جست‌وجوی استاد و پژوهشگر

Paul Munroمشاهده پروفایل

دانشیار

• Connectionist systems
• Neural information processing
• Image processing
• +۵ مورد دیگر

Paul Munro is an Associate Professor at the University of Pittsburgh’s School of Computing and Information, where he holds a PhD from Brown University. His research focuses on connectionist systems, neural information processing, cognitive science, and artificial life. He has contributed to advancements in neural network architectures, including applications in healthcare and spatial cognition modeling. His educational background includes a PhD in a field related to computational neuroscience, with a strong emphasis on interdisciplinary approaches. Research Interests: Connectionist systems; neural information processing; image processing; modeling and simulation; cognitive science; models of learning; visualization; genetic algorithms/artificial life. Munro’s publications span over three decades, with notable contributions to understanding neural learning rules (e.g., BCM theory), adversarial machine learning, and applications of neural networks in healthcare. Recent work includes predicting falls in elderly populations and studying Alzheimer’s disease risk factors through large-scale health data analysis. Despite prolific publishing, no scientific awards or grants are explicitly listed in the provided texts. His advising record is also unspecified, though his research group likely engages in collaborative projects given the interdisciplinary nature of his work. He is reachable via pwm@pitt.edu at the Information Sciences Building.

Mattia Setzuمشاهده پروفایل

پژوهشگر ارشد

• Explainable AI
• Machine Learning
• Black Box Models
• +۳ مورد دیگر

Mattia Setzu is a Research Fellow at the Department of Computer Science, University of Pisa, where he also pursues his PhD. His work focuses on Explainable Artificial Intelligence (XAI), particularly on developing methods to interpret and explain the decisions made by black box machine learning models. His educational background includes: Bachelor's degree in Computer Science from the University of Cagliari (2016) with 103/110 Master's degree in Computer Science from the University of Pisa (2018) with 110/110 cum laude Setzu's research centers on the explainability problem in artificial intelligence, addressing what he refers to as the "black box syndrome" that machine learning models suffer from. His quote "To be human is to wonder. To go beyond the empirical data and grasp the essence of things is the cornerstone of human understanding and allows us to be active agents in the world" reflects his philosophical approach to explainable AI. His methodological evolution spans from semantic web applications for source code analysis to sophisticated frameworks for generating global explanations from local ones using clustering approaches, topology-aware algorithms, and Bayesian techniques. His publication record demonstrates a clear progression in XAI research, with recent work connecting explainability methods to critical applications in pandemic response. This interdisciplinary approach shows how his core research on making AI systems transparent has practical implications in high-stakes domains like public health and social policy. Setzu contributes to major research initiatives: XAI - Science and technology for the eXplanation of AI decision making TAILOR - Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization His collaborative work extends across multiple domains, frequently appearing as co-author with researchers from diverse backgrounds including public health, social science, and traditional computer science disciplines. This network reflects the interdisciplinary nature of modern AI research and the growing importance of explainability across application domains.

Steve Drekicمشاهده پروفایل

استاد

• Insurance Risk Modeling
• Queueing Theory
• Actuarial Science
• +۳ مورد دیگر

Steve Drekic is a Professor and Associate Chair for Undergraduate Studies in the Department of Statistics and Actuarial Science at the University of Waterloo. His research focuses on stochastic modeling, queueing theory, actuarial science, and risk analysis. He holds a faculty position within the Faculty of Mathematics and is affiliated with the university's interdisciplinary research groups. His work spans theoretical contributions to applied probability and practical applications in insurance risk, maintenance systems, and healthcare operations. Dr. Drekic has extensively studied multi-class systems, priority queueing models, and ruin theory in insurance contexts. Notable recent projects include analyzing waiting times in transplant systems and applying statistical methods to sports analytics. His publications demonstrate expertise in stochastic processes, with a particular emphasis on developing analytical solutions for complex systems under uncertainty. While no specific awards or grants are listed, his academic contributions are reflected in over 50 peer-reviewed articles in top-tier journals. He currently advises students in actuarial science and probability theory, though no specific advisee names are documented here. Dr. Drekic's research laboratory focuses on computational probability and its applications to real-world problems in insurance, healthcare, and telecommunications. His work bridges theoretical developments with practical implementation, emphasizing the importance of mathematical rigor in decision-making processes.

Giovanna Maria Dimitriمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۷ مورد دیگر

Giovanna Maria Dimitri is a tenure-track Assistant Professor in Artificial Intelligence at the Università degli Studi di Milano (Statale), with strong affiliations at the University of Siena (DIISM) and the University of Cambridge. She conducts research in deep learning, machine learning, and AI applications across domains such as computer vision, bioinformatics, climate modeling, and healthcare. She also lectures in Business Intelligence at the University of Siena and serves as a Guest Lecturer in Data Science at the University of Cambridge's Institute of Continuing Education. PhD in Artificial Intelligence, University of Cambridge MPhil in Advanced Computer Science (Distinction), University of Cambridge Master's and Bachelor's in Computer and Automation Engineering (110/110 cum laude), University of Siena Her research spans foundational AI models and their applications in diverse fields including neuroscience, environmental science, and medical diagnostics. She develops methodologies in deep learning, graph neural networks, and multimodal data analysis, with a growing interest in sustainable AI and ethical implications of machine learning systems. Her recent publications reflect a strong trend in applying AI to real-world challenges: from detecting synthetic images and analyzing brain signals to modeling climate impacts and assessing sustainable development goals. Her work bridges theoretical innovation with practical implementation across healthcare, environmental monitoring, and digital humanities. Scientific Awards and Editorial Roles: Ai-Net Fellows Scholarship (DAAD, 2023) Associate Editor, Neurocomputing (Elsevier) Associate Editor, IEEE Transactions on Technology and Society (since May 2024) Giovanna has extensive experience in teaching, supervising, and science communication. She has delivered seminars at top institutions like the University of Cambridge and has been featured in Italian media for her AI expertise. She mentors students and collaborates internationally, including with Prof. Gemma Roig at the DAAD-sponsored Ai-Net program. She is actively involved in academic service, conference organizing, and public engagement, contributing to both technical and societal aspects of AI advancement. She is a life member of Clare Hall College, University of Cambridge, and maintains active research collaborations across Europe. Her lab and research group focus on developing robust, interpretable, and sustainable AI systems, with ongoing projects in medical AI, climate informatics, and ethical machine learning.

Christine Largouëtمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Explainable AI
• Timed Behavioral Models
• +۵ مورد دیگر

Christine Largouët is a tenured Associate Professor in Computer Science at Institut Agro Rennes-Angers , where she leads the Computer Science Teaching Unit since 2006. She is an associate researcher with the DREAM team at IRISA (Institute for Research in Computer Science and Random Systems) and part of the LACODAM team at IRISA/INRIA. Her academic journey includes teaching positions at University of New-Caledonia (2003-2005) and research collaborations with INRAE, IRISA, and INRIA. HDR : Université de Rennes 1 (2019) PhD : Université de Rennes 1 (2000) Her research bridges Artificial Intelligence with Agroecology , focusing on: Learning Timed Behavioral Models Explainable AI for decision transparency Complex Systems Modelling and Analysis Data-Driven Decision Support frameworks Applications to Ecosystem Management and Precision Livestock Farming She employs formal methods like Timed Automata and Model-Checking to analyze agricultural systems and animal behavior patterns. Recent publications demonstrate her work on: Explainable AI interfaces for behavioral data interpretation Timed Automata for agricultural system modeling Machine Learning applications in swine nutrition and welfare Intrusion Detection Systems for agricultural IoT Environmental Modeling with formal verification Her methodological contributions include persistence-based discretization and role-adaptive explanation frameworks.

• Mathematical Logic
• Fibring of Logics
• Modal Logic
• +۶ مورد دیگر

Maria Cristina De Sales Viana Serôdio Sernadas is a distinguished professor at Instituto Superior Técnico (IST), Universidade de Lisboa, specializing in mathematical logic and its applications. Her academic career spans several decades with significant contributions to the field of logic systems. Research Interests Professor Sernadas' research focuses on the theoretical foundations and practical applications of logical systems. Her primary areas of interest include: Fibring and combination of logics Modal and intuitionistic logic Probabilistic and quantum logic Proof theory and decidability Temporal and evidence-based logics Applications of logic to computer science and quantum computing Scientific Contributions With over 100 publications spanning more than three decades, Professor Sernadas has established herself as a leading figure in mathematical logic. Her recent work (2021-2025) continues to advance the field with innovative approaches to logical systems, particularly in the areas of evidence logic, quantum observations, and complexity measures in proof structures. She has co-authored several influential books that serve as important references in the field. Awards and Recognition Professor Sernadas maintains an active scholarly profile with an ORCID identifier (0000-0002-5510-3512) and an ISI ResearcherID (D-2269-2011), reflecting her standing in the academic community. Her work is regularly published in top-tier logic journals and has influenced research directions in multiple subfields of mathematical logic. Academic Supervision Professor Sernadas has supervised numerous PhD students whose work has significantly contributed to the field of logic. Her supervision spans topics including modal logic fusion, probabilistic systems, and first-order logic frameworks, demonstrating her broad expertise and commitment to developing the next generation of logicians.

Rattikorn Hewettمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۱۰ مورد دیگر

Rattikorn Hewett is a Professor of Computer Science at Texas Tech University's College of Engineering, where she joined the Department of Computer Science in 2004. Previously, she served as a faculty member at Florida Atlantic University, Washington State University Vancouver, and was a research scientist at the Institute for Human and Machine Cognition. She completed her postdoctoral fellowship at Stanford University (1987-1990) after earning her Ph.D. in Computer Science from Iowa State University in 1986. Her research spans multiple areas of artificial intelligence with significant contributions to intelligent data understanding, model-based reasoning, agent control mechanisms, and blackboard systems. She has developed the SORCER system for classification rule induction from databases and has applied her expertise to diverse domains including software risk assessment, genomic data analysis, and hydrological forecasting. Her work on blackboard systems introduced efficient activation and agenda maintenance mechanisms that significantly improved performance in BB1-like systems. Hewett's publications demonstrate consistent contributions across several domains, with recent work focusing on software risk assessment, bioinformatics applications of machine learning, and intelligent data understanding. Her research shows a clear progression from foundational work in knowledge representation and blackboard architectures to practical applications in healthcare, environmental science, and software engineering. She has successfully secured funding from prestigious sources including NSF, DARPA, ONR, NASA, EPRI, and industry partners like Boeing and IBM. Scientific Awards and Recognition: National Science Foundation Research Initiation Award (1993-1997) Exceptional Professor award from Florida Atlantic University Hewett has established a research program on Capability Engineering at TTU to apply and enhance AI techniques for emerging problems in software automation and security. She has served as a referee for numerous prestigious journals and currently serves on the editorial boards of Advances in Artificial Intelligence, Journal of Computational Intelligence in Bioinformatics, and other scholarly publications. Her teaching portfolio includes graduate courses in Artificial Intelligence, Knowledge Discovery and Data Mining, and undergraduate courses in Algorithms and Formal Language Theory. Her research laboratory has focused on developing practical applications of AI techniques to real-world problems, with notable projects including automated assessment of software risks, classification of genetic phenotypes of Osteogenesis Imperfecta, prediction of Lake Okeechobee inflows, and intelligent domain-specific software synthesis control. These projects reflect her commitment to applying theoretical AI concepts to solve concrete problems in healthcare, environmental management, and software engineering.

• Fuzzy Logic Programming
• Fuzzy Deductive Databases
• Automatic Program Transformation
• +۴ مورد دیگر

Pascual Julián-Iranzo is an Associate Professor in the Information Technologies and Systems Department at the Higher School of Computer Science, University of Castilla-La Mancha (UCLM), where he has served since April 2003. He is a founding member and current co-director of the DEC-tau research group focused on Declarative Programming and Automatic Program Transformation. His academic career includes over 27 years of university teaching experience across various positions, with significant contributions to curriculum development including the Degree Curriculum in Computer Engineering (2008/09 and 2009/10). Education Background: Doctor in Computer Science (1994-2000) from Universitat Politècnica de València, Department of Computer Systems and Computation His research centers on declarative programming languages with emphasis on fuzzy logic programming, functional-logic paradigm integration, and automatic program transformation. He pioneered work in proximity-based reasoning systems like Bousi-Prolog and FASILL, developing novel approaches for similarity-based unification, thresholded tabulation, and fuzzy deductive databases. His publications demonstrate consistent innovation in fuzzy logic semantics, particularly in handling truth degrees and semantic similarity measures for knowledge representation. Analysis of his 15 most recent publications reveals a strong trajectory in formalizing fuzzy logic programming frameworks, with increasing focus on practical implementations (2018-2023) after establishing theoretical foundations (2015-2017). Key thematic clusters include proximity-based unification algorithms (2018-2021), semantic frameworks for truth degree management (2016-2017), and real-world applications in natural language processing (2021) and security systems (2017). Research Recognition: Positive evaluation by CENAI for three consecutive research periods: [1997-2002], [2003-2008], and [2009-2014] Consistent publication record with 24 scientific journal articles (16 JCR-indexed) As an academic leader, Julián-Iranzo has supervised 2 doctoral theses and chaired the PROLE 2021 Conference Program Committee. His grant portfolio includes significant European funding, notably the MERINET project (2016-2019) supported by ERDF and MINECO. He has served on 12 conference program committees and reviewed for top journals including Journal of Theory and Practice of Logic Programming. His DEC-tau research group maintains active international collaborations, evidenced by coordination of the UCLM node in the ALFA Lernet Project (2005-2009) and partnership with Argentina's National University of San Luis (2009-2013).

H. M. Zaferمشاهده پروفایل

دانشیار

• Arabian and African Languages
• Historical Linguistics
• Medieval Studies
• +۷ مورد دیگر

H. M. Zafer is an Associate Professor at the University of Washington , specializing in Arabian and African Languages through pre-modern networks of knowledge spanning 6th-16th century Horn of Africa and Western Arabian societies. He also serves as a Senior Lecturer in the Program in African Studies. Education: PhD from Cornell University (2014) Zafer's research focuses on historical linguistics, medieval studies, and Islamic studies, particularly analyzing the Ecumenical Community in the context of Red Sea wars and Early Muslim communitarianism. His current projects include The Comrades of the Ship (Early Muslim historiography) and The Ethiopic Quran (16th-century Ge'ez commentary on the Quran). His scholarly contributions bridge interdisciplinary themes in comparative literature, religious studies, and computational methodologies through his earlier work in machine learning applications for bioinformatics and natural language processing. Scientific Awards: Mellon-Sawyer Fellow, University of Notre Dame Katz Fellow, University of Pennsylvania His publications from 1997-2009 explore hierarchical classification, Bayesian aggregation techniques, and neural networks applied to genomic prediction, linguistic analysis, and automated puzzle generation, demonstrating technical expertise in algorithm development and data modeling.

Joaquín Ariasمشاهده پروفایل

دانشیار

• Constraint Logic Programming
• Answer Set Programming
• Event Calculus
• +۴ مورد دیگر

Joaquín Arias is an Associate Professor at King Juan Carlos University since 2025. His career includes 2020-2025 as Assistant Professor at the same institution and prior pre-doctoral research at IMDEA Software Institute (2013-2020). He has collaborated with institutions like University of Texas at Dallas and Aalto University. Education: Ph.D. in Computer Science (2020, Universidad Politécnica de Madrid) M.Sc. in Computer Science (2015) B.Sc. in Computer Science (2014) M.Arch. in Architecture (2002) Research interests revolve around Constraint Logic Programming , Answer Set Programming , and their applications in Event Calculus , Stream Data Analysis , and Value-Aware Systems . He has developed frameworks like s(CASP) for non-grounded reasoning and Mod TCLP for tabled constraints. Recent articles demonstrate expertise in integrating Large Language Models with logic programming, automated legal reasoning , and real-time system verification . His work emphasizes explainability, constraint handling, and semantic coherence in AI systems. Scientific awards include the best paper prize at CAEPIA 2021 . He has contributed to proceedings as editor for ICLP workshops and authored numerous papers in TPLP , PADL , and AI & LAW .

• Computational Neuroscience
• Machine Learning
• Simulation-Based Inference
• +۳ مورد دیگر

Pedro J. Goncalves is a computational neuroscientist and machine learning researcher serving as Group Leader at VIB-NERF and Associate Professor at KU Leuven . His work bridges theoretical neuroscience and advanced machine learning methodologies to study neural computation. Organizational Affiliations VIB-NERF (NERF.be/en#/) KU Leuven (www.kuleuven.be/english/kuleuven) Co-developer of the sbi Python package His research focuses on simulation-based inference (SBI) for modeling neural circuits, with applications in neurodegeneration studies (e.g., Parkinson's disease), uncertainty quantification in brain imaging, and development of open-source tools for computational neuroscience. Key projects involve: Collaborative work with Patrik Verstreken's lab on ML methods for brain-on-chip data analysis Leadership in the SBI Hackathon for improving open-source inference workflows PhD recruitment via VIB.AI International PhD Call 2025 He actively contributes to community initiatives like the SBI Discord Server and interdisciplinary collaborations across Europe. Publications include work on masked variational autoencoders for neural data modeling in Cell Reports and foundational SBI methodologies.

• Predictive Analytics
• Recommender Systems
• AI Applications
• +۳ مورد دیگر

Sung-Hyuk Park is an Assistant Professor at KAIST College of Business, focusing on predictive analytics, recommender systems, and AI applications. His work spans marketing technology, healthcare informatics, and computer vision, with recent publications in journals like IEEE Transactions and Biomedical Signal Processing . Current research integrates deep learning, social network analysis, and optimization models across domains.

• Monetary Economics
• Macroeconomics
• Econometrics
• +۱۱ مورد دیگر

Francisco J. Ruge-Murcia is Professor of Economics at McGill University, Montréal, where he also served as Department Chair from 2019 to 2024. He is an elected President of the Canadian Economics Association (2021-2022) and a Senior Houblon-Norman Fellow at the Bank of England (Fall 2025). His research blends macroeconomics, monetary economics, and econometrics to study central-bank behaviour, inflation dynamics, and collective decision-making in policy committees. Education Ph.D. in Economics, University of Virginia, USA M.A. in Economics, University of Virginia, USA B.Sc. in Industrial Engineering, Universidad Industrial de Santander, Colombia Research Focus Professor Ruge-Murcia’s work centres on monetary economics , macro-econometrics , and collective decision-making in monetary policy committees . He develops and estimates nonlinear DSGE models to analyse how central banks set policy, how shocks propagate through production networks, and how price rigidity and skewness risk affect aggregate dynamics. A unifying theme is the interplay between committee governance (voting records, deliberation, chairman influence) and macro-financial outcomes (inflation, bond prices, exchange rates). Recent projects use high-frequency data—including Twitter sentiment—to measure the impact of trade-policy uncertainty on exchange rates. Scientific Awards & Fellowships President, Canadian Economics Association, 2021–2022 Bank of Canada Fellowship Award, 2017–2021 Postdoctoral Research Fellowship, Bank of Spain, 1999–2000 Top-5 % author ranking in RePEc across multiple metrics Grants & Institutional Support His research has been supported by the Bank of Canada Fellowship, the Bank of Spain, and recurring grants from CIREQ. Ongoing collaborations with the Federal Reserve Banks of Richmond and Minneapolis, the IMF, and the Bank of England underpin his international network. Labs & Research Groups He is affiliated with the Centre interuniversitaire de recherche en économie quantitative (CIREQ) and maintains active ties to policy institutions such as the Bank of Canada, the Bank of England, and the Society for Economic Dynamics.

Toni Karvonenمشاهده پروفایل

استادیار

• Statistics and Probability
• Applied Mathematics
• Numerical Analysis
• +۱۰ مورد دیگر

Toni Karvonen is an Assistant Professor of Applied Mathematics at LUT University's Computational Engineering Department, School of Engineering Sciences, since September 2024. His research focuses on statistics, applied mathematics, and computational methods with emphasis on Gaussian processes, uncertainty quantification, and kernel-based approximation. He has held postdoctoral positions at the Alan Turing Institute (2020–21) and the University of Helsinki (2021–24). Research Areas: Statistics and Probability, Applied Mathematics, Numerical Analysis, Gaussian Processes, Positive Definite Kernels Key Trends: His recent work explores probabilistic numerics, Bayesian quadrature, kernel methods for uncertainty quantification, and approximation theory in reproducing kernel Hilbert spaces. Email: Toni.Karvonen@lut.fi

Sandro Stuckiمشاهده پروفایل

مدرس

• Programming Languages
• Formal Methods
• Type Systems
• +۴ مورد دیگر

Sandro Stucki is a Lecturer in the Department of Computer Science and Engineering at Chalmers University of Technology. He has previously worked as an applied scientist at Amazon and as a postdoctoral researcher at Chalmers University of Technology and the University of Gothenburg. He completed his doctoral studies at the Programming Methods Laboratory (LAMP) at EPFL under the supervision of Professor Martin Odersky. His research focuses on programming languages, with particular interest in formal methods, type systems and theory, and the semantics and implementation of domain-specific languages. He applies formal methods and type theory to problems in privacy and security, investigates type safety of Scala and related type systems, and develops type soundness proofs using Agda. His work also includes designing domain-specific languages for modeling probabilistic and stochastic systems, especially biochemical systems. He has contributed to the Scala ecosystem by developing a GNU/Emacs mode for Kappa, a modeling language for systems biology. His recent publications demonstrate strong expertise across programming language theory, formal verification, privacy-preserving systems, and applications in systems biology. His work bridges theoretical foundations with practical applications, particularly in the Scala programming language ecosystem. Stucki is actively involved in academic service, having served on program committees for numerous conferences including ECAI, GPCE, NWPT, and as a steering committee member for the Scala Symposium series. He has also organized several academic events, including the Scala Symposium 2016. He currently teaches courses on Data Science and AI, Fundamentals of Program Development, and Neuro-symbolic AI at Chalmers/Gothenburg University, and has previously taught courses on Parallel Functional Programming, Principles of Concurrent Programming, and Types and Programming Languages.