Find Academic Supervisors

• Computer Vision
• Machine Learning
• Deep Learning
• +5 more

Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.

• AI in Healthcare
• Electronic Health Records Analysis
• Biomedical Time Series Analysis
• +6 more

Dr. Kezhi (Ken) Li is an Associate Professor of AI in Healthcare at the Institute of Health Informatics, University College London (UCL). He leads the AI for Health research group and has established himself as a leading expert in applying artificial intelligence to solve complex problems in healthcare, with over 130 publications in leading journals (total impact factor greater than 400). Dr. Li earned his Doctor of Philosophy from Imperial College of Science, Technology and Medicine in 2013, followed by research positions at the Medical Research Council (2015-2017), University of Cambridge (2014-2015), and Royal Institute of Technology (KTH) (2013-2014). His academic journey reflects a consistent trajectory from technical AI research toward increasingly healthcare-focused applications. Dr. Li's research focuses on solving physiological, medical, clinical, and operational problems in healthcare using AI techniques. His specific expertise includes AI in healthcare using electronic health records (EHR), biomedical time series analysis using monitors/wearables, diabetes management, large language models (LLM) in healthcare (especially mental health), patient flow optimization, and digital health with federated learning. His work bridges the gap between cutting-edge AI methodologies and practical healthcare applications, with a strong focus on improving patient outcomes and healthcare system efficiency. Analysis of Dr. Li's publication history reveals a strong emphasis on diabetes management technologies, particularly blood glucose prediction systems using advanced neural network architectures. More recently, his work has expanded into mental health applications of large language models, blockchain-based federated learning for healthcare data, and mortality prediction in critical care settings. His research demonstrates a clear evolution from purely technical AI development toward increasingly clinically impactful applications, with growing emphasis on explainability, privacy preservation, and real-world implementation challenges. Dr. Li has received numerous prestigious awards recognizing his contributions to healthcare AI: Best Application Award of IEEE Global Blockchain Conference (2025) Fellow of British Computer Society (2025) Fellow of the Royal Society for Public Health (2024) Healthcare Partnership of the Year category at the London Higher Awards (2024) ECR Promising Project Award (2023) Gallivan Award finalists (2022) Stylianos Kalaitzis PhD Award Winner (2022) HDR UK Team of the Year (COVID-19) Award (2021) As an educator, Dr. Li serves as the Director of MRes study (AI-enabled Healthcare Systems) at UCL. He leads multiple key modules including Healthcare Artificial Intelligence Journal Club, Dissertation in Artificial Intelligence Enabled Healthcare, and Advanced Machine Learning for Healthcare. His supervision extends across dissertation projects and junior researchers in his AI for Health group. His research has been supported by various grants, including those from HDR UK, focusing on translating AI innovations into practical healthcare solutions. Dr. Li leads the AI for Health research group (https://ai4hucl.github.io/ai4h_webs/), which comprises researchers with diverse expertise in machine learning, healthcare systems, and clinical domains. The group maintains strong collaborations with healthcare providers and industry partners to ensure their research addresses real-world healthcare challenges and can be effectively translated into clinical practice.

Chinmay KulkarniView profile

Assistant Professor

• Human Computer Interaction
• Large-scale Learning
• Automation
• +5 more

Chinmay Kulkarni is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute, where he leads the Expertise@Scale lab. His research integrates large-scale data and automation to transform learning, work, and mentoring systems. Education : Ph.D. in Computer Science from Stanford University (recipient of the Arthur P Samuel Award) Previous Affiliations : Microsoft Research, Barcelona Supercomputing Center His research spans: Human-Computer Interaction design for massive collaboration Voice-controlled interfaces and AI tools Future of work in remote/hybrid environments Behavioral economics through tech interventions Creative entrepreneurship support systems Algorithmic feedback in education Recent publications with AI and education focus show strong trends in voice technology, peer feedback mechanisms, and scalable learning platforms. His lab's systems have been used by >100,000 users across 150 countries. Scientific Awards : Arthur P Samuel Award (Stanford thesis award) Advising & Grants : NSF grant recipient US Department of Education funding Office of Naval Research support Departmental fellowship Labs : Directs Expertise@Scale lab developing systems adopted by Coursera and edX. Current research group includes PhD students Yasmine Kotturi, Julia Cambre, Pranav Khadpe and Masters student Sayan Chaudhry.

• Networking
• Communications
• Machine Learning
• +7 more

Christopher G. Brinton is the Elmore Associate Professor of Electrical and Computer Engineering at Purdue University, where he leads the ION research lab. He is affiliated with the Department of Electrical and Computer Engineering in the College of Engineering at Purdue University's West Lafayette campus. Dr. Brinton received his PhD from Princeton University, where he was previously the Associate Director of the EDGE Lab and a Lecturer of Electrical Engineering. His research focuses on the intersection of networking, communications, and machine learning, with particular emphasis on Fog computing systems, the Internet of Things (IoT), NextG Wireless, and social learning networks. His research integrates foundational techniques including convex and non-convex optimization, machine learning, and signal processing to address challenges in networked intelligent systems. The ION lab under his leadership develops both theoretical frameworks and practical implementations for next-generation networking solutions, with strong industry collaborations including Qualcomm, Nokia, Intel, Cisco, Dell, and Ericsson. Recent publications reveal a strong trend toward federated learning, decentralized algorithms, and edge intelligence, with significant contributions to model partitioning, communication-efficient learning, and robust network architectures. His work increasingly bridges traditional communication theory with modern machine learning techniques to solve emerging challenges in distributed networked systems. NSF CAREER Award ONR Young Investigator Program (YIP) Award DARPA Young Faculty Award (YFA) AFOSR Young Investigator Program (YIP) Award Intel Rising Star Faculty Award (RSA) Dr. Brinton teaches several courses including ECE 647: Performance Modeling of Computer Communication Networks, ECE 301: Signals and Systems, and ECE 547: Introduction to Computer Communication Networks. He has co-authored the book 'The Power of Networks: Six Principles That Connect Our Lives' and taught three Massive Open Online Courses (MOOCs) with over 400,000 cumulative students. While not currently actively recruiting students, he remains open to connecting with highly motivated individuals. Dr. Brinton leads the ION (Intelligent Optimization and Networking) research lab, which focuses on creating theoretical foundations and practical implementations for next-generation networked systems. The lab has recently published significant work on 6G taxonomy in collaboration with major industry partners and continues to push boundaries in distributed learning and network optimization.

Alina LandowskaView profile

Assistant Professor

• Cultural Studies
• Discourse Analysis
• Cultural Evolution
• +3 more

Alina Landowska serves as Assistant Professor in the Department of Cultural Studies at the Faculty of Humanities, SWPS University of Social Sciences and Humanities in Warsaw. Her academic profile integrates cultural studies, computational linguistics, and ethics, with significant contributions to understanding moral foundations in digital discourse and technological futures. Her educational background includes degrees in Management and Economics from Gdańsk University of Technology and European Integration from the Pontifical University of John Paul II in Krakow, complemented by international scholarship experiences: SDG Academy (United Nations) Tantur Ecumenical Institute, Jerusalem (University of Notre Dame) Baltic University Program, Uppsala Swedish Institute of Environmental Research, Kalmar Royal Danish Student Fund, Copenhagen Landowska's research investigates cultural evolution through computational discourse analysis, focusing on morality-technology intersections. She pioneers text-mining methodologies to examine moral foundations in social media, anticipatory rhetoric in digital communication, and value-based management frameworks. Her work bridges humanities with data science, analyzing polarization mechanisms and proleptic cues in online environments while exploring cooperation ethics in business contexts. Recent publications demonstrate methodological innovation in mapping technological futures through sentiment/emotion analysis and moral-value detection systems. Her article corpus reveals consistent thematic threads: digital rhetoric analysis (particularly prolepsis functions), moral psychology applications in AI/social media, and cultural evolution studies linking cooperation theory with business ethics. This interdisciplinary approach positions her at the nexus of computational social science and humanistic inquiry. No scientific awards or prizes are documented in her current professional profile. As an educator, Landowska mentors students in discourse analysis methodologies and socio-economic media studies while serving as executive coach with EMCC Poland. Her research leadership extends to co-founding the Institute for Development think tank and representing Employers of Poland at the OECD's Business and Industry Advisory Committee (2016-2018). She previously held vice-presidential roles in the Polish Association of Businesswomen. Landowska maintains active research affiliations with the Humanistic Management Center (University of Lucerne), International Council for Small Business (George Washington University), and ArgDiaP association. Her 2022-2024 tenure with New Ethos Lab advanced dialogue studies in persuasion frameworks, complementing her current work on digital rhetoric's ethical dimensions.

Sebastijan DumancicView profile

Assistant Professor

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +2 more

Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

Nezihe Merve GürelView profile

Assistant Professor

• Machine Learning
• Computer Science
• Pattern Recognition
• +4 more

Nezihe Merve Gürel is an Assistant Professor in Computer Science at Delft University of Technology (TU Delft), affiliated with the Pattern Recognition & Bioinformatics Group within the Intelligent Systems Department of the Faculty of Electrical Engineering, Mathematics and Computer Science. Her research focuses on developing robust, reliable, and efficient machine learning methods with enhanced reasoning capabilities, bridging theoretical rigor and practical applications. She emphasizes data-centric approaches to improve ML systems. Education: PhD in Computer Science from ETH Zurich, MSc from EPFL (Switzerland). Research Interests: ML robustness, reliability, reasoning, data-centric ML, federated learning, and explainable AI. Her recent work includes certified robustness for retrieval-augmented models and time-efficient learning algorithms. She has contributed to the Journal of Data-centric Machine Learning Research as an executive editor and served as a reviewer for top ML conferences (NeurIPS, ICML, ICLR). She previously held roles at IBM Research, Stanford University's Human-Centered AI Lab, and Westlake Institute for Advanced Study. Her awards include the Generation Google Scholarship and Cisco Research Funding . Scientific Awards : Generation Google Scholarship (2021) Cisco Research Center University Funding Labs & Teams : She leads research in the Pattern Recognition Laboratory at TU Delft and collaborates with international institutions like Stanford and Westlake Institute for Advanced Study.

Mitchell L. StevensView profile

Professor

• Alternative Schooling
• Educational Policy
• Higher Education
• +2 more

Mitchell L. Stevens is a Professor at Stanford University’s Graduate School of Education and holds a courtesy appointment in the Department of Sociology. He co-directs the Stanford Center on Longevity and leads the Pathways Network and Futures Project on Education and Learning for Longer Lives . His research bridges organizational sociology and educational innovation, focusing on equity in academic pathways, data-driven institutional practices, and the sociology of higher education. Education: PhD in Sociology (Northwestern University, 1996); BA in Sociology (Macalester College, 1988). Stevens’ scholarly work examines alternative schooling , educational policy , gendered academic decision-making , and lifelong learning . His recent publications analyze the intersection of machine learning and enrollment trends , merit rituals in admissions , and the marketization of higher education . He employs mixed methods, including large-scale data analysis and comparative-historical frameworks. Stevens mentors doctoral students and postdoctoral researchers, with advisees including Daniela Ganelin, Philip Hernandez, Hansol Lee, Léon Marbach, Melanie Shimano, Joao M. Souto-Maior, Bernardo Mackenna, and Katie Spoon. His lab, the Pathways Network , develops analytics tools to enhance educational equity and institutional practices. Current teaching includes courses on Higher Education , Stanford’s Historical Context , and Organizational Analysis .

Santiago BarredaView profile

Associate Professor

• Speech Perception
• Phonetics
• Acoustic Phonetics
• +4 more

Santiago Barreda is an Associate Professor in the Department of Linguistics at the University of California, Davis, specializing in speech perception and phonetic analysis. His research examines how acoustic properties of speech convey speaker characteristics including age, gender, and physical attributes. Education: Ph.D. in Linguistics (Phonetics), University of Alberta, 2013 M.A. in Hispanic Studies (Language and Linguistics), University of Western Ontario, 2008 B.A. in Linguistics and Spanish Language and Literature, University of Western Ontario, 2006 Research Focus: Dr. Barreda employs behavioral experiments and statistical modeling to investigate perceptual mechanisms in speech recognition. His work bridges theoretical phonetics with practical applications, particularly in vowel normalization techniques and formant tracking algorithms. Key questions address how listeners extract speaker identity from acoustic cues and interpret social characteristics through vocal signals. Publication Trends: Recent publications (2020-2025) reveal three dominant themes: computational phonetic tools (FastTrack, phonTools), perception of social/physical speaker characteristics from children's voices, and interdisciplinary public health research on speech-related aerosol transmission. His work demonstrates strong methodological consistency in combining acoustic analysis with perceptual validation. Scientific Awards: No scientific awards were mentioned in the source material. Advising and Grants: The provided documentation does not specify graduate student advising roles or external grant funding. Technical Contributions: Dr. Barreda develops open-source phonetic analysis software including FastTrack (Praat-based formant tracking) and the phonTools R package, which have become standard resources in acoustic phonetic research.

• Semantic Web
• Ontology Engineering
• Knowledge Graphs
• +4 more

Birgitta König-Ries is a Professor at the Department of Computer Science, University of Jena, Germany. She is a leading researcher in semantic technologies, ontology engineering, and knowledge graph management for biodiversity and life sciences. Her work focuses on reproducibility, provenance tracking, and data integration using semantic approaches. Research Interests: Semantic Web, Ontology Engineering, Knowledge Graphs, Biodiversity Informatics, Reproducibility of Scientific Experiments Key Collaborations: Sheeba Samuel, Nora Abdelmageed, Samira Babalou, Alsayed Algergawy, Felicitas Löffler, Vamsi Krishna Kommineni Her recent publications emphasize automated knowledge graph construction, domain-specific language models (e.g., BiodivBERT), benchmarking semantic table interpretation (KG2Tables, BiodivTab), and tools for provenance management (MLProvLab, MLProvCodeGen). She contributes to FAIR data principles and interdisciplinary research, particularly in biodiversity and public administration transparency. Her work bridges theoretical advances with practical implementations, including open-access benchmarks (tFood, tBiodiv, tBiomed) and collaborative platforms like BiodivPortal and fusion-jena. Notable Tools & Benchmarks: BiodivBERT, KG2Tables, BiodivTab, MLProvLab, tBiodiv, tBiomed

Gavin SchwarzView profile

Professor

• Organizational Change
• Organizational Failure and Inertia
• Virtual Teams
• +2 more

Gavin Schwarz is a Professor and Head of School at the School of Management and Governance within the UNSW Business School . He specializes in organizational change , organizational failure and inertia , and the dynamics of virtual teams , with a focus on how organizations fail during change processes and how to develop applied strategies for change management . His work spans diverse sectors including healthcare, technology, and education, with publications in leading journals such as Academy of Management Learning and Education and Administrative Science Quarterly . Education : PhD in Management (University of Queensland), MPhil (Hons) in Management (University of Auckland), BA in Management and English (University of Auckland). Grants : 2020 Brock University grant for "University communication in times of COVID-19" , 2020 UNSW Medicine grant for "Translation and change: Embedding effective change management in health" , and earlier Australian Research Council and Gordon J. Samuels Fellowship awards. His research explores the development of knowledge in organizational theories , with an emphasis on collective responses to change , HR management during crises , and technology strategy . He has contributed to understanding organizational communication , team innovation , and structural inertia . His 15 most recent publications cover topics from AI’s role in organizational change to pandemic-driven research adaptation, with keywords spanning management science , behavioral economics , and digital transformation . Scientific Awards 2021-2023 : Outstanding Reviewer Awards (Academy of Management Review) 2017-2020 : Best Reviewer Awards (Journal of Organizational Behavior) 2019, 2013, 2011 : Best Paper Finalist/Awardee (Academy of Management divisions) 2007, 2006 : Editorial Board Excellence (Academy of Management Journal) and Gordon J. Samuels Fellowship As an active supervisor in organizational change and HR development , his work supports healthcare innovation and digital transformation. He serves as Editor-in-Chief for the Journal of Applied Behavioral Science and is on the editorial boards of Academy of Management Review , Journal of Management , and Journal of Organizational Behavior . Contact: g.schwarz@unsw.edu.au

Thomas PepinskyView profile

Professor

• Comparative Politics
• Political Methodology
• Political Economy
• +4 more

Thomas Pepinsky holds the prestigious Walter F. LaFeber Professorship of Government and Public Policy at Cornell University, where he is a faculty member in the Department of Government within the College of Arts and Sciences. He additionally serves as a Nonresident Senior Fellow at the Brookings Institution, bridging academic research with policy engagement. His academic career, which began at Cornell in 2008 after earning his Ph.D. from Yale University, has established him as a leading scholar in comparative politics with deep regional expertise in Southeast Asia. Professor Pepinsky's research examines the complex interactions between political and economic systems globally, with particular attention to democratic backsliding in Southeast Asia and Europe, the evolution of social categories like ethnicity in the Malay world, and methodological approaches to social science explanation. His work consistently connects theoretical political science with empirical analysis of real-world political phenomena, often employing innovative methodological frameworks to understand complex political dynamics across different contexts. His scholarly output demonstrates a clear trajectory from foundational work on Southeast Asian politics to increasingly comparative analyses examining democratic erosion, electoral behavior during political transitions, historical persistence of political attitudes, and the intersection of social categories with political economy. Recent publications reveal growing attention to contemporary crises including refugee flows, pandemic politics, and the challenges to democratic institutions worldwide. Among his significant professional activities, Dr. Pepinsky serves as editor of the Journal of East Asian Studies and is a co-founder and Executive Board member of the Southeast Asia Research Group (SEAREG). He also contributes to the Association for Analytical Learning about Islam and Muslim Societies (AALIMS) and the International Political Economy Society (IPES), reflecting his interdisciplinary approach. At Cornell, he actively participates in the Southeast Asia Program (SEAP) and teaches courses including Introduction to Comparative Government and Politics, Southeast Asian Politics at both undergraduate and graduate levels, and independent study opportunities. His international scholarly engagement is evident through visiting appointments at institutions including Amsterdam, the Australian National University, Freiburg, Griffith University, the Hertie School, and the National University of Singapore.

• Mathematical Optimization
• Algorithmic Intelligence
• Energy Systems Modeling
• +5 more

Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.

Jason GoulahView profile

Professor

• Ikeda/Soka Studies in Education
• Transformative Learning
• Socioecological Perspectives in Education
• +6 more

Jason Goulah is a Professor at DePaul University’s College of Education, where he directs the Institute for Daisaku Ikeda Studies in Education and the Value-Creating Education for Global Citizenship Doctoral Program. His work bridges Ikeda/Soka studies, transformative learning, and socioecological approaches to language education. Education: Ph.D. in Education, State University of New York, Buffalo LL.M. in International Law, Kwansei Gakuin University, Japan His research explores intersections of language, culture, identity, and sustainability, with a focus on Soka education, global citizenship, and eco-ethical consciousness. He has published extensively in journals and edited volumes, including works on climate change in TESOL and the role of dialogue in peacebuilding. Scientific Awards: 2018 DeGarmo Award for Exemplary Scholarship 2015 American Educational Studies Association Critics Choice Book Award 2013 DePaul University Excellence in Teaching Award 2009 Stephen A. Freeman Award for Best Language Education Article He has held visiting positions at the University of Guelph-Humber, Soka University of America, and the University of Evansville, and has developed curriculum for Chicago Public Schools and New York City Schools. His recent articles analyze Anthropocene-era education and East-West philosophical convergences.

Seth FreyView profile

Associate Professor

• Computational Social Science
• Governance of Commons
• Complex Systems
• +5 more

Seth Frey is an Associate Professor in the Department of Communication at the University of California, Davis, with affiliate status at Indiana University's Ostrom Workshop and as Research Director at Metagov. His research focuses on computational social science approaches to understanding self-governance in complex social systems, particularly through the lens of online communities as model institutions. Education: Ph.D. in Cognitive Science and Informatics (complex systems), Indiana University, 2013 B.A. in Cognitive Science, UC Berkeley, 2004 Research Interests: Frey specializes in computational approaches to institutional analysis and the cognitive science of strategic behavior . His work examines how communities design governance systems to overcome collective action problems, with emphasis on: Emergent institutional structures in digital commons Policy-as-data through NLP and institutional grammar frameworks Cognitive mechanisms underlying cooperative behavior Design principles for participatory change in online platforms His methodology integrates large-scale data analysis, web-based experiments, and computational modeling across diverse contexts including Minecraft, Reddit, and professional sports ecosystems. Publication Trends: Recent publications (2023-2025) demonstrate a cohesive trajectory toward computational institutional analysis, with increasing focus on NLP-driven policy analysis (e.g., NLP4Gov), decentralized governance architectures (DAOs, multi-level platform governance), and the cognitive foundations of collective action. His work consistently bridges theoretical institutional analysis with practical applications in digital community design, showing particular growth in translating Ostrom's design principles into computational frameworks. Awards: Honorable Mention Award for Best Paper at ACM CSCW 2019 Advising and Grants: Frey mentors students interested in data science applications at the intersection of communication, cognition, and complex systems, emphasizing resourcefulness and intellectual curiosity. His research has secured substantial funding from: National Science Foundation (NSF) NASA Ford Foundation Google Open Source Foundation He actively encourages aspiring graduate students with strong self-directed research skills to explore computational approaches to social phenomena. Labs and Teams: He leads the Computational Communication Lab at UC Davis and co-directs the Institutional Grammar Research Initiative. Through Metagov, he develops the 'Governance API' framework for modular community governance. His past affiliations include Disney Research (Walt Disney Imagineering) where he applied complexity science to theme park systems, and the New England Complex Systems Institute (NECSI). Current collaborations span Ethereum governance, Minecraft server ecosystems, and Colorado's cannabis monitoring infrastructure.