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Chloé ClavelView profile

Professor

• Affective Computing
• Artificial Intelligence
• Natural Language Processing
• +5 more

Chloé Clavel is a Professor of Affective Computing at Télécom Paris (Institut Polytechnique de Paris) and Senior Researcher at INRIA Paris . She coordinates the Social Computing theme at Télécom Paris and leads research at the intersection of Affective Computing , Artificial Intelligence , and Human-Agent Interaction . Her work spans multiple disciplines including speech processing, natural language understanding, and social robotics. Research Focus: Socio-emotional behavior modeling Applications: Health, Education, Social Robotics Funding: H2020 ITN ANIMATAS, ANR SINNet Her recent publications demonstrate expertise in NLP and AI with topics including: Language model evaluation Contextual word representations Multimodal attention networks Stance detection Emotion recognition Awarded the AAAI-22 Outstanding Student Paper award for her work on the InfoLM metric, she has developed significant resources like the 3MT_French dataset and SILICONE benchmark . Her teaching includes: Master's Degree in Artificial Intelligence Courses on affective computing and NLP Industry training at Allianz and Sagemcom

Natalie SchluterView profile

Associate Professor

• Theoretical Computer Science
• Machine Learning
• Natural Language Processing
• +3 more

Natalie Schluter is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), and a Machine Learning Researcher at Apple's Machine Learning Research Organisation under Samy Bengio. She previously held roles including Head of Programme for the Data Science BSc at the IT University of Copenhagen, Senior Research Scientist at Google Brain/DeepMind, Lead Data Scientist at MobilePay, and postdoctoral researcher positions at the University of Copenhagen and Malmö University. Her education includes a PhD in Natural Language Processing from Dublin City University, an MSc in Mathematics from Trinity College Dublin, an MA in Linguistics (Semantics) from Université de Montréal, and a BA in French and Spanish from the University of British Columbia. Her research interests span Theoretical Computer Science (Graph Algorithms and Automata), Machine Learning, and their applications in Natural Language Processing and Data Science. Key areas include text summarization, parsing, cross-lingual NLP, and algorithmic fairness. She has contributed significantly to multilingual NLP datasets like MassiveSumm and DaNewsroom, and her work bridges theoretical foundations with practical NLP systems. Publications highlight innovations in neural network regularization, dependency parsing, and cross-lingual inference, with a focus on Danish and low-resource languages. Current advisees include PhD student David Sasu. She has advised numerous students and researchers, contributing to impactful NLP tools like the UniParse toolkit. Her career reflects a blend of academic and industry leadership, driving advancements in NLP theory and application while fostering education through pioneering programs like the Data Science BSc.

Collin McMillanView profile

Associate Professor

• Software Engineering
• Natural Language Processing
• Program Comprehension
• +3 more

Collin McMillan, Ph.D., is an Associate Professor of Computer Science at the University of Notre Dame's College of Engineering. He earned his Ph.D. from William & Mary under Denys Poshyvanyk. His research focuses on software engineering and natural language processing, particularly in automated documentation generation, program comprehension, and human-AI collaboration in code analysis. Education: Ph.D. in Computer Science (William & Mary), advised by Denys Poshyvanyk. His work integrates eye-tracking studies, neural networks, and graph-based models to improve code summarization and developer tooling. Research interests include source code summarization, developer behavior analysis, privacy behavior detection in Android apps, and tools for program comprehension. His lab, the Automatic Program Comprehension Lab (APCL), develops datasets and tools like CodeGRITS, available via Huggingface and GitHub. Publications span 2007–2025, emphasizing developer-centric AI models, code summarization techniques, and empirical studies on programmer attention. Key themes include context-aware models, eye-tracking integration, and LLM applications in software engineering. Awards: ASEE Teaching Award (2020), NSF CAREER Award (2015), Distinguished Paper Award (2021). Grants: NSF funding for AI/SE integration, totaling over $2.5M since 2015. Service: Associate Editor for TSE and EMSE, program committee roles at ICSE, FSE, and ASE. Current students include Maria Dhakal (2023), Robert Wallace (2023), and Chia-Yi Su (2022). Graduates have moved to faculty positions (e.g., Clemson University) and industry roles at Google, Qualtrics, and Ascend Health.

Michael John DeckerView profile

Assistant Professor

• Software Engineering
• Source Code Analysis
• Program Comprehension
• +3 more

Michael John Decker is an Assistant Professor at Bowling Green State University specializing in software engineering research and education. He teaches courses including Software Architecture & Design, CS Capstone, and Advanced Software Engineering from his office in Hayes 242, with research deeply integrated into source code analysis and software evolution infrastructure development. His educational background includes a Ph.D. from Kent State University (2017) and a Master's degree from The University of Akron (2012), both in Computer Science. Decker's research centers on software engineering with emphasis on source code analysis, program comprehension, and software maintenance/evolution. He actively develops the srcML infrastructure for syntactic representation of source code and explores natural language processing applications for identifier analysis and documentation generation. His work bridges empirical studies of developer practices with tool development to enhance code understandability and maintenance efficiency. Analysis of his recent publications reveals consistent focus on syntactic differencing techniques, identifier semantics, and automated documentation systems. The research combines empirical validation with practical tooling, demonstrating strong trends in leveraging domain knowledge for code change comprehension and natural language integration in software artifacts. His scientific recognition includes: Most Influential Paper Award at SCAM'21 (2021) for 'Lightweight Transformation and Fact Extraction with the srcML Toolkit' Best Challenge Entry Award at DysDoc3 for 'Automatically Redocumenting Source Code' $750K NSF Grant for Syntactic Differencing Infrastructure development Decker actively secures research funding including the significant NSF grant and serves in leadership roles at major conferences (ICPC'23 Tool Track Co-Chair, ICPC'23 Session Chair). While specific student advisees aren't listed, his research involves extensive collaboration through labs and conference committees. He is a core researcher in the Software DeveloMent Laboratory (SDML) advancing srcML infrastructure and the Source Code Analysis and Natural Language Lab (SCANL) focusing on NLP applications for source code. Current projects include syntactic differencing enhancements and identifier analysis systems for improved software evolution support.

• Artificial Intelligence
• Computational Linguistics
• Natural Language Processing
• +7 more

Sandra Carberry is a Professor in the Department of Computer and Information Sciences at the University of Delaware with a joint appointment in Linguistics. Her research integrates artificial intelligence, natural language processing, and user modeling to develop intelligent systems for digital libraries, medical decision support, and educational applications. She leads the NLP/AI laboratory and teaches core AI and data mining courses. Her primary research focuses include: Intelligent Interfaces : Developing systems that recognize user goals and generate tailored responses through advanced user modeling Digital Libraries : Pioneering methods to extract meaning from information graphics using perceptual effort analysis and Bayesian reasoning Medical Informatics : Creating TraumaCASE for generating clinical training scenarios and improving trauma care decision support Educational Systems : Building adaptive medical tutoring systems that dynamically adjust case complexity Dialogue Systems : Advancing transformation-based learning for dialogue act recognition and cooperative response generation Her publication trend (2002-2007) reveals a strategic shift toward information graphics understanding, where she established that 61% of graphics convey unique information not present in text. This work combines cognitive psychology with machine learning to recognize graphic intentions through perceptual effort metrics and communicative signals, significantly advancing digital library capabilities. Key recognitions include: James Chen 2006 Award for Best Paper (UMUAI Journal) Best Paper Award at Diagrams Conference (2006) Springer Best Paper Award at User Modeling Conference (2007) Funded by NSF grant IIS-0534948, her digital libraries project demonstrates strong grant acquisition capability. Her extensive co-authorship with junior researchers indicates active mentorship, though formal student lists aren't published. University service includes departmental committees and professional activities documented in her vita. She directs the NLP/AI laboratory, which maintains active research in natural language processing, artificial intelligence applications, and multimodal information systems, with current work extending graphic understanding to complex visual representations.

• Machine Learning
• Graph Analysis
• Natural Language Processing
• +7 more

Michalis Vazirgiannis is a Professor at the Department of Computer Science, École Polytechnique, France, leading the Data Science and Mining (DaSciM) group. He holds degrees in Physics (Athens University), Informatics (PhD, Athens University), and AI (Heriot-Watt University). His research focuses on machine learning for graphs, NLP, combinatorial optimization, and applications in fraud detection, biomedical tasks, and social networks. He has supervised 29 PhD theses and published over 250 papers. Awards include the ERCIM Fellowship, Marie Curie EU Fellowship, and Tencent's Rhino-Bird Award. Educations Bachelor's in Physics, Athens University Master's in AI, Heriot-Watt University PhD in Informatics, Athens University Research Interests His work spans graph neural networks (GNNs), community detection, text mining, and biomedical applications. He emphasizes explainability in GNNs and has developed models like RW-GNN. Current projects include the ANR-HELAS Chair on heterogeneous data and the X/AXA Data Science Chair. Industrial collaborations include Airbus, Google, and Tencent. Grants & Chairs ANR-HELAS Chair (2020–25): Deep learning for graphs and NLP AXA Data Science Chair (2015–18) ANR-XCOVIF (2020–21): Social impact analysis of COVID-19 via social media Labs & Teams The DaSciM group has 15 completed PhD theses and collaborates with industry on projects like predictive maintenance and recommendation systems. Key projects include graph generative models for biomedical data and privacy-preserving synthetic medical records.

• Natural Language Processing
• Biomedical Informatics
• Health Communication
• +3 more

Yue Guo is an Assistant Professor in the Department of Information Sciences at the University of Illinois. Her research focuses on natural language processing (NLP), biomedical informatics, and healthcare communication. She explores how large language models (LLMs) can improve lay language summarization of complex scientific content, particularly in healthcare contexts. Her work emphasizes personalized jargon detection, factuality evaluation of summaries, and accessibility of health information for diverse audiences. Guo has also conducted extensive studies on radiation oncology outcomes, including xerostomia (dry mouth) recovery in head and neck cancer patients and the impact of radiation dose patterns on patient-reported symptoms. Her interdisciplinary research bridges computational methods with clinical needs, aiming to enhance patient understanding and care through advanced NLP techniques. Her recent articles (2021–2025) highlight trends in LLM-driven biomedical communication, evaluation of health summaries, and data-driven approaches to cancer treatment optimization. Notable projects include APPLS (a summarization metric framework) and personalized jargon identification systems for interdisciplinary collaboration. Guo teaches IS 504: Sociotechnical Information Systems , integrating technical and human factors in information systems design.

• Cognitive Psychology
• Human Factors
• Ergonomics
• +7 more

Dr. Kyung Hun Jung is a faculty member at Kennesaw State University, affiliated with the Department of Psychology. He teaches courses including Cognitive Psychology, Engineering Psychology, Experimental Design, and Research Methods and Statistics. Ph.D. in Experimental Psychology (University of New Mexico, 2013) M.S. in Experimental Psychology (Korea University, 2006) B.A. in Psychology (Korea University, 2004) His research spans cognitive psychology, human factors, and ergonomics, with recent work focusing on automated vehicle interaction and driver training using moving-base driving simulators and virtual-reality headsets. Earlier research includes studies on attentional mechanisms, facial attractiveness, color perception, and document similarity analysis. Recent publications highlight applications of human factors in transportation technology and usability engineering. Data and code from his studies are publicly shared for replication purposes.

Atefeh FarzindarView profile

Part-Time Lecturer

• Natural Language Processing
• Health Informatics
• Artificial Intelligence
• +4 more

Atefeh Farzindar is a Part-Time Lecturer in the Department of Computer Science at the University of Southern California (USC), affiliated with the Viterbi School of Engineering. She holds a PhD in Computer Science from the University of Montreal and a Doctorate in automatic summarization of legal documents from Paris-Sorbonne University. Her academic roles include Adjunct Professorships at the University of Montreal and Polytechnique Montréal, alongside leadership positions in organizations like the Canadian Artificial Intelligence Association and the Natural Sciences and Engineering Research Council of Canada (NSERC). Her research focuses on Natural Language Processing (NLP), Health Informatics, Machine Learning, and their applications in healthcare and social media analysis. She has authored/co-authored influential books including Natural Language Processing for Social Media and Transforming Healthcare with Big Data and AI . Her work bridges technology and art, reflected in her painting exhibitions and a published collection of science fiction short stories, Gazelle in New York . Farzindar actively engages in industry-academia collaboration, serving as CEO and co-founder of NLP Technologies Inc., and leading non-profit initiatives like "Dreams Without Borders" to promote ethical AI development. She has received awards such as the Femmessor-Montréal contest for balancing innovation in technology and art.

• Machine Learning
• Explainable AI
• Fairness in AI
• +3 more

Carlos Guestrin is the Fortinet Founders Professor of Computer Science at Stanford University, Director of the Stanford AI Lab (SAIL), and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI). He also serves as Chief Scientist at Visual Layer and Virtue AI, and is a Member of the National Academy of Engineering. His research centers on Machine Learning Methods, Explainability, Fairness & Ethics of AI, and Machine Learning Systems. He develops interpretable and reliable models, addresses algorithmic fairness, and builds efficient large-scale ML systems through frameworks like XGBoost. His work bridges theoretical rigor with real-world applications in healthcare and human-centered AI. His recent publications (2023–2025) demonstrate leadership in generative AI evaluation, model reliability, and ethical frameworks. Key trends include developing live benchmarks for research synthesis, on-device calibration techniques, multi-objective optimization with constraints, and societal impact assessment tools—showcasing a trajectory from foundational ML systems to responsible AI deployment. Honors include: Member of the National Academy of Engineering Details about his advising and grant activities were not provided in source materials, though his leadership roles indicate extensive mentorship and funding oversight. As Director of SAIL, he shapes one of the world’s premier AI research centers, while his HAI fellowship drives interdisciplinary initiatives ensuring AI advances human welfare. His industry roles at Visual Layer and Virtue AI translate academic research into practical AI solutions.

• Natural Language Processing
• Machine Learning
• Text Mining
• +5 more

Manuel Javier Palomar Sanz is a Professor of Languages and Computer Systems at the University of Alicante's Higher Polytechnic School, where he has held faculty positions since 1991. He served as Rector of the University (2012-2020), Vice-Rector for Research (2005-2011), and Director of his department (2000-2004), while maintaining active research through the Institute of Computer Research and Language Processing and Information Systems (GPLSI) group. He earned a PhD in Computer Science (1996) and Bachelor's degree in Computer Science (1989) from the Polytechnic University of Valencia. His research centers on Natural Language Processing , with expertise in information retrieval, automatic summarization, text mining, and machine learning applied to healthcare, tourism, and legal domains. As promoter of the Digital Intelligence Center (CENID), he advocates integrating technology with ethics, education, and sustainability for societal transformation. Palomar Sanz held leadership roles including President of the Spanish Society for Natural Language Processing (SEPLN; 1996-2006) and Board Member of the Confederation of Scientific Societies of Spain (COSCE; 2004-2009), demonstrating sustained impact in academic governance and professional communities.

David CorsarView profile

Senior Lecturer

• Knowledge Representation
• Knowledge Reasoning
• Semantic Web
• +11 more

Dr. David Corsar is a Principal Lecturer in Informatics at Robert Gordon University's School of Computing, Engineering and Technology. He is an active researcher in Artificial Intelligence with a focus on knowledge representation, reasoning methods, and explainable AI systems. His academic journey began with a Bachelor's Degree in Computing Science (Artificial Intelligence) from the University of Aberdeen (2000-2004), followed by a PhD in Computing Science (2004-2009) where he researched knowledge reuse through ontology mapping and ontology based knowledge acquisition. After his doctoral studies, he worked as a Research Fellow at the University of Aberdeen in interdisciplinary teams before joining RGU. Dr. Corsar's research interests span across multiple AI domains: Knowledge Representation and Reasoning Semantic Web and Knowledge Graphs Provenance in AI systems Explainable Artificial Intelligence (XAI) Case-Based Reasoning Integration of machine learning with symbolic AI Transparency and trustworthiness of AI systems His recent publication record (2023-2025) shows a strong emphasis on developing practical XAI solutions, particularly through the iSee platform which addresses the challenge of personalized explanations for diverse AI users. His work bridges theoretical AI research with practical applications in healthcare, regulatory compliance, and industrial domains like Oil and Gas decommissioning. Dr. Corsar has received research funding for multiple projects: iSee: Intelligent Sharing of Explanation Experience (2021-2024) Prophecy Project: Intelligent Decommissioning Materials Management (2019-2020) Intelligent System for assisted medical diagnosis for CARDIOvascular diseases (2020-2021) Building resilience in Health and Social Care students during Covid-19 (2020) As an educator, Dr. Corsar coordinates several technical modules including Native Android App Development, Internet of Things, Data Structures and Algorithms, and Knowledge Modelling and Reasoning. He is actively involved in supervising PhD students working on cutting-edge AI research topics and welcomes Honours and Masters projects aligned with his research areas. Dr. Corsar is a member of the Artificial Intelligence & Reasoning Research Group at RGU, contributing to the university's research theme 'Living in a Digital World.' His work on the W3C Provenance Working Group has helped develop standards for provenance information interchange on the Web, demonstrating his commitment to advancing foundational aspects of AI systems.

• Case-Based Reasoning
• Deep Metric Learning
• Explainability
• +3 more

Dr. Kyle Martin is a Lecturer in the School of Computing, Engineering & Technology at Robert Gordon University (RGU), where he works in the Artificial Intelligence & Reasoning Research Group. He completed his PhD in 2021, after having been hired as a full-time lecturer and researcher in 2019. His academic focus centers on making AI systems more transparent and explainable, with applications across multiple sectors including digital health (with partners Jiva and Walk With Path), fintech (Sticklr), and horizon scanning (Citizen Advice Scotland). Dr. Martin's primary research interests include: Case-Based Reasoning Deep Metric Learning Explainability in AI systems Applied Machine Learning across various domains His recent publications (2023-2025) demonstrate a strong focus on explainable AI, particularly in developing case-based reasoning approaches to enhance the transparency of machine learning systems. He has published extensively on counterfactual explanations, legal question-answering systems, and automated essay grading, with 48 research outputs spanning multiple publication types. His work often bridges theoretical AI concepts with practical applications in real-world domains where understanding AI decision-making is critical. Dr. Martin is actively involved in research funding and supervision: Co-investigator on the iSee project (European consortium) focused on personalized explanation experiences Available for PhD supervision in Case-Based Reasoning, Deep Learning, Explainability, and Applied Machine Learning Program committee member for ICCBR and SGAI conferences Organizer of international workshops on Case-Based Reasoning and Deep Learning As a member of the Artificial Intelligence & Reasoning Research Group at RGU, Dr. Martin contributes to advancing reasoning capabilities in AI systems and developing practical applications of these technologies. His research has resulted in multiple publications in prestigious venues including ECAI, ICCBR, and Knowledge-Based Systems, demonstrating his growing impact in the AI research community.

Simone ScalabrinoView profile

Assistant Professor

• Software Quality
• Software Testing
• Software Security
• +2 more

Simone Scalabrino is an Assistant Professor at the University of Molise, Italy, where he is part of the STAKE lab. He also serves as CSO at Datasound. His academic career includes teaching courses such as Automated Software Delivery and Object-Oriented Programming at the University of Molise. Dr. Scalabrino received his Ph.D. from the University of Molise in 2019 with a thesis entitled "Automatically Assessing and Improving Code Readability and Understandability," supervised by Prof. Rocco Oliveto. He earned his Master's Degree in Computer Science from the University of Salerno in 2015 and his Bachelor's Degree in Computer Science from the University of Molise in 2013. His research interests focus on Software Quality, Software Testing, Software Security, and Empirical Software Engineering . Dr. Scalabrino's work spans multiple areas including code readability assessment, software testing methodologies, Docker container analysis, game quality assessment, and voice user interface testing. His research often combines empirical methods with machine learning techniques to address practical software engineering challenges. Analysis of his recent publications reveals a strong focus on improving software quality through various approaches. His work spans code readability assessment, Docker container analysis, game quality testing, and voice user interface evaluation. There's a clear trend toward applying machine learning techniques to traditional software engineering problems, particularly in the areas of code understanding, defect prediction, and quality assessment. His research often involves large-scale empirical studies with real-world data from open source projects and commercial applications. Scientific Awards Distinguished Reviewer Award @ FSE 2025 Distinguished Reviewer for TSE (2023) Best Reviewer Award for JSS (2022) ACM Distinguished Paper Award @ MSR 2019 ACM Distinguished Paper Award @ ASE 2017 ACM Distinguished Paper Award @ ICPC 2016 Dr. Scalabrino has been actively involved in mentoring students through research projects, though specific student names are not listed in the provided information. He has served on program committees for numerous prestigious conferences including ASE, ICSE, ICPC, and FSE. His service extends to reviewing for top-tier journals such as Transactions on Software Engineering and Empirical Software Engineering. He leads or contributes to several research projects including DevProDev, which focuses on developer-centered recommendation systems, and ATTICUS, a tele-monitoring system for ambient-assisted living. Dr. Scalabrino has also developed multiple research tools such as Code Readability Predictor, TIRESIAS, OCELOT, CLAP, and ACRyL to address various software engineering challenges.