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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.

• Intelligent Tutoring Systems
• Cognitive Modeling
• Learning Sciences
• +5 more

John Stamper is an Associate Professor at Carnegie Mellon University's Human-Computer Interaction Institute (HCII) and serves as Technical Director of the Pittsburgh Science of Learning Center DataShop, the world's largest open repository of educational log data. He earned his PhD in Computer Science from the University of North Carolina at Charlotte under Dr. Tiffany Barnes, following over a decade of industry experience including roles as Vice President of Development at VSI Technologies, Inc. and MCSE/MCDBA certifications. Stamper's research leverages big data to transform educational systems through expertise in Educational Data Mining, Learning Sciences, and Intelligent Tutoring Systems. His work spans Human-Centered AI, Social Computing, and Data Visualization, driving projects like DataShop, LearnSphere, and the Pittsburgh Science of Learning Center to identify effective instructional conditions and improve online learning. He actively mentors graduate students including Huy Anh Nguyen, Christine Kwon, Marshall An, Yumou (James) Wei, and Ruiwei Xiao, while bridging academic research with practical applications through his startup TutorGen. Stamper maintains strong community engagement in educational technology conferences including LAK, EDM, AIED, and L@S, with upcoming participation in 2025 events in Dublin and Palermo.

• Educational Data Mining
• Learning Sciences
• Educational Technologies
• +4 more

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.

• Robotics
• Multi-Agent Systems
• Machine Learning
• +4 more

Terry E. Lee is an Associate Teaching Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science . Contact available at TCS Hall 240, Pittsburgh, PA 15213. Education : PhD in Software Engineering, PhD in Societal Computing. Teaching Focus includes courses like Web Application Development, Data Structures for Application Programmers, and Privacy Technology. Collaborative teaching with Professors Austin Henley, Jeff Eppinger, and Hammad Ahmad highlights interdisciplinary engagement.

• Software Engineering
• Privacy Technology
• Information Security
• +3 more

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.

• Technology Ethics
• Machine Learning
• Data Ethics
• +4 more

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.

• Text Mining
• Data Mining
• Machine Learning
• +4 more

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

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +5 more

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.

• Value of Digital Things
• Service Design and Social Computing
• Ubiquitous/Mobile Computing
• +3 more

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.

• Artificial Intelligence
• Machine Learning
• Agent-Based Modeling
• +5 more

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.

• Computer Vision
• Robotics
• Medical Robotics
• +6 more

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)

• Computational Biology
• Bioinformatics
• Machine Learning
• +3 more

Robert Harper is a Professor in the Computer Science Department at Carnegie Mellon University, Pittsburgh, PA. His research focuses on the intersection of type theory and programming language design, with emphasis on formal verification, cost semantics, and concurrency models. University: Carnegie Mellon University Department: Computer Science Academic Rank: Professor Email: rwh@cs.cmu.edu Harper's research program integrates cost analysis with behavioral properties in type theory, including Synthetic Tait Computability for logical frameworks and Amortized Analysis via Coalgebraic Methods . He explores foundational issues in Higher-Order Recursion and resource-sensitive programming through Decalf , a directed effectful cost-aware framework. His work combines theoretical rigor with practical implementation in systems like Twelf and Red* . Recent publications demonstrate his contributions to Computational Adequacy , Session-Typed Concurrency , and Compiler Verification . Collaborations with institutions like the Max Planck Institute for Software Systems and researchers such as Stephanie Balzer and Jan Hoffmann extend his impact to concurrent systems and cost-sensitive programming. Scientific Distinctions ACM SIGPLAN Programming Languages Achievement Award (2021) ACM Fellow (2005) Allen Newell Award for Research Excellence (2001) He advises students on projects like Cost Verification in Calf and Parallel Scan Verification , while teaching advanced courses including 15-413: Advanced Topics in Principles of Programming Languages (Spring 2025) and 15-312: Principles of Programming Languages (Fall 2025). Research funded by AFOSR grants including MURI A210038S002.

• Programming Languages
• Type Theory
• Cost Semantics
• +3 more

Dave Andersen is an Associate Professor at the School of Computer Science, Carnegie Mellon University , with research focusing on memory and power-efficient computing, robust distributed systems, and networked environments. He also serves as CTO of Enriched Ag . Education: Ph.D. and M.S. in Computer Science from MIT (2001, 2004), B.S. in Computer Science and Biology from the University of Utah (1995). Research Trends: Explores systems design in the post-Moore's Law era, emphasizing concurrency, low-latency geo-replicated storage, and RDMA in datacenters. Key projects include MemC3, Eiger, Cuckoo Filter, and FAWN. Teaching: Courses in Advanced OS, Distributed Systems, Low-Power Computing, and Network Security. Professional Service: Program committee roles at SOSP, NSDI, SIGCOMM, and DARPA ISAT advisory group. Personal: Active in running, climbing, and outdoor activities with detailed route guides for Pittsburgh and Boston.

• Computer Systems
• Distributed Systems
• Networked Environments
• +12 more

Weina Wang is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, joining in Fall 2018. Her research lies at the intersection of applied probability , stochastic systems , and reinforcement learning , focusing on decision-making in large-scale systems with applications to computing resource orchestration, data privacy, and graph statistics. She has received prestigious awards including the NSF CAREER Award (2022) , ACM MobiHoc Best Paper (2022) , and ACM SIGMETRICS Rising Star Research Award (2023) . PhD in Electrical Engineering, Arizona State University (2016) Bachelor’s in Electronic Engineering, Tsinghua University (2009) Her recent publications span restless bandits , queueing theory , and attributed graph alignment , reflecting her dual focus on fundamental limits and algorithmic solutions. Notable collaborations include work on privacy-preserving data routing , phase-aware scheduling , and erasure-coded servers for heterogeneous traffic. She has advised PhD students Jalani Williams , Tuhinangshu Choudhury , and Yige Hong . Her research has been recognized with best paper awards and grants like the NSF CAREER . She also contributes to professional societies, recently joining the INFORMS Applied Probability Society council . Her teaching includes courses like Probability and Computing and Fundamentals of MDPs and Reinforcement Learning .

• Applied Probability
• Stochastic Systems
• Reinforcement Learning
• +7 more