جست‌وجوی استاد و پژوهشگر

Esteban Guerrero Roseroمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Multi-Agent Systems
• Knowledge Representation
• +۸ مورد دیگر

Esteban Guerrero Rosero is an Associate Professor in Social-Aware Artificial Intelligence at the Department of Computer Science, Umeå University (Sweden). His work bridges multi-agent systems , knowledge representation , and neuro-symbolic methods to develop trustworthy hybrid intelligence systems. He has co-led multidisciplinary projects in Sweden and Finland, focusing on AI applications in health , economics , and sports . Education: Ph.D. in Computer Science, Umeå University M.Sc. in Computer Science, Malmö University B.Eng. in Electronics and Telecommunications, University of Cauca (Colombia) His research emphasizes software agents and non-monotonic reasoning to model human activities in domains like health informatics and ambient intelligence . By integrating argumentation frameworks and neuro-symbolic approaches , he develops systems that adapt to social and behavioral contexts, with applications in digital coaching and behavior change interventions . Recent publications highlight trends in value-based decision-making , financial robo-advisors , and AI regulations , reflecting his focus on trustworthy AI and ethical reasoning . His work often involves cross-disciplinary collaboration , particularly in healthcare and personalized systems. Guerrero Rosero has led the Formal Methods for Trustworthy Hybrid Intelligence research group since 2019. He is involved in projects like STAR-C (2018–2024), Socially Intelligent Autonomous Systems (2017–2019), and NTC - Nordic Telemedicine Center (2015–2018), aiming to create AI systems that support human activities in complex social contexts.

• Statistical Relational Artificial Intelligence
• Human-aware AI
• Explainable AI
• +۱۲ مورد دیگر

Tanya Braun is a Junior Professor in the Institute of Computer Science at the University of Münster, Department of Mathematics and Computer Science. She leads the Data Science research group, focusing on statistical-relational AI, human-aware AI, and text understanding. Her work bridges formal AI methods with real-world applications in healthcare, digital humanities, and public sector systems. Education: Bachelor's and Master's in Computational Informatics, Hamburg University of Technology Doctorate in Computer Science, University of Lübeck (2020), thesis: 'Rescued from a Sea of Queries - Exact Inference in Probabilistic Relational Models' Her research centers on probabilistic inference in relational domains , with a focus on lifted inference techniques that exploit symmetries to scale reasoning. She investigates human-aware AI , particularly how AI systems can reconcile learned models with human expectations to improve explainability and trust. Her work on text understanding addresses challenges in data-scarce settings such as digital humanities, where traditional large language models fail. She has developed methods for identifying and enriching subjective content descriptions, topic modeling in specialized domains, and feedback-driven model improvement. The 15 most recent publications highlight a strong trajectory in lifted inference, model compression, privacy-preserving AI, and explainability . Her work integrates formal AI foundations with practical concerns in high-stakes domains like healthcare. She frequently publishes in top venues such as AAAI, IJCAI, ECAI, and Artificial Intelligence, often in collaboration with Ralf Möller, Marcel Gehrke, and Jan Speller. Scientific Awards: No specific awards listed in the provided text. Tanya Braun actively advises students and leads the HAPPI project, which focuses on human-AI model reconciliation using lifted probabilistic inference. She has supervised multiple theses and mentored researchers including Jan Speller (PostDoc), Nazlı Nur Karabulut, and Sagad Hamid. She has secured funding from the Ministry of Culture and Science of North Rhine-Westphalia for her research. She is deeply involved in academic service: serving as program co-chair for KI 2025, guest-editing special issues in journals like Künstliche Intelligenz and Annals of Mathematics and Artificial Intelligence , and organizing major conferences including ICCS and KR. Labs and Teams: She leads the Data Science Group at the University of Münster, which conducts research in AI, probabilistic modeling, and data science. The group is actively involved in teaching and mentoring students in advanced AI topics.

Yael Grushka-Cockayneمشاهده پروفایل

استاد

• Data Science
• Forecasting
• Project Management
• +۴ مورد دیگر

Professor Yael Grushka-Cockayne is a faculty member at the University of Virginia's Darden School of Business. Her academic career spans research and teaching in Data Science, Forecasting, Project Management , and Behavioral Decision-Making . She holds degrees from Ben-Gurion University (B.Sc.), London School of Economics (M.Sc.), and London Business School (Ph.D., M.Res.). Current affiliations: Darden School of Business (University of Virginia) Academic rank: Professor Professional memberships: INFORMS, Decision Analysis Society, Operational Research Society, Project Management Institute (PMI) Her research focuses on data-driven decision-making , project scheduling , and forecasting methodologies . Recent publications emphasize AI applications in business coding and technical decision frameworks . She has authored influential studies on ensemble forecasting , quantile evaluation , and behavioral biases in project planning . Key trends in her recent work (2023-2024) include: AI integration in business coding and software development Technical decision economics and resource allocation Project management methodology innovation Behavioral aspects of software development and digital transformation Scientific recognition includes: Darden Morton Leadership Faculty Award (2011) Mead-Colley Award (2012) Darden Outstanding Faculty Award (2013, 2022) University of Virginia All University Teaching Award (2015) Multiple Transformational Faculty Awards (2024, 2025) She leads executive education programs like AI Essentials for Government Leaders and has developed digital learning platforms such as the Coursera MOOC Fundamentals of Project Planning and Management (350,000+ learners). As associate editor at Management Science and Operations Research , she shapes academic discourse in decision analysis and operations management.

Alan Zameمشاهده پروفایل

استاد

• Mathematics
• Statistics
• Probability Theory
• +۴ مورد دیگر

Alan Zame is a Professor in the Department of Mathematics at the University of Miami , College of Arts and Sciences. His research spans diverse areas in mathematics, statistics, and probability theory. Education & Training: (Details not explicitly provided in the text) Research Interests: Dr. Zame's work includes combinatorics , probability theory , game theory , and number theory . He has contributed to understanding stochastic processes, tournament planning, urn schemes, and subgroup chains. Publication Trends: His publications from 1984–2010 focus on probabilistic modeling, strategic games, and mathematical structures. Key topics include random sequences, gambling strategies, string matching, and combinatorial fairness in tournaments. Advising & Grants: No specific students or grants are listed in the provided text.

Philipp Georg Haselwarterمشاهده پروفایل

استادیار

• Programming Languages
• Formal Verification
• Probabilistic Programming
• +۳ مورد دیگر

Philipp Georg Haselwarter is an Assistant Professor at the Department of Computer Science , Aarhus University . His expertise lies in programming languages , formal verification , and cryptographic proofs . His research interests include Higher-order probabilistic programming Separation logic for program analysis Modular cryptographic proofs in Coq Resource-aware and cost-bounded formal methods Key trends in his recent publications span Formal verification techniques for probabilistic and cryptographic systems Integration of separation logic with cost and error analysis Development of frameworks like SSProve for cryptographic proofs Applications of type theory and higher-order logic in programming languages

Marco Carboneمشاهده پروفایل

استاد

• Theoretical Computer Science
• Session Types
• Concurrency Theory
• +۷ مورد دیگر

Marco Carbone is a Professor of Theoretical Computer Science at IT University of Copenhagen. His research focuses on session types , concurrency theory , structured communication , and formal verification of distributed systems. He leads the Center for Information Security and Trust and serves as Head of Education for the Master of Science in Computer Science program. Research Areas: Session Types, Concurrency, Security Protocols, Trust Management, Programming Logic Projects: GAINER (2023-2024), MECHANIST (2021-2025), PROBABILIST (2025-2028), BeHApi (2018-2023) Scientific Awards: International Prize (2018) His recent work explores probabilistic choreographies, asynchronous session subtyping, and mechanized proofs for session type systems. He has published extensively in venues like Logical Methods in Computer Science and Lecture Notes in Computer Science .

• Formal Methods in Software Engineering
• Programming Languages
• Neural Network Testing
• +۶ مورد دیگر

Chih-Duo Hong is an Assistant Professor at the Department of Management Information Systems , College of Business , National Chengchi University , holding this position since February 2023. He earned his Ph.D. in Computer Science from University of Oxford (2017-2022). Research Focus : Formal methods, automated reasoning, neural network testing, and software security Grants : Principal Investigator for multiple National Science and Technology Council projects on data transformation systems (2023-2026) His work spans parameterized verification , probabilistic systems , and neural network fairness , with publications in journals like IEEE Transactions on Software Engineering and ACM TOPLAS. He contributes to concolic testing , array systems abstraction , and constraint-based software optimization . Recent Topics : Formal verification of anonymity/uniformity in probabilistic systems Individual fairness testing for neural networks Regular abstractions for array constraints Collaborations include institutions like University of Oxford, National Chengchi University, and research partners Anthony Lin, Philipp Rümmer, Fang Yu.

Ziyang Liمشاهده پروفایل

استادیار

• Neurosymbolic Programming
• Machine Learning
• Programming Languages
• +۵ مورد دیگر

Ziyang Li is an Assistant Professor of Computer Science at Johns Hopkins University and a member of the Data Science and AI Institute. He holds a Ph.D. in Computer Science from the University of Pennsylvania (2025) and dual bachelor's degrees in Computer Science and Mathematics from UCSD (2019). Research Areas: Neurosymbolic Programming, AI4Code His research bridges programming languages and machine learning, focusing on neurosymbolic methods that combine symbolic reasoning with learning-based techniques. Applications span software security, computer vision, natural language processing, bioinformatics, and clinical decision-making. He developed Scallop , a neurosymbolic programming language, and Lobster , a GPU-accelerated framework for neurosymbolic applications, with impacts in cybersecurity and biomedical domains. Recent publications highlight neurosymbolic approaches for RNA structure prediction, Long COVID modeling, and safety-critical systems. His work emphasizes data-efficient learning, weak supervision, and hybrid AI for scalable reasoning. Scientific Awards : AWS Fellowship (2023) KPCB Fellows, Engineering (2018) NIH L3C Honorable Mention Award Li has mentored students including Jason Liu, Felix Zhu, and Eric Zhao, and served as Teaching Assistant for courses at UPenn and UCSD. He co-organized the TACPS Workshop and reviewed for NeurIPS, ICLR, and ICML.

• Analytic Number Theory
• Probability Theory
• Gender Equality Policy in Education
• +۳ مورد دیگر

Roma Kačinskaitė is a Professor at the Department of Mathematics and Statistics within the Faculty of Informatics at Vytautas Magnus University. Her research focuses on analytic number theory, probability theory, and gender equality policies in education. Doctor of Sciences (2002) ORCID: 0000-0003-2656-1052 Scopus ID: 6504101661 Her work spans zeta function theory (Riemann, Hurwitz, Matsumoto classes), joint universality theorems, and computational mathematics. Recent supervised theses explore topics like: ICT integration in calculus pedagogy Discrete universality theorems for zeta function classes Functional independence of periodic zeta functions Public-key cryptographic algorithms Computer-assisted evaluation of Hurwitz zeta function values Student advising emphasizes mathematical olympiads, zeta function analysis, and computational methods. Publications reflect interdisciplinary approaches combining pure mathematics with educational innovation and cryptographic applications.

Matthieu EXBRAYATمشاهده پروفایل

مدرس

• Machine Learning
• Data Analysis
• Data Visualization
• +۷ مورد دیگر

Matthieu EXBRAYAT is a Lecturer in Computer Science at the University of Orleans, affiliated with the Fundamental Computer Science Laboratory of Orléans (LIFO). He holds significant administrative responsibilities including Vice President for Digital and Educational Innovation at the University of Orleans and previously served as Co-head of the IT department (2019-2021) and Head of the IMIS Computer Science Masters (2015-2021). His research spans multiple domains with a primary focus on Machine Learning applications in data analysis and visualization, time series analysis, and computer vision applications in archaeology. His work demonstrates strong interdisciplinary connections between computer science and archaeological documentation, particularly in ceramic sherd classification using deep learning techniques. Earlier in his career, he contributed significantly to high-performance databases and probabilistic relational learning, especially Markov logic networks. Analysis of his publication history reveals an evolving research trajectory: beginning with database systems and parallel join algorithms in the early 2000s, progressing through Markov logic networks and clustering algorithms in the 2010s, and more recently focusing on machine learning applications in archaeological imaging and programming language analysis. His recent publications show a clear shift toward practical applications of machine learning in diverse domains while maintaining theoretical rigor. Throughout his career, EXBRAYAT has maintained extensive collaborative research networks, with Lionel Martin appearing as a co-author on 20 publications, followed by Guillaume Cleuziou (14), Jacques-Henri Sublemontier (9), and Quang-Thang Dinh (7). These collaborations span multiple research domains and demonstrate his ability to work across disciplinary boundaries. As an educator, he teaches distributed information systems, programming languages, databases, AI/data mining, and geographic information systems. He has also been actively involved in scientific mediation, delivering numerous public lectures on artificial intelligence topics since 2017, including conferences such as "Living in harmony with AIs" (2023) and "Artificial Intelligence: My new toaster is an AI!" (2019). His administrative contributions are substantial, including roles as Communications Correspondent for the Computer Science Disciplinary Center, Facilitator of the DataCenters ComUE Centre Val de Loire reflection group, and Vice President of Digital Resources at the Leonardo da Vinci Confederal University. He also served as LIFO website webmaster for over a decade (2002-2014) and was responsible for the STIC degree program (now IT degree) from 2004-2008.

• Control Engineering
• Automation
• Marine Icing
• +۵ مورد دیگر

Sushmit Dhar serves as an Associate Professor in the Department of Automation and Process Technology at UiT The Arctic University of Norway, focusing on control engineering and automation solutions for Arctic maritime challenges. His work bridges theoretical control methods with practical applications in extreme environments. His research spans: Classical and modern control methods (PID, LQR, MPC) State estimation and Kalman filtering Physics-Informed Neural Networks and Reinforcement Learning for decision support IoT-based automation for maritime operations Marine icing phenomena and sea spray measurement Recent publications demonstrate cutting-edge work in marine icing prediction using LiDAR and capacitive sensors, Arctic road safety systems, and optimization of maritime operations. His research integrates advanced sensing technologies with data-driven modeling to address safety challenges in cold climates, particularly through sea spray measurement innovations and route planning algorithms for fishing vessels and search-and-rescue operations. Dr. Dhar actively contributes to collaborative initiatives: Member of the Sustainable Technology and Safety (STS) research group Member of the IR, Spectroscopy, and Numerical Modelling Research Group Key participant in the SPRICE project (Multidisciplinary approach for spray icing modelling and decision support) His teaching portfolio includes foundational courses in Linear Systems (AUT-2604) and Control Engineering (AUT-2801) for Automation Engineering undergraduates, preparing students for technical challenges in Arctic engineering contexts.

Giuseppe Sanfilippoمشاهده پروفایل

استاد

• Probability Theory
• Conditional Events
• Mathematical Logic
• +۵ مورد دیگر

Giuseppe Sanfilippo is a Full Professor of Probability (MAT/06) in the Department of Mathematics and Computer Science at the University of Palermo, Italy. He holds the position of FULL PROFESSOR (MATH-03/B) and maintains office hours on Thursdays from 9:00 to 11:00 at DMI, Via Archirafi 34, second floor, Room 213. His academic appointments include teaching positions across multiple schools at the University of Palermo: School of Basic and Applied Sciences (Mathematics program) School of Basic and Applied Sciences (Artificial Intelligence program) Polytechnic School (Statistics for Data Analysis program) School of Basic and Applied Sciences (Computer Science program) Professor Sanfilippo's research focuses on the theoretical foundations of probability theory, particularly exploring the intersection between probability, mathematical logic, and conditional reasoning. His work centers on conditional events, coherence principles, trivalent logics, and connexive logic. He has made significant contributions to understanding the probabilistic interpretation of Aristotelian syllogisms, entropy and extropy measures, and the mathematical structures underlying compound conditionals. His research has important applications in artificial intelligence, uncertainty management, and decision theory, with over two decades of publications showing consistent development of these themes. Professor Sanfilippo has been actively involved in the academic community, organizing and participating in numerous international conferences including SUM (Scalable Uncertainty Management), ECSQARU (European Conferences on Symbolic and Quantitative Approaches to Reasoning with Uncertainty), and specialized workshops on connexive logic and probabilistic reasoning. His work bridges theoretical developments with practical applications in knowledge representation and reasoning under uncertainty, as evidenced by his extensive conference participation from 2014-2024 across Europe. He mentors students through various academic programs and has supervised numerous theses in probability theory and its applications. His teaching portfolio includes core courses such as 'Calculation of Probabilities' across Mathematics, Artificial Intelligence, Statistics for Data Analysis, and Computer Science programs, as well as specialized courses like 'Uncertain Reasoning and Probability,' reflecting his commitment to both foundational education and advanced research training. Professor Sanfilippo maintains an active research laboratory focused on probabilistic reasoning, where interdisciplinary teams explore the mathematical foundations of uncertainty and their applications in artificial intelligence and decision systems. His current research agenda includes extending coherence principles to complex conditional structures and developing scalable methods for uncertainty management in AI systems, as demonstrated by his upcoming conference chair position for SUM 2024 in Palermo.

• Modular development of specification languages
• Algorithms for models of computation
• Coalgebra
• +۴ مورد دیگر

Alexandra Silva is a Professor of Computer Science at Cornell University with prior affiliations as a Royal Society Wolfson Fellow and Professor of Algebra, Semantics, and Computation at University College London . She leads a research group focusing on the modular development of specification languages and algorithms for models of computation, emphasizing coalgebra as a unifying mathematical framework. Research Interests Her work spans foundational and applied areas in theoretical computer science, including: Coalgebraic methods for formal verification Automata theory and learning algorithms Probabilistic programming and semantics Programming language design (e.g., NetKAT, Kleene Algebra with Tests) Concurrency theory and distributed systems Algebraic structures in computation Recent publications address network verification (StacKAT), symbolic automata learning, probabilistic regular expressions, and outcome logic for correctness/incorrectness reasoning. She is actively involved in organizing academic events like OPLSS 2025 and co-authoring foundational works in Formal Aspects of Computing and Theoretical Computer Science . Scientific Awards Distinguished Paper Award (ACM SIGPLAN POPL, 2020) Best Paper Award (RTA, 2015) She teaches courses on Kleene Algebra with Tests (KAT) and verification at summer schools like Marktoberdorf 2025 , and her research includes collaborations on probabilistic network verification (ProbNV) and stochastic system modeling.

• Artificial Intelligence
• Machine Learning
• Probabilistic Reasoning
• +۴ مورد دیگر

Stuart J. Russell is the Smith-Zadeh Professor in Engineering at the University of California, Berkeley, leading the Center for Intelligent Systems and founding the Center for Human-Compatible AI in 2016. His research spans foundational AI domains including: Logical and probabilistic reasoning Knowledge representation Machine learning and reinforcement learning Bounded rationality (pioneering bounded optimality) AI ethics and safety Probabilistic programming (Bayesian logic/BLOG) Russell revolutionized AI education through Artificial Intelligence: A Modern Approach (with Peter Norvig), used in 1,500+ universities globally. His NETVISA algorithm applies Bayesian logic to nuclear explosion detection for treaty verification. He spearheads the international campaign against lethal autonomous weapons, co-creating the viral film Slaughterbots and authoring Human Compatible: AI and the Problem of Control (2019). Major honors include: ACM AAAI Allen Newell Award (2022) for foundational AI contributions ACM Karl V. Karlstrom Educator Award (2005) ACM Fellowship (2003) IJCAI Computers and Thought Award (1995) He has mentored over 45 students/postdocs who now lead AI research in academia and industry, while his advocacy includes UN addresses, Nature commentaries, and global AI community mobilization for ethical frameworks.

• Explainable AI
• Human-like machine learning
• Explanation Generation
• +۷ مورد دیگر

Professor Ute Schmid is a Full Professor of Cognitive Systems at the University of Bamberg, where she has been a faculty member since September 2004. She leads the Cognitive Systems Group within the Bamberg Center of AI (BaCAI), focusing on creating AI systems that generate human-like explanations and reasoning processes. Her research bridges cognitive science and artificial intelligence to develop methods for explanation generation, inductive programming, and interactive machine learning. Professor Schmid's work emphasizes practical applications of explainable AI across diverse domains including image classification, medical diagnosis, and educational technologies. Her research on contrastive explanations, near misses, and human-AI alignment has significantly advanced the field of XAI. She has also pioneered research on AI literacy, recognizing the growing importance of basic AI understanding for responsible tool usage by non-experts. Her publication record demonstrates exceptional productivity and impact, with numerous articles in top-tier venues including Nature Machine Intelligence, IEEE Transactions on Visualization and Computer Graphics, and the Journal of Web Semantics. Her 2025 paper 'Aligning generalization between humans and machines' represents a significant theoretical contribution to understanding human-machine cognitive alignment. Professor Schmid actively contributes to gender diversity research in computer science through studies examining why women pursue PhDs in the field. She has also made important contributions to computing education, investigating how students acquire programming skills and how AI tools like code generators are integrated into learning processes. As an educator and researcher, Professor Schmid maintains strong international collaborations, with co-authors spanning multiple countries and institutions. Her interdisciplinary approach is evident in her diverse publication venues and collaborative work that bridges computer science, cognitive science, education, and application domains.