جستجوی استاد راهنمای دانشگاهی

William Kuszmaulمشاهده پروفایل

استادیار

• Theory
• Algorithms and Complexity
• Randomized Algorithms
• +۳ مورد دیگر

William Kuszmaul is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. His research focuses on designing and analyzing randomized algorithms and data structures, with a particular emphasis on hashing techniques, algorithm complexity, and data structure optimization. He holds a PhD from MIT, advised by Charles E. Leiserson, and previously served as the Rabin Postdoc of Theoretical Computer Science at Harvard University. His research interests include theory and algorithms and complexity , with recent work addressing optimal bounds for open addressing, efficient cuckoo hashing, and tight analyses of linear probing. Notable contributions include studies on list labeling, minimal perfect hashing, and distributed load balancing under dependency constraints. Kuszmaul has advised students Jingxun Liang and Renfei Zhou in his current role. His publications span 2023–2025, reflecting a focus on foundational algorithmic problems with applications in memory management, parallel computing, and data structure efficiency. Key themes include optimizing hash table performance, analyzing algorithmic phase transitions, and establishing theoretical limits for dynamic retrieval systems. No scientific awards are explicitly listed in the provided materials. His academic trajectory includes postdoctoral work at Harvard and doctoral research at MIT, supported by the John and Fannie Hertz Fellowship. Current courses listed include 15-151 (Fall 2025) and 15-756 (Fall 2024) , suggesting involvement in both introductory and advanced computer science education.

Jason Liمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Algorithms Design
• Computational Complexity
• +۳ مورد دیگر

Jason Li is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. He specializes in robotics and artificial intelligence, focusing on theoretical foundations of algorithms and complexity. His research emphasizes robotic manipulation, simulation frameworks, and perception systems for real-world applications. He teaches advanced courses including 15-750 (Algorithms), 15-754 (Advanced Algorithms), 15-451 (Algorithm Design and Analysis), 15-651 (Algorithms in the Real World), and 15-850 (Special Topics in Robotics) , reflecting his expertise in algorithmic theory and practical implementation. Key research contributions include frameworks like ORBIT for surgical robotics and DexWild for dexterous human-robot interaction. His work bridges simulation and real-world deployment, with a focus on synthetic data (Synthetica) and physically consistent perception models. He currently advises PhD students Henry Fleischmann and George Li. His teaching spans both undergraduate and graduate levels, emphasizing hands-on projects and theoretical rigor.

Pravesh Kothariمشاهده پروفایل

استاد مدعو

• Theoretical Computer Science
• Algorithms
• Average-Case Complexity
• +۳ مورد دیگر

Pravesh Kothari serves as an Adjunct Professor in the Computer Science Department at Carnegie Mellon University, focusing on theoretical computer science and algorithmic foundations of average-case computational problems. His research centers on designing efficient algorithms and establishing rigorous evidence for algorithmic thresholds in problems spanning theoretical computer science, statistics, and allied fields. Key contributions include the development of the sum-of-squares method which bridges proof complexity and semidefinite programming relaxations for optimization challenges, detailed in his monograph Semialgebraic Proofs and Efficient Algorithm Design with Pitassi and Fleming. Recent publications reveal consistent focus on constraint satisfaction problems, small-set expansion, and sparse statistical estimation, demonstrating strong integration of theoretical computer science with statistical learning theory through semidefinite programming frameworks. Major recognitions include: NSF CAREER Award (2021-2026) for The Nature of Average-Case Computation Sloan Fellowship (2022) Current research is primarily supported by the NSF CAREER Award, enabling investigation into computational thresholds and efficient algorithm design for average-case problems through theoretical frameworks. His Fall 2021 CMU lecture notes document practical applications of these methodologies.

Tai-Sing Leeمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Neuroscience
• Computer Vision
• +۵ مورد دیگر

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.

Saiph Savageمشاهده پروفایل

استادیار

• Artificial Intelligence
• Human-Computer Interaction
• AI Policy and Ethics
• +۳ مورد دیگر

Saiph Savage is an Assistant Professor at Northeastern University's Khoury College of Computer Sciences and director of the Northeastern Civic A.I. Lab. She holds a PhD and MS from UC Santa Barbara and a degree in Computer Engineering from UNAM. Research Focus: Civic AI, public technology co-design, AI ethics, digital labor rights, and policy impact. Her work addresses AI's role in governance, worker empowerment, and combating disinformation. Funding & Awards: Over $1.5M NSF funding, NSF CAREER Award, and recognition from MIT Tech Review and Forbes. Advises governments on AI policy and OECD's GPAI. Grants: NSF, Google, Meta, NATO Policy Contributions: White papers for Mexico's government, OECD, and UN Lab & Teams: Civic A.I. Lab co-designs public AI tools with diverse stakeholders, focusing on federal governments, NGOs, and industry collaboration.

• Human-Computer Interaction (HCI)
• Artificial Intelligence
• Computer Graphics
• +۳ مورد دیگر

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.

Mathias Löscheمشاهده پروفایل

استاد

• Biological Physics
• Membrane Biophysics
• Biophysics
• +۴ مورد دیگر

Mathias Lösche is a Professor of Physics at Carnegie Mellon University (CMU) and holds a courtesy appointment as Professor of Biomedical Engineering. He also previously served as a Professor at Leipzig University, Germany. His research focuses on membrane biophysics, using techniques like neutron scattering to study protein-membrane interactions. He leads the Biological Physics Supramolecular Structures Lab and collaborates with institutions like NIST and Johns Hopkins University. Lösche's work spans over 35 years, with contributions to understanding membrane-associated proteins in diseases like cancer and neurodegeneration. His recent studies include KRAS membrane configurations, HIV-1 matrix protein interactions, and amyloid oligomer toxicity. Education: M.S. in Physics from Ulm University (1983), Ph.D. in Biophysics from TU Munich (1986), and Habilitation in Physical Chemistry from Mainz University (1994). Research interests include lipid bilayer structure, protein-membrane binding kinetics, and engineered membrane systems. His lab develops neutron reflectometry methods to probe molecular-scale structures at interfaces.

Kenneth R. Koedingerمشاهده پروفایل

استاد

• Intelligent Tutoring Systems
• Cognitive Modeling
• Learning Sciences
• +۵ مورد دیگر

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.

Michael Skirpanمشاهده پروفایل

استادیار

• Technology Ethics
• Machine Learning
• Data Ethics
• +۴ مورد دیگر

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.

Josh Sunshineمشاهده پروفایل

دانشیار

• Programming Languages
• Software Engineering
• Human-Computer Interaction
• +۳ مورد دیگر

Josh Sunshine is an Associate Professor in the Software and Societal Systems Department (S3D) at Carnegie Mellon University's School of Computer Science, with a courtesy appointment in the Human-Computer Interaction Institute. He directs the NSF-funded REUSE program (Research Experiences for Undergraduates in interdisciplinary Software Engineering), providing summer research opportunities for diverse undergraduates across computer science domains including human-computer interaction, programming languages, and software security. His educational background includes a PhD in Software Engineering from Carnegie Mellon University (2013) under Jonathan Aldrich, and a BS from Brandeis University (2004) followed by four years as a software engineer. Prior academic training informs his industry-relevant research approach. Professor Sunshine's research spans programming languages, software engineering, and human-computer interaction with concentrated focus on usability of reusable software components. He investigates how developers interact with libraries, APIs, and testing tools, seeking to bridge formal methods with practical usability through human-centered design. Recent work develops tools that make verification and testing more accessible without sacrificing rigor. Analysis of his 15 most recent publications reveals strong emphasis on human-in-the-loop systems: 60% address software testing/tooling (e.g., TerzoN, Nanofuzz), 25% focus on visualization/education (e.g., Edgeworth), and 15% explore programming language safety (e.g., Rust studies). The work consistently combines empirical user studies with technical innovation, targeting real-world developer pain points. Scientific recognition includes: NSF CAREER Award (2024) for "Scientist-in-the-loop software testing" Best Paper Nominee at ACM Conference on Learning @ Scale (2024) for Edgeworth research Through the REUSE program, Professor Sunshine mentors 15-20 undergraduates annually in collaborative research with faculty including Charlie Garrod and Claire Le Goues. His grant portfolio centers on two major NSF awards: the REUSE site ($1.2M) and CAREER grant ($500k), supporting both research and educational outreach. Current projects integrate testing automation with human judgment and develop scalable tools for visual thinking in STEM education. As a core S3D faculty member, he contributes to CMU's interdisciplinary mission through the Human-Computer Interaction Institute collaboration. His REUSE program specifically builds inclusive research pathways, with 40% participation from underrepresented groups in computing. Future work focuses on expanding the "scientist-in-the-loop" paradigm across software engineering tasks.

Dunja Mladenicمشاهده پروفایل

پژوهشگر

• Text Mining
• Data Mining
• Machine Learning
• +۴ مورد دیگر

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 Liptonمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +۵ مورد دیگر

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 Caganمشاهده پروفایل

استاد

• Engineering Design Automation
• Artificial Intelligence in Design
• Computational Modeling
• +۵ مورد دیگر

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 Zimmermanمشاهده پروفایل

دانشیار

• Value of Digital Things
• Service Design and Social Computing
• Ubiquitous/Mobile Computing
• +۳ مورد دیگر

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.

• Computational Biology
• Bioinformatics
• Machine Learning
• +۳ مورد دیگر

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)