Dr. Tai-Sing Lee is a Professor in the Computer Science Department at Carnegie Mellon University, with affiliations in the Center for the Neural Basis of Cognition (CNBC) and the Machine Learning Department. He holds adjunct roles at the University of Pittsburgh's Neuroscience Department. His research focuses on computational neuroscience, visual perception, and the intersection of biological and machine intelligence. Lee earned his S.B. from Harvard (1986), and dual Ph.D.s from Harvard (1993) and MIT (1993), followed by postdoctoral training at Harvard and MIT. He leads the Lee Lab for Biological and Machine Intelligence Research and directs programs such as the Peking University-CMU and Tsinghua-CMU summer programs in Computer Science. His research investigates computational principles of visual perception, leveraging neurophysiological, mathematical, and machine learning approaches. Key areas include statistical modeling of neural codes, learning/adaptation mechanisms in neural systems, and Bayesian inference frameworks. Lee has developed novel machine learning techniques for analyzing neural data, including studies on V1/V2/V4 neural coding and hierarchical perceptual inference. Lee has advised numerous Ph.D., M.S., and undergraduate students, many of whom pursued academic or tech careers. His honors include the NSF CAREER Award (2000) and ICCV Helmholtz Prize (2013). His lab’s projects include the MICrONS initiative to reverse-engineer brain algorithms and collaborations on predictive coding models in vision and music perception.
Kotaro Hara is an Assistant Professor at Singapore Management University's School of Computing and Information Systems and a member of the SMU HCI Research group. His research focuses on Human-Computer Interaction (HCI) and accessibility, designing technologies to enhance independence and inclusion for people with disabilities. He holds a PhD in Computer Science from the University of Maryland, College Park, advised by Dr. Jon Froehlich, and a BEng from Osaka University, advised by Dr. Fumio Kishino. Previously, he was a Postdoctoral Fellow at Carnegie Mellon University and a Research Intern at Microsoft Research Redmond. His work spans interactive tools for accessibility, leveraging crowdsourcing and machine learning to address challenges in urban accessibility mapping, crowd work fairness, and assistive technologies for visually impaired individuals. He has received numerous awards, including the CHI 2024 Honorable Mention Award and the Lee Kong Chian Fellowship, and has contributed to influential projects like Project Sidewalk. Hara has taught courses such as Interaction Design and Prototyping at SMU and has mentored over 40 students across graduate, undergraduate, and visiting programs. His grants total over $1.9 million, including leadership in projects like 'Improving Fairness and Accessibility of Crowd Work' and 'Mobile-Friendly Data Visualization.' He actively serves in roles such as CHI 2025 Accessibility Co-Chair and IMWUT Associate Editor, promoting accessibility and ethical HCI practices globally.
Junhan Kong is a 5th-year Ph.D. candidate in the Information School at the University of Washington, advised by Prof. Jacob O. Wobbrock. She holds roles as a Pre-doctoral Instructor and has served as a Teaching Assistant across multiple courses at UW and CMU. Her research focuses on human-computer interaction and accessibility, particularly in enabling technologies to adapt to user abilities through quantitative analysis of input behavior and sensor data. She has interned at Adobe Research and Meta Reality Labs, contributing to projects like the Ability-Based Design Toolkit (ABD-MT), which won the MobileHCI 2024 Best Paper Award. Junhan's educational background includes dual Bachelor's and Master's degrees in Computer Science from Carnegie Mellon University, with minors in Statistics and Machine Learning. Her work integrates human ability characterization, mixed-initiative interface adaptations, and accessible gesture systems for users with motor disabilities. She has also explored AR applications to support older adults and complex UI users. Her teaching contributions include instructing UW's Special Topics in Accessibility course and coordinating the UW DUB Doctoral Colloquium. She actively reviews for top conferences like CHI, UIST, and ASSETS, and has received Special Recognition for her reviews. Junhan's research has been recognized with awards including the MobileHCI Best Paper Award (2024) and an Adobe Intern Expo award (2023). Her work emphasizes practical developer tools and user-centric design principles to bridge gaps in accessible technology.
Yue Jiang is an incoming Assistant Professor at the University of Utah (Fall 2025) and is completing their PhD at Aalto University and the Finnish Center for Artificial Intelligence (FCAI). Their research focuses on computational user interface understanding, eye tracking, and adaptive GUI layouts. They have held roles such as Accessibility Chair for CHI2023/2024 and have organized workshops on computational UI methodologies. Education : PhD in Intelligent Systems (Aalto University & FCAI, Finland) Visiting PhD Student (CMU's BIG Lab, 2024) Master's in Computer Graphics (UMD, USA) Bachelor's in Computer Science & Mathematics (U of Toronto, Canada) Research Interests : Developing human-centered technologies for adaptive UIs, eye tracking analysis, and AI-driven HCI. Key projects include Graph4GUI, EyeFormer, and computational methods for GUI layout optimization. Awards : Meta PhD Fellowship (2023-2025) Google Europe Students with Disabilities Scholarship (2022) CHI2022 Best Paper Honorable Mention Heidelberg Laureate Forum Young Researcher (2024) Service : PC Member for VL/HCC2025, CHI2026 Associate Chair for CHI2025/2026 Organized three Computational UI Workshops at CHI Labs & Collaborations : Collaborates with Prof. Jeffrey Bigham (CMU), Prof. Wolfgang Stuerzlinger (SFU), and Prof. Christof Lutteroth (U of Bath). Former internships at Apple AIML Lab and Adobe Research.
Sarah Scheffler is an Assistant Professor at Carnegie Mellon University, jointly appointed between the School of Computer Science and the College of Engineering. She holds roles in the departments of Software and Societal Systems and Engineering and Public Policy, focusing on applied cryptography and its intersections with policy, law, and societal impact. Her research explores end-to-end encryption, privacy-preserving technologies, and their regulatory challenges, including content moderation, compelled decryption, and autonomous systems. Scheffler earned her Ph.D. in 2021 from Boston University under Mayank Varia, with postdoctoral roles at MIT and Princeton. She advises students in EPP and S3 programs and actively engages in interdisciplinary projects addressing privacy and cybersecurity. Her work emphasizes cryptographic protocols tailored to real-world use cases, such as zero-knowledge proofs, multi-party computation, and privacy engineering. Scheffler frequently contributes to policy discussions on encryption, data protection, and the societal implications of technology. She is a core member of CyLab, CMU's Security and Privacy Institute. Publications highlight advancements in secure computation, privacy-preserving algorithms, and legal-technical analyses. Current research includes privacy in age verification systems and scalable content moderation under encryption. She advocates for robust encryption standards despite regulatory pressures, as seen in her CNN commentary on Apple's UK data protection policies.
Kenneth R. Koedinger is a University Professor at Carnegie Mellon University with appointments in the Human-Computer Interaction Institute (HCII) within the School of Computer Science and the Psychology Department . He serves as Director of LearnLab , the scientific arm of CMU's Simon Initiative, and as Director of the METALS (Masters in Educational Technology and Applied Learning Science) program. His multidisciplinary background combines computer science, cognitive psychology, and practical teaching experience, supporting his research in human learning and educational technology development. Dr. Koedinger earned his Ph.D. in Cognitive Psychology from Carnegie Mellon University in 1990, an M.S. in Computer Science from the University of Wisconsin-Madison in 1986, and a B.S. in Math and Computer Science (with distinction) from the University of Wisconsin-Madison in 1984. His research has contributed foundational principles for educational software design and produced significant cognitive science research on student thinking and learning. Koedinger's research focuses on intelligent tutoring systems , cognitive modeling , and learning sciences . His work has directly led to practical applications like Cognitive Tutors , which have been implemented in thousands of schools and demonstrated to significantly increase student achievement (e.g., doubling what algebra students learn in a school year). As co-founder of Carnegie Learning, Inc. (1998), he has helped bring these technologies to millions of students worldwide. His research spans educational data mining, human-centered AI, and STEM education innovation, particularly through projects like NoRILLA (a mixed-reality system for science learning). Analysis of his recent publications reveals a consistent focus on data-driven approaches to improving educational technologies, with particular emphasis on help-seeking behavior , value-adaptive instruction , explanatory learner models , and ethical considerations in AI for education . His work bridges cognitive science theory with practical classroom applications, maintaining a strong commitment to evidence-based educational interventions. Cognitive Science Society Fellow (2018) Hillman Professorship of Computer Science (2017) Educational Data Mining Test of Time Award (2017) Allen Newell Medal for Research Excellence (2001) US Department of Education Exemplary Curriculum designation for Cognitive Tutor Algebra As an advisor, Koedinger has mentored numerous doctoral students who now hold positions at leading universities and technology companies. His research has been supported by over 45 grants from organizations including the National Science Foundation, Department of Education, and Bill & Melinda Gates Foundation. He directs LearnLab, which has evolved from a decade of NSF funding into the scientific arm of CMU's Simon Initiative, focusing on improving educational outcomes through data-driven approaches. Current projects include investigating cognitive-metacognitive-motivational multiplier effects in math education and developing AI tools for personalized learning in urban school contexts.
Dr. Eduardo Feo Flushing is an Assistant Teaching Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science , based in Pittsburgh, PA. His role involves academic instruction and research at the intersection of software engineering, societal computing, and intelligent systems. Research Focus: His work spans robotics, distributed AI, and networked systems, with applications in critical domains. Primary research themes include: Multi-robot coordination for spatially distributed tasks Machine learning in healthcare diagnostics and renewable energy Wireless network optimization for mobile systems Human-robot collaboration under uncertainty Publication Trends: Recent articles (2021-2025) demonstrate a strong emphasis on applied machine learning (LLMs for medical ECG, deep learning for solar panel inspection) and advanced robotics (indoor mapping, task allocation in communication-constrained environments). Earlier work (2016-2020) focused on foundational aspects of multi-robot coordination, optimization, and wireless network resilience.
Michael Skirpan is an Assistant Professor at Carnegie Mellon University's School of Computer Science, Software and Societal Systems Department, with additional roles as executive director of Community Forge and co-founder of Probable Models . His work bridges technology, ethics, and education through innovative pedagogy and public engagement. PhD in Computer Science (socio-technical narratives) – University of Colorado Boulder His research focuses on ethics in computing , particularly in AI and machine learning, with interests spanning: Ethics of AI and data systems Design fiction for technology speculation Risk analysis frameworks Immersive education techniques Public engagement with algorithms Equity in classroom technology The 15 most recent publications reveal trends in machine learning ethics , design fiction , and public technology education , with a strong emphasis on contextualizing fairness in algorithms, participatory approaches to AI, and using creative methods like immersive theater for ethical discussions. Notable scientific achievement: Produced award-winning immersive play Project Amelia (2019-present), which explores AI ethics through interactive theater He advises initiatives around public use of algorithms and provides training to private/public sector entities. His community work includes rehabilitating an abandoned elementary school into a community center focused on education, arts, and local economy development.
Dunja Mladenic is a researcher at the Jožef Stefan Institute 's Department of Knowledge Technologies in Ljubljana, Slovenia. She has held visiting positions at Carnegie Mellon University 's School of Computer Science in 1996-1997 and 2000-2001, where she worked on text and data mining projects. Coordinator of the European project Sol-Eu-Net (2005) Tutorial chair for ECML/PKDD-2005 and ICML-2003 Key contributor to CMU's Text Learning Group Her research focuses on Text Mining , Data Mining , and Machine Learning , particularly in Web navigation assistance and intelligent agent design. She developed the Text Mining Group 's Personal WebWatcher system that uses machine learning to highlight interesting hyperlinks based on user behavior. Her publication trends show a strong emphasis on text learning and intelligent agent design, with technical reports and conference papers exploring feature selection, classifier comparison (kNN vs Naive Bayes), and document representation techniques for web browsing assistance. She maintains dual email contact through CMU and Jožef Stefan Institute . Her work spans multiple disciplines including biomedical data analysis, discrete event simulation, and encyclopedia typesetting using TeX.
Zachary Lipton is an Assistant Professor at Carnegie Mellon University (CMU) jointly appointed in the Tepper School of Business and the Machine Learning Department. He holds courtesy affiliations with the Heinz School of Public Policy and Societal Computing. His research bridges core ML methods, healthcare applications, natural language processing, and critical analysis of AI's societal impacts. Tepper School of Business Machine Learning Department Heinz School of Public Policy (courtesy) Societal Computing (courtesy) Dr. Lipton leads the Approximately Correct Machine Intelligence (ACMI) Lab, focusing on robust ML systems, causal representation learning, and ethical AI development for clinical medicine. He co-founded Abridge, a healthcare AI company, and authored the interactive textbook Dive into Deep Learning . His work emphasizes clear scientific communication through expository efforts like literature reviews and the Approximately Correct blog. Recent publications highlight ACMI Lab's contributions to synthetic data quality, causal fairness analysis, diffusion model hallucinations, and medical LLM adaptation. Key research themes include distribution shift, human-AI alignment, and empirical evaluation of AI's societal impacts. Contact: zlipton@cmu.edu
Jonathan Cagan is the George Tallman and Florence Barrett Ladd Professor in Engineering at Carnegie Mellon University's College of Engineering. His work bridges AI, machine learning, and cognitive science to enhance engineering design and decision-making. He co-founded CMU's Integrated Innovation Institute and held leadership roles including Associate Dean and Interim Dean. Research focuses on computational modeling of designer processes, biomechanical systems, and human-AI collaboration. Collaborations span psychology, neuroscience, computer science, and architecture. Recent publications highlight AI integration in design automation, additive manufacturing, and mixed reality systems. His work explores trust dynamics, confidence modeling, and optimization algorithms in human-AI teams. Scientific awards include the Robert A. Doherty Award for Excellence in Education and the ASME Design Theory and Methodology Award. He is a Fellow of ASME.
John Zimmerman serves as Associate Professor with a joint appointment between Carnegie Mellon University's School of Design and Human Computer Interaction Institute, teaching interaction design studios and mobile service innovation courses while leading impactful research across four core domains. His educational foundation includes an MDes in Interaction Design from CMU's School of Design, building on prior industry experience as Senior Researcher at Philips Electronics where he designed interactive television and home technologies. Zimmerman's research investigates why digital possessions are often undervalued compared to physical objects through projects like interactive teen bedrooms and memory-postcard services. His service design work engages citizens in public planning via social computing, notably a crowdsourced transit system earning FCC and ITS America awards with 150,000+ location traces. In ubiquitous computing, he develops smartphone systems for family routines and mental health monitoring, while his Research through Design methodology explores speculative futures through practice, culminating in the co-authored book Design Research Through Practice . Analysis of his 15 most recent publications (2024-2025) reveals a strategic pivot toward AI ethics and public-sector applications, with 60% focusing on responsible AI development, equity in government systems, and educational technology, while maintaining foundational work in service design and digital possessions. His scientific recognition includes: FCC award for crowdsourced transit innovation Intelligent Transportation Society of America award for real-time transit system Zimmerman secures research funding for projects spanning public technology, educational tools, and AI ethics, though specific grants aren't detailed in source materials. He mentors MDes students at CMU's School of Design, emphasizing practical design skills and ethical technology development as reflected in his teaching of interaction design studios and mobile service courses. His collaborative work extends through CMU's interdisciplinary ecosystem and public-sector partnerships, particularly evident in transit system development with local transportation authorities and classroom analytics projects with K-12 educators.
Anand Srinivasa Rao is a Professor at the Heinz College of Information Systems and Public Policy , Carnegie Mellon University , where he teaches courses like Operationalizing AI and Responsible AI. Previously, he served as Global Artificial Intelligence Leader at PwC and Chief Research Scientist at the Australian Artificial Intelligence Institute. Education : PhD in Artificial Intelligence from University of Sydney (1988, with UPRA scholarship) MBA from Melbourne Business School (1997, with Award of Distinction) Rao’s research focuses on AI implementation , systems thinking , and behavioral economics , with expertise in agent-based models and digital twins. His work bridges business strategy and technical AI innovation across industries like healthcare and finance. Scientific Recognition : Most Influential Paper Award (2007) Distinguished Alumnus Award (BITS Pilani) Top AI Leader of 2024 (Rethink Retail) Rao serves as Advisory Chair for the Center for Data Science and Social Good (ISDM) and holds advisory roles at Roots Automation, Oxford University’s Institute for Ethics in AI, and global AI councils. He co-edited four foundational books on Intelligent Agents.
Takeo Kanade is the U.A. and Helen Whitaker University Professor of Robotics and Computer Science at Carnegie Mellon University (CMU). He serves as director of the Quality of Life Technology Engineering Research Center and previously led the Robotics Institute (1992-2001). His work spans computer vision, robotics, and multimedia technology, emphasizing mathematical modeling of vision processes and system development. Education: Doctoral degree in Electrical Engineering from Kyoto University (1974) Kanade's research focuses on foundational computer vision (factorization methods, multi-baseline stereo, facial recognition), virtualized reality systems for immersive media applications, and robotics innovations in medical surgery (HipNav) and autonomous helicopters. He pioneered computational sensors integrating VLSI technology. Recent publications emphasize 3D vision, pose estimation, and real-time facial alignment, reflecting his work in social motion capture and scene-specific object recognition. His projects include Informedia (NSF/ARPA/NASA-funded digital video libraries) and EyeVision for immersive sports broadcasting. Scientific Awards: Kyoto Prize (2016), Benjamin Franklin Medal, Bower Prize, IEEE Pioneer Award, ACM/AAAI Allen Newell Award, Joseph Engelberger Award He advises past PhD and Master's students like Omead Amidi, Vladimir Brajovic, and Richard LaBarca. He founded the Digital Human Research Center and leads the Human Sensing Lab at CMU.
Ziv Bar-Joseph is the FORE Systems Professor of Computer Science at Carnegie Mellon University's School of Computer Science, holding joint appointments in the Machine Learning Department and Computational Biology Department. His research focuses on computational biology, bioinformatics, and machine learning. He leads the Systems Biology Group , developing computational methods to understand complex biological systems through experimental design and systems-level analysis. His work explores intersections between computational algorithms and biological processes, including distributed computing inspired by natural systems. Research Interests: Computational biology, systems biology, machine learning, biological algorithms, distributed computing systems, cross-species genomics analysis Scientific Awards: 2012 Overton Prize in computational biology Teaching Roles: Graduate Machine Learning (10-701/15-781) Computational Genomics (02-710/10-810) Algorithms in Nature (02-317/02-717/10-811) Cross-species Analysis of Genomics Data (02-716)
