Prof. Eirini Ntoutsi is a Professor of Open Source Intelligence at the CODE Research Institute for Cybersecurity and Smart Data , Bundeswehr University Munich . She leads the Artificial Intelligence & Machine Learning (AIML) research group , focusing on adaptive learning, responsible AI, and generative AI. Research Interests: Developing intelligent algorithms for real-world data challenges, addressing fairness-aware machine learning, explainable AI, and generative models. Projects: Co-leads the EU-funded MAMMOth (Multimodal AI for Trustworthy Human-Centric Applications) and STELAR (Spatio-Temporal Linked Data for Agri-food) initiatives. Applications: Deploying AI solutions in education, social networks, banking, agriculture, manufacturing, and engineering. Key Contributions: Developed the MMM-Fair open-source toolkit for fairness analysis with no-code interface. Actively contributes to conferences like ECML PKDD , FAccT , IJCNN , and WWW .
James Henderson is a Senior Researcher at Idiap Research Institute where he heads the Natural Language Understanding group. He currently serves as Action Editor for Transactions of the Association for Computational Linguistics (TACL) and was recently awarded an ERC Advanced Grant for his project 'Interpretable Beliefs and Programmable Knowledge with Bayesian Attention in Large Language Models' (BALM). Previously, Henderson held positions as Chargé de Cours at University of Geneva's Department of Computer Science and Principal Scientist at Xerox Research Centre Europe (now Naver Labs Europe). Henderson's research focuses on machine learning methods for natural language processing, with pioneering work on recurrent neural networks for syntactic and semantic parsing. His current investigations include representation learning for language semantics, graph-to-graph deep learning models, entity induction, and variational-Bayesian attention-based representation learning. His research bridges Bayesian inference, transformer architectures, and structured prediction for NLP tasks. His publication portfolio demonstrates consistent contributions to core NLP methodologies, with recent emphasis on transformer optimization, Bayesian neural methods, efficient model architectures, and graph-based language representations. Research frequently appears in top venues including ACL, EMNLP, ICLR, and NeurIPS. Honors: ERC Advanced Grant (2023) Henderson leads the Natural Language Understanding group at Idiap, currently recruiting PhD students and postdoctoral researchers for his ERC project. He obtained his PhD and MSc from University of Pennsylvania and BSc from Massachusetts Institute of Technology, all in computer science.
Gianluca Demartini is a prominent researcher in human-in-the-loop AI systems, crowdsourcing, and information retrieval. His work spans interdisciplinary collaborations with institutions across Australia, Europe, and Asia, focusing on enhancing media literacy, managing data bias, and improving human-AI collaboration frameworks. Key affiliations include University of Queensland, University of Padua, and University of Basel Research areas: Crowdsourcing, Large Language Model applications, Misinformation detection Research Interests : Bias Management : Developing tools for identifying and mitigating biases in AI systems Misinformation Mitigation : Human-AI strategies for truthfulness assessment Collaborative Knowledge Systems : Hybrid human-machine approaches to data quality LLM Applications : Personas, synthetic data generation, and ethical considerations Publication Trends show a focus on AI ethics, human-AI collaboration, and social media analysis, with recent work exploring LLMs' role in content moderation and misinformation detection. Scientific Awards : 2023 ICTIR Best Paper Award 2020 ISWC Best Demo Award 2013 ISWC Best Paper Nominee 2011 ISWC Best Demo Award 2020 CSCW Honorable Mention Advising : Mentoring junior researchers across multiple institutions in areas like data bias, crowdsourcing, and AI ethics.
Dr. Jie Du serves as Chair and Associate Professor in the Department of Information Sciences and Technologies within the College of Computing at Grand Valley State University. As the founding chair of her department, she plays a pivotal leadership role in shaping academic programs and research directions. Her research spans critical intersections of artificial intelligence, information security, and decision support systems. Key focus areas include cybersecurity behavior modeling, electronic health record adoption, vehicular network security, and AI-driven financial systems. Her work consistently bridges theoretical frameworks like the Health Belief Model with practical technological implementations. Dr. Du has received significant recognition including the Faculty Excellence Award and Professor of Well-Being Award. Her scholarly contributions appear in journals such as the Journal of Cybersecurity and Journal of Information Systems Applied Research. Faculty Excellence Award Professor of Well-Being Award She actively mentors student research on impactful topics including EHR adoption, intelligent roadside units in vehicular networks, and AI stockbroker systems. Her teaching encompasses core information systems curriculum with emphasis on experiential learning. Dr. Du maintains active engagement with the Association of Information Systems through conference participation and journal manuscript reviews.
Robert Krauthgamer is the Harry Weinrebe Professor of Computer Science and currently serves as Department Head in the Department of Computer Science & Applied Mathematics at the Weizmann Institute of Science , within the Faculty of Mathematics and Computer Science . He is a leading researcher in theoretical computer science, particularly in the analysis of algorithms. Research Interests: His research focuses on Analysis of Algorithms , with deep expertise in Data Analysis and Massive Data Sets , Combinatorial Optimization , Approximation Algorithms , Hardness of Approximation , Embeddings of Finite Metrics , and Routing and Peer to Peer Networks . He also maintains a broad interest in Discrete Mathematics and High-Dimensional Geometry . His recent publications highlight work in graph algorithms, parameterized complexity, streaming algorithms, and metric embeddings. Publication Trends: His most recent work, including papers from SODA 2016, demonstrates a strong trend in the design and analysis of efficient algorithms for fundamental problems in graph theory, optimization, and data streams. Key themes include kernelization and sampling techniques for dynamic graph streams, subexponential parameterized algorithms, deterministic derandomization of the polynomial method, and structural results for graph modification problems. His research often bridges theoretical insights with applications in computational biology and network science. Service and Recognition: Journal Editorial: Editor-in-Chief of SIAM Journal on Computing (2019–2025), Associate Editor (2012–2017); Managing Editor of Theory of Computing (2007–2018), and current Editorial Board Member. Conference Leadership: Program Committee Chair for SODA 2016 and HALG 2018; Steering Committee member for SODA, ESA, and HALG; and committee member for the Gödel Prize (2019–2021). Workshops: Organizer of numerous workshops on sublinear algorithms, fine-grained complexity, and high-dimensional data. Teaching and Mentorship: He regularly teaches advanced courses such as Randomized Algorithms and Sublinear Time and Space Algorithms . He advises a large group of MSc and PhD students and hosts postdoctoral researchers, demonstrating a strong commitment to training the next generation of computer scientists. His former students have gone on to successful academic and research careers. Laboratories and Research Groups: He is a key member of the Foundations of Computer Science (theory) seminar at Weizmann and has organized the TheoryLunch and Reading Group in Algorithms, fostering a vibrant research community within the department.
Pavel Veselý is an Assistant Professor at the Computer Science Institute of the Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic. His research focuses on the design and analysis of efficient algorithms and data structures, particularly in streaming, online, and approximation algorithms, with applications in bioinformatics and data privacy. His research interests include: Streaming algorithms for quantile estimation, geometric problems, and adversarial robustness Online algorithms, especially in scheduling and buffer management Approximation algorithms, including shortest superstrings and their use in genomic data (k-mer sets) Randomized algorithms and algorithmic data privacy His recent work has advanced the state of the art in streaming quantile estimation (KLL sketch), indexed k-mer representations using masked superstrings, and streaming facility location in high dimensions. His publications appear in top venues such as FOCS, STOC, PODS, and SODA. Notable scientific awards include: Best Paper Award at PODS 2021 2022 ACM SIGMOD Research Highlight Award Supervision of award-winning student theses, including Dean’s Award and Czech-Slovak competition wins He actively advises students, including PhD and master’s level researchers, and leads a research group focused on data sketches and streaming algorithms. He has taught courses such as Algorithmic Data Privacy, Randomized Algorithms, and Streaming Algorithms, and previously served as a postdoc at the University of Warwick under Graham Cormode.
Peter Nelson is a Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC), where he conducts research through the Artificial Intelligence Laboratory. His work spans multiple domains including Artificial Intelligence , Intelligent Transportation Systems , Manufacturing Optimization , Bioinformatics , and High-Availability Computer Clustering . He has led major ITS projects like the ADVANCE initiative (1991–1995) and developed real-time traffic visualization systems with over 300 million annual hits. Education: Ph.D. and M.S. in Computer Science from Northwestern University (1988, 1986); B.A. in Computer Science and Mathematics from North Park College (1984). Research: Pioneered perimeter search algorithms and applied AI to transportation, manufacturing, bioinformatics, and clustering. His manufacturing work with Motorola optimized chip shooter systems globally using genetic algorithms and tabu search. Grants: NSF IGERT co-PI ($3.2M), Sun Microsystems, National Research Council, and multiple FHWA/IDOT collaborations. Awards: Silver Circle Teaching Award Finalist (1990), UIC College of Engineering Research Award (1999), and FHWA National Award for Traveler Information Web Sites (2003).
Abraham J. Wyner is a tenured Professor of Statistics and Data Science at the Wharton School of Business, University of Pennsylvania, where he serves as Chair of the Undergraduate Program in Statistics and Data Science and Faculty Co-Director of the Wharton Sports Analytics and Business Initiative. With over 11 years at Wharton, he is a leading expert in probability models and statistical methodology. His educational background includes: Bachelors in Mathematics from Yale University (Magna Cum Laude, Stanley Prize for excellence in Mathematics) PhD in Statistics from Stanford University (NSF Graduate Fellowship, Abrams Prize, Herz Foundation fellowship) Professor Wyner's research spans baseball analytics, boosting, data compression, entropy, information theory, probabilistic modeling, and temperature reconstructions, with primary focus on Applied Probability, Information Theory, and Statistical Learning. His work bridges theoretical statistics with real-world applications across finance, sports, and health sciences. His publication trends reveal dual expertise: sports analytics (baseball strategy, pitch framing, bracket pools) and machine learning (neural networks, random forests, AdaBoost). Recent work integrates statistical learning with time series dependence for applications like sleep scoring in mice, demonstrating cross-disciplinary impact in neuroscience and sports. Key honors include: Stanley Prize for excellence in Mathematics (Yale) National Science Foundation Graduate Fellowship (Stanford) Abrams Prize and Herz Foundation fellowship (Stanford) NSF post-graduate fellowship Mathematical Sciences Post-Doctoral Fellow, NSF (1995-1998) Professor Wyner has secured research funding from NSF, NIH, and private industry. His consulting portfolio spans: TiVo, Inc. (early personalization software development) Surfnotes, Inc. (CTO for online data summarization tools) Banks, marketing firms, and law practices in Philadelphia, NY, and DC ESPN-funded MLB player evaluation research (Principal Investigator) Hedge funds and private equity statistical analysis As Faculty Co-Director of the Wharton Sports Analytics Initiative, he leads collaborations between academia and sports industry partners including ESPN, fostering interdisciplinary teams for projects in baseball analytics, sleep research, and machine learning applications.
Monica Agrawal is an Assistant Professor at Duke University with joint appointments in the Division of Translational Biomedical (Biostatistics & Bioinformatics), Trinity College of Arts & Sciences (Computer Science), and Pratt School of Engineering (Biomedical Engineering). Holding a Ph.D. from MIT (2023), her work bridges machine learning, clinical data analysis, and health equity through biomedical AI systems. Research Focus: Combines natural language processing, graph networks, and EHR analysis to address medical challenges. Key areas include polypharmacy side effects, health knowledge graphs, and human-AI collaboration in clinical settings. Scientific Contributions: Pioneering applications of large language models in health equity promotion, clinical information extraction, and EHR-based research. Collaborates with Harvard Medical School and Harvard School of Public Health on translational health projects. Teaching: Instructs courses on natural language processing (COMPSCI 572) and research independent study (COMPSCI 393/394), emphasizing hands-on AI development for healthcare. Recent Publications: Explore medical conversational AI, ambient scribing tools, and LLM safety in clinical communication. Her 2025 paper on health equity highlights AI's potential to reduce disparities.
Amy Pavel is an Assistant Professor in the Department of Computer Science at the University of Texas at Austin. Prior to this role, she was a Postdoctoral Fellow at Carnegie Mellon University and a Research Scientist at Apple. Her research bridges Human-Computer Interaction and Accessibility, focusing on AI-driven systems for efficient and inclusive communication. Education: PhD in Computer Science from UC Berkeley (2019), advised by Björn Hartmann and Maneesh Agrawala. Teaching: Regularly teaches Human-Computer Interaction courses at UT Austin and UC Berkeley. Her work addresses accessibility challenges through systems like Rescribe (audio descriptions), CrossA11y (video accessibility), and GenAssist (image generation). Recent projects explore AI applications for low-vision learners, photosensitivity warnings in VR, and collaborative video editing. Award highlights include 2023 UIST Best Paper 2022 UIST Best Paper 2020 CHI Honorable Mention (twice) 2018 UC Berkeley EECS Outstanding Graduate Student Instructor She advises PhD students like Mina Huh and Yi-Hao Peng, as well as undergraduates and masters students. Her lab collaborates with institutions including Google, Carnegie Mellon, and UC Berkeley.
Kathleen McKeown is the Henry and Gertrude Rothschild Professor of Computer Science at Columbia University , where she has been a faculty member since 1982. She served as Founding Director of the Data Science Institute (2012–2017), Department Chair (1998–2003), and Vice Dean for Research (2003–2005) at the School of Engineering and Applied Science . Education : PhD in Computer Science and Information Science (1982) and MS in Computer and Information Science (1979) from the University of Pennsylvania , AB in Comparative Literature (1976) from Brown University Her research focuses on Natural Language Processing , Text Summarization , and Natural Language Generation , with applications in Social Media Analysis , Disaster Response , and Energy Behavior Messaging . She has pioneered neural methods for summarization, sentiment analysis in low-resource languages, and multi-media explanations. Her work includes 15 recent publications (2011–2013) on topics like discourse relation disambiguation , MT error correction , influencer detection , and hierarchical web summarization , reflecting trends in deep learning , cross-lingual NLP , and social media analytics . Honors & Awards : IEEE Innovations in Societal Infrastructure Award (2023) AAAI Fellow (1994) ACM Fellow (2003) Founding ACL Fellow (2012) Anita Borg Women of Vision Award (2010) NSF Presidential Young Investigator (1985) She leads the NLP Group at Columbia, focusing on real-time disaster updates , behavioral NLP , and sentiment analysis for marginalized communities. Her career spans leadership roles , grant-funded research , and foundational contributions to text generation and summarization .
Yinong Chen serves as a Teaching Professor at Arizona State University's School of Computing and Augmented Intelligence within the Ira A. Fulton Schools of Engineering. With over two decades of academic experience since joining ASU in 2001, he maintains active roles in research, teaching, and professional service. His institutional affiliations include continuous editorial board memberships with Simulation Modeling Practice and Theory (Elsevier), International Journal of Simulation and Process Modeling, and Journal of Systems and Software. Dr. Chen's educational background includes a Ph.D. in Computer Science from the University of Karlsruhe/Karlsruhe Institute of Technology (1993), an M.S. from Chongqing University (1984), and a B.S. in Software Engineering from the same institution (1982). Prior to ASU, he served as Lecturer and Senior Lecturer at the University of the Witwatersrand (1994-2000) and completed postdoctoral research at LAAS-CNRS in France. His research spans service-oriented computing, visual programming environments, and computer science education innovation. Chen has developed the VIPLE (Visual IoT/Robotics Programming Language Environment) framework, which integrates robotics, IoT, and AI education through visual programming. Current work focuses on GPU-accelerated cloud services, AI-enhanced educational tools, and smart city applications. His recent publications reveal a strong emphasis on applying service-oriented principles to emerging domains like quantum machine learning education and assistive technologies. Professional service includes editorial responsibilities for four major journals and continuous committee involvement at ASU since 2004, particularly in freshman mentorship and computing resource management. His grant history demonstrates sustained funding from Intel for robotics education initiatives and multiple NSF-related projects focused on service-oriented computing pedagogy. Arizona Robotics Challenge Initiative (Intel, 2007-2010) Visual Programming for IoT in Engineering Education (Intel, 2012-2013) Preparing High School Teachers for Service-Oriented CS Education (NSF, 2007-2011) Chen directs the VIPLE research group, which develops visual programming tools for robotics and IoT education. Current projects include the ASU Instructor Assistant GPT and multi-modal systems for blind obstacle detection. His work bridges theoretical service-oriented computing with practical educational applications, maintaining strong industry partnerships while advancing computer science pedagogy.
Oren Tsur is an Assistant Professor in the Department of Software & Information Systems Engineering (SISE) at Ben Gurion University of the Negev, where he heads the NLP and Social Dynamics Lab (NAS-LAB) and directs the Center for the Study of Digital Politics and Strategy (DPS@BGU). He joined the university in September 2017 and teaches core courses including Natural Language Processing, Social Network Analysis, and Introduction to NLP at both undergraduate and graduate levels. His research focuses on computational modeling of social dynamics through language and network analysis. Key interests include social contagion, language evolution, political network analysis, and sentiment analysis. He employs methodologies from exponential random graph models (ERGM), machine learning, and natural language processing to study how language shapes and is shaped by social interactions, particularly in political contexts and online communities. His publication trends reveal strong interdisciplinary work spanning computational linguistics, political science, and social network analysis. Recent research emphasizes hate speech detection in platforms like Parler, suicide risk identification in counseling services, and modeling semantic drift in emoji usage. His work consistently bridges technical NLP innovations with real-world social applications, particularly in political discourse and community dynamics. NSF Political Network Fellowship (2014, 2015, 2016) TIME Magazine's 50 Best Inventions of 2010 for sarcasm detection research Work featured in Science Magazine, BBC, The Atlantic, CNET, and Politico Best Paper award at HICSS 2020 Tsur actively mentors prospective students through his lab, emphasizing excellence in candidates through academic transcripts and research interest statements. His grants include multiple NSF Political Network Fellowships supporting computational social science research. He serves as Director of BGU's Cyber Politics and Policy Research Center and organizes major NLP workshops including the Natural Language Processing and Computational Social Science series at top conferences. His NAS-LAB focuses on data-driven modeling of social coordination and influence, with applications in sentiment analysis, dialogue systems, and digital forensics. The lab maintains strong industry connections through past collaborations with IBM Research (Watson Debater project), Yahoo! Research, and startup consulting.
Luis Rodríguez Benitez is an Associate Professor at the University of Castilla-La Mancha, affiliated with the Department of Information Technologies and Systems. His academic work bridges theoretical computer science and applied domains, with sustained contributions to computational intelligence methodologies. His research integrates: Fuzzy systems for linguistic data description Machine learning in transportation and environmental contexts Time-series analysis using evolutionary and prototyping approaches Software development for scientific computing (e.g., EEG analysis tools) Recent publications (2014–2025) demonstrate: Advancements in kernel methods for discrete choice modeling Novel applications of fuzzy logic in sustainability science Efficiency-focused algorithms for large datasets Interdisciplinary work spanning neurocomputing, transportation, and IoT
Cristina Sirangelo is a Professor of Computer Science at Université Paris Cité, affiliated with the Institut de Recherche en Informatique Fondamentale (IRIF) and on INRIA delegation at ENS Paris within the VALDA team. Her work bridges database theory, logic, and automata theory, focusing on query answering in incomplete data environments and XML processing. Ph.D., University of Calabria (2005) Habilitation à diriger des recherches, ENS de Cachan (2014) Her research explores computational complexity in consistent query answering, Datalog and first-order logic for XML querying, and automata-based validation. Recent projects emphasize dichotomy theorems for query complexity and algorithmic solutions for database constraints under primary keys. She has been recognized with the ICDT 2013 Test of Time Award for foundational work on XML with incomplete information. Her publications span top-tier venues like PODS, ICDT, JACM, and LMCS, with a focus on logical frameworks and efficient data processing. ICDT 2013 Test of Time Award : For pioneering work on XML with incomplete data. As a teaching leader, she co-organizes the master DATA program and the MIDS double master in data science. Her collaborative efforts include co-authoring key papers on XML reasoning, data exchange, and histogram-based data summarization.
