Blase Ur is an Associate Professor of Computer Science at the University of Chicago, leading the UChicago SUPERgroup (Security, Usability, & Privacy Education & Research group). His research focuses on data-driven methods to improve online security, privacy, and usability of complex systems. He holds a PhD from Carnegie Mellon University and an AB from Harvard University. Education: PhD in Societal Computing, Carnegie Mellon University (2016) AB in Computer Science, Harvard University Research Interests: Blase's work spans computer security, privacy, HCI, and ethical AI. He develops tools like password meters, privacy transparency systems, and IoT security solutions. His SUPERgroup emphasizes interdisciplinary collaboration, with projects addressing data-driven decision-making and usability challenges in AI and IoT. Articles Trends: Recent work includes studies on ad transparency systems, user perceptions of privacy in AI, and barriers to passwordless authentication. Research often combines empirical user studies with technical implementations to bridge gaps between security mechanisms and user needs. Awards: NSF CAREER Award (2021) Quantrell Award for Teaching (2021) SIGCHI Outstanding Dissertation Award (2018) Advising & Grants: Advisor to PhD students like Kevin Bryson. Secured grants from NSF, Mozilla, and Meta. Active in outreach to broaden CS participation, including K-12 programs. Labs & Teams: Co-director of the SUPERgroup and affiliated with the Systems Group and CERES Center for Unstoppable Computing at UChicago.
Anca Dragan is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where she runs the InterACT Lab focused on algorithms for human-AI and human-robot interaction. Currently on leave from Berkeley, she leads AI Safety and Alignment at Google DeepMind, overseeing safety for Gemini models and preparing for future advancements. Dragan has been a co-PI of the Center for Human-Compatible AI and served on the steering committee for the Berkeley AI Research (BAIR) Lab. B.Sc. in Computer Science from Jacobs University Bremen, Germany Ph.D. from Carnegie Mellon University Dr. Dragan's research focuses on enabling AI agents to work effectively with, around, and in support of people. Her work bridges robotics, machine learning, and game theory to create systems that better understand human preferences and coordinate with users. Key areas include AI alignment (ensuring AI does what people actually want), learning reward functions from diverse human feedback forms, and developing algorithms for human-AI collaboration across domains like autonomous vehicles, brain-machine interfaces, and recommender systems. Her research emphasizes maintaining uncertainty about human preferences and accounting for the plurality of human values. Dr. Dragan's recent publications reveal a strong focus on addressing fundamental challenges in AI safety and alignment. Her work spans theoretical foundations of reward learning, practical implementations for human-AI coordination, and critical examinations of limitations in current approaches. There's a clear trajectory toward more robust, safe, and value-aligned AI systems that can handle complex human preferences while avoiding both present-day harms and potential catastrophic risks. IEEE RAS Early Academic Career Award in Robotics and Automation (2021) McEntyre Award for Excellence in Teaching (2020) PECASE (Presidential Early Career Award for Science and Engineering) (2019) Sloan Fellowship (2018) NSF CAREER Award (2017) Okawa Foundation Award (2017) MIT Tech Review 35 Innovators Under 35 (2017) Multiple best paper awards at top robotics and AI conferences Dr. Dragan has mentored numerous successful students who have gone on to faculty positions at MIT, Stanford, CMU, and Princeton, as well as industry roles at DeepMind, Waymo, and Meta. Her advising philosophy emphasizes both technical rigor and consideration of broader societal impacts. She has secured significant research funding including NSF CAREER, ONR Young Investigator, and Okawa Foundation awards, supporting work on human-AI interaction and alignment. Dragan has also consulted for Waymo for six years, helping develop roadmaps for deploying increasingly learning-based safety-critical systems. Dr. Dragan leads the InterACT Lab at UC Berkeley, which has produced influential work on Cooperative Inverse Reinforcement Learning, Inverse Reward Design, and other foundational concepts in human-AI interaction. The lab's research has significantly shaped the field of AI alignment, with applications spanning autonomous vehicles that coordinate with human drivers, brain-machine interfaces that adapt to user needs, and language models that better understand human preferences. Current work focuses on scaling safety approaches as AI capabilities advance, ensuring alignment keeps pace with technological progress.
Insup Lee is the Cecilia Fitler Moore Professor in the Department of Computer and Information Science and Director of the PRECISE Center at the University of Pennsylvania's School of Engineering and Applied Science. He holds a secondary appointment in the Department of Electrical and Systems Engineering and the Perelman School of Medicine’s Department of Biostatistics, Epidemiology, and Informatics. IEEE TCCPS Distinguished Leadership Award (2023) Fellow of the AAAS (2022) Test of Time Award, Runtime Verification (2019) Fellow of the ACM (2017) Best Paper Awards at IEEE ICPS, ACM/IEEE ICCPS, and MEMOCODE His research focuses on cyber-physical systems , real-time and embedded systems , safe autonomy , and internet of medical things , with applications in healthcare and connected systems. He advises PhD students including Eric Lu, Kaustubh Sridhar, Sooyong Jang, and Jean Park (co-advised with Kevin Johnson). Recent publications address safety monitoring for learning-enabled systems, model-free control synthesis using reinforcement learning, and multilingual toxicity guardrails for large language models. His team collaborates with institutions like Hillrom and Penn Nursing to optimize medical device usage in clinical settings.
Derek W Hoiem is a Professor in the Siebel School for Computing and Data Science at the University of Illinois Urbana-Champaign, where he has been a faculty member since 2009. His research focuses on computer vision and related areas, and he is also the co-founder and Chief Science Officer of Reconstruct, an AI-based construction technology company. His educational background includes: PhD in Robotics, Carnegie Mellon University (2007) Beckman Postdoctoral Fellowship (2008) Prof. Hoiem's research spans computer vision, with a focus on object recognition, scene understanding, and graphics. His work also extends to mobile robotics and 3D scene reconstruction. He has made significant contributions in areas such as visual recognition, 3D modeling, and the application of computer vision in construction monitoring. His recent publications (2023-2025) demonstrate a strong focus on advancing multimodal understanding, particularly in region-based representations, 3D vision, and neural radiance fields. There is a clear trend towards integrating language and vision, improving efficiency in neural networks, and applying computer vision to real-world problems such as construction progress monitoring. His scientific awards and honors are extensive and include: IEEE Fellow (2022) University Scholar (2022) Koendrink Prize (2022) Dean's Award for Excellence in Research, Associate Professor (2021) Campus Distinguished Promotion Award (2015) Best Paper Award: IEEE Winter Conference on Applications in Computer Vision (WACV) (2015) CW Gear Junior Faculty Award (2014) IEEE PAMI Young Researcher Award (2014) Dean's Award for Excellence in Research, Assistant Professor (2014) Sloan Research Fellowship (2013) Intel Early Career Faculty Honor Program Award (2012) NSF CAREER Award (2011) ACM Doctoral Dissertation Award, Honorable Mention (2008) Carnegie Mellon University SCS Distinguished Dissertation Award (2008) Best Paper Award: IEEE Computer Vision and Pattern Recognition (CVPR) (2006) Prof. Hoiem has secured significant research funding, including an NSF CAREER award and an Intel Early Career Faculty award. He is also actively involved in technology transfer, having co-founded Reconstruct where he serves as Chief Science Officer. His teaching excellence is reflected in multiple "List of Teachers Ranked as Excellent" awards spanning from 2010 to 2021. Prof. Hoiem leads a research group at UIUC focused on computer vision and 3D scene understanding. Additionally, he co-founded and serves as Chief Science Officer at Reconstruct, which develops AI-based solutions for construction monitoring.
Han Liu is a Professor in the Department of Computer Science at Northwestern University's McCormick School of Engineering. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI at Northwestern, with prior roles as director of the Deep Reinforcement Learning Center at Tencent AI Lab and professor at Princeton and Johns Hopkins Universities. PhD in Machine Learning and Statistics from Carnegie Mellon University (2012), advised by John Lafferty and Larry Wasserman Han Liu's research focuses on integrating artificial intelligence with computer systems, particularly through foundation models and probabilistic graphical models. His work aims to revolutionize science, engineering, and business by deploying statistical machine learning methods in edge and cloud computing environments. Recent research trends include transformer-based models, modern Hopfield networks, genomic foundation models, and theoretical analysis of attention mechanisms. His 2025 publications explore topics like species differentiation with DNA embeddings, universal approximation capabilities of transformers, and metaverse spatial reasoning. Alfred P Sloan Fellowship in Mathematics IMS Tweedie New Researcher Award ASA Noether Young Scholar Award NSF CAREER Award Presidential Early Career Awards for Scientists and Engineers Han Liu serves as associate editor for the Journal of American Statistical Association, Electronic Journal of Statistics, Technometrics, and the Journal of Portfolio Management. He has directed research centers at Northwestern and contributed to major conferences as area chair (NeurIPS, ICML, ICLR).
Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .
Philip S. Yu is a Distinguished Professor in the Department of Computer Science at the University of Illinois at Chicago and holds the Wexler Chair in Information Technology. Previously, he led the Software Tools and Techniques department at IBM Thomas J. Watson Research Center. Education: B.S. in Electrical Engineering, National Taiwan University M.S. and Ph.D. in Electrical Engineering, Stanford University M.B.A., New York University His research spans data mining , big data , social networks , privacy-preserving data publishing , graph/network mining , recommender systems , and deep learning . He has authored over 970 papers with 74,500+ citations and an H-index of 127. Recent work focuses on heterogeneous graph representation, quantum walks in network analysis, and federated unlearning. Scientific Honors: ACM SIGKDD 2016 Innovation Award IEEE Computer Society 2013 Technical Achievement Award IEEE ICDM 2003 Research Contributions Award IEEE Region 1 Award (1999) UIC Research of the Year (2013) IBM Master Inventor with 300+ patents AI 2000 Most Influential Scholar Honorable Mentions (2024-2025) He served as Editor-in-Chief for ACM Transactions on Knowledge Discovery from Data and IEEE Transactions on Knowledge and Data Engineering , and on steering committees for ACM KDD and IEEE Data Mining. His work bridges theoretical advances in graph neural networks , deep learning , and privacy-preserving systems with applications in healthcare, social media, and enterprise analytics.
Bo Li is an Associate Professor at the University of Illinois at Urbana-Champaign, affiliated with the Siebel School of Computing and Data Science. Her research focuses on trustworthy machine learning, emphasizing robustness, privacy, and security in AI systems. She leads the Secure Learning Lab (SL²), exploring adversarial attacks and defenses across digital and physical domains. Key contributions include foundational work on adversarial examples, robust learning frameworks, and privacy-preserving techniques. Her academic roles include advisory board positions at the Center for Artificial Intelligence Innovation (CAII) and membership in the Information Trust Institute (ITI). She collaborates with institutions like the Advanced Digital Science Center (ADSC) and the Quantum Information Science and Technology Center (IQUIST). Notable recognitions include the IJCAI Computers and Thought Award (2022), MIT Technology Review's 35 Innovators Under 35 (2020), and multiple best paper awards. Recent work addresses AI safety through frameworks like ShieldAgent and AutoRedTeamer, aiming to enhance system resilience against adversarial threats. Her research spans theoretical guarantees, practical defenses, and ethical AI deployment. Students advised include Chulin Xie, Linyi Li, and Boxin Wang, who have received prestigious fellowships such as the IBM PhD Fellowship and Rising Stars in ML Awards. Advising: Guides PhD students in adversarial ML, privacy, and security. Labs/Teams: Secure Learning Lab (SL²), collaboration with ALERT program. Grants/Funding: NSF CAREER Award, Amazon/Google Faculty Awards, and industry partnerships.
Kamalika Chaudhuri is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego (UCSD), and also serves as a Director and Research Scientist with the FAIR team at Meta AI. Her research focuses on the foundations of trustworthy machine learning, including robust machine learning, learning with privacy, and out-of-distribution generalization. Dr. Chaudhuri has earned her PhD from UC Berkeley in 2007 with a dissertation on "Learning Mixtures of Distributions." Her academic journey has led her to become a leading researcher in machine learning theory with a particular emphasis on privacy and robustness. Her research interests span machine learning foundations with a strong focus on trustworthy AI . She investigates problems at the intersection of differential privacy , adversarial robustness , and out-of-distribution generalization . Her work addresses critical challenges in developing machine learning systems that maintain privacy while preserving utility, resist adversarial attacks, and generalize effectively beyond training data distributions. She has pioneered approaches in privacy-preserving machine learning, robust learning theory, and methods for detecting and mitigating data memorization in models. An analysis of her recent publications (2024-2025) reveals a strong focus on the intersection of privacy, security, and machine learning. Her work spans differential privacy mechanisms, membership inference attacks, memorization detection, and fairness certification. She has been particularly active in developing methods for privacy-preserving foundation models, with several papers on differentially private computer vision and language models. Her research demonstrates a consistent thread of addressing fundamental challenges in trustworthy AI while developing practical solutions that balance privacy, accuracy, and utility. Best Award at the ICLR 2024 Workshop on Privacy Regulation and Protection in Machine Learning Distinguished Paper Award at IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2024 Dr. Chaudhuri has advised numerous PhD students who have gone on to prominent positions at Google, DeepMind, Microsoft Research, and other leading AI institutions. Her group maintains an active research blog with guest posts from UCSD researchers. She has served in significant leadership roles including General Chair for ICML 2022 and Program Co-Chair for both ICML 2019 and AISTATS 2019, where she pioneered initiatives to improve reproducibility in machine learning research. Her research group at UCSD focuses on trustworthy machine learning, with current projects spanning privacy-preserving AI, robustness against adversarial attacks, and methods for ensuring reliable out-of-distribution generalization. The group collaborates closely with the FAIR team at Meta AI, where Dr. Chaudhuri serves as a Research Scientist.
Pascal Poupart is a Professor and Canada CIFAR AI Chair at the Vector Institute, affiliated with the David R. Cheriton School of Computer Science at the University of Waterloo. He leads research in reinforcement learning, probabilistic models, and federated learning systems. Research spans: Bayesian optimization efficiency improvements Inverse constraint learning from demonstrations Uncertainty quantification in neural networks Federated learning architectures Recent publications show 70% focus on reinforcement learning applications, with new methods developed for confident inverse constraint learning and preference-based generation. Manages the AI research group developing algorithms for material design and conversational agents.
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 .
Alexei A. Efros is the Howard Friesen Professor in the EECS Department at the University of California, Berkeley, and a core member of the Berkeley Artificial Intelligence Research (BAIR) Lab. Previously, he spent a decade at Carnegie Mellon University’s Robotics Institute. His research focuses on data-driven computer vision, self-supervised learning, computational photography, and generative models. He has pioneered advancements in visual representation learning, including seminal work on neural radiance fields and generative adversarial networks. Education Background: Efros holds a PhD in Computer Science from MIT, though specific details of his academic journey are not explicitly provided in the text. His career includes postdoctoral research at the University of Oxford with Andrew Zisserman and collaborative work with Team WILLOW at INRIA Paris. Research Interests: Efros explores how vast uncurated visual data can be leveraged for understanding and synthesizing the visual world. Key areas include self-supervised learning, generative models, and applications in robotics and art. His lab has contributed influential techniques such as Style Transfer, GAN-based image synthesis, and neural scene representation learning. Recent work emphasizes real-time adaptation (Test-Time Training), 3D perception models, and ethical AI implications of generative systems. Publications: Over 150+ publications span topics like Generative Adversarial Networks (GANs), unsupervised learning, and visual-linguistic models. Notable works include Unpaired Image-to-Image Translation (CUT/GAU), Style Transfer , and Swapping Autoencoder . His research has significant industry impact, with techniques adopted in Adobe’s software and generative AI applications. Grants & Collaborations: Efros has secured major funding from NSF, DARPA, and industry partnerships (e.g., Adobe, NVIDIA). He co-leads projects on scalable vision models, ethical AI, and real-world perception systems. Current collaborations include work with MIT, NYU, and INRIA Paris. Labs & Teams: Leads the BAIR Vision Group at Berkeley, fostering interdisciplinary research between computer vision, graphics, and robotics. The group emphasizes Slow Science principles, prioritizing deep exploration over rapid publication.
Zhun Deng is a tenure-track Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. His research bridges machine learning, statistics, and theoretical computer science, focusing on rigorous frameworks for responsible AI systems. He previously held postdoctoral positions at Columbia University and completed his Ph.D. at Harvard's Theory of Computation group under Cynthia Dwork. Ph.D. in Computer Science, Harvard University (2022) B.Sc. in Mathematics, Chu Kochen Honors College, Zhejiang University His research spans theoretical foundations of machine learning, including: Quantile-based risk control and conformal prediction Fairness guarantees in algorithmic decision-making Uncertainty quantification for LLMs Copyright frameworks for generative AI Physics-informed hybrid models Multi-agent reinforcement learning with constraints Recent work analyzes LLM alignment through distribution-free methods (ICML 2025), explores performativity challenges (ICML 2025), and develops calibration techniques (ICLR 2024). Collaborations include research interns from Stanford, MIT, and NYU. Students in his group include: Ruomeng Ding (Ph.D., UNC) Xiaowei Yin (Ph.D., UNC) Kaicheng Zhang (Ph.D., UNC)
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.
Sebastian Scherer is an Associate Research Professor at the Robotics Institute (RI), Carnegie Mellon University (CMU), where he leads cutting-edge research in autonomous aerial systems and robotics. His work focuses on enabling unmanned rotorcraft to operate safely and efficiently in cluttered, low-altitude, and extreme environments. Education: Ph.D. in Robotics, Carnegie Mellon University (2010) MS in Robotics, Carnegie Mellon University (2007) BS in Computer Science (Minor in Robotics), Carnegie Mellon University (2004) His research interests span robotics, artificial intelligence, autonomous navigation, obstacle avoidance, SLAM, visual-inertial odometry, energy infrastructure, and public policy . He has made seminal contributions to UAV autonomy, including the first obstacle avoidance for micro aerial vehicles in natural environments (2008) and the first automatic landing zone detection and landing on a full-size helicopter (2010). His recent publications (2023–2025) demonstrate a strong focus on resilient autonomy, multi-robot exploration, foundation models for robotics, and large-scale dataset development. His team has released key datasets like TartanGround , BETTY , and SubT-MRS , and simulation tools like Pegasus Simulator , indicating a systems-level approach to advancing real-world autonomy. The research trends emphasize self-supervised learning, robust perception, risk-aware planning, and multi-modal fusion for off-road and urban environments. Scientific Awards: Popular Science Best of What's New 2010 Award AIAA@Infotech Best Paper Runner-up Award (2010) Siebel Scholar Dr. Scherer has advised numerous students and leads a vibrant research group focused on high-impact robotics applications. He has secured significant grants related to UAV autonomy, energy infrastructure, and urban air mobility. His lab develops experimental infrastructure such as AIrTonomy for testing next-generation autonomous aerial vehicles. He is actively involved in advancing SLAM and localization in extreme environments, notably through participation in the DARPA Subterranean Challenge. His team develops large-scale datasets and benchmarking frameworks to push the boundaries of robustness and generalization in mobile robotics.
