Jiaxuan You is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign, leading the U Lab focused on achieving Artificial General Intelligence (AGI) in digital environments. His research spans graph neural networks (GNNs), relational data, foundation models, and machine learning systems. PhD and MS in Computer Science from Stanford University (2021) Developed GraphGym and PyTorch Geometric (PyG) for graph learning Core member at Kumo AI (2021-2023) His research explores: Graph-enhanced LLMs: Integrating relational structures into foundation models AGI Development: Self-optimizing AI agents and tool utilization ML Systems: Scalable architectures and redundancy-free computation Interdisciplinary Applications: Financial networks, crop yield prediction, and metro systems Recent publications focus on temporal reasoning, multi-agent dynamics, and hybrid architectures for LLMs. He actively develops open-source tools like DBGYM and GraphRouter. Scientific recognition includes: JPMC PhD Fellowship Baidu Scholarship Best Student Paper at AAAI 2017 World Bank Big Data Innovation Challenge winner He mentors PhD and intern students, emphasizing machine learning systems expertise. His lab collaborates on AGI workshops (e.g., ICLR 2024) and industry projects.
Olga Russakovsky is an Associate Professor of Computer Science at Princeton University and Associate Director of the Princeton Laboratory for Artificial Intelligence. Her research focuses on computer vision , machine learning , human-computer interaction , and fairness, accountability, and transparency in AI systems. Princeton University faculty member since 2025 Affiliated with Princeton's Center for Statistics and Machine Learning and Center for Information Technology Policy Scientific Recognition: Presidential Early Career Award for Scientists and Engineers (2025) PAMI Young Researcher Award (2022) AnitaB.org Emerging Leader Abie Award (2020) CRA-WP Anita Borg Early Career Award (2020) MIT Technology Review 35-under-35 Innovator (2017) PAMI Everingham Prize (2016) As a co-founder and current Board Chair of AI4ALL , she drives initiatives to expand diversity in AI. Her recent publications demonstrate expertise in vision-language models , deepfake detection , and ethical AI systems .
Celestine Mendler-Dünner is a Principal Investigator at the ELLIS Institute in Tübingen, co-affiliated with the Max Planck Institute for Intelligent Systems and the Tübingen AI Center. She leads the Algorithms and Society research group, focusing on machine learning in social contexts and the role of prediction in digital economies. Her work bridges theoretical machine learning with practical societal impact, developing tools for safe, reliable, and equitable AI ecosystems. Her educational background includes a PhD from ETH Zurich in collaboration with IBM Research, followed by an SNSF postdoctoral fellowship at UC Berkeley hosted by Moritz Hardt. She was previously a group leader at the Max Planck Institute for Intelligent Systems before joining the ELLIS Institute. Mendler-Dünner's research spans several interconnected themes including performative prediction (where predictions change the behavior they aim to predict), algorithmic collective action (how participants can steer AI systems toward common goals), and the role of LLMs in social science research. Her work combines theoretical foundations with practical implementations, addressing challenges in interactive machine learning, optimization in dynamic environments, and context-specific evaluation of AI systems. She particularly examines how algorithmic predictions mediate services and platforms at societal scale, exploring concepts of economic power in digital markets. Her publication record shows a clear evolution from system-aware machine learning algorithms (including foundational work on IBM Snap ML) toward increasingly sociotechnical questions at the intersection of machine learning, economics, and policy. Recent work focuses on measuring performative power in digital economies, evaluating LLMs as risk scores, and developing frameworks for algorithmic collective action in recommender systems and labor markets. Among her notable recognitions are the ETH Medal for her dissertation, the IBM Research Division Award, the Fritz Kutter Award, and the IBM Eminence and Excellence Award. She is an ELLIS Scholar, a fellow of the Elisabeth-Schiemann-Kolleg, and affiliated with several prestigious research programs including the International Max Planck Research School for Intelligent Systems and the Max Planck ETH Center for Learning Systems. ETH Medal (dissertation award) IBM Research Division Award Fritz Kutter Award IBM Eminence and Excellence Award SNSF Early Postdoc Mobility Fellowship Mendler-Dünner actively mentors the next generation of researchers, advising PhD student Patrik Wolf and supervising research interns including Joachim Baumann, Haiqing Zhu, and Anna Badalyan, as well as Master's student Dorothee Sigg. She serves as core faculty for the International Max Planck Research School and associated faculty for the Max Planck ETH Center for Learning Systems. Her group has secured significant research funding through fellowships and institutional support, enabling work on projects like Powermeter (measuring search engine influence) and Snap ML (resource-efficient machine learning library with over 1 million PyPI downloads). She leads the Algorithms and Society research group, which examines machine learning as part of broader sociotechnical ecosystems. The group explores human-population interactions with algorithmic systems and incorporates these insights into learning system fundamentals. Current projects include investigating economic incentives in digital platforms, developing tools for systematic LLM evaluation in social science contexts, and creating frameworks for collective action in algorithmic systems. Mendler-Dünner also co-organizes the Algorithmic Collective Action workshop at NeurIPS 2025, demonstrating her leadership in emerging research directions at the AI-society interface.
April Yi Wang is a tenure-track Assistant Professor in the Department of Computer Science at ETH Zürich, where she directs the Programming, Education, and Computer-Human Interaction Lab (PEACH Lab). She is a core faculty member at the Institute for Intelligent Interactive Systems and associated with the ETH AI Center. Wang is also an active member of ETH HCI and Swiss CHI communities, contributing significantly to human-computer interaction and educational technology research. Dr. Wang's educational background includes: Ph.D. in Information Science from University of Michigan (2023), advised by Steve Oney and Christopher Brooks M.Sc. in Computer Science from Simon Fraser University (2018), advised by Parmit Chilana B.Eng in Computer Science from Zhejiang University (2016) Dr. Wang's research focuses on human-centered approaches to programming and data science. Her work reimagines programming as a form of literature that communicates with both machines and people, exploring creative representations like text, shapes, animations, and everyday objects. She investigates how to make programming more natural and intuitive through literate programming environments, with applications in professional and educational contexts. Her research spans human-computer interaction, educational technology, and AI-assisted programming tools. Analysis of Dr. Wang's recent publications reveals a strong focus on AI-enhanced educational tools, particularly for programming and data literacy. Her work increasingly integrates large language models to scaffold learning while maintaining user agency. There's a clear trajectory toward developing situated learning approaches that connect abstract concepts to real-world contexts through augmented reality and tangible interfaces. Her research bridges HCI, education, and AI to create more accessible and engaging technical learning experiences. Dr. Wang has received numerous prestigious awards including: 2023 Gary M. Olson Award and Honourable Mention Award at ACM CHI 2022 Rising Stars in EECS and Heidelberg Laureate Forum Young Researcher 2020 Best Short Paper Award at IEEE VL/HCC and Honourable Mention at ACM CHI 2019 Best Paper Award at ACM CSCW Dr. Wang actively mentors students through thesis projects at ETH Zürich, supervising numerous bachelor's and master's students on topics ranging from AI-assisted programming to data literacy tools. Her lab, PEACH Lab, has secured funding including the recent innovedum funding for the Coducate project. She serves on program committees for major conferences including CHI and UIST, and regularly reviews for top HCI and education journals. The PEACH Lab, directed by Dr. Wang, focuses on creating expressive, intelligent, and human-centered systems that make technical topics more accessible. The lab explores textual, visual, and embodied representations for programming, with emphasis on enhancing communication, collaboration, and learning. Current research directions include balancing automation with user agency, supporting diverse learning needs, and developing tools for interdisciplinary technical communication.
Brenden Lake is an Associate Professor of Computer Science and Psychology at Princeton University, starting Fall 2025. Previously, he was an Associate Professor of Psychology and Data Science at New York University. He is the principal investigator of the lab for Human & Machine Intelligence, which moved from NYU to Princeton in 2025 and is jointly affiliated with the Department of Computer Science and the Department of Psychology. His lab is located in Princeton's Peretsman Scully Hall, rooms 117, 120, and 121. Ph.D., Massachusetts Institute of Technology, 2014 Lake's research focuses on the intersection of human and machine intelligence, specifically examining human cognitive abilities that elude current AI systems. His work centers on few-shot learning of new concepts, learning by generating new goals, learning by asking questions, and learning by producing novel combinations of known components. He employs modern neural network modeling approaches including meta-learning, fine-tuning LLMs, neuro-symbolic modeling, and learning from child headcam videos. His research aims to advance both psychology and computer science by exploring what makes human intelligence unique and using those insights to develop more powerful AI systems. Lake's recent publications demonstrate significant trends in grounded language acquisition through child perspectives, systematic generalization in neural networks, and the intersection of developmental psychology with AI. His work has appeared in top-tier venues including Science (2024) and Nature (2023), with multiple publications exploring how insights from human cognition can improve machine learning systems. His research shows how incorporating human cognitive ingredients can make AI systems more powerful and human-like while addressing longstanding debates about neural network capabilities. Science publication (2024) on Grounded language acquisition through the eyes and ears of a single child Nature publication (2023) on Human-like systematic generalization through a meta-learning neural network Multiple publications covered by major media outlets including New York Times and Washington Post Lake advises Ph.D. students in computer science, psychology, and related fields through his lab. His research is supported by publications in top venues across computer science and cognitive science. He teaches courses including Computational Cognitive Modeling and Advancing AI through Cognitive Science, bridging the theoretical and practical aspects of his research. Lake leads the lab for Human & Machine Intelligence, which studies the ingredients of intelligence in humans and machines. The lab investigates human cognitive abilities that current AI systems cannot replicate, with the dual goal of advancing psychological understanding of human intelligence while developing more capable artificial intelligence systems. Current research focuses on few-shot concept learning, learning through goal generation, and learning by asking questions.
Marlon Dumas is a Professor of Information Systems at the University of Tartu's Faculty of Science and Technology, with a 20-year academic career spanning Estonia and Australia. He holds a PhD in Computer Science from the University of Grenoble 1, France, and has served as Head of Chair and Programme Director in Software Engineering programs. Specializes in Business Process Management (BPM) and Process Mining Current research focuses on prescriptive process monitoring, simulation modeling, and data privacy Recipient of 25+ scientific awards, including multiple Test of Time Awards and the Estonian National Research Award in Technical Sciences Editorial leadership: Area Editor for Information Systems (Elsevier) and ERC Starting Grant Panel Chair His work bridges theoretical advancements in BPM with practical applications in financial services and anti-money laundering. He has developed tools like SIMOD, Kronos, and Kairos for process optimization and analysis. His research integrates AI/ML techniques (reinforcement learning, causal inference) with traditional process modeling. Key trends in his recent publications include: Prescriptive monitoring systems combining causal inference and machine learning Privacy-preserving process mining techniques (differential privacy, anonymization) Resource availability modeling and multi-objective process optimization LLM applications for process analysis and intervention policies Scientific honors include: 2024 BPM Best Paper & Prototype Awards 2023 ICPM Best Prototype Award 2019 ERC Advanced Grantee 2017 Estonian National Research Award in Technical Sciences 2019 MODELS Test of Time Award 2017 BPM Best Prototype Award He has mentored PhD candidates as thesis examiner and contributed to 30+ conference program committees, including General Co-Chair roles at ESEC-FSE 2019 and CAiSE 2018. His work has been supported by European Research Council grants and Estonian Science Foundation projects.
Christopher Potts is Professor and Chair of Linguistics at Stanford University, with a courtesy appointment as Professor of Computer Science. He serves as Director Emeritus of the Stanford Center for the Study of Language and Information (CSLI) and leads the Pragmatic Enrichment & Contextual Interface Lab. His work bridges theoretical linguistics and computational approaches to language understanding. Education: B.A. in Linguistics from New York University (1999) Ph.D. in Linguistics from University of California, Santa Cruz (2003) Potts' research focuses on how computational methods can illuminate linguistic phenomena, particularly in the areas of semantics, pragmatics, and sentiment analysis. His work explores how emotion is expressed in language and how linguistic production and interpretation are influenced by context. He has made significant contributions to understanding conventional implicatures, sentiment analysis frameworks, and the application of neural networks to linguistic problems. His recent work has increasingly focused on the interpretability of large language models and the development of frameworks like DSPy for building reliable AI systems. An analysis of Potts' recent publications reveals a strong trend toward the intersection of linguistic theory and practical AI applications. His work spans theoretical linguistics (e.g., compositionality, preposing constructions), neural network interpretability, and practical NLP systems (e.g., ColBERT, DSPy). The research demonstrates consistent focus on making language models more transparent, controllable, and linguistically informed, with particular attention to how context shapes meaning. Scientific Awards: Best Paper Award at 2024 ACL for 'Mission: Impossible Language Models' Outstanding Paper Award at 2024 ACL for 'CausalGym' ACL Test of Time Award 2023 Dean's Award for Distinguished Teaching (2015-2016) Best New Data Set or Resource Award at 2015 EMNLP Potts has secured numerous research grants as PI or Co-PI from major organizations including Google, Amazon, NSF, Office of Naval Research, and Stanford's HAI institute. His current projects focus on evaluation of retrieval-augmented generation systems, LLM-mediated communication in organizations, interpretability techniques for language models, and frameworks like DSPy for building next-generation AI systems. He has mentored numerous researchers who have gone on to make significant contributions in NLP and computational linguistics. As Director of CSLI (2013-2020) and current Chair of Linguistics at Stanford, Potts has played a key leadership role in shaping interdisciplinary research at the intersection of language, computation, and cognition. His Pragmatic Enrichment & Contextual Interface Lab continues to be a hub for innovative research combining formal linguistic theory with cutting-edge computational methods.
Zaid Harchaoui is an Adjunct Professor in the Department of Statistics at the University of Washington. His research focuses on machine learning, generative AI, and algorithmic optimization, with applications spanning ecology, neuroscience, and artificial intelligence. He explores learning under distributional shifts and develops tools for scalable generative models in language and vision domains. University: University of Washington Department: Statistics Research Focus: Learning from data with computational, inferential, and mathematical rigor; distributional shift adaptation; generative model scaling Email: zaid@uw.edu His recent work emphasizes generative AI applications in ecology and neuroscience, stochastic optimization for robustness, and algorithmic efficiency in large-scale learning. Key contributions include techniques for distributionally robust optimization, interpretable authorship obfuscation, and uncertainty quantification in behavior classification. Scientific awards and honors are not explicitly mentioned in the provided text. Collaborative efforts often intersect with nonlinear control algorithms, spectral analysis, and privacy-preserving machine learning frameworks.
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
Lexing Xie is a Professor in Computer Science at the Australian National University. He leads the ANU Computational Media Lab ( http://cm.cecs.anu.edu.au ) and the ANU Integrated AI Network. His work focuses on the intersection of machine learning, social media analysis, and multimedia understanding. Dr. Xie's research broadly focuses on innovative design and use of machine learning algorithms, especially on large-scale graph data and collective behaviour. His recent work spans several key areas: Popularity in social media -- understanding, predicting, and optimization Multimedia knowledge graphs, vision and language integration Humanising machine intelligence through better understanding of social dynamics His publications reveal a strong trend toward understanding information diffusion patterns in social media, particularly through visual content. He has made significant contributions to the study of visual memes, popularity prediction using point processes, and multimodal learning that connects vision with language. His work often bridges theoretical machine learning with practical applications in social media analysis. Dr. Xie has received recognition for his research, including an Honourable Mention at CSCW 2019 for his work on attention flow in online video networks. His research has been supported by collaborations with major institutions including IBM Research and Columbia University. As an advisor, Dr. Xie has mentored numerous students who have gone on to contribute significantly to publications in top-tier conferences. His lab, the ANU Computational Media Lab, serves as a hub for interdisciplinary research connecting computer science with social sciences.
Fei Fang is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University (CMU) , where she explores the intersection of artificial intelligence and multi-agent systems . Her work integrates machine learning with game theory to address challenges in security , sustainability , and mobility , aligning with the AI for Social Good mission. Ph.D. in Computer Science, University of Southern California (2016) B.Eng. in Electronic Engineering, Tsinghua University (2011) Recent research focuses on reinforcement learning , large language models (LLMs) , and human-AI collaboration . Her team’s work has been recognized with 15+ awards across prestigious venues like IAAI, AAAI, and IJCAI. Notable accolades include the 2023 Allen Newell Award , 2022 Sloan Fellowship , and NSF CAREER Award (2021) . She actively contributes to educational initiatives , including teaching "Demystifying AI for Everyone" at CMU, and has sought part-time teaching assistants for course development. Her research spans 15+ domains , including AI ethics , cyber defense , traffic optimization , and public health .
Melanie Mitchell is a Professor at the Santa Fe Institute, focusing on artificial intelligence, cognitive science, and complex systems. She is an inaugural Fractal Faculty member and originated the Complexity Explorer platform. PhD in Computer Science from University of Michigan (1990) Faculty/research roles at Santa Fe Institute, Portland State University, Los Alamos National Laboratory, and others Her research explores abstraction, analogy-making, and reasoning in AI and human intelligence. She has authored six books, including the award-winning Complexity: A Guided Tour and Artificial Intelligence: A Guide for Thinking Humans . Recent work examines large language models, conceptual understanding, and cognitive parallels between humans and AI. Her 15 most recent publications address topics like AI reasoning, language model robustness, cognitive metaphors, and the Turing test's evolving relevance. Awards include the Phi Beta Kappa Science Book Award and the Herbert A. Simon Award. She advises numerous PhD and postdoctoral students and leads public outreach through online courses and media appearances.
Professor Nancy W. Y. Law is a leading scholar in Learning Sciences and Technology at the University of Hong Kong's Faculty of Education . She has served as Founding Director of the Centre for Information Technology in Education (CITE) (1998-2013) and currently holds the Chair of Learning Sciences and Technology . Her career spans over 25 years of international comparative studies, policy analysis, and large-scale educational innovation. Current roles: Associate Dean (Strategic Development), HKU Key affiliations: Sciences of Learning Strategic Research Theme (co-convener) Research Expertise Her work focuses on technology-enhanced learning , digital literacy , knowledge building communities , and multilevel educational change . She pioneered network models of teacher innovation and socio-technical frameworks for sustainable pedagogical transformation, particularly in STEM education and Cyberbullying prevention . Scientific Leadership With over 25 major research grants (2000-2023) exceeding HK$50 million, she leads projects like: Learning and Assessment for Digital Citizenship Jockey Club Self-directed Learning in STEM Design-Aware Learning Analytics A Fellow of the International Society of Learning Sciences and recipient of the HKSAR Humanities and Social Sciences Prestigious Fellowship , she has supervised numerous international comparative studies including SITES 2006, ICILS 2013, and longitudinal digital citizenship research (2019-2021).
Giuseppe Ateniese is a Professor and Eminent Scholar in Cybersecurity at George Mason University, affiliated with the Department of Computer Science and Cyber Security Engineering. Formerly, he held the Farber Endowed Chair and chaired the Department of Computer Science at Stevens Institute of Technology. His research focuses on cloud security, applied cryptography, blockchain technology, and AI-driven cybersecurity solutions. He has received prestigious awards including the NSF CAREER Award and Google/IBM Faculty Awards. AIFurther, he teaches courses on blockchains, cryptography fundamentals, and cryptofinance. His work includes pioneering contributions to rewritable blockchains, password cracking via AI (e.g., PassGAN), and secure decentralized systems. Education: PhD in Computer Science, University of Genoa (2000) Laurea (M.Sc.), University of Salerno (1995) Master of Engineering (Honoris Causa), Stevens Institute of Technology (2020) Research Interests: Ateniese’s work bridges theoretical and applied cryptography, with emphasis on blockchain scalability, privacy-preserving machine learning, and secure data storage. He has developed foundational techniques like proxy re-cryptography and accountable storage systems. His recent efforts address AI’s dual role as both a cybersecurity tool and threat vector, including AI-based password cracking and defense strategies against LLM-driven attacks. Awards: NSF CAREER Award (for privacy/security research) Google Faculty Research Award (cloud security) IBM Faculty Award IEEE CISTC Technical Recognition Award Teaching & Labs: He teaches advanced courses on blockchains (CS695), cryptography fundamentals (CYSE/ECE476), and introductory cryptography (CS487/587). His research team focuses on cryptographic protocols, decentralized systems, and AI-security intersections. Collaborations include projects with Accenture on editable blockchain prototypes and NSF-funded work on password security.
Professor Janet B. Pierrehumbert is a leading academic in computational linguistics and natural language processing, holding the position of Professor of Language Modelling at the Oxford e-Research Centre, University of Oxford. She is also a Senior Research Fellow at Trinity College and affiliated with the Faculty of Linguistics, Philology and Phonetics. Her work bridges interdisciplinary research in phonology, sociolinguistics, and computational models of language dynamics. Education: PhD in Linguistics from MIT (1980), A.B. in Linguistics from Harvard University (1975). Research Interests: Focuses on computational linguistics, dialect variation, language dynamics, and the societal impacts of NLP. Her group develops algorithms for analyzing social media discourse, forecasting trends, and modeling language evolution. Recent work includes studies on dialect fairness in LLMs and semantic shifts in political discourse. Key Projects: EPSRC-funded research on online forum dynamics, the Wordovators project on lexical innovation, and collaborations with institutions like the Oxford Man Institute. Her work emphasizes robust NLP systems and theoretical linguistics. Awards: ISCA Medal (2020), National Academy of Sciences membership (2019), Fellowships from the American Academy of Arts and Sciences and Cognitive Science Society. Grants & Advising: Over £6M in research funding, including EPSRC and Templeton grants. Supervised over 30 PhD students and postdocs, many now leading academics and industry researchers in NLP and linguistics. Labs/Teams: Leads the Pierrehumbert Language Modelling Group, collaborating with the Oxford e-Research Centre and international partners like Stanford and the University of Canterbury.
