Find Academic Supervisors

• Educational Technology
• Informatics Education
• Programming Pedagogy
• +3 more

Snježana Babić serves as Associate Professor and Head of the Graduate Study Program Informatics - Teaching Major at the Faculty of Informatics in Pula , University of Pula. With a PhD obtained in 2016 and university affiliation since 2017, she teaches across undergraduate and graduate programs focusing on ICT fundamentals, web design, digital competencies, and informatics education methodology. Her educational background includes graduation in 1993 followed by doctoral studies culminating in 2016. Though specific institutions aren't detailed in the text, her career trajectory shows progression from foundational ICT teaching to specialized informatics education leadership. Babić's research centers on educational technology adoption with particular emphasis on programming pedagogy for young learners through the MEMA method. Her work investigates digital competencies across multiple educational contexts, from primary schools to higher education. Recent publications demonstrate rapid adaptation to emerging technologies, with 2025 studies examining ChatGPT integration in Croatian educational settings and cross-cultural applications of programming methods. Analysis of her 15 most recent publications reveals consistent focus on technology acceptance models applied to Croatian educators and students. The research trajectory shows evolution from foundational e-learning systems (2016-2020) to cutting-edge AI and robotics applications (2022-2025), maintaining strong methodological focus on teacher motivation , implementation barriers , and culturally-specific adoption factors . Her teaching portfolio demonstrates deep integration with research interests: Undergraduate: ICT Fundamentals, Web Design and Application Graduate: Digital Competencies in Education, Informatics Education Methodology, Practicum for IT Educators Cross-disciplinary: ICT in Nursing, Healthcare Informatics Babić actively contributes to institutional leadership as program head while maintaining robust research productivity. Her work bridges theoretical technology adoption frameworks with practical implementation challenges in Croatian educational contexts, particularly examining how emerging technologies like humanoid robots and AI assistants can be effectively integrated into teaching practices.

David StillwellView profile

Professor

• Psychometrics
• Computational Social Science
• Big Data Analytics
• +3 more

David Stillwell serves as Professor of Computational Social Science at Cambridge Judge Business School and Academic Director of The Psychometrics Centre, University of Cambridge. His research leverages big data to understand human psychology and behavior, with significant contributions in personality prediction from digital footprints and personalized advertising applications. His educational background includes: BSc in Psychology from the University of Nottingham (2007) MSc in Research Methods from the University of Nottingham (2008) PhD in Decision Making from the University of Nottingham (2012) Dr. Stillwell's research centers on computational social science and psychometrics, pioneering the myPersonality Facebook application that collected data from over 6 million users. His work demonstrates computers can predict personality as accurately as spouses, reveals psychological targeting's advertising effectiveness, and establishes links between personality-matched spending and life satisfaction. He also explores linguistic honesty markers through profanity analysis and personality-based dating patterns. His recent publications (2019-2025) reveal a strong trajectory toward AI evaluation using psychometric frameworks, particularly in medical and general-purpose AI assessment. Key themes include fairness metrics in language models, crisis emotional responses through social media, and computational personality recognition - demonstrating consistent innovation at the psychology-technology intersection. He has received significant recognition: Top 10 most influential papers of 2013 by Altmetric Nieman Journalism Lab highlight of 2013 Named in 'top 30 thinkers under 30' by Pacific Standard Magazine Dr. Stillwell maintains active industry engagement through consultancy with Barclays, Hilton, and Ubisoft on projects spanning computer-adaptive testing systems to interactive experiences like Predictive World for Watch Dogs 2. His policy impact is substantial, with citations by the European Data Protection Supervisor, World Bank, and multiple national governments, leading to speaking engagements at the European Parliament and Bank of England. As Academic Director of The Psychometrics Centre, he leads a global hub for advancing psychological assessment through innovative methods including Concerto open-source software, collaborating with organizations ranging from the European Commission to major corporations on psychometric applications in people analytics and digital behavior.

• Natural Language Processing
• Large Language Models
• Digital Libraries
• +7 more

Min Yen Kan is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore's School of Computing, Department of Computer Science. With a PhD from Columbia University (2002), he leads the Web Information Retrieval / Natural Language Processing Group (WING.NUS) and serves as ACL Ethics Committee co-chair. His research spans Natural Language Processing , Large Language Models , Digital Libraries , and Information Retrieval , with specific focus on scientific discourse analysis, fact verification, and multimodal systems. Current projects include Scholarly Document Information Extraction (TRL 6), Task-Oriented Dialogue Systems (TRL 4), and Recommendation Systems (TRL 5). Recent publications reveal strong trends in LLM limitations (bias, hallucination, evaluation), conversational recommendation systems , and misinformation detection . His work consistently bridges theoretical NLP with real-world applications in digital libraries and scientific communication. Award highlights include: CIKM 2019 Best Paper Award ACL Distinguished Service Awards Vannevar Bush Best Paper Award (JCDL 2012) ACM Distinguished Speaker designation Kan mentors PhD students with placements at Google and USTC, and serves as associate editor for Information Retrieval and survey editor for Journal of AI Research . His lab WING.NUS develops practical tools like SciWING for scientific document processing and FANG for fake news detection. Media engagements include commentary on AI regulations in Southeast Asia and workforce implications in the AI era.

Eytan AdarView profile

Professor

• Human-Computer Interaction
• Artificial Intelligence
• Data Mining
• +7 more

Eytan Adar is a Professor of Information and Computer Science at the University of Michigan, holding dual appointments in the School of Information and the College of Engineering's Electrical Engineering and Computer Science department. His work sits at the intersection of human-computer interaction and artificial intelligence, focusing on large-scale systems analysis and novel interface design. Adar's research spans multiple domains including social media analysis (Twitter, Reddit), academic citation networks, meme propagation, information extraction, and political networks. His methodological expertise includes data mining, data visualization, and graph-based network analysis. His work often operates at internet scale, examining language, creativity, social network dynamics, and text production. His recent publications reveal a strong focus on AI-human collaboration, with particular attention to generative AI interfaces, visualization techniques for complex data, and ethical considerations in AI systems. His work shows a consistent pattern of bridging theoretical insights with practical system implementations. Best Paper Award at ProtoAI: Model-Informed Prototyping for AI-Powered Interfaces, IUI'21 Honorable Mention Award at CHI'24 for feminist interaction techniques research Best Paper Award at ICWSM'18 for Wikipedia language edition analysis Best Paper Award at ICML 2016 Workshop for neural language model visualization Best Student Paper at WSDM'09 for web dynamics research Best of CHI at CHI 2008 for web revisitation patterns analysis Adar has advised numerous PhD students who have gone on to prominent positions at organizations including Google, Apple, RAND, Northwestern University, and Stanford. His research is generously supported by the NSF, IARPA, NIH, the Education Department, and major technology companies including Adobe, Microsoft, Facebook, Google, and Yahoo. He is also a founder of the International Conference on Web and Social Media (ICWSM) and has served as Co-General Chair for WSDM and Co-Program Chair for UIST.

Kevin D. AshleyView profile

Professor

• Computational Social Science
• Artificial Intelligence
• Algorithms
• +7 more

Kevin D. Ashley is a Professor of Law at the University of Pittsburgh School of Law. He is also a senior scientist at the Learning Research and Development Center and an adjunct professor of computer science at the University of Pittsburgh. His interdisciplinary work bridges artificial intelligence, legal analytics, and ethical reasoning. JD, Harvard Law School M.A. and PhD, University of Massachusetts BA, Princeton University His research focuses on computational modeling of legal reasoning , AI and ethics , legal text analytics , and case-based reasoning . He has pioneered AI applications for legal decision support, automated argumentation, and bias detection in legal corpora. Recent publications include studies on large language models in legal annotation, argument mining, and empirical legal analysis. His work has been supported by multiple National Science Foundation grants, emphasizing fairness in AI and access to justice. 2022 Codex Prize for Computational Law 2015 University of Massachusetts Outstanding Achievement Award 2002 AAAI Fellow for AI in Law contributions 2000 Chancellor’s Distinguished Research Award A former President of the International Association for Artificial Intelligence and Law, Ashley has held visiting roles at the University of Bologna, Stanford CodeX, and IBM Watson Research Center. He co-edits the journal Artificial Intelligence and Law and teaches courses on applied legal analytics.

• Computational Social Science
• Network Science
• Data Visualization
• +7 more

Yu-Ru Lin is an Associate Professor at the School of Computing and Information, University of Pittsburgh, and serves as Research & Academic Director at the Institute for Cyber Law, Policy and Security (Pitt Cyber). She leads the Pitt Computational Social Dynamics Lab (PICSO Lab) and holds secondary appointments in Political Science, Computer Science, and the Intelligent Systems Program. PhD in Computer Science from Arizona State University Postdoctoral research at Harvard University and Northeastern University Her research focuses on computational approaches for: Networked social dynamics High-dimensional social information summarization Trust and distrust propagation Misinformation detection Policy diffusion analysis Recent publications span 2014-2020, covering: Social media crisis response Graph visualization techniques Policy diffusion patterns Misinformation mitigation Temporal topic modeling Scientific support includes: National Science Foundation (NSF) awards Minerva/ONR funding AFOSR grant for distrust modeling DARPA Understanding Group Biases program As director of PICSO Lab, she leads interdisciplinary research teams on: Digital accountability Urban mobility patterns Health informatics via crowdsourcing Trust-influence dynamics

Bimal ViswanathView profile

Associate Professor

• Cybersecurity
• Privacy Engineering
• Machine Learning
• +4 more

Bimal Viswanath is an Associate Professor in the Department of Computer Science at Virginia Tech's College of Engineering. His research focuses on cybersecurity, privacy engineering, and machine learning, particularly addressing adversarial AI and ML system vulnerabilities. He holds a Ph.D. from Saarland University and Max Planck Institute for Software Systems, and has held postdoctoral positions at UCSB and Nokia Bell Labs. Education Ph.D., Computer Science, Saarland University/Max Planck Institute (2016) M.S., Computer Science, IIT Madras (2008) B.Tech, Computer Science, Cochin University (2005) Research interests include: Adversarial attacks using/against ML systems Generative AI security (deepfakes, chatbots) Cybersecurity for online services Grants & Funding Recipient of CCI, NSF, and 4-VA grants supporting projects like robust AI defense mechanisms and adversarial image classification. Recent grants include $1.2M for AI security initiatives (2022-2025). Awards Distinguished Paper Award (SOUPS 2014) Best Paper Award (COSN 2015) AI2000 Most Influential Scholar Honorable Mention (2020) Advising Guided over 15 students including Jiameng Pu (PhD 2022), Connor Weeks (MS 2023), and current advisees Sifat Muhammad Abdullah (PhD 2021-). Notable student achievements include Facebook Fellowship nominations and industry placements at CVENT and University of Michigan. Labs & Teams Leading Virginia Tech's AI Security Lab focusing on generative models, adversarial ML, and applied cybersecurity solutions.

Sezgin AyabakanView profile

Associate Professor

• Healthcare Analytics
• Business Value of Information Systems
• Data Analytics Techniques
• +5 more

Dr. Sezgin Ayabakan is an Associate Professor in the Department of Management Information Systems at the Fox School of Business and Management, Temple University . He holds a PhD in Management Science from the University of Texas at Dallas, with a focus on Management Information Systems (MIS), an MS in Industrial and Systems Engineering from the University of Florida, and a BS in Industrial Engineering from Bilkent University in Turkey. His research focuses on healthcare analytics , particularly exploring how health information technology (HIT) impacts healthcare costs, quality, and utilization. Key research interests include data analytics applications in healthcare and social media, the business value of information systems, and open-source production strategies. He has examined topics such as telehealth efficacy, electronic health record (EHR) adoption challenges, hospital readmission reduction programs, and the interplay between healthcare IT and clinician well-being. Dr. Ayabakan teaches undergraduate and graduate courses in information systems, including MIS 2101: Digital Systems and SBM 3586: Diamond Peer Teachers - Internship II . His work has been published in top-tier journals like MIS Quarterly and Health Services Research and Management Epidemiology , with recent studies addressing telehealth utilization, EHR strategy impacts, and the role of organizational routines in clinician satisfaction. While no formal awards are listed, his contributions to healthcare IT research demonstrate significant academic impact. His advisory and grant activities are not detailed in the provided text, but his research often intersects with practical healthcare system challenges, suggesting potential collaborations with healthcare institutions and policy organizations.

Matt KingView profile

Associate Professor

• Medieval History
• Crusades Studies
• Digital Humanities
• +3 more

Matt King is an Associate Professor and Graduate Director in the Department of History at the University of South Florida (USF), within the College of Arts and Sciences. He specializes in medieval Mediterranean history, digital humanities, and the Crusades era, particularly focusing on Norman-Sicily and Zirid-Ifriqiya relations. King holds a Ph.D. from the University of Minnesota (2018) and teaches courses on medieval history, digital methods, and historical pedagogy. His research explores the interconnectedness of medieval Mediterranean polities, leveraging Arabic and Latin sources alongside digital tools like the Old World Drought Atlas. His first book, Dynasties Intertwined (2022), examines Zirid-Norman relations. He has co-authored translations of al-Idrisi’s geography and is currently researching the historical perception of goblins. Education: Ph.D., University of Minnesota (2018); M.A. (2014), B.A. (2012) in History and Latin. King’s teaching emphasizes active learning and digital humanities, including workshops for K-12 educators. He has received grants totaling over $40,000 for research and community projects. Awards include the Outstanding Graduate Instructor Award (UMN, 2018) and the Subvention Award (USF, 2022). His publications span peer-reviewed journals (e.g., Journal of North African Studies , The History Teacher ) and edited volumes, with a focus on medieval Mediterranean dynamics, digital pedagogy, and environmental history. King advises graduate students in medieval Mediterranean history, emphasizing research in Arabic, Latin, or Greek. He is involved in digital initiatives, including departmental websites and archaeological 3D modeling projects.

Srijan KumarView profile

Assistant Professor

• Data Science
• Applied Machine Learning
• Network Analysis
• +7 more

Srijan Kumar is an Assistant Professor in the School of Computational Science and Engineering at Georgia Institute of Technology's College of Computing. His research focuses on data science, AI for security, and online safety, addressing challenges in detecting malicious users, misinformation, and enhancing AI robustness. His work has been deployed in platforms like Flipkart and Wikipedia, and recognized through awards such as the NSF CAREER and Forbes 30 Under 30. Education: B.Tech from Indian Institute of Technology, Kharagpur Ph.D. in Computer Science from University of Maryland, College Park Postdoctoral training at Stanford University Research Interests: Multi-modal/multi-lingual detection of harmful content and users Adversarial robustness of AI models Graph and network analysis for early detection Responsible recommender systems Awards & Grants: NSF CAREER Award (2023) Kavli Fellow (2022) Facebook/Adobe Faculty Awards NSF Convergence Accelerator Phase II grant ($5M) Advising & Labs: Leads the CLAWS Lab, advising over 20 students. Active in mentoring through conferences and NSF-funded projects.

• Computer Vision
• Computer Graphics
• Machine Learning
• +4 more

Prof. Matthias Nießner is a Professor at the Technical University of Munich , where he leads the Visual Computing Lab . Prior to this, he held a Visiting Assistant Professor position at Stanford University . His work bridges computer vision , graphics , and machine learning , focusing on 3D reconstruction , semantic scene understanding , and AI-driven video synthesis . Prof. Nießner has published over 150 works in top venues like SIGGRAPH , CVPR , and ECCV , with several receiving best paper awards (SIGCHI’14, HPG’15, SPG’18, SIGGRAPH’16 Emerging Tech). His research has garnered international media attention, including features in the New York Times , Wall Street Journal , and MIT Technological Review , as well as TV demonstrations (e.g., Jimmy Kimmel Live for Face2Face technology). Awards : TUM-IAS Rudolph Moessbauer Fellowship (2017–ongoing) Google Faculty Award (2017) Nvidia Professor Partnership Award (2018) ERC Starting Grant (2018, €1.5M) Eurographics Young Researcher Award (2019) Research Trends : 3D Gaussian Splatting for real-time rendering Neural Radiance Fields (NeRF) with mesh supervision Audio-driven facial animation via diffusion models Latent space diffusion for 3D scenes Self-supervised and zero-shot methods for 3D and image analysis As a co-founder and director of Synthesia Inc. , he drives democratization of synthetic media. His YouTube channel has over 5 million views, reflecting his impact beyond academia.

Sara StymneView profile

Senior Lecturer

• Computational Linguistics
• Natural Language Processing
• Cross-lingual NLP
• +7 more

Sara Stymne is a Senior Lecturer in Computational Linguistics at the Department of Linguistics and Philology, Uppsala University, where she has been working since 2012. She initially joined as a post-doc (2012-2015), then worked as a researcher (2015-2017), and served as an assistant professor (2017-2023) before her current position as Senior Lecturer. Prior to Uppsala, she was a researcher at Linköping University's Department of Computer and Information Science. Dr. Stymne earned her PhD in Computational Linguistics from Linköping University in 2012 with the thesis 'Text Harmonization Strategies for Phrase-Based Statistical Machine Translation,' following a Licentiate degree in Computational Linguistics (2009) and a Master's degree in Cognitive Science (2006), both also from Linköping University. During her doctoral studies, she spent the autumn of 2010 and spring of 2009 at Xerox Research Centre Europe in Grenoble, France. Her primary research interests focus on cross-lingual natural language processing and digital humanities, with particular emphasis on multilingual dependency parsing. Dr. Stymne is passionate about applying computational linguistics to solve research questions in other fields, including language history, literary analysis, and political science. Her earlier work concentrated on machine translation, with specific interests in discourse-aware translation, compound processing, and error analysis. She has made significant contributions to the development of language technology tools for analyzing dialogue, narrative, and stylistic features in literature. Analysis of Dr. Stymne's recent publications reveals a strong focus on cross-lingual and cross-domain natural language processing. Her work spans multiple subfields including dependency parsing across genres and topics, discourse relation analysis in low-resource languages like Egyptian Arabic, direct speech identification in Swedish literature, and causality detection in governmental documents. A notable trend is her application of NLP techniques to digital humanities problems, particularly in analyzing literary texts and historical language change. Her research often involves creating and utilizing specialized datasets for specific linguistic phenomena across multiple languages. Dr. Stymne actively supervises graduate students, having guided numerous master's and bachelor's theses on topics ranging from speech recognition to multilingual parsing and causality detection. She leads or participates in several research projects including 'Fictional prose and language change' (funded by VR, 2021-2023) and 'Enabling climate-resilient development' (funded by Marianne and Marcus Wallenberg Foundation, 2023-2027), demonstrating her commitment to interdisciplinary research with practical applications. Her work has resulted in several notable software resources including uuPronPred for cross-lingual pronoun prediction, uuparser for dependency parsing, and Docent for document-level machine translation. Within the Computational Linguistics and Language Technology group at Uppsala University, Dr. Stymne contributes to multiple research initiatives focused on developing language technology tools for digital humanities applications. Her team works closely with literary scholars and historians to create computational methods for analyzing large corpora of literary texts, particularly focusing on Swedish literature across different historical periods. Her research bridges the gap between theoretical computational linguistics and practical applications in the humanities, creating new methodologies for quantitative analysis of literary and historical texts.

Jenna WiensView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Healthcare Data Analytics
• +5 more

Jenna Wiens is an Associate Professor of Computer Science and Engineering at the University of Michigan's College of Engineering. She serves as Associate Director of the Artificial Intelligence Lab and co-Director of AI & Digital Health Innovation. Leading the MLD3 research group, her work focuses on machine learning and AI applications for healthcare data. PhD from MIT (2014) under John Guttag NSF CAREER Award recipient (2016) Humboldt Foundation Carl Friedrich von Siemens Award (2024) Her research addresses four key technical thrusts: Time-series analysis for predicting clinical outcomes Robust machine learning against spurious correlations Decision-making with causal inference and offline reinforcement learning Human-AI collaboration frameworks Notable methodological contributions include the FIDDLE preprocessing pipeline for clinical time-series data, foundational work on survival analysis, and novel approaches to model selection in healthcare reinforcement learning. Her work has led to real-world AI deployments in infection prevention and patient risk stratification. Scientific honors include: Forbes 30 Under 30 (2015) MIT Tech Review 35 Innovators Under 35 (2017) Sloan Research Fellowship in Computer Science (2020) Sarah Goddard Power Award (2023) Wiens collaborates with clinicians across disciplines, emphasizing clinician-in-the-loop AI systems and ethical implementation in healthcare workflows.

Andrew HeadView profile

Assistant Professor

• Human-Computer Interaction
• Programming
• Reading
• +13 more

Andrew Head is an Assistant Professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on human-computer interaction, programming, and reading, particularly in developing technology for interactive reading and reasoning. He advises PhD students Alyssa Hwang, Hita Kambhamettu, Litao Yan, Jeffrey Tao, and Jessica Shi, and co-leads the Penn Human-Computer Interaction (Penn HCI) group with Danaé Metaxa. His work is published in top venues like ACM CHI, UIST, and ICSE. Research Interests: Andrew's work bridges interactive systems with programming environments, aiming to enhance how scientists and programmers interact with their tools. Key areas include AI-assisted code understanding, math notation accessibility, and medical note interpretation through interactivity. He employs user studies to identify needs and builds interactive systems to address them. Recent Article Trends: His publications emphasize systems-centric HCI approaches, integrating AI into code and document interfaces. Topics span code explanation (e.g., Ivie), property-based testing (e.g., Tyche), math notation augmentation (e.g., FreeForm), and medical informatics (e.g., Explainable Notes). Recent work also explores notebook environments (e.g., Bolt-on, Tyche) and literate programming (e.g., Colaroid). Scientific Awards: Distinguished Paper Award, ICSE 2024 Best Paper Awards at CHI (2024, 2023, 2022, 2019), UIST (2023, 2018), and others. Nominated for Best Paper at CHI 2023 and VL/HCC 2015. Advising & Grants: Andrew advises multiple PhD students and has secured significant grants, including a $1M NSF award for 'Property-based Testing for the People' (2024). His group collaborates with Penn’s MindCORE center and teams like PLClub and PennNLP. Labs & Teams: He co-leads the Penn HCI group, which works closely with other Penn research teams and labs. The group focuses on creating interactive tools that enhance scientific and programming workflows, supported by grants and cross-institutional partnerships.

Karthik R. NarasimhanView profile

Professor

• Language Agents
• Reinforcement Learning
• Natural Language Processing
• +3 more

Karthik R. Narasimhan is a Professor at Princeton University's School of Engineering and Applied Science in the Department of Computer Science. Previously, he earned his PhD from MIT under Regina Barzilay and served as a visiting research scientist at OpenAI during 2017-18. His research focuses on the intersection of language and decision-making, building autonomous agents that learn from both experience and human knowledge. His research spans multiple high-impact areas including language agents (Text-DQN, CALM, ReAct, Tree of Thoughts), reinforcement learning (h-DQN, Multi-Objective RL), and AI safety (Toxicity in ChatGPT, DataMUX). He has developed critical datasets and benchmarks such as WebShop, InterCode, SWE-bench, and SILG that have become standard evaluation tools in the field. Current work emphasizes agent capabilities, software engineering automation, and multimodal interaction. His publication trends show strong focus on practical agent deployment (SWE-agent, Tree of Thoughts), safety evaluation (Probing AI Safety), and efficiency improvements (DataMUX). Recent work increasingly addresses real-world challenges in software engineering, security, and human-AI collaboration through rigorous benchmarking. Co-author of foundational GPT (2018) paper Key developer of Text-DQN (2015), CALM (2020), ReAct (2022), Tree of Thoughts (2023) Creator of influential benchmarks: WebShop (2022), SWE-bench (2023), InterCode (2023) He actively advises students through Princeton's computer science program, with research supported by multiple grants focused on autonomous agent development and language-based decision systems. His GitHub repositories (nlp-datasets, text-world-player) demonstrate strong community engagement in open-source research tools. Current projects include advancing language agent capabilities through Reflexion (2023) and Tree of Thoughts (2023) frameworks while addressing critical safety and efficiency challenges.