prior to Brown, he taught at the University of Vermont for 20 years. His research focuses on epistemology, formal and informal, with emphasis on rational belief constraints, evidential support, and epistemic disagreement. Current work explores how rational self-doubt should shape ideal rationality frameworks. Christensen’s notable contributions include Putting Logic in its Place (2004) and co-editing The Epistemology of Disagreement: New Essays (2013). His scholarship bridges formal methods with practical questions about belief revision and peer disagreement. Research interests include: logical constraints on belief, higher-order evidence, epistemic akrasia, and the implications of peer disagreement. He examines how self-aware agents navigate cognitive biases and conflicting evidence while maintaining rational integrity. Recent work emphasizes self-critique mechanisms and the ineliminable role of epistemic rationality. Publications span over 30 years, addressing topics like evidential symmetry, Bayesian epistemology, and the limits of rational reflection. His articles consistently explore foundational questions in epistemology using both formal and conceptual analysis. Christensen’s work is characterized by rigorous argumentation and interdisciplinary engagement with philosophy of science and decision theory. Teaching responsibilities include directing undergraduate studies and courses on epistemology, logic, and philosophy of science. No formal awards are listed, though his academic contributions are widely cited in epistemology literature. Labs/Teams: No specific research groups are mentioned, though his work contributes broadly to collective epistemology and formal methods in philosophy.

Riccardo Tommasiniمشاهده پروفایل

دانشیار

• Stream Processing
• Knowledge Graphs
• Query Languages
• +۳ مورد دیگر

Riccardo Tommasini is an Associate Professor at INSA Lyon , a leading engineering institution in France. He leads the Stream Processing and Knowledge Graphs research within the DB Team at LIRIS laboratory under Professor Angela Bonifati. His academic journey began with a PhD in Computer Science from Politecnico di Milano under Emanuele Della Valle, with a dissertation titled Velocity on the Web to be published as a Springer book. Research Interests : Advancing stream processing for real-time data systems Extending knowledge graphs with dynamic data Designing graph databases for big data applications Creating query languages for heterogeneous data environments Building data engineering pipelines with Apache Airflow Enabling big graph processing in distributed settings Key Contributions : Developed Zodiac framework for Datalog reasoning under rule amendments (ICDE 2025) Co-authored foundational Streaming Linked Data book with Springer (2023) Created RSP4J API for RDF stream processing (ESWC 2021) Designed challenge-based learning curriculum for Data Engineering courses Scientific Recognition : Received ANR JCJC grant for POLYFLOW project (2024) Awarded Best Resource at ESWC 2021 Managed industrial collaborations with Neo4j, InfluxData, and Confluent Advising & Teaching : Supervises Mohamed Ragab (PhD candidate at University of Tartu) Course Leadership : Foundational Data Engineering course at INSA Lyon and University of Tartu Structured around Apache Airflow , Docker, and graph databases

Pierre Marquisمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Representation
• Automated Reasoning
• +۶ مورد دیگر

Pierre Marquis is a distinguished Professor of Computer Science at Université d'Artois , affiliated with the Centre de Recherche en Informatique de Lens (CRIL-CNRS, UMR 8188) . Since December 2024, he has served as the vice-president for research and doctoral studies at Université d'Artois. His research focuses on Artificial Intelligence , particularly knowledge representation , automated reasoning , inconsistency handling , and knowledge compilation , with recent emphasis on Explainable AI (XAI) . Research Interests: Marquis's work spans foundational AI topics including abduction , induction , belief revision , and preference modeling . He has pioneered knowledge compilation techniques to optimize AI tasks and developed frameworks for reasoning under inconsistency through paraconsistent logics and argumentation. His EXPEKTATION chair (2020-2026) under France's national AI program drives his current focus on interpretable machine learning models. Scientific Awards: 2025: CNRS Silver Medal 2022: AAIA Fellow 2017: Senior Member of Institut Universitaire de France (IUF) 2009: EurAI (ECCAI) Fellow Doctoral Students: Mentoring Clément Lens (critical patient monitoring systems) and Mehdi Sabiri (data-knowledge integration for AI explanations). Collaborating with students like Louenas Bounia (formal XAI models) and Romain Wallon (pseudo-Boolean constraints). Grants & Projects: Leads the EXPEKTATION research chair (2020-2026) and participates in ANR PING/ACK (2019-2023), ANR THEMIS (2021-2025), CNRS IRP MAKC (2020-2024), and H2020 TAILOR (2020-2024). Previously led PIA4 MAIA (2023-2032) and Pint (2022-2023). Labs & Teams: Active in CRIL-CNRS, contributing to PyXAI (Python XAI library) and d4 (model counting), while mentoring teams on consensus belief merging and dynamic constraint processing .

Fabrizio Riguzziمشاهده پروفایل

استاد

• Logic Programming
• Statistical Relational Artificial Intelligence
• Probabilistic Logic Programming
• +۳ مورد دیگر

Fabrizio Riguzzi is a Full Professor at the Department of Mathematics and Computer Science of the University of Ferrara, Italy. His academic career spans over two decades at the same institution, having served as Associate Professor (2014-2020) and Assistant Professor/Ricercatore (1999-2014). He is an active researcher in the fields of Logic Programming and Statistical Relational Artificial Intelligence with numerous publications and leadership roles in international conferences. His educational background includes: PhD in Electronic and Computer Engineering from the University of Bologna (1999) Laurea in Computer Engineering from the University of Bologna (1995) Riguzzi's research focuses on probabilistic approaches to artificial intelligence, particularly probabilistic logic programming and statistical relational AI. His work bridges symbolic reasoning with probabilistic methods, developing frameworks for uncertain knowledge representation and reasoning. He has made significant contributions to probabilistic answer set programming, neuro-symbolic integration, and applications in areas like network intrusion detection and knowledge graph completion. His research demonstrates how logical formalisms can be enhanced with probabilistic reasoning to tackle real-world problems with uncertainty. An analysis of his recent publications reveals a strong trend toward integrating neural and symbolic approaches in AI, with significant work on probabilistic answer set programming frameworks. His research spans theoretical foundations of probabilistic logic programming, practical implementations, and applications in cybersecurity, knowledge graphs, and decision-making under uncertainty. The interdisciplinary nature of his work connects computer science theory with practical AI applications. His notable awards include: Alain Colmerauer 10-Year Test-of-Time Award at ICLP 2021 Best Paper Award for "BUNDLE: A Reasoner for Probabilistic Ontologies" at RR-2013 Highly Commended Paper Award for "Probabilistic declarative process mining" at KSEM 2010 Riguzzi has supervised several PhD students to completion, including Elena Bellodi, Riccardo Zese, and Giuseppe Cota, who have gone on to win prestigious awards for their theses. He has served in numerous editorial roles, including Associate Editor of the Journal of Artificial Intelligence Research and Editor in Chief of Intelligenza Artificiale. His leadership extends to organizing major conferences like ILP 2018 and serving on program committees for top AI venues including IJCAI, AAAI, and ECAI. He is a member of the ML@unife research group and has developed several online systems including cplint, TRILL, and an Online AUC calculator. His work has fostered collaborations across the AI research community, particularly in the areas of probabilistic logic programming and neuro-symbolic AI.

Johannes Sternمشاهده پروفایل

استاد مدعو

• Philosophical Logic
• Formal Theories of Truth
• Modality
• +۷ مورد دیگر

Johannes Stern is an Adjunct Professor at the Department of Philosophy, University of Bristol. He completed his Master's in Logic and Philosophy of Science at the University of Munich and earned his PhD in 2012 from the University of Geneva under Fabrice Correia and Karl-Georg Niebergall. His work spans philosophical logic, formal theories of truth/modality, paradox, and the philosophy of language and mathematics. Key research areas: Predicate approaches to modality, semantic paradoxes, and the intersection of truth theory with mechanized reasoning. Notable collaborations include Volker Halbach (Oxford) and contributions to debates on Gödel's and Penrose's arguments against computationalism. His publications bridge proof-theoretic and semantic methods, with a focus on reconciling classical reasoning with self-referential truth theories. Recent work critiques and extends Kripke's reduction of modal predicates to truth-modified operators. Selected publications analyze the interplay between axiomatic truth systems and their semantic counterparts, including formalizations of Montague's theorem and reducibility criteria for multimodal paradoxes. His 2018 paper on supervaluation-style truth theories remains among his most cited works.

• Software Engineering
• Formal Methods
• Safety and Security Assurance
• +۷ مورد دیگر

Marsha Chechik is a Professor in the Department of Computer Science at the Faculty of Arts and Science, University of Toronto. She previously served as Department Chair from 2019-2022 and as Acting Dean in the Faculty of Information from July-December 2022. Her academic career spans numerous research contributions and leadership roles within the software engineering community. Professor Chechik's primary research interests focus on software engineering with emphasis on formal methods to enhance software quality. Her work encompasses scalable automated verification techniques including model-checking and theorem-proving, formal specification languages, verification of protocols, non-classical logics, and reasoning under inconsistency. She has made significant contributions to model management, software product lines, safety and security assurance, and automotive safety systems. Her research bridges theoretical foundations with practical applications, particularly in managing uncertainty in software models and developing techniques for automotive safety verification. Her recent publications demonstrate a strong focus on model management and transformations, software product lines and variability analysis, safety and security assurance cases, and semantic analysis of software evolution. The integration of formal methods with practical software engineering challenges, especially in safety-critical domains like automotive systems, represents a consistent theme throughout her work. Professor Chechik has been recognized with multiple prestigious awards including a Best Paper Award at RE'12, a SIGSOFT Distinguished Paper Award at ICSE'12, a Best Student Paper Award at CASCON'07, and a Distinguished Paper Award at ICSE'07, highlighting the impact and quality of her research contributions. She actively supervises graduate students and has successfully guided numerous Ph.D. candidates to completion. Her group has produced graduates who predominantly pursue research careers in both academic institutions and industrial research labs. She currently leads several funded projects including the Automotive Safety project (in collaboration with General Motors) and the Software Evolution project, focusing on practical applications of her research interests. Professor Chechik leads the Software Engineering Lab at the University of Toronto, where innovative projects like Matchmakers (a serious game for software engineering) are developed. Her collaborative network extends across institutions, with notable partnerships including Julia Rubin at the University of British Columbia, demonstrating her commitment to interdisciplinary research and academic collaboration.

• Behavioral Economics
• Experimental Economics
• Behavioral Finance
• +۶ مورد دیگر

Florian Zimmermann is Research Director at IZA and Professor at the University of Bonn. Previously, he served as Professor and Research Director at the briq Institute on Behavior & Inequality (2019–2023). His affiliations include the University of Zurich, University of Mannheim, University of California, Los Angeles (UCLA), University of California, San Diego (UCSD), and Harvard University. Education: PhD in Economics, Bonn Graduate School of Economics (2013) Bachelor/Master in Economics, University of Mannheim and UCLA Research Interests: A leading expert in behavioral and experimental economics, his work focuses on motivated cognition, belief formation, information processing, and their implications for financial markets, labor economics, and social behavior. Key themes include discrimination, climate change denial, trust, and self-assessment mechanisms. Publications: His research appears in top journals like the American Economic Review , Quarterly Journal of Economics , Management Science , and Nature Climate Change , with recurring themes of cognitive biases, decision-making under uncertainty, and market psychology. Editorial Roles: He serves as Associate Editor at the Quarterly Journal of Economics and Journal of the European Economic Association , and is on the editorial board of the Review of Economic Studies .

• Simulation in Physical Therapy Education
• Cardiopulmonary Rehabilitation
• Cardiovascular and Pulmonary Physical Therapy
• +۳ مورد دیگر

Rachel W Pata serves as Clinical Professor of Physical Therapy and Director of Simulation at Quinnipiac University's School of Health Sciences, concurrently holding the role of Capstone Coordinator for the Department of Physical Therapy. Board certified as a Cardiovascular and Pulmonary Specialist by the American Physical Therapy Association and designated as a Certified Healthcare Simulation Educator by the Society for Simulation in Healthcare, she maintains active clinical practice in acute-care and inpatient rehabilitation settings with emphasis on cardiopulmonary compromise. Her educational foundation includes: BS in Physical Therapy from Quinnipiac University MPT from Quinnipiac University (2003) DPT from Simmons University (2009) Dr. Pata's research program demonstrates exceptional focus across three interconnected domains: simulation-based learning methodologies for clinical skill development, cardiopulmonary rehabilitation protocols addressing practice inconsistencies, and physiological impacts of athletic overtraining. Her nutritional education studies reveal critical gaps in physical therapists' preparedness for patient counseling, while her marathon-related biomarker research provides novel insights into physiological stress responses. This multifaceted approach bridges classroom theory with clinical application through innovative educational frameworks. Recent scholarly output (2020-2025) reveals strategic progression toward evidence-based simulation integration in healthcare education, with growing emphasis on interdisciplinary applications including obstetrics and pulmonary rehabilitation. Her publications consistently address practical clinical challenges through rigorous methodology, appearing in journals like the Journal of Cardiopulmonary Physical Therapy and presentations at APTA Combined Sections Meetings. The recurring themes of assessment reliability, care standardization, and technology-enhanced learning underscore her commitment to elevating physical therapy practice standards. Professional recognitions include: CHEFA Grant (2024-2025) awarding $75,000 for OBGYN simulation development Certified Healthcare Simulation Educator credential (2019) Cardiovascular and Pulmonary Specialist certification (2014) Basic Life Support certification (2003) As Capstone Coordinator, Dr. Pata guides student research projects while securing external funding like the CHEFA Grant. Her leadership extends to service as APTA CT Board Vice President (2025-present) and Director (2023-present), where she shapes state-level professional standards. The simulation program she directs has evolved into a multidisciplinary training hub serving multiple healthcare disciplines. Under Dr. Pata's direction, the School of Health Sciences simulation program has developed into a comprehensive training ecosystem featuring high-fidelity scenarios across acute care, rehabilitation, and specialty domains. She cultivates cross-departmental collaboration to design clinically relevant simulations, emphasizing debriefing techniques that transform experiential learning into actionable clinical competence. Her team integrates emerging technologies while maintaining focus on assessment validity and educational outcomes.

• Knowledge Representation and Reasoning
• Description Logics
• Formal Concept Analysis
• +۳ مورد دیگر

Francesco Kriegel is a Postdoctoral Research and Teaching Associate at the Institute of Theoretical Computer Science within the Faculty of Computer Science at Dresden University of Technology. He is a key member of the International Center for Computational Logic (ICCL), working under Professor Franz Baader's research group. His academic journey began with Mathematics as his major (focusing on Algebra and Analysis) and Computer Science as his minor, culminating in a doctoral degree in 2019 with a dissertation on constructing and extending Description Logic ontologies using Formal Concept Analysis. Dr. Kriegel's research is deeply rooted in Knowledge Representation and Reasoning, with a particular focus on Description Logics (especially the EL family), ontology repair, and the integration of Formal Concept Analysis with Description Logics. His work has significantly advanced the field of ontology engineering through the development of optimal repair frameworks that preserve maximal consequences while removing errors. He has explored theoretical foundations such as the EL subsumption hierarchy's structure and navigational properties, while also developing practical applications for ontology construction and maintenance. His publication record from 2016-2025 reveals a consistent research trajectory focused on ontology repair mechanisms, with increasing sophistication in handling quantified ABoxes, hierarchical concrete domains, and interactive repair systems. The research demonstrates a clear progression from theoretical foundations to practical implementations, with recent work emphasizing user-centered approaches to ontology repair that balance theoretical optimality with practical usability. Best Paper Award at CLA 2015 Principal Investigator for DFG-funded project 'Construction and Repair of Description-logic Knowledge Bases' Research Associate at ScaDS.AI (Center for Scalable Data Analysis and Artificial Intelligence) Former Research Associate in DFG project 'Repairing Description Logic Ontologies' and CPEC (Center for Perspicuous Computing) Dr. Kriegel has been actively involved in teaching since his third semester and throughout his doctoral studies, primarily conducting tutorials for foundational and advanced computer science courses. In summer semester 2024, he delivered his first full lecture series 'Building and Maintaining Ontologies in the Description Logic EL', reflecting his expertise in the field. He serves on program committees for major AI conferences including IJCAI, AAAI, and KR, and reviews for prestigious journals such as Artificial Intelligence Journal and Journal of Artificial Intelligence Research.

Ronald Faginمشاهده پروفایل

پژوهشگر ارشد

• Applications of logic to computer science
• Database theory
• Finite model theory
• +۵ مورد دیگر

Ronald Fagin is a Research Fellow at IBM Research - Almaden, renowned for his groundbreaking contributions to database theory, finite model theory, and reasoning about knowledge. His work bridges logic with practical applications in computer science, including entity resolution, query answering, and uncertainty modeling. Ph.D. in Mathematics, UC Berkeley B.A. in Mathematics, Dartmouth College His research focuses on applying logic to computer science, particularly in database theory, finite model theory, and knowledge representation. His recent work explores quantifier complexity, multi-structural games, and ontology-driven data integration. His publications span database theory, logic, and information extraction, with a strong emphasis on formal frameworks and complexity analysis. Key themes include entity linking, uncertainty reasoning, and algorithmic voting theory. Member of National Academy of Sciences Member of National Academy of Engineering Recipient of Gödel Prize and IEEE Technical Achievement Award Laurea Honoris Causa (Italy) and Docteur Honoris Causa (France) Senior Fellow of ACM, IEEE, and AAAS Ronald Fagin has mentored prominent collaborators in database theory and information extraction. His research includes leading projects on imprecise probabilistic logic and developing frameworks for knowledge representation under uncertainty.

Cor Stegingمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Hybrid Intelligence
• Explainable AI
• +۳ مورد دیگر

Dr. Cor Steging is a Postdoctoral Researcher at the University of Groningen's Faculty of Science and Engineering, specializing in Artificial Intelligence through the Bernoulli Institute. His work focuses on bridging machine learning with structured reasoning, particularly in legal contexts, with strong emphasis on ethical and transparent AI development. Research interests center on Hybrid Intelligence systems that combine learning and reasoning, with specialization in Responsible AI and Explainable AI methodologies. Key focus areas include: Alignment of machine learning outputs with human-understandable reasoning Application of AI in legal domains and justice systems Development of transparent AI systems for high-stakes decision environments Handling incomplete/inconsistent data in critical applications Ethical implications of prediction systems in law Recent publications reveal a clear trajectory toward practical implementation of Responsible Hybrid Intelligence , with growing emphasis on legal applications and ethical frameworks. The 2024 doctoral thesis establishes foundational methodology for aligning learning and reasoning, while 2023 publications deepen exploration of legal domain applications and workshop organization for responsible AI development. Collaborative research demonstrates strong network connections with prominent AI researchers including Bart Verheij, Silja Renooij, and Trevor Bench-Capon. Current work addresses critical gaps in AI transparency for legal decision-making, with particular attention to small/inconsistent datasets and ethical design choices in machine learning systems.

Manling Liمشاهده پروفایل

استادیار

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Manling Li is an Assistant Professor at the Department of Computer Science, Northwestern University. She previously served as a postdoc at Stanford University's Vision and Learning Lab under Prof. Jiajun Wu and received her Ph.D. from the University of Illinois at Urbana-Champaign (advisor: Prof. Heng Ji). Her research spans Language + Vision + Robotics with applications in Embodied AI and AI for Science . Key Research Areas : Knowledgeable Foundation Models, Reasoning & Planning, Compositionality, Multimodal Knowledge Extraction, Factuality & Trustworthiness in AI Leadership Roles : Organizing Committee for ACL 2025, NAACL 2025, EMNLP 2024 Research Trends in her 15 most recent publications show: Advancing Embodied AI through structured reasoning and planning frameworks Developing Vision-Language Models for 3D layout optimization and video understanding Addressing LLM Hallucinations via knowledge shadowing and mechanistic interpretability Creating collaborative agent systems with out-of-sync recovery mechanisms Scientific Recognition : ACL 2024 Outstanding Paper SoCal NLP 2024 Best Paper Microsoft Research Fellowships (PhD & Postdoc) DARPA Riser & EE CS Rising Star Advising Impact : Mentored 19 students in developing the UIUC information extraction system. Currently advising 12 students across PhD, Master's, and undergraduate levels, with particular emphasis on supporting underrepresented groups. Led teams to rank 1st in DARPA AIDA evaluations.

Rodrigo Borgesمشاهده پروفایل

استادیار

• Epistemology
• Philosophy of Language
• Philosophy of Mind
• +۳ مورد دیگر

Dr. Rodrigo Borges is an Assistant Instructional Professor in the Department of Philosophy at the University of Florida. He holds a PhD in Philosophy from Rutgers University. His research focuses on epistemology, with particular interests in the Gettier problem, formal epistemology, and the history of philosophy (especially Plato and Descartes). He is currently pursuing a project on 'Knowledge and Reasoning,' integrating traditional and formal methods across epistemology, metaphysics, and psychology. Dr. Borges has edited several volumes, including Illuminating Errors: New Essays on Knowledge from Non-Knowledge (Routledge, 2023) and co-edited special issues in Synthese and Manuscrito . His work bridges analytical philosophy with historical and interdisciplinary approaches. Research Interests : Epistemology: Gettier problem, epistemic justification, anti-luck epistemology Philosophy of Mind: theories of knowledge and belief History of Philosophy: pre-modern epistemology and rationalism Formal Methods: logical analysis of epistemic concepts Recent Publications : His recent articles explore themes like diachronic epistemic thresholds, group knowledge, and historical precedents to the Gettier problem. These works emphasize methodological pluralism in addressing core epistemological challenges. Presentations : He has presented at venues like the Penn-Rutgers-Princeton Social Epistemology Conference and the Meeting of the National Association of Graduate Programs in Philosophy. No scientific awards are explicitly mentioned in the provided materials. His advising and grant activities remain unspecified in the current data. He has no listed lab or collaborative team affiliations.

Sergio Grecoمشاهده پروفایل

استاد

• Database Theory
• Data Integration
• Inconsistent Data Processing
• +۳ مورد دیگر

Sergio Greco is Full Professor at University of Calabria's DIMES Department, Coordinator of the Computer Engineering Degree, and Vice-president of the Computer Engineering national Group. His research spans database theory, data integration, inconsistent data processing, and computational logic. Research Focus: Development of theoretical frameworks for data management including argumentation frameworks, knowledge base querying under inconsistency, and decentralized learning systems. Recent work integrates machine learning with formal reasoning methods. Honors: Best Paper Award at International Conference on Logic Programming (2020) Best Paper Award at RuleML Symposium (2014) Leads projects on cybersecurity and data management funded by EU and Italian Ministry of Research, coordinating national and international research groups.

Marty Allenمشاهده پروفایل

دانشگاهی

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Marty Allen is an Associate Teaching Professor and Director of Online Programs in the Department of Computer Science at Tufts University's School of Engineering. He holds a PhD in Computer Science from the University of Massachusetts Amherst (2009), with earlier degrees in Philosophy from Simon Fraser University (1996 B.A.) and the University of Pittsburgh (2001 M.A.). His research focuses on multiagent reinforcement learning and applications of AI to biological systems, including influenza phenotype prediction and complex ecological modeling. He has authored or co-authored over 40 publications across journals like Emerging Microbes and Infections and conferences such as AAAI and NIPS. Allen has led multiple grants including a $1.2M REU program and a Disc Seed Grant for AI-driven viral research. He teaches courses in discrete mathematics, algorithms, and capstone projects, and has held leadership roles in curriculum development including the Strategic Planning Transformational Education Action Group at University of Wisconsin–La Crosse (2016–2019). Professional activities include service on Tufts' Computer Science External Advisory Board and equity initiatives as Department Equity Liaison. His work bridges theoretical computing with interdisciplinary applications in both natural and social sciences.