جست‌وجوی استاد و پژوهشگر

Tanya Khovanovaمشاهده پروفایل

مدرس

• Recreational Mathematics
• Combinatorics
• Probability
• +۷ مورد دیگر

Tanya Khovanova is a Lecturer at the Massachusetts Institute of Technology (MIT) in the Department of Mathematics . Her career spans academia and industry, with extensive experience in recreational mathematics, combinatorics, probability, and algorithm design. She has held positions at MIT since 2008, including Visiting Scholar and Research Affiliate, and has contributed to programs like PRIMES and RSI for mentoring high school students. Ph.D. in Mathematics from Moscow State University (1988), advised by I.M. Gelfand Former roles include Lead Analyst at BAE Systems (2003-2008) and Princeton University (2001-2003, 1996-1998) Her research bridges recreational mathematics with applications in game theory , coding theory , and combinatorial puzzles . She is a prolific blogger and popularizer of mathematics, known for her work in creating innovative puzzles and educational tools like the Math Alive course at Princeton. Recent publications include explorations of EvenQuads games (2025), Chip-Firing algorithms (2022-2025), and SET game generalizations (2025). Her work often intertwines group theory , graph theory , and error-correcting codes . Scientific Awards : 2021 MLK Leadership Award (MIT) 2019 Infinite Mile Award (MIT) 1976 Gold Medal at International Mathematics Olympiad 1975 Silver Medal at International Mathematics Olympiad She mentors students through programs like PRIMES and RSI , emphasizing puzzle-based learning. Her blog and public talks continue to engage global audiences with mathematics.

Mina Young Pedersenمشاهده پروفایل

پژوهشگر

• Logic
• Formal Epistemology
• Social Network Analysis
• +۵ مورد دیگر

Dr. Mina Young Pedersen is a postdoctoral researcher at the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam, where she is affiliated with the Epistemology & Philosophy of Science research group and the Research Priority Area in Human(e) AI. She joined the university in April 2025 after completing her PhD in Information Science at the University of Bergen, for which she won the prestigious Norwegian Artificial Intelligence Society Dissertation Award in 2024. Her academic journey includes: PhD in Information Science, University of Bergen (2019-2024) MSc in Logic, Institute for Logic, Language and Computation, University of Amsterdam (2017-2019) BA in Philosophy, University of Oslo (2015-2017) BSc in Mathematics, Norwegian University of Science and Technology (2012-2016) Dr. Pedersen's research sits at the intersection of mathematical logic, philosophy, and artificial intelligence. Her work focuses on applying modal and temporal logic frameworks to analyze social networks, with particular attention to how agents (including potentially malicious ones) behave within these networks. She has made significant contributions to understanding visibility in social networks, bot detection, group polarization, and echo chambers. Her publication record demonstrates consistent output in top-tier venues, with work appearing in journals such as Mathematical Structures in Computer Science , Synthese , and Journal of Logic and Computation . Her research combines theoretical rigor with practical applications to contemporary challenges in social media and AI systems. Dr. Pedersen has received multiple research grants including two consecutive Meltzer Project Grants. Her recognition includes: Norwegian Artificial Intelligence Society Dissertation Award (2024) Induction into the Logicians Liberation League as 'Magister of Malicious Networks' (2024) Multiple travel and research grants supporting international collaboration She actively contributes to the academic community through journal reviewing, conference organization, and service on program committees. As a postdoc at ILLC, Dr. Pedersen continues to develop her research program at the intersection of logic, social networks, and AI ethics, with implications for understanding and addressing challenges related to misinformation and ethical AI development.

Steve Awodeyمشاهده پروفایل

استاد

• Category Theory
• Homotopy Type Theory
• Modal Logic
• +۴ مورد دیگر

Steve Awodey is a Professor in the Department of Philosophy at Carnegie Mellon University, holding the Dean's Chair in Logic. He serves as an external member of the Munich Center for Mathematical Philosophy (MCMP) at Ludwig Maximilian University (LMU), spending several months annually in Munich. His research spans Category Theory, Logic, and Foundations of Mathematics, with groundbreaking work in Homotopy Type Theory (HoTT) and the Univalent Foundations Program. Education: University of Marburg (Philosophy, Mathematics), University of Chicago (Graduate degrees). Awodey's research interests include: Homotopy Type Theory and its applications to logic Modal Logic and topological interpretations Algebraic Set Theory Philosophy of Mathematics Formalization of mathematical structures via category theory His recent publications focus on HoTT, model categories, and categorical logic. Key awards include the Dean's Chair in Logic and editorship of Rudolf Carnap's Collected Works. He has advised numerous PhD students across Philosophy, Mathematics, and Computer Science, shaping research in synthetic homotopy theory and type theory. Awodey's work bridges abstract mathematics with philosophical inquiry, emphasizing structuralism and invariance in mathematical foundations.

• Artificial Intelligence
• Natural Language Processing
• Logic Programming
• +۷ مورد دیگر

María Josefina Sierra Santibáñez is an Associate Professor in the Department of Computer Science at the School of Computer Science of Barcelona (FIB), Universitat Politécnica de Catalunya (UPC). She is a member of the ALBCOM research group (Algorithms, Bioinformatics, Complexity and Formal Methods) and has maintained consistent research activity for over three decades. Her academic credentials include a Licenciatura in Mathematical Sciences and a Doctorado in Computer Science. Her research profile shows 73 documented academic activities spanning from 1992 to 2024, demonstrating remarkable longevity and continued relevance in her field. Dr. Sierra Santibáñez specializes in the computational modeling of language acquisition systems, particularly for logical expressions. Her work uses agent-based modeling to investigate how language systems for boolean coordination and logical constructions emerge, evolve, and are transmitted among agents. She bridges artificial intelligence, cognitive science, and formal linguistics to understand the processes by which logical reasoning capabilities develop through social interaction. Her publication trends show a consistent focus on the computational aspects of language acquisition for logical expressions, with significant contributions to natural language processing through her work on fluid construction grammar and logic programming approaches. She has published in top venues including AAAI conferences and journals such as Artificial Intelligence and Autonomous Agents and Multi-Agent Systems. Scientific Committee Member: AAAI Conferences (2014-2018) Scientific Committee Member: International Conference on Principles of Knowledge Representation and Reasoning (2012) Scientific Committee Member: AAAI Fall Symposium on Biologically Inspired Cognitive Architectures (2009-2010) She has collaborated extensively with researchers across UPC, particularly with Ricard Gavaldà, Marta Arias, and other members of the ALBCOM research group. Her work often involves interdisciplinary teams combining expertise in computer science, linguistics, and cognitive science, reflecting the inherently interdisciplinary nature of her research questions.

Claude Sammutمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Inductive Logic Programming
• +۵ مورد دیگر

Professor Claude Sammut is affiliated with the School of Computer Science and Engineering at the University of New South Wales. He is a leading figure in artificial intelligence, machine learning, and robotics, with foundational contributions to Inductive Logic Programming (ILP) and Behavioral Cloning. Professor at University of New South Wales School of Computer Science and Engineering Editorial board member of key journals (Journal of Machine Learning Research, Machine Learning Journal, New Generation Computing) Leadership roles in RoboCup Federation and ICML conferences His research focuses on Machine Learning applications in Robotics , including work with the UNSW teams that won multiple RoboCup competitions. His career spans pioneering theoretical work in relational learning and practical applications in autonomous systems. He has contributed to editorial leadership as co-editor-in-chief of Springer's Encyclopedia of Machine Learning and Data Mining, and organized major conferences like ICML 2002 and ICML 2007. His work bridges theoretical AI research with real-world robotic implementations.

Davide Anconaمشاهده پروفایل

دانشیار

• Formal Methods
• Runtime Verification
• Programming Languages
• +۳ مورد دیگر

Davide Ancona is an Associate Professor at the University of Genoa, Italy, in the Department of Computer Science, Bioengineering, Robotics, and Systems Engineering (DIBRIS). He holds key governance roles: Member of the School Council, School of Mathematical, Physical and Natural Sciences Member of the Department Board, DIBRIS His teaching portfolio spans Internet of Things, Healthcare IoT, Wearable Devices, Programming Languages, and Pervasive Computing across Computer Science and Bioengineering degree programs. His research concentrates on: Formal Methods and Programming Languages (specializing in corecursive streams and type systems) Runtime Verification for software correctness and multi-agent systems Internet of Things applications in healthcare contexts His work bridges theoretical foundations with practical verification tools for critical systems. Analysis of his 2023-2024 publications reveals: Foundational advances in corecursive stream equivalence and expressivity Novel applications of logic programming to runtime monitoring Techniques for verifying mutable object behavior in Java Extensions to robotic multi-agent frameworks like JaCaMo These contributions demonstrate a cohesive trajectory from theoretical programming language research to real-world IoT and healthcare validation systems.

José Fragoso Santosمشاهده پروفایل

استادیار

• JavaScript Verification
• Formal Methods
• Symbolic Execution
• +۳ مورد دیگر

Welcome to my home page. I am an Assistant Professor at the Department of Computer Science and Engineering, Instituto Superior Técnico, University of Lisbon, and a researcher at INESC-ID's SAT group. My work focuses on embedding formal methods into software development, particularly for JavaScript programs. PhD in Computer Science, University of Nice Sophia Antipolis (2014) MSc in Information Systems and Computer Engineering, Instituto Superior Técnico (2008) My research spans JavaScript verification, symbolic execution, and separation logic. I developed JaVerT, the first separation-logic-based tool for JavaScript analysis, used by Amazon to verify the AWS Encryption SDK and recognized with a Facebook Research Award. Recent publications highlight trends in symbolic execution, JavaScript security, and WebAssembly analysis. Key projects include Gillian (multi-language symbolic execution platform), Rexstepper (regular expression debugger), and Wasmati (WebAssembly vulnerability scanner). Facebook Research Award Supervised students include PhD researcher Gabriela Cunha Sampaio and MSc students Pedro Lopes, Carolina Costa, and others. Current teaching subjects: Analysis and Synthesis of Algorithms, Object-Oriented Programming, Software Security. Affiliated with the SAT group at INESC-ID and the Verified Trustworthy Software Specification group at Imperial College London.

• 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.

Antoine Cornuéjolsمشاهده پروفایل

استاد

• Computer Science
• Machine Learning
• Online Learning
• +۴ مورد دیگر

Antoine Cornuéjols is a Professor in Computer Science at AgroParisTech and head of the LINK (Learning and Integration of Knowledge) team within the UMR518 MIA-Paris research unit. His career spans decades of work in artificial intelligence and machine learning, with a focus on adaptive learning frameworks that challenge classical assumptions of stationary environments. Doctorate from UCLA and Orsay University Co-author of two seminal books: Machine Learning: Concepts and Algorithms (2 editions) and Phase Transitions in Machine Learning His research explores online learning , transfer learning , and collaborative learning , addressing theoretical foundations and practical applications. He has contributed extensively to major journals and conferences, served on program committees, and examined the philosophical underpinnings of induction in machine learning, particularly through the lens of Hume's critiques and modern frameworks like the No-Free-Lunch theorem.

• Artificial Intelligence
• Machine Learning
• Data Visualization
• +۵ مورد دیگر

Dr. Martin Možina is an Assistant Professor at the University of Ljubljana , affiliated with the Artificial Intelligence Laboratory since 2004. His work bridges classical machine learning with argumentation theory and focuses on interpretable AI methods. Research includes argument-based machine learning for knowledge-driven AI Developed nomogram visualization techniques for linear models Created automated chess tutors for move explanation Research projects span from 2004 to 2025, including: Current DRIFT project (2022-2025) on deep learning for power grid optimization Deep reinforcement learning applications in energy systems Argumentation for medical prognosis (lung cancer) and knowledge acquisition Publications show interdisciplinary work between: AI explainability (4/6 articles) Education technology (2/6) Healthcare AI applications (2/6) Graphical model interpretation (1/6) Multi-agent research (1/6) Teaching includes Decision Systems courses, with a focus on applied AI methods.

• Natural Language Processing
• Applied Data Science
• Computational Linguistics
• +۳ مورد دیگر

Dr. Pablo Mosteiro Romero is an Assistant Professor at Utrecht University's Faculty of Social and Behavioural Sciences within the Department of Methodology and Statistics . His work bridges Natural Language Processing (NLP) , Applied Data Science , and Human-centered Artificial Intelligence , focusing on language change, morphology-syntax trade-offs, and applications in healthcare and open societies. Education: B.Sc. in Physics, Rutgers University (Summa Cum Laude, 2007) Ph.D. in Computer Science, Princeton University (2014, Centennial Fellowship) Research Interests revolve around computational linguistics, information-theoretic approaches to linguistics, and ethical AI. He investigates synonym evolution, morphosyntactic interactions, and fairness in mental health-focused AI systems. Recent work includes de-identification in medical texts and taxonomy induction via reinforcement learning. Publications span top venues like ACL, IEEE, and Springer, covering topics such as topic modeling interpretability, bias discovery in ML, and multimodal negation processing. Collaborations with colleagues like Scheepers, Spruit, and Kaymak highlight interdisciplinary applications in electronic health records. Awards include the Centennial Fellowship at Princeton and Summa Cum Laude at Rutgers. As a University Teaching Qualification holder, he teaches courses in data science, Python programming, and research methods, supervising theses in AI and Business Informatics. His work aligns with Utrecht's Institutions for Open Societies (IOS) theme, addressing gender diversity and global justice in data-driven systems.

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.

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۷ مورد دیگر

Andreas Karwath is an Associate Professor at the University of Birmingham, affiliated with the Cancer and Genomic Sciences Institute, Institute for Interdisciplinary Data Science and AI, and The Centre for Computational Biology. His research spans computational biology, artificial intelligence, and data science applications in healthcare and cancer genomics. His research interests include: Machine Learning and Artificial Intelligence Computational Biology and Bioinformatics Phenotype Analysis and Ontology Cancer Genomics and Radiomics Differential Diagnosis Systems Dr. Karwath's research fingerprint reveals significant contributions to Inductive Logic Programming (100%), Semantic Similarity (89%), Phenotype Profiles (80%), Machine Learning (74%), Learning Systems (74%), Ontology (71%), Differential Diagnosis (66%), and Neural Networks (63%). His work consistently applies computational approaches to biomedical challenges, with recent focus on cancer outcomes prediction, cardiovascular monitoring through wearables, and phenotype library development for health data research. His scientific contributions include 73 research outputs spanning two decades, with notable recent publications in high-impact journals including Nature Medicine, Machine Learning, and Frontiers in Oncology. His work has been cited across multiple platforms with significant attention from news outlets and academic readers. Dr. Karwath serves as Co-Investigator on several major research projects: PETNECK2 - Radiomics in Outcome Predictive Models for Head and Neck Cancer (2025-2027, NIHR) ACORN: Assessment of Chronic Opioid Risk using Neurobiology (2024-2027, Wellcome Leap Inc.) PINK - Provision of Integrated Computational Approaches for Safe-and-Sustainable-by-Design Chemicals and Materials (2024-2027, UKRI) NIHR BRC Theme - Data, Diagnostics & Decision Tools (2022-2027, NIHR) His collaborative network spans multiple UK institutions and international partners, with frequent collaboration with Gkoutos, G.V. across multiple projects. His research has practical clinical applications, particularly in cancer treatment outcomes prediction and digital health monitoring systems.

Xiaokang Qiuمشاهده پروفایل

دانشیار

• Programming Languages
• Software Engineering
• Program Verification
• +۶ مورد دیگر

Xiaokang Qiu is an Associate Professor at the Elmore Family School of Electrical and Computer Engineering , Purdue University , with a Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2013). His research focuses on Programming Languages and Software Engineering , particularly theories, algorithms, and tools for program synthesis, verification, and logic-based analysis. Research Interests His work addresses: Formal methods for heap-manipulating programs using separation logic Automated deduction and decision procedures for data structures Syntax-guided synthesis of concurrent and bit-vector programs Integration of machine learning with formal verification Scalable verification of hardware memory consistency Network protocol optimization through program synthesis Recent Publications Recent work includes PLDI 2025 on concurrent string synthesis, POPL 2024 on bit-vector synthesis, and POPL 2023 on comparative network design. His tools like STRAND and VCDryad enable automated verification of complex data-structure manipulations. Grants & Awards Recipient of: NSF SHF Small Award (2024, co-PI, $593K) NSF FMitF Award (2023, PI, $750K) Tenure at Purdue (2023) Professional Service Active in program committees for PLDI , POPL , CAV , and ATVA conferences. Developed tools like DryadSynth (PLDI 2020), ImpSynt (OOPSLA 2017), and JSketch (ESEC/FSE 2015).

Ruth Ganssمشاهده پروفایل

استاد

• Cancer Microenvironment
• Tumor Immunology
• Vascular Biology
• +۳ مورد دیگر

Professor Ruth Ganss is Head of the Cancer Microenvironment Lab at the Harry Perkins Institute of Medical Research and holds a professorial appointment at the University of Western Australia. She obtained her PhD at the University of Heidelberg/Germany in 1994 and conducted postdoctoral research with Prof. Douglas Hanahan at the University of California, San Francisco before establishing her laboratory at the German Cancer Research Center in Heidelberg (1997-2006). Professor Ganss's research focuses on the integration of cancer, immunology and vascular biology, with over 20 years of expertise in studying the tumor microenvironment using transgenic and orthotopic tumor models. Her current work centers on translational anti-cancer aspects of stromal remodeling and immunotherapy, particularly investigating vascular normalization, pericyte biology, and the development of strategies to overcome immune suppression in tumors. Her groundbreaking research has demonstrated how modulating the tumor vasculature can enhance immune cell infiltration and improve immunotherapy outcomes. Her publication record reveals a strong focus on tumor vascular biology, with recent work emphasizing pericyte phenotype switching, induction of tertiary lymphoid structures, and the role of TRAIL signaling in vascular stability. These research themes represent a logical progression from her seminal 2008 Nature paper on Rgs5-deficient tumors, which established the critical link between vascular normalization and improved immune destruction of tumors. Scientific Recognition Inaugural Woodside Cancer Research Fellow Professor Ganss leads an active research program that continues to advance our understanding of the tumor microenvironment and develop novel approaches to enhance cancer immunotherapy through vascular and stromal remodeling. Her work bridges basic science and translational applications, with particular emphasis on making tumors more responsive to immune-based treatments.