جست‌وجوی استاد و پژوهشگر

Joseph Ramseyمشاهده پروفایل

پژوهشگر

• Causal Inference
• Machine Learning
• Computational Epistemology
• +۴ مورد دیگر

Joseph Ramsey is a Researcher in the Department of Philosophy at Carnegie Mellon University , affiliated with the Dietrich College of Humanities and Social Sciences . He serves as Director of Research Computing and has been instrumental in developing computational infrastructure and algorithms for causal inference. Core projects: Tetrad (causal search algorithms), AProS (proof generator for logic), Causality Lab , and Laboratory for Symbolic and Educational Computing . His research spans causal modeling, algorithm design, and applications in neuroscience, bioinformatics, and education. He has contributed to software tools like Causal-learn and Py-Tetrad , enabling scalable causal discovery in high-dimensional datasets. He has received funding from NASA, NSF, and the University of Pittsburgh for projects ranging from Martian rover software to glaucoma detection models. His work integrates philosophy, computer science, and applied statistics.

Daniel Cardoso Llachمشاهده پروفایل

دانشیار

• Architectural Automation
• Human-Machine Interaction
• Digital Technology History
• +۳ مورد دیگر

Daniel Cardoso Llach is an Associate Professor at Carnegie Mellon University's School of Architecture , where he chairs the Master of Science in Computational Design program and co-directs the CoDe Lab . His scholarship merges history, science and technology studies (STS), and computational design , focusing on the cultural and socio-technical dimensions of design automation. Education: PhD and MS in Architecture: Design and Computation from MIT , BArch from Universidad de los Andes Research Grants: Supported by the Graham Foundation for historical CAD exhibitions and by the Alexander Von Humboldt Foundation for postwar computational design research in Germany His work interrogates the politics of software, the materiality of computational systems , and the ethical implications of AI/robotics in architectural practice. Recent projects include reconstructing early CAD systems and analyzing data-driven urban technologies. Scientific awards include: Alexander Von Humboldt Fellowship (2024–2025) ACM CSCW Methods Mention for emulation-based software research (2021)

Yihan Sunمشاهده پروفایل

استادیار

• Parallel Computing
• Algorithms
• Data Structures
• +۱۲ مورد دیگر

Yihan Sun is an Assistant Professor at the University of California, Riverside (UCR) since January 2020. He earned his Ph.D. in Computer Science from Carnegie Mellon University (CMU) , advised by Guy Blelloch , and holds a Bachelor's degree in Computer Science from Tsinghua University . Research Interests: Yihan Sun focuses on the theory and practice of parallel computing , including Parallel algorithms and data structures Write-efficient algorithms for Non-Volatile Memory (NVM) Computational geometry (range trees, Delaunay triangulations) Graph algorithms (SSSP, SCC, cluster-based BFS) Concurrent and persistent data structures Multi-version concurrency control (MVCC) with garbage collection Applications in databases, transactional systems, and computational biology Recent Research Trends: His work on join-based parallel balanced trees has been foundational, supporting four balancing schemes (AVL, red-black, weight-balanced, treaps) and enabling efficient implementations in graph analytics, spatial queries, and dynamic programming. Recent publications focus on output-sensitive algorithms , scalable graph libraries (PASGAL) , and pedagogical approaches to teaching parallel algorithms. Teaching: He teaches CS260 (Parallel Algorithms) at UCR and has served as a guest lecturer for MIT 6.886 (Algorithm Engineering) and CMU 15-859 (Algorithms in the real world) . He also contributed to algorithm education through a tutorial at the ACM Symposium on Principles and Practice of Parallel Programming (PPoPP 2019) . Labs & Collaborations: Yihan is a core contributor to the PAM (Parallel Augmented Maps) library, which has been integrated into systems like Aspen (graph-streaming) and C-trees . He collaborates with teams at CMU-Parlay , PBBS , and Ligra , with his code available on Github for community feedback.

Andrew O. Arnoldمشاهده پروفایل

استاد مدعو

• Machine Learning
• Natural Language Processing
• Quantitative Trading
• +۵ مورد دیگر

Andrew O. Arnold is a Principal Applied Machine Learning Engineer at Shopify and an Adjunct Professor at New York University's Tandon School of Engineering, Department of Finance and Risk Engineering. He earned his Ph.D. in Machine Learning from Carnegie Mellon University and a BA in Computer Science and Artificial Intelligence from Columbia University. Education Ph.D., Machine Learning, Carnegie Mellon University BA, Computer Science and Artificial Intelligence, Columbia University His research focuses on robust machine learning , developing models that perform well in low signal-to-noise regimes, handle distributional shifts (transfer learning), and extract features from unstructured data. Key applications include time series analysis and natural language processing in financial and other domains. Recent publications highlight work on large language models (LLMs) for code generation, including multitask pretraining, contrastive learning, and quantization techniques for efficiency. He has contributed to understanding model robustness and adapting NLP methods to dynamic market conditions. Arnold teaches NYU FRE GY 7871: News Analytics and Machine Learning , covering NLP and ML techniques for quantitative trading strategies. The course emphasizes practical applications of sentiment analysis, text relevance, and novelty detection in financial contexts. He has led teams at Amazon Web Services (AI Labs), served as Chief Scientist at Oracle Alpha, and worked at Microsoft Research, IBM Research, and other institutions. His technical expertise spans code generation , anomaly detection , and NLP for commerce , with patents in these areas.

David Garlanمشاهده پروفایل

استاد

• Software Engineering
• Software Architecture
• Self-Adaptive Systems
• +۴ مورد دیگر

David Garlan is a Professor at the Software and Societal Systems Department within the School of Computer Science at Carnegie Mellon University , where he also serves as Associate Dean for Master’s Programs . He received his Ph.D. from Carnegie Mellon in 1987 after working in industry as a software architect. His research focuses on controlling complexity in large software systems through formalized architectural design, self-adaptive systems, and cyber-physical systems. He developed AcmeStudio , a widely used architecture design environment, and pioneered formal representation and analysis of software architecture. Education : Ph.D. in Computer Science (Carnegie Mellon, 1987) Research Interests include: Software Architecture: Formal methods for architectural design, end-user composition, and architectural styles Self-Adaptive Systems: Stochastic planning, model checking, security adaptation, and uncertainty reduction Cyber-Physical Systems: Multi-view design methods, consistency checking, and automotive systems Recent Article Trends address microservice resiliency, hybrid planning (combining formal methods and ML), simulation-augmented robotics, and sustainable machine translation. Themes include stochastic modeling , probabilistic verification , and adaptive decision-making . Scientific Awards : Stevens Award Citation (2005) ACM SIGSOFT Outstanding Research Award (2011) Allen Newell Award for Research Excellence (2016) IEEE TCSE Distinguished Education Award (2017) Nancy Mead Award (2017) Fellow of IEEE and ACM Advising and Grants : He has advised 25+ graduate students and collaborated on projects with Toyota and the Software Engineering Institute. His work includes model-based adaptation, automated planning, and formal verification of adaptive systems. Labs & Teams : Affiliated with the Institute for Software Research and works on tools like AcmeStudio, Rainbow, and IPL for architectural modeling and self-adaptation.

Jack Mostowمشاهده پروفایل

استاد پژوهشی

• Educational Technology
• Artificial Intelligence
• Natural Language Processing
• +۳ مورد دیگر

Jack Mostow is a Research Professor affiliated with the Human-Computer Interaction Institute at Carnegie Mellon University. His work focuses on educational technology, artificial intelligence, and natural language processing. He is renowned for developing Project LISTEN's automated reading tutor that listens to children's oral reading, integrating speech recognition and machine learning to improve literacy. His research addresses challenges in child literacy, automated assessment, and the application of AI in education. Key contributions include advancing speech recognition accuracy for children’s voices, designing adaptive tutoring systems, and exploring EEG and visual features to gauge learning states. He has contributed to global education initiatives, such as the XPRIZE field study, and developed tools for iterative improvement of intelligent tutoring systems. His work emphasizes scalable educational technologies for under-resourced regions and data-driven approaches to enhance learning outcomes. Mostow’s publications span over two decades, addressing topics like reinforcement learning for instructional policies, semi-supervised affect perception, and difficulty-controllable question generation. His research bridges AI, education, and human-computer interaction, aiming to create systems that adapt to learners’ needs while advancing computational methods for educational analytics.

Claire Le Gouesمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Program Analysis
• +۹ مورد دیگر

Claire Le Goues is an Associate Professor in the School of Computer Science at Carnegie Mellon University , affiliated with the Software and Societal Systems Department (formerly Institute for Software Research). She holds a Ph.D. and M.S. in Computer Science from the University of Virginia and a B.A. in Computer Science from Harvard College. Her research focuses on software engineering with emphasis on program analysis , transformation , and search-based repair . She leads the squaresLab group and co-directs the REUSE@CMU summer program. Her work spans automated program improvement (stochastic/formal approaches), software assurance, quality metrics, and systems from open source to robotics. Recent scientific awards include the ACM FSE 2025 Test of Time Award Honorable Mention and the Presidential Early Career Award for Scientists and Engineers (PECASE) . She mentors students in software engineering and actively collaborates on projects like SearchRepair and GenProg , supporting empirical benchmarks such as ManyBugs and IntroClass . She teaches software engineering and program analysis at undergraduate, master’s, and doctoral levels, addressing challenges in scaling modern systems. Her lab focuses on software repair , code transformation , and AI-driven testing .

Rayid Ghaniمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Science
• +۵ مورد دیگر

Rayid Ghani is a Professor at Carnegie Mellon University (CMU), affiliated with both the Machine Learning Department (School of Computer Science) and the Heinz College of Information Systems and Public Policy. He co-leads CMU’s Responsible AI Initiative and leads the Data Science and Public Policy Group and the Data Science for Social Good Program. His work focuses on applying machine learning, AI, and data science to address social and policy challenges in health, criminal justice, education, public safety, workforce development, and sustainability, with an emphasis on fairness, equity, and transparency in AI systems. Education: PhD in Software Engineering PhD in Societal Computing Rayid’s research spans three pillars: (1) building AI systems for human collaboration to improve decision-making, (2) embedding fairness and equity in AI design, and (3) ensuring reliability and resilience of AI systems in dynamic environments. He emphasizes application-grounded experimental design and stakeholder engagement. His recent publications include frameworks for fair ML experimentation, analyses of fairness-accuracy trade-offs, and AI applications in child welfare, public health, and housing policy. He has also contributed to policy discussions, including congressional testimonies on responsible AI procurement. Advising & Grants: Rayid collaborates with governments, NGOs, and academic institutions on projects like eviction prevention, HIV care retention, and bias mitigation in public policy. He advises non-profits and startups on data science strategy and ethics. Labs & Teams: He leads the Data Science and Public Policy Group and the Data Science for Social Good Program at CMU, fostering interdisciplinary collaborations between computer scientists, social scientists, and policymakers.

• Privacy
• Security
• Requirements Engineering
• +۵ مورد دیگر

Travis D. Breaux is an Associate Professor in the School of Computer Science at Carnegie Mellon University, where he directs the Requirements Engineering Lab . His research bridges software engineering, privacy, security, and legal compliance, with a focus on developing formal methods to ensure software systems adhere to regulatory frameworks. He holds appointments in the Software and Societal Systems Department and directs the Masters of Software Engineering (MSE) Professional Programs. Breaux's research investigates privacy policy compliance , empirical extraction of legal requirements , and risk quantification in system design. His work employs AI, formal specification, and empirical methods to resolve ambiguities in policies and quantify privacy/security risks. Key themes include regulatory alignment, automated reasoning for compliance, and human factors in risk perception. Recent publications emphasize AI-driven requirements engineering , including LLM applications for goal modeling, legal requirement extraction, and automated question generation. His work consistently addresses the intersection of formal methods, policy analysis, and scalable compliance verification. Awards and Honors: NSF CAREER Award (2015) IEEE RE Distinguished Paper Award (2018) Distinguished Reviewer Awards (ICSE 2018, RE 2023) IEEE RE Most Influential Paper Award (Honorable Mention, 2016) Breaux advises PhD and Master's students in privacy engineering and requirements formalization. He has led NSF-funded initiatives including the Workshop on Designing Accountable Software Systems (DASS) . Current courses include Prompt Engineering and Artificial Intelligence for Software Engineering , focusing on LLM applications and AI ethics.

Gary L Millerمشاهده پروفایل

استاد

• Spectral Graph Theory
• Algorithms
• Computational Geometry
• +۴ مورد دیگر

Gary L. Miller is a Professor of Computer Science at Carnegie Mellon University's School of Computer Science. His research focuses on Spectral Graph Theory, Algorithms, Computational Geometry, and Scientific Computing. He has developed influential methods in graph partitioning, mesh generation, and numerical linear algebra solvers. Teaching includes advanced courses like Spectral Graph Theory, Algorithms, and Computational Geometry. Active in publishing, recent work involves weighted Cheeger inequalities, exact manifold metric computations, and adaptive graph sketching techniques. Projects include Orasis, 3D Meshing Software, Tumble, and Sangria. His work bridges theoretical foundations with applications in machine learning, image processing, and scientific computing. Current projects emphasize efficient graph algorithms and scalable numerical methods.

Keenan Craneمشاهده پروفایل

دانشیار

• Computer Science
• Mathematics
• Scientific Computing
• +۷ مورد دیگر

Keenan Crane is the Michael B. Donohue Associate Professor of Computer Science and Robotics at Carnegie Mellon University , with membership in the Center for Nonlinear Analysis and mentorship in the Geometry Collective . His research bridges differential geometry and computer science to develop fundamental algorithms for geometric data processing. Education : BS from University of Illinois at Urbana-Champaign, PhD from Caltech Fellowships : Google PhD Fellow, NSF Mathematical Sciences Postdoctoral Fellow Research focuses on Discrete Differential Geometry , addressing PDE solutions, mesh processing, and geometric modeling through methods like: Walk on Spheres for PDEs Intrinsic Triangulations for robust geometry Repulsive Energy formulations for collision avoidance Recent publications span 2025–2021 , emphasizing grid-free algorithms , anisotropic mesh generation , and differentiable systems . Scientific accolades include Packard Fellowship and NSF CAREER Award . Students include Nicole Feng , Olga Gutan , and Zoë Marschner . During his 2024 sabbatical at Roblox , he does not accept new researchers. Key software contributions include Penrose (math diagram generation) and I♥Mesh (domain-specific language for mesh algorithms).

Timothy Verstynenمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Cognitive Science
• Computational Neuroscience
• +۱ مورد دیگر

Timothy Verstynen is a Professor of Psychology and Neuroscience Institute at Carnegie Mellon University, affiliated with the Dietrich College of Humanities and Social Sciences. His research focuses on cognitive neuroscience, cognitive science, computational modeling, and learning science. He investigates how neural pathways regulate action planning, skill learning dynamics, and structure-function relationships in the brain. His work integrates psychophysics, computational models, and neuroimaging techniques (fMRI, TMS, diffusion imaging). Key research themes include: 1) Action selection and stopping mechanisms under sensory input; 2) Neurobiological bases of skill acquisition timelines; 3) White matter architecture's role in cognitive functions. Recent studies explore links between brain structure, cardiovascular health, and decision-making processes using multimodal imaging and machine learning. Notable publications address cortico-basal ganglia-thalamic circuits' role in decision policies, stress-brain connectivity, and reward-based learning. His CoAx Lab develops tools like CBGTPy for modeling decision-making systems. Research spans neuroimaging methodological advancements (e.g., local connectome fingerprinting) and translational health neuroscience projects.

Carol J. Smithمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence
• User Experience (UX)
• Ethical AI
• +۳ مورد دیگر

Carol J. Smith is an Adjunct Instructor at Carnegie Mellon University's Human-Computer Interaction Institute (HCII) and a Research Scientist at the Software Engineering Institute (SEI). She focuses on improving the design of AI/ML systems through UX and HCI methods to build trustworthy technologies. Her work emphasizes ethical AI, user trust, and human-AI collaboration. Research Interests: Trustworthy AI systems Ethical design of AI User experience (UX) in AI development Bias mitigation in machine learning Human-AI teaming frameworks Awards: 2023: Listed among 100 Brilliant Women in AI Ethics™ 2021–2025: ACM Distinguished Speaker Labs/Teams: Software Engineering Institute (SEI) HCII's AI Ethics and Trustworthiness Research Group

Russell Schwartzمشاهده پروفایل

استاد

• Cancer Genomics
• Tumor Evolution
• Computational Phylogenetics
• +۳ مورد دیگر

Russell Schwartz is Professor of Biological Sciences and Computational Biology at Carnegie Mellon University, with courtesy appointments in Computer Science and Machine Learning. He serves as Head of the Ray and Stephanie Lane Computational Biology Department and Co-Director of the Joint CMU-University of Pittsburgh PhD Program in Computational Biology. His research develops computational models for complex biological systems, focusing on cancer genomics and macromolecular assembly. Key areas include reconstructing tumor clonal evolution through integration of bulk and single-cell sequencing data, developing algorithms for stochastic simulation of self-assembly systems, and creating optimization methods for fitting models to experimental data. Recent publications demonstrate advancements in single-cell lineage tracing, circulating tumor DNA analysis, and generative AI for biological databases. His team's BrM-Phylo software enables phylogeny reconstruction from matched transcriptome data in breast cancer brain metastases. NSF CAREER Award Alfred P. Sloan Research Fellowship Schwartz leads multiple NIH-funded projects on tumor evolution modeling and mentors students in computational cancer research. His laboratory develops open-source tools including FISHtrees for tumor phylogenetics and TUSV for integrating structural variants in clonal reconstruction.

Reza Zadehمشاهده پروفایل

استاد مدعو

• Machine Learning
• Distributed Computing
• Discrete Mathematics
• +۷ مورد دیگر

Reza Zadeh is a Computational Mathematics professor at Stanford University's School of Engineering and Founder & CEO of Matroid . He previously served as a Technical Advisory Board member for Databricks and leads the Spark Tutorial at Stanford. Research Interests: Specializing in Machine Learning and Distributed Computing , his work bridges theoretical mathematics with practical implementations in big data systems. Key focus areas include Optimization of Apache Spark 3D Convolutional Neural Networks Discrete Mathematics and Graph Theory Medical Imaging Applications Academic Contributions: His publications reveal trends across multiple disciplines: Adapting machine learning for medical diagnostics (2019-2022) Advancing distributed computing frameworks (2014-2016) Developing mathematical foundations for social networks (2009-2013) Creating scalable optimization algorithms (2014-2016) Scientific Awards: Best Paper Award runner-up at KDD 2016 Academic Leadership: He has taught SMACC Consulting and designed courses including CME 323: Distributed Algorithms and Optimization (2015-2024) and CME 305: Discrete Mathematics and Algorithms (2010-2017). His lectures cover graph theory, approximation algorithms, and spectral sparsification. Labs & Teams: Organized Spark Summit workshops and leads Scaled Machine Learning Conference . Collaborates with Stanford's ICME computational consulting services.