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Németh T EnikőView profile

Professor

• Linguistics
• Pragmatics
• Discourse Analysis
• +5 more

Overview Enikő Németh T. is a Full Professor and Head of the Department of General Linguistics at the University of Szeged's Faculty of Humanities and Social Sciences since 2017. She has held leadership roles including Head of the Doctoral School of Linguistics (2018–present), Head of the Hungarian Research Centre for Pragmatics (2012–present), and Chair of the Theoretical Linguistics PhD Program (2007–present). She earned her Candidate's degree (PhD equivalent) in 1992 and became a Doctor of the Hungarian Academy of Sciences in 2017. Education 1987: MA in Russian Language and Literature and General/Applied Linguistics (University of Szeged) 1992: Candidate's degree (PhD equivalent) in Linguistics 2017: Doctor of the Hungarian Academy of Sciences in Linguistics Research Interests Her work focuses on pragmatics , discourse analysis , and the interaction between grammar and pragmatics . She explores implicit arguments in Hungarian, perspectives in language use , and cognitive pragmatics . Her research emphasizes empirical methodologies and theoretical frameworks to understand linguistic phenomena like pro-drop, anaphora, and lexical semantics. Awards & Honors 2020: Hungarian Academy of Sciences Publication Award 2013: Hajdú Péter Guest Researcher Fellowship 2004–2007: Bolyai János Research Fellowship 2000–2003: Széchenyi Professor Fellowship Academic Leadership & Grants She leads major initiatives like the MTA-DE-SZTE Research Group for Theoretical Linguistics (2006–present) and has directed national and international research projects. Her grants include long-term fellowships from the Hungarian Academy of Sciences. Labs & Teams Directs the Hungarian Research Centre for Pragmatics and oversees the MTA-DE-SZTE Research Group for Theoretical Linguistics , fostering collaborative research in pragmatics and theoretical linguistics.

Ricardo A. CalixView profile

Professor

• Machine Learning
• Natural Language Processing
• Deep Learning
• +4 more

Ricardo A. Calix is a Professor of Computer Information Technology at Purdue University Northwest. His research focuses on Machine Learning, Natural Language Processing, AI applications in Cyber Security and Healthcare, and Deep Learning methodologies. He holds a Ph.D. in Engineering Science from Louisiana State University. Key research areas include ethical AI model analysis, reinforcement learning for aerospace control systems, and industrial automation through machine learning. His work spans cybersecurity tools (e.g., CyberSecTK) and healthcare data mining from social media. Calix has secured grants as PI/Co-PI for projects like 'Detection of Potential Drug Effects from Twitter Data' (NIH) and 'A Smart and Fast IDS' (Northrop Grumman). He authored Getting Started with Deep Learning: Programming and Methodologies Using Python (2017). Recent publications (2023-2024) address biases in AI models, blast furnace automation, and autonomous aircraft control via reinforcement learning. His work bridges theoretical AI with practical applications in industry and security.

Alexander Miserlis HoyleView profile

Researcher

• Computational Social Science
• Natural Language Processing
• Machine Learning
• +6 more

Alexander Hoyle is a Researcher at the ETH Zürich AI Center, concurrently contributing to natural language processing/machine learning and social science groups. He holds a PhD in Computer Science from the University of Maryland (advised by Philip Resnik) and a Master's in Computational Statistics from University College London (advised by Sebastian Riedel and Jeff Mitchell). His research focuses on computational social science, emphasizing methods for latent construct identification (e.g., topic models, ideal point models) and evaluation frameworks grounded in validity. Key areas include bias/fairness in AI, political science applications, and mental health constructs like suicidality. He pioneered frameworks like PairScale (attitude measurement via pairwise comparisons) and TopicGPT (prompt-based topic modeling). Education: PhD in Computer Science, University of Maryland (2020-2023) MS in Computational Statistics & Machine Learning, University College London (2018) Bachelor's degree (pre-PhD details omitted) Research Interests: Combining NLP with social science needs, particularly in evaluation rigor and interpretability. Active in interdisciplinary work between NLP and computational social science (e.g., measuring attitude evolution on Reddit, improving topic model validity). Advocates for human-in-the-loop approaches to address LLM limitations in tasks like document clustering and sentiment analysis. Grants & Projects: Contributed to a landmark $2.2B DOJ settlement on NYC public housing via econometric modeling at The Brattle Group. Active in graduate labor advocacy (Maryland state legislature testimony) and mentorship (Científico Latino's mentorship program). Labs & Teams: Leads initiatives at the ETH Zürich AI Center, collaborating with groups like Microsoft Research (FATE) and AI2's AllenNLP. Involved in multi-university projects (e.g., University of Maryland's Computational Linguistics lab).

Muriel WaterlotView profile

Assistant Professor

• Translation Didactics
• Intercultural Communication
• Phraseology
• +4 more

Dr. Muriel Waterlot serves as an Assistant Professor at the Department of Dutch Language and Literature within the Faculty of Humanities at the John Paul II Catholic University of Lublin. Her research focuses on translation didactics , intercultural competence , and contrastive linguistic analysis between Polish, Dutch, and Flemish contexts. Supervised 18+ diploma theses on topics like Zeeuws dialect preservation, Dutch-English modal verbs, and Polish-Dutch phraseological units Contributed to translation theory through publications and presentations at international conferences including TransELTE and THERMOS Active in editorial and expert councils for academic journals and PhD programs Her recent work explores terminological variants in Polish-Dutch-French contexts , multilingual translation approaches , and pedagogical models for foreign language teaching . She advocates for task-based translation methodologies and cross-cultural business communication frameworks. Key projects involve EU-funded initiatives like Language in Business – Business in Language (Erasmus+) Developed the Waterlot Model for tourism web-text translation Coordinated international translation education programs

• Logic Programming
• Knowledge Representation
• Artificial Intelligence
• +6 more

Bart Bogaerts is an Associate Professor in the Department of Computer Science at KU Leuven's Faculty of Engineering Science. He is affiliated with the Declarative Languages and Artificial Intelligence (DTAI) research unit and is a member of Leuven.AI - KU Leuven Institute for Artificial Intelligence. Bogaerts serves on the Council of the Faculty of Engineering Science as senior academic staff and participates in the Programme Committee for Artificial Intelligence curriculum development. His research focuses on foundational aspects of logic programming and knowledge representation, with particular expertise in approximation fixpoint theory, higher-order logic programming, and non-monotonic reasoning. Bogaerts investigates the theoretical underpinnings of stable model semantics, justification frameworks, and executable query languages. His work bridges theoretical computer science with practical applications in artificial intelligence and knowledge-based systems. Bogaerts' publication record demonstrates consistent contributions to top venues in logic programming and artificial intelligence. His recent work shows increasing focus on category-theoretic approaches to approximation theory, distributed web traversal specifications, and certified model expansion techniques. The publications reveal a strong emphasis on formal methods with applications spanning from theoretical mathematics to practical AI systems. As a promotor for multiple significant research projects, Bogaerts leads investigations into certified answer set programming (CertifASP), first-order model expansion (CertiFOX), proof generation for combinatorial optimization, distributed configuration problems, and knowledge integration paradigms. These projects, funded through 2028-2029, demonstrate his leadership in advancing the theoretical foundations of AI and logic programming. Bogaerts is actively involved in teaching courses on knowledge representation and reasoning, contributing to the development of next-generation AI researchers. His work within the DTAI research unit positions him at the forefront of declarative AI approaches in Belgium's leading research university.

Yao LiView profile

Assistant Professor

• Programming Languages
• Formal Verification
• Dependent Types

Yao Li is an Assistant Professor of Computer Science at Portland State University, specializing in Programming Languages, Formal Verification, and Dependent Types. They earned a Ph.D. in Computer and Information Science from the University of Pennsylvania (2022), supervised by Stephanie Weirich, and hold a Master’s and Bachelor’s in Software Engineering from Shanghai Jiao Tong University (2016, 2013) under Zhengwei Qi. Ph.D., Computer and Information Science, University of Pennsylvania (2022) M.S., Software Engineering, Shanghai Jiao Tong University (2016) B.S., Software Engineering, Shanghai Jiao Tong University (2013) Their research focuses on formal verification of functional programming languages, particularly Haskell, and mechanized proofs using tools like Coq. They work on specifying and verifying networked servers, lazy program cost analysis, and embedded systems through program adverbs and Tlön embeddings. Recent publications (2018-2025) demonstrate expertise in Haskell verification, formal methods, and interaction trees. Key trends include bridging the gap between mechanized formalization and real-world code, advancing lazy evaluation semantics, and enhancing software verification through dependent types. Yao Li actively participates in academic service as a committee member and session chair in conferences like POPL, ICFP, and PLDI, contributing to artifact evaluation, program organization, and student research competitions.

Cătălin HriţcuView profile

Professor

• Formal Methods for Security
• Secure Compilation
• Compartmentalization
• +14 more

Cătălin Hriţcu is a tenured faculty member at the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany, where he heads the Formally Verified Security group. He is also an Adjunct Professor in the Faculty of Computer Science at Ruhr University Bochum (RUB) and a member of the Horst Görtz Institute for IT Security (HGI) and the CASA Cluster of Excellence at RUB. He received a PhD from Saarland University and a Habilitation from École Normale Supérieure (ENS) Paris. Prior positions include Tenured Researcher at Inria Paris, Postdoctoral Associate at the University of Pennsylvania, and Visiting Researcher at Microsoft Research. His research bridges formal methods for security (secure compilation, memory safety, information flow), programming languages (proof assistants, dependent types, verification), and security-critical systems (reference monitors, cryptographic implementations). Key projects include the F* verification system and a formally verified HTTPS stack. Awards & Honors: Distinguished Paper Award, CSF Symposium (2019, 2021, 2025) Major Grants: ERC Starting Grant for formally secure compilation . He leads the Formally Verified Security group at MPI-SP, focusing on high-assurance systems via formal verification.

Kartik NagarView profile

Assistant Professor

• Automated Formal Verification
• Program Analysis
• Programming Languages
• +4 more

Kartik Nagar is an Assistant Professor at the Department of Computer Science and Engineering, IIT Madras . He specializes in developing verification and analysis techniques to enhance the reliability, security, and efficiency of computer systems, focusing on concurrent and distributed systems, computer architecture, and real-time systems.

Nate FosterView profile

Professor

• Programming Languages
• Networking
• Formal Verification
• +3 more

Nate Foster is a Professor of Computer Science at Cornell University and a Visiting Researcher at Jane Street . During the 2023-24 academic year, he served as a Visiting Professor at EPFL in the Data Center Systems Laboratory. His research bridges Programming Languages and Networking , focusing on formal verification, data plane programming, and language design for networked systems. His recent work includes developing symbolic automata for network verification (e.g., Active Learning of Symbolic NetKAT Automata , StacKAT ), creating efficient verifiers like KATch , and advancing type safety in data plane programming (e.g., SafeP4 ). These efforts span formal methods, algorithm optimization, and practical networked systems. Notable scientific awards include: NSF CAREER Award Sloan Research Fellowship ACM SIGCOMM Rising Star Award ACM SIGPLAN Robin Milner Award He actively contributes to PLDI, POPL, OOPSLA, and ICFP, serving as author, editor, and committee member. His GitHub repositories include tools for academic website templates and programming language research codebases.

James BrotherstonView profile

Professor

• Logic
• Computation
• Separation Logic
• +5 more

James Brotherston is Professor of Logic and Computation in the Programming Principles, Logic and Verification Group (PPLV) at University College London's Department of Computer Science. His research spans logic, computation, and verification, with a focus on separation logic, cyclic proofs, and automated reasoning for program correctness. Research interests include: Foundations of program verification using separation logic Automated reasoning via cyclic proof systems Concurrency and deadlock detection Formal methods for security and resource management Applications of substructural logics in computer science His publications demonstrate consistent focus on logical foundations of verification, with recent work expanding into concurrency testing and atomic operations compatibility. Trends include theoretical advances in proof systems alongside practical verification tools. Scientific awards and grants: Facebook Testing and Verification Research Award (2019) £6M EPSRC Programme Grant for IRIS project (2018-2024) Facebook Faculty Grant (2015) Multiple EPSRC fellowships and grants totaling over £1.2M (2008-2017) He has supervised 12+ PhD students and research fellows, with current students working on verification of concurrent systems. Leads research in the PPLV group and collaborates internationally on logical frameworks.

• Information Systems
• Business/IT Alignment
• Machine Learning
• +5 more

Lerina Aversano is an Associate Professor in the Department of Engineering (DING) at the University of Sannio, specializing in Information Processing Systems (ING-INF/05). Her research focuses on the intersection of business processes and information technology, with particular expertise in business/IT alignment, process mining, and machine learning applications. Her research interests include: Business/IT alignment and strategic integration Machine learning and deep learning applications in healthcare diagnostics Process mining and predictive analytics for business processes Software engineering and service-oriented computing Semantic integration of heterogeneous data sources Over her extensive career, Aversano has published 145 research items with a clear evolution from foundational work in business/IT alignment to cutting-edge applications of AI in healthcare and business process management. Her most recent publications demonstrate expertise in explainable AI for process prediction, AI applications in medical diagnosis (including Parkinson's and thyroid diseases), and security for IoT systems, showing her ability to adapt to emerging technologies while maintaining focus on core alignment issues between business needs and technological solutions. She maintains an active research group with frequent collaborations with Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, and Maria Tortorella, producing significant contributions to information systems literature. Her work appears in reputable venues including IEEE conferences, Springer publications, and journals like Information and Software Technology.

Emmanuel HaucourtView profile

Professor

• Directed Algebraic Topology
• Concurrency Theory
• Static Analysis of Concurrent Programs
• +3 more

Emmanuel Haucourt is a Professor of Computer Science at École Polytechnique, conducting research at the Computer Science Laboratory (LIX) within the Cosynus team. His work focuses on applying directed algebraic topology to analyze concurrent programs. Research Interests: Directed Algebraic Topology, Concurrency Theory, Geometric Models in Computer Science, Static Analysis of Concurrent Programs, Software Verification, and Theoretical Computer Science. Publications Trends: His publications emphasize the use of higher-dimensional geometric structures (e.g., d-spaces, cubical areas, non-Hausdorff manifolds) to model concurrency, enabling deadlock detection and state-space reduction through theoretical advances in category theory and topology. Laboratory: Affiliated with the Computer Science Laboratory (LIX) at École Polytechnique and the Cosynus team.

• Software Engineering
• Artificial Intelligence
• Cybersecurity
• +5 more

Xiaofei Xie is an Assistant Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). He received his PhD from Tianjin University in 2018 and was a postdoctoral researcher at Nanyang Technological University (2018-2021) before joining SMU in 2022. His research focuses on software engineering, AI systems, and cybersecurity. Dr. Xie's primary research areas include program analysis, software testing, vulnerability detection, and quality assurance of AI systems. His work spans: Testing methodologies for autonomous systems and games AI security including backdoor detection and model robustness Automated program repair and code generation Formal methods and semantic code analysis His recent publications demonstrate strong emphasis on AI/ML system testing, cybersecurity applications, and program analysis techniques. Research trends show increasing focus on LLM-based program repair, autonomous system validation, and federated learning security. Major Awards: ACM SIGSOFT Distinguished Paper Awards (ASE'23, ISSTA'22, ASE'19, FSE'16) CCF Outstanding Doctoral Dissertation Award (2019) 3rd place in AI Singapore's Trusted Media Challenge (2022) Wallenberg-NTU Presidential Postdoctoral Fellowship (2019) APSEC Best Paper Award (2020) He currently advises 7 PhD/Master's students including CHENG Mingfei, KONG Jiaolong, and YU Jiongchi. Dr. Xie leads research in software reliability and AI security at SMU's SCIS.

Noortje VenhuizenView profile

Assistant Professor

• Computational Semantics
• Discourse Processing
• Language Comprehension
• +3 more

Noortje Venhuizen is an Assistant Professor in the Department of Cognitive Science and Artificial Intelligence at the Tilburg School of Humanities and Digital Sciences, Tilburg University. She serves as Academic Director for the BSc Cognitive Science and Artificial Intelligence program since 2024, and has held academic positions at Saarland University (2015-2022) including roles as Scientific Staff member and Principal Investigator in SFB 1102 projects. PhD in Computational Semantics (University of Groningen, 2015) MSc in Logic (ILLC, University of Amsterdam, 2011) BSc in Artificial Intelligence (Utrecht University, 2009) Her research focuses on expectation-based language comprehension, neurocomputational modeling of semantic processing, distributional formal semantics, and pragmatic reasoning in discourse. Key contributions include PDRT-SANDBOX (Haskell NLP library) and DFS Tools (Prolog implementation of distributional formal semantics). Recent publications (2023-2025) explore multimodal word meaning, informativity in reference production, and neurocognitive models of surprisal processing. Her work combines formal semantics with cognitive neuroscience and computational modeling. LOT Grotevragenprijs essay contest - Second Prize She has presented research at major conferences including CogSci, AMLaP, and Sinn und Bedeutung. Current teaching includes courses on artificial intelligence, statistics, and semantic theory.

Anna FarihaView profile

Assistant Professor

• Data Management
• Human-Centered Systems
• Usability Engineering
• +4 more

Anna Fariha is an Assistant Professor in the Kahlert School of Computing at the University of Utah. She co-leads the Data Management Research Center for Human-centered, Efficient, and Scalable Systems. Her research focuses on enhancing data system usability, explainability, and trustworthiness through algorithmic innovations and practical implementations. She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts, Amherst, under Prof. Alexandra Meliou. Education: Ph.D. in Computer Science, University of Massachusetts Amherst (2021) Master's in Computer Science, University of Massachusetts Amherst (2020) Undergraduate work in Computer Science, unspecified institution Her research interests include data wrangling tools, constraint discovery, conversational AI for data science, and human-centered database systems. Recent work emphasizes automated data summary recommendations, constraint violation detection, and educational tools for data science programming. Grants & Awards: NSF CIRC: ENS/Grand: POWDER-ENS Award (2024) Stena Center Seed Grant for Fintech Data Analysis (2025) Microsoft Research Dissertation Grant (2020) SIGMOD 2022 Comprehensive Reproducibility Award Teaching: Recently taught Advanced Database Systems , Human-Centric Data Management , and Deep Learning courses at the University of Utah.