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Lionel ParreauxView profile

Assistant Professor

• Programming Languages
• Type Systems
• Compiler Optimization
• +3 more

Lionel Parreaux is an Assistant Professor at the Hong Kong University of Science and Technology (HKUST) in the Department of Computer Science and Engineering since February 2021. He obtained his PhD in Computer Science from the Swiss Federal Institute of Technology, Lausanne (EPFL) in 2020, where he developed the Squid type-safe metaprogramming library for Scala. Research Interests: His work spans programming languages, type systems, functional programming, generative programming, language design, and compiler optimizations. He focuses on improving program correctness and performance through advanced type theory and compilation techniques. Publications: His recent articles concentrate on type inference methodologies, compiler optimizations (especially for functional programs), metaprogramming, and formal verification of language features. Key themes include constraint-based type systems, deforestation techniques, polymorphic subtyping, and error message improvements. Research Group: He leads a team of 8 PhD students and 1 MPhil student, with 2 MPhil students graduated in 2024. Actively recruits students for projects in programming languages and compilers. Projects: A Lightweight Type System for Scope and Effect Safety (Huawei-funded, 2024–) Optimizing Functional Programs via Graph Reduction (RGC Early Career Scheme, 2022–2025)

• Educational Technology
• Learning Analytics
• Computer-Supported Collaborative Learning
• +5 more

Minhong Wang is a Professor at the Faculty of Education, University of Hong Kong, with additional affiliation at the University of Edinburgh's Usher Institute. With an extensive publication record spanning over two decades from 2005 to 2025, Wang has established themselves as a leading researcher in educational technology and learning sciences. Their work bridges the gap between computer science and education, developing innovative technology-enhanced learning environments that support complex skill development and knowledge construction. Wang's research interests focus on educational technology, learning analytics, computer-supported collaborative learning, and cognitive mapping approaches. Their work examines how technology can enhance problem-solving processes, support self-regulated learning, and improve educational outcomes across various contexts. Recent research has expanded into multimodal learning analytics, teacher professional development through technology, and the application of advanced computational methods to educational challenges. Wang's approach integrates theoretical frameworks with practical implementations, creating systems that transform how educators and learners interact with digital environments. Analysis of Wang's recent publications (2023-2025) reveals a strategic expansion of research scope while maintaining core educational technology focus. The work demonstrates increasing sophistication in learning analytics methodologies, with growing integration of computer vision techniques and advanced machine learning approaches. This reflects a trend toward more comprehensive multimodal analysis of learning processes, moving beyond traditional text-based interactions to incorporate visual, spatial, and behavioral data in educational contexts. The research maintains strong practical applications while advancing theoretical understanding of how technology mediates learning. Wang has collaborated extensively with researchers across multiple institutions globally, particularly in Hong Kong, mainland China, and international partners. Their work demonstrates consistent funding support through numerous research projects, though specific grant details aren't visible in the publication record. The collaborative nature of the work suggests leadership in research teams focused on developing and evaluating innovative educational technologies.

Rachel OliverView profile

Assistant Professor

• Space Domain Awareness
• Spaceflight Dynamics and Controls
• Satellite Tracking
• +7 more

Rachel Oliver is an Assistant Professor at the Air Force Institute of Technology (AFIT), located at Wright-Patterson Air Force Base, Ohio. She is affiliated with the Graduate School of Engineering and Management and the Department of Aeronautics and Astronautics. As a Major in the U.S. Air Force, she contributes to national space capabilities through education and research in Space Domain Awareness and spaceflight dynamics and controls. Education: Ph.D. in Aeronautical Engineering, Cornell University, 2024 M.S. in Aeronautical Engineering, Cornell University, 2022 M.S. in Astronautical Engineering, Air Force Institute of Technology, 2017 B.S. in Mechanical Engineering, United States Military Academy, 2015 Rachel Oliver's research focuses on advancing space surveillance through novel sensing technologies, particularly event-based vision sensors for space domain awareness. Her work spans thermal systems for spacecraft, satellite tracking, cislunar situational awareness, and infrared imaging. She applies computational modeling, sensor simulation, and heuristic algorithms to solve complex problems in space operations and spacecraft engineering. Her recent publications reflect a strong trend in leveraging neuromorphic and event-based vision sensors for space applications, including sensor modeling, noise analysis, tracking, and simulation frameworks. These works are consistently presented at the Advanced Maui Optical and Space Surveillance Technologies Conference and other high-impact venues, demonstrating sustained contributions to space surveillance and sensor technology development. Scientific Awards: No scientific awards explicitly listed in the provided text. Rachel Oliver advises graduate students and collaborates on research projects in satellite tracking and thermal systems. She leads research initiatives at AFIT and has been involved in significant projects such as infrared imaging of spacecraft and oscillating heat pipe technologies. She currently serves as deputy director for the Center of Space Research and Assurance, supporting institution-wide space research efforts. While specific grant details are not provided, her research scope suggests involvement in Department of Defense and Air Force Research Laboratory-sponsored programs. Rachel Oliver is actively involved in the Center of Space Research and Assurance at AFIT, where she contributes to strategic space research and assurance initiatives. Her work integrates academic research with military space operations, fostering innovation in space domain awareness and next-generation astronautical engineering education.

Rosina WeberView profile

Professor

• Explainable Artificial Intelligence (XAI)
• Case-Based Reasoning
• Machine Learning
• +6 more

Rosina Weber is a Professor of Information Science and Computer Science at Drexel University's College of Computing & Informatics. She specializes in explainable artificial intelligence (XAI), case-based reasoning (CBR), and combining neural/symbolic methods for use-inspired AI applications. Her research addresses interpretability challenges, medical decision support, and ethical AI frameworks. Education: PhD in Production Engineering (Federal University of Santa Catarina, Brazil, 1998) MS in Production Engineering (Federal University of Santa Catarina, Brazil) BA in Business Administration (Federal University of Rio Grande do Sul, Brazil) Research Interests: Focus areas include XAI, causal discovery, medical AI applications, and AI ethics. Recent work emphasizes interpretable models for clinical diagnostics and military medical triage scenarios. Key Projects: Co-PI for DARPA ITM project with Parallax Advanced Research Former co-lead in DHS-EPA Center for Microbial Risk Assessment Swedish Vinnova-funded international XAI collaboration Awards: 2023 Research Excellence Award (College-level) Best Paper Awards (various conferences) Grants & Collaboration: Holds active grants from DARPA, NCATS-NIH, and Swedish Vinnova. Her work has been featured in Good Day Philadelphia and NBC Nightly News . Labs/Teams: Leads AI research teams focused on biomedical data translation, CBR integration, and XAI implementation in healthcare systems.

Pranabendu MisraView profile

Assistant Professor

• Algorithms and their applications
• Graph Theory
• Parameterized Complexity
• +10 more

Pranabendu Misra is an Assistant Professor in the Department of Computer Science at Chennai Mathematical Institute (CMI), India. He previously held postdoctoral positions at the Max Planck Institute for Informatics, Germany, and the University of Bergen, Norway. His research is centered in theoretical computer science with strong contributions to algorithms and complexity. Current Affiliation: Assistant Professor, Department of Computer Science, Chennai Mathematical Institute, India Previous Positions: Postdoctoral Fellow, Max Planck Institute for Informatics; Researcher, University of Bergen Education: PhD from Institute of Mathematical Sciences (IMSc), Chennai, advised by Prof. Saket Saurabh; M.Sc. and B.Sc. from CMI in Computer Science and Mathematics Pranabendu's research interests span a broad spectrum of algorithmic theory, including Graph Theory , Parameterized Complexity , Approximation Algorithms , Matroids , Algebraic Methods , Derandomization , Algorithmic Game Theory , Streaming and Dynamic Graphs , and increasingly, AI/ML and Deep Learning . His work combines deep theoretical analysis with practical algorithmic insights. His recent publications in top-tier venues like SODA, STOC, ICALP, and EC reveal a strong trend toward developing meta-theorems, kernelization techniques, and FPT approximation algorithms for fundamental graph problems such as vertex cover, feedback vertex set, connectivity augmentation, and disjoint paths. He also explores fair division in algorithmic game theory and fault-tolerant network design. His scientific honors include: Google India Research Award (2022) SERB Startup Research Grant (2022) Fujitsu Research Grant (2023) Pranabendu actively contributes to academic advising and teaching, offering courses such as Advanced Machine Learning , Approximation Algorithms , and Parameterized Algorithms . He has collaborated extensively with leading researchers including Daniel Lokshtanov, Saket Saurabh, Meirav Zehavi, and Kurt Mehlhorn. While specific grant details are limited, his funded projects suggest strong support for research in algorithms and AI/ML. He teaches at CMI and has previously taught at the Max Planck Institute. He is affiliated with a vibrant research group focusing on algorithms and complexity, working on both foundational and applied algorithmic problems. His recent interest in AI/ML indicates an expanding research scope bridging theoretical guarantees with modern machine learning applications.

Tilia EllendorffView profile

Researcher

• Natural Language Processing
• Biomedical Informatics
• Text Mining
• +3 more

Tilia Ellendorff is a researcher at the University of Zurich , affiliated with the Department of Computational Linguistics under the Faculty of Arts and Social Sciences . She contributes to the Text Crunching Center (TCC) by developing solutions for text analytics, information extraction, and natural language processing . Her work bridges computational methods with biomedical and health-related domains, as evidenced by her involvement in the Digital Society Initiative (DSI) Community Health. Fields of Interest: Natural Language Processing, Biomedical Informatics, Text Mining, Information Extraction, Computational Linguistics, Machine Learning Her research focuses on creating annotated corpora, optimizing language models for clinical and biomedical texts, and advancing text mining tools for tasks like entity recognition and causal network extraction. She has contributed to collaborative initiatives such as BioCreative V and SMM4H, emphasizing hybrid approaches and multi-task learning. Contact: ellendorff@cl.uzh.ch

Stephan OepenView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Language Technology
• +3 more

Stephan Oepen is a Professor at the Department of Informatics, University of Oslo, specializing in Computational Linguistics and Natural Language Processing (NLP). He leads the Research Group for Language Technology and serves as Academic Chair in Artificial Intelligence for the Circle U European University Alliance. His research bridges formal linguistics with large-scale computation, focusing on semantic parsing, dependency structures, and multilingual language models. 2002–present: Researcher → Professor at UiO 2011–2017: Head of Language Technology Research Group 2019–20: Section Head for Machine Learning 2021–24: Department Head for Informatics Current projects include HPLT (High-Performance Language Technologies), Nordic Language Processing Laboratory (NLPL), and OpenEuroLLM . He co-directs the 2017 ACL Shared Task on Extrinsic Parser Evaluation and contributes to international NLP conferences like ACL, EMNLP, and CoNLL. Recent publications focus on: Uniform semantic graph representations Hybrid parsing architectures Continual training for small languages Diagnostic parser evaluation Copyright implications in LLMs Community language resource infrastructure He actively participates in editorial boards and technical coordination roles, including the LUMI Scientific Advisory Group and Horizon Europe's High-Performance Language Technologies project.

Nicole Danaher-GarciaView profile

Assistant Professor

• Healthcare Data Analytics
• Quantitative Analysis
• Predictive Analytics
• +5 more

Nicole Danaher-Garcia is an Assistant Professor in the School of Healthcare Leadership at the MGH Institute of Health Professions . She teaches quantitative analysis and predictive analytics in the Master of Healthcare Data Analytics and Master/PhD of Health Professions Education programs. Her research focuses on network analysis and Bayesian techniques applied to healthcare data and animal behavior, particularly in dolphin social dynamics. She holds a BS in Biology from Duke University and a PhD in Integrative Biology from UMass Dartmouth. Her doctoral work on Atlantic spotted dolphins in The Bahamas revealed unexpected social behaviors, such as intergroup alliances that defied terrestrial mammal norms. This research gained significant media attention from outlets like the New York Times and Science Magazine. She currently applies network analysis and predictive modeling to healthcare challenges, emphasizing interdisciplinary collaboration between technical and non-technical stakeholders. Education: BS, Biology, Minor: Environmental Science, Duke University PhD, Integrative Biology, UMass Dartmouth Research Interests: Social network analysis in healthcare systems Bayesian statistical modeling for behavioral studies Telehealth etiquette and clinical competency Predictive analytics for healthcare resource allocation Media Highlights: "Breaching Extinction: Merging for Dolphin Societies" (2022) "Dolphins make peace and love—not war—when they encounter strangers" (2022) Dr. Danaher-Garcia’s courses emphasize critical thinking and problem-solving in data analytics, preparing students to address complex healthcare challenges. She is developing new methodologies to predict clinical technology impacts and optimize healthcare workflows through interdisciplinary approaches.

Ralf LämmelView profile

Professor

• Software Language Engineering
• Model-Driven Development
• Semantic Web
• +2 more

Ralf Lämmel is Professor and Head of the Software Languages group at the University of Koblenz. His research spans software language engineering, model-driven development, and semantic web technologies. He specializes in API analysis, data validation (SHACL constraints), and variability management for software systems. Recent work focuses on AI-assisted workflows for archaeology and trust analysis in large language models. Publications demonstrate growing interest in knowledge graph validation and semantic web applications. He has developed tools like the Virtual Platform for software variability and ProGS for property graph validation. Service includes program committee memberships for software engineering conferences and editorial roles. He leads research groups exploring API evolution and RDF validation techniques.

Ezgi AydemirView profile

Lecturer

• Graph Theory
• Algebraic Graph Theory
• Topological Indices
• +2 more

Ezgi Aydemir serves as a Lecturer in the Department of Algebra and Number Theory within the Faculty of Arts and Sciences at Iğdır University. Her academic profile combines rigorous teaching with specialized research in graph theory and its applications. Education: Doctorate (2013-2018): Selcuk University, Institute of Science/Mathematics/Algebra and Number Theory Department; Thesis: 'New parameters in graphs and some results' Master's Degree (2010-2013): Selcuk University, Institute of Science/Mathematics (MA) with Thesis; Thesis: 'Some parameters in graphs and their relations' Bachelor's Degree (2005-2010): Balikesir University, Faculty of Arts and Sciences/Department of Mathematics Research Focus: Dr. Aydemir's scholarly work centers on graph theory with particular emphasis on topological indices, spectral graph properties, and applications in mathematical chemistry. Her research investigates various graph invariants including Co-PI indices, Zagreb indices, harmonic indices, and spanning trees. She has recently expanded her research to include applications of AI techniques like GPT-4 for sentiment analysis through mathematical modeling, demonstrating interdisciplinary reach beyond traditional graph theory. Publication Trends: Dr. Aydemir's research output shows a clear progression from foundational work on Co-PI indices (2015-2017) to more complex spectral analyses (2018) and recently to interdisciplinary applications (2021-2024). Her publications consistently address theoretical aspects of graph invariants while increasingly exploring practical applications in chemistry and computational fields. The 2024 paper applying differential equations and GPT-4 to sentiment analysis represents a significant expansion of her research scope. Teaching and Administration: At Iğdır University, Dr. Aydemir teaches a comprehensive range of mathematics courses including Graph Theory (graduate level), Abstract Algebra, Linear Algebra, and Number Theory. She has held significant administrative positions as Department Vice Chair (2020) and Dean's Assistant (2019), demonstrating leadership and commitment to institutional development. Her course portfolio reflects both deep theoretical knowledge and practical teaching skills across multiple levels of mathematics education.

• web mining
• social mining
• recommender systems
• +2 more

Dr. Le Duc Trong is an Assistant Professor (equivalent to Lecturer) at the University of Engineering and Technology (UET), Vietnam National University (VNU) in Hanoi, where he currently serves as Head of the Computer Science Department . He holds a PhD from Singapore Management University (2019) and a Bachelor's in Information Technology from VNU-UET (2011). He leads the Reliable Machine Learning research group , focusing on AI robustness and real-world applications. His research spans web/social mining, recommender systems, multimodal learning, and reliable ML , with emphasis on fairness-aware algorithms, emotion recognition, and healthcare AI. Recent work integrates graph networks, causal inference, and curriculum learning to address challenges like modality imbalance and catastrophic forgetting. Publication analysis (2019-2025) reveals three dominant themes: (1) Multimodal emotion recognition (5+ papers), advancing fusion techniques for conversational/clinical settings; (2) Reliable recommendation systems addressing fairness, causality, and user intent modeling; (3) Medical/healthcare AI including bronchoscopy datasets and mental disorder detection frameworks. His work consistently appears in premier venues (e.g., ACM MM, IJCAI, AAAI). Awards & Service: IJCAI Student Travel Award (2017) Program Committee: AAAI, IJCAI, PAKDD (2020-2022) Co-Session Chair: KSE 2023 Administrative Roles: Head, Computer Science Department (2022–present) Deputy Head, Computer Science Department (2022)

Lionel ParreauxView profile

Assistant Professor

• Programming Languages
• Type Systems
• Functional Programming
• +3 more

Lionel Parreaux is an Assistant Professor at The Hong Kong University of Science and Technology (HKUST) in the Department of Computer Science and Engineering since February 2021. He is actively recruiting students for his research group focusing on programming languages, type systems, and compiler optimization. He earned his PhD in 2020 from École polytechnique fédérale de Lausanne (EPFL) under the Data Analysis Theory and Applications Laboratory (DATA), where he developed the Squid type-safe metaprogramming library for Scala. His research spans Programming Languages Type Systems Functional Programming Generative Programming Compiler Optimization Language Design Recent publications (2024) cover type inference, metaprogramming, and compiler optimization. Notable works include Scope-safe metaprogramming Deforestation techniques Conditional syntax design Subtyping constraints Lazy memory management These reflect his focus on theoretical and practical language implementation challenges. Current research projects include "A Lightweight Type System for Scope and Effect Safety" (2024, Huawei Technologies) "Optimizing Functional Programs by Building on Optimal Graph Reduction Techniques" (2022-2025, RGC Early Career Scheme) He supervises 8 PhD/MPhil students and serves on program committees for POPL, APLAS, GPCE, and SPLASH symposia.

Lasha AbzianidzeView profile

Assistant Professor

• Natural Language Processing (NLP)
• Computational Linguistics (CL)
• Formal Semantics
• +5 more

Dr. Lasha Abzianidze is an Assistant Professor at Utrecht University's Department of Languages, Literature and Communication (Faculty of Humanities). He is affiliated with the Institute for Language Sciences, contributing to the Logic, Language, Information group and Computational Linguistics group. His research focuses on NLP, computational semantics, and formal reasoning systems like LangPro, a tableau-based theorem prover for NLI. He co-leads the Parallel Meaning Bank (PMB) project for semantically annotated corpora and the NALOMA workshop series. He holds a PhD from Tilburg University (2017), with earlier roles as a postdoc in the PMB and ROCKY projects, and lectureships at the University of Groningen. His expertise includes semantics, NLP, computational linguistics, and human-centered AI. Education: PhD in Linguistics (Tilburg University, 2017); Double MSc in Language & Communication Technologies (Charles University & University of Lorraine); BSc in Pure Mathematics (Tbilisi State University). Research Interests: Natural Language Inference (NLI), formal semantics, theorem proving, cross-lingual semantic annotation, and explainable AI. Current projects include explainable natural language reasoning via the NWO XS grant and developing spatial reasoning datasets like SpaceNLI. His work bridges logic-based methods with deep learning, emphasizing transparent and interpretable systems. Grants & Supervision: Holder of an NWO XS grant (2023) for explainable NLI research. Supervises AI master's theses, favoring students from his courses on formal languages and NLP. Involved in teaching courses like 'Reasoning with Natural Language' and 'Machine Learning for Vision and Language'. Labs/Teams: Leads the NALOMA workshop series and contributes to the PMB project. Active in the Computational Linguistics group at ILS, collaborating on semantic parsing tools like CCGweb and LangPro. His work integrates logic-based reasoning with modern NLP techniques to advance explainable AI systems.

• Natural Language Processing
• Computational Linguistics
• Large Language Models
• +7 more

Yulan He is an active researcher in Natural Language Processing and Computational Linguistics with numerous publications in top-tier conferences including ACL, EMNLP, and COLING from 2023-2025. Their work spans both theoretical advancements in Large Language Model architectures and practical applications in healthcare, social media analysis, and information retrieval. Research interests focus on Large Language Model optimization , including improving faithfulness in rationale generation, enhancing reasoning capabilities, personalizing outputs to user preferences, and optimizing computational efficiency. Significant contributions include frameworks for debiasing opinion summarization, improving depression detection in clinical interviews, and developing methods for Theory-of-Mind reasoning in LLMs. Their work addresses critical challenges in LLM reliability, interpretability, and efficiency. Analysis of recent publications reveals consistent focus on bridging the gap between theoretical LLM capabilities and practical applications , with particular attention to healthcare contexts, social media analysis, and complex reasoning tasks. Their research demonstrates how to make LLMs more reliable, efficient, and aligned with human needs across diverse domains. Scientific contributions include: Novel frameworks for LLM faithfulness and reasoning (Drift, EnigmaToM) Efficient inference methods (SCOPE, PECAN) Bias mitigation techniques (LASS, Rehearse With User) Personalization approaches (PROPER) Healthcare applications (Explainable Depression Detection) As evidenced by senior authorship positions across numerous publications, Yulan He leads research projects and likely supervises graduate students in NLP research. Their work demonstrates strong technical expertise combined with practical problem-solving approaches to real-world NLP challenges.

Simon KnightView profile

Associate Professor

• Learning Analytics
• Epistemic Cognition
• AI Ethics
• +4 more

Associate Professor Simon Knight is a member of the University of Technology Sydney Centre for Research on Education in a Digital Society (CREDS), which he founded and directs. He holds the position of Associate Professor at UTS TD School and is a 2023 Australian Research Council Discovery Early Career Research Award (ARC DECRA) Fellow. His academic work spans multiple institutions, including honorary positions at University College London (2023-2026) and previous visiting roles at New York University, UCL, and Cambridge. PhD, The Open University (2012-2015) MA (Philosophy of Education), UCL Institute of Education (2009-2011) MPhil (Educational Research Methods), University of Cambridge (2011-2012) BSc (International Honours, Philosophy and Psychology), University of Leeds (2004-2008) Graduate Certificate in Higher Education Teaching and Learning, UTS (2018-2020) Dr Knight's research focuses on how people learn to navigate uncertainty, disagreement, and evidence in digital environments, with particular attention to the mediating role of technology. His work explores data as evidence, design for learning toward epistemic and ethical reasoning, and the intersection of critical thinking with information problem solving in the age of misinformation and generative AI. He has made significant contributions to the field of learning analytics, particularly in understanding epistemic cognition and its relationship to information seeking behaviors. His recent publication trends reveal a strong focus on AI ethics, critical information navigation, and the development of tools to support students in complex information landscapes. Knight's work increasingly addresses the challenges posed by generative AI in educational contexts, with multiple 2025 publications examining how learners can develop critical thinking skills when interacting with AI systems and navigating conflicting information sources. 2023 Australian Research Council Discovery Early Career Research Award (ARC DECRA) Fellow UTS 2020 Early Career Research Excellence Award UTS 2023 Medal for Excellence in Research and Teaching Integration Multiple UTS teaching awards As Co-Editor in Chief of the Journal of Learning Analytics (2015-2022) and active participant in numerous scholarly conferences, Dr Knight has significantly shaped the learning analytics field. He has secured research funding through his ARC DECRA fellowship and other grants, focusing on how technology can support evidence-based reasoning and critical thinking. His professional activities extend to serving on editorial boards, acting as an ARC Assessor, and contributing to policy development through submissions to government inquiries on digital transformation and civics education. Dr Knight leads the Centre for Research on Education in a Digital Society (CREDS) at UTS, which serves as the primary hub for his research activities. His work involves close collaboration with researchers across disciplines and institutions, including partnerships with UCL Knowledge Lab, KTH Digital Futures, and various Australian universities. The INFO-NAVIGATE project represents a key current initiative, developing tools to support critical thinking in complex information environments.