جستجوی استاد راهنمای دانشگاهی

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.

Shuangning Liمشاهده پروفایل

استادیار

• Econometrics
• Statistics
• Machine Learning
• +۵ مورد دیگر

Shuangning Li is an Assistant Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business. He holds a Ph.D. from Stanford University's Department of Statistics, advised by Professors Emmanuel Candès and Stefan Wager, and a Bachelor of Science from the University of Hong Kong. Prior to his current role, he was a postdoctoral fellow in Statistics at Harvard University. His research focuses on causal inference, machine learning, and statistical methodology with applications in econometrics, networks, and genomics. **Education:** Ph.D. in Statistics, Stanford University (Advisors: Emmanuel Candès, Stefan Wager) Bachelor of Science, University of Hong Kong **Research Interests:** Causal inference in complex systems (e.g., networks, high-dimensional data) Statistical methods for experimental design and robustness Machine learning applications in genomics and reinforcement learning Randomization-based testing and knockoff filters **Recent Work Trends:** His articles emphasize methodological innovations in causal effect estimation, network interference modeling, and transfer learning. Recent work addresses challenges in stochastic congestion, multi-environment analysis, and cooperative learning frameworks. His 2024 paper advances covariate shift correction for conditional randomization tests, while his 2023 studies explore robustness in model-X inference and dyadic reinforcement learning dynamics. **Advising & Academic Background:** His doctoral training under Candès and Wager shaped his focus on rigorous statistical foundations. He has not yet listed advising relationships in available materials, but his research collaborations span academia and industry.

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.

Feras Saadمشاهده پروفایل

استادیار

• Probabilistic Programming
• Artificial Intelligence
• Programming Languages
• +۴ مورد دیگر

Feras Saad is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the Principles of Programming and Artificial Intelligence groups. He received his Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) in 2022, where his dissertations on probabilistic programming systems earned him the George M. Sprowls PhD Thesis Award and Charles & Jennifer Johnson MEng Thesis Award. His research focuses on developing scalable computing systems for probabilistic modeling and inference, integrating ideas from programming languages and probabilistic AI. Key research themes include probabilistic programming languages, automated probabilistic model discovery, statistical estimation and testing, random sampling algorithms, and applications in science and engineering. His lab explores new techniques to improve reasoning systems through automation, accuracy, and scale. Dr. Saad has published extensively in top venues including PLDI, POPL, ICML, and Nature Communications. His work spans foundational computational questions to practical software systems for probabilistic inference. Research trends show consistent focus on bridging theoretical computer science with practical applications in probabilistic modeling, with recent advances in random sampling algorithms and probabilistic programming systems. Awards and honors include: George M. Sprowls PhD Thesis Award in Artificial Intelligence and Decision Making (2023) Charles & Jennifer Johnson MEng Thesis Award in Computer Science (2017) Editor's Highlight for Nature Communications paper (2024) He currently advises graduate students Gaurav Arya and Thomas Draper in the Probabilistic Computing Systems Lab. His research is supported by software libraries including GenSQL, BayesNF, and SPPL that enable practical applications across scientific domains.

Clinton Conleyمشاهده پروفایل

دانشیار

• Descriptive Set Theory
• Ergodic Theory
• Combinatorics
• +۴ مورد دیگر

Clinton Conley is an Associate Professor and Director of Graduate Studies in the Department of Mathematical Sciences at Carnegie Mellon University, part of the Mellon College of Science. He holds a Ph.D. in Mathematics from UCLA and has held postdoctoral appointments at the Kurt Gödel Research Center (Vienna, Austria) and Cornell University. His research focuses on descriptive set theory, measurable group actions, and Borel graphs, exploring intersections with ergodic theory, combinatorics, and set theory. Key research areas include measurable chromatic numbers, Borel combinatorics, and hyperfinite equivalence relations. His work often bridges abstract set-theoretic principles with concrete problems in dynamics and graph theory. Notable awards include the Julius Ashkin Award. Education: Ph.D. in Mathematics, University of California, Los Angeles Postdoctoral Appointments: KGRC (Vienna), Cornell University Recent publications span topics like measurable regular subgraphs, quasi-invariant measures, and hyperfiniteness in Borel combinatorics. Teaching includes advanced courses on descriptive set theory, set theory, and mathematical paradoxes. Advising and grants sections remain unspecified in available data. No lab or team affiliations are noted.

• Statistical Genetics
• Spatial Statistics
• Machine Learning
• +۳ مورد دیگر

Arkajyoti Saha is an Assistant Professor in the Department of Statistics at the Donald Bren School of Information and Computer Sciences , University of California, Irvine. Previously, he was a UW Data Science Postdoctoral Fellow at the University of Washington, working with Drs. Daniela Witten and Jacob Bien. His academic journey includes a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health (advised by Drs. Nilanjan Chatterjee and Abhirup Datta), and bachelor's/master's degrees in Statistics from the Indian Statistical Institute, Kolkata. Research Focus: His work bridges statistical methodology and computational tools for high-dimensional and spatially dependent data. Key areas include scalable algorithms for spatial genomics, environmental monitoring, and machine learning applications such as random forests for dependent data. He also develops R packages like RandomForestsGLS to address challenges in correlated data analysis. Publications: His recent work spans spatial variable gene identification, fuzzy clustering theory, and environmental sensor calibration. He emphasizes methodological innovation in statistical genetics and geospatial statistics. Education & Mentorship: Encourages prospective students to contact him directly. His academic background reflects a strong foundation in theoretical and applied statistics, with a focus on bridging computational efficiency and statistical rigor.

Ashrujit Ghoshalمشاهده پروفایل

پژوهشگر ارشد

• Cryptography
• Theoretical Computer Science
• Cryptographic Hashing
• +۵ مورد دیگر

Ashrujit Ghoshal is a Postdoctoral Fellow at Carnegie Mellon University, working under the supervision of Elaine Shi. He received his PhD in Computer Science from the University of Washington, advised by Stefano Tessaro and Rachel Lin. Prior to this, he was a research intern at NTT Research during summers of 2021 and 2022 with Ilan Komargodski, and earned his Bachelor of Technology (Hons.) in Computer Science and Engineering from the Indian Institute of Technology, Kharagpur, advised by Debdeep Mukhopadhyay. Education: PhD in Computer Science from the University of Washington Bachelor of Technology (Hons.) in Computer Science and Engineering from IIT Kharagpur His research focuses on the theory and foundations of cryptography, particularly private information retrieval, hash functions, authenticated encryption, time-space tradeoffs, memory-tight proofs, and side-channel security. His work spans theoretical analysis and practical implementations, including lightweight cryptographic systems for resource-constrained environments. Recent publications include advancements in scalable multi-server PIR, memory-tight security proofs for cryptographic protocols, and studies on time-space tradeoffs in hashing. He has contributed to top-tier conferences like EUROCRYPT, CRYPTO, and FSE, with a particular emphasis on balancing computational efficiency with robust security guarantees. Service: Program Committee, CRYPTO 2025 Program Committee, CHES 2025

Anil Adaمشاهده پروفایل

استاد آموزشی

• Theoretical Computer Science
• Education
• Communication Complexity
• +۵ مورد دیگر

Anil Ada is an Associate Teaching Professor at the Computer Science Department of Carnegie Mellon University . He was born in Istanbul and completed his B.Sc. (Hon.), M.Sc., and Ph.D. in Mathematics and Computer Science at McGill University , advised by Denis Thérien and Hamed Hatami. Research Focus : Theoretical computer science with an emphasis on communication complexity, circuit complexity, and the intersection of structure vs randomness in computation. Education : B.Sc. (Hon.) in Mathematics and Computer Science from McGill University M.Sc. in Computer Science from McGill University Ph.D. in Computer Science from McGill University Academic Contributions : His research has explored foundational questions in computational complexity, including pseudorandomness, Boolean function analysis, and additive combinatorics. He has published in venues such as Computational Complexity , ICALP , and International Journal of Foundations of Computer Science . Teaching Innovation : He developed Panda Notes , a web application combining document collaboration with AI-assisted educational tools. He teaches courses like 15-251: Great Ideas in Theoretical Computer Science and 15-155: The Computational Lens , with prior teaching roles at McGill University.

David S. Choiمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Network Analysis
• +۵ مورد دیگر

David S. Choi is an Associate Professor at Heinz College, Carnegie Mellon University, which houses the Schools of Public Policy and Information Systems. He maintains a courtesy appointment in the Department of Statistics at the same institution. His academic credentials include a Ph.D. in Electrical Engineering from Stanford University under Benjamin Van Roy. Professional experience encompasses roles as a technical staff member at MIT Lincoln Laboratory, postdoctoral research at Harvard University's School of Engineering, and a visiting postdoc at UC Berkeley's Department of Statistics. Choi's research centers on statistical and machine learning methodologies for network-structured data. His work pioneers community detection techniques, latent variable network modeling, unsupervised learning frameworks, and causal inference methods that account for interference in social networks. These innovations address fundamental challenges in analyzing interconnected systems across public policy, healthcare, and social sciences. Publication analysis reveals consistent contributions to causal inference in networked experiments, particularly in characterizing interference and spillover effects. His scholarship spans network clustering algorithms, biological network modeling, and policy evaluation methodologies, with impactful work published in Journal of the American Statistical Association, PNAS, and Annals of Statistics. No scientific awards were documented in the source material. The available information does not reference doctoral students supervised or research grants managed by Professor Choi. Specific research laboratories or collaborative teams were not mentioned in the provided documentation.

Marios Savvidesمشاهده پروفایل

استاد

• Biometric Identification
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Marios Savvides is the Bossa Nova Robotics Professor of Artificial Intelligence and a Full Tenured Professor in the Electrical and Computer Engineering Department at Carnegie Mellon University (CMU). He is also the Founder and Director of the CyLab Biometrics Center. His research focuses on AI algorithms for biometrics, facial recognition, iris scanning, and object detection under challenging conditions. He holds a BEng from the University of Manchester, an MS in Robotics from CMU, and a PhD in Electrical and Computer Engineering from CMU. Education: BEng, Microelectronics Systems Engineering, University of Manchester Institute of Science and Technology (1997) MS, Robotics, Carnegie Mellon University (2000) PhD, Electrical and Computer Engineering, Carnegie Mellon University (2004) Research Interests: Core AI and machine learning for robust biometric systems Long-range iris capture and matching Low-shot object detection and scene understanding Applications in retail automation, airport security, and medical imaging Key Achievements: Recipient of seven Best Paper Awards and the 2022 PIPLA Inventor of the Year Recipient of the 2015 Edison Gold Award, 2018 Immigrant Entrepreneur Award, and 2020 AI Excellence Award Developed AI algorithms deployed in over 3 million ADT security cameras and Bossa Nova robots Spun off startups including HawXeye and Oosto, serving as CTO/Chief AI Scientist Grants and Partnerships: Collaborations with Bossa Nova Robotics, Egen, UltronAI, and CMKL University Research on facial recognition for stock market prediction and medical imaging privacy risks Labs and Teams: Director of the CyLab Biometrics Center Member of IEEE Biometric Council and contributor to the IEEE Certified Biometrics Professional program

• Experimental Mathematics
• Artificial Intelligence
• Legal aspects of technology
• +۴ مورد دیگر

Michael Ian Shamos is a Distinguished Career Professor at Carnegie Mellon University's School of Computer Science, with appointments in the Language Technologies Institute and Software and Societal Systems Department. His career spans academia, law, and technology entrepreneurship, with expertise in experimental mathematics, artificial intelligence, and legal aspects of technology. Dr. Shamos earned his educational credentials through an impressive multidisciplinary path: A.B. in Physics from Princeton University (1968) under John Wheeler M.A. in Physics from Vassar College (1970) M.S. in Technology of Management from American University (1972) M.S. and M.Phil. in Computer Science from Yale University (1973-1974) Ph.D. in Computer Science from Yale University (1978) J.D. from Duquesne University (1981) His research interests bridge multiple domains with exceptional depth. In experimental mathematics, he develops computational systems that automatically generate and prove novel mathematical theorems, particularly in number theory, having contributed hundreds of new results to the field. As a leading expert in electronic voting security, he has examined over 120 voting systems for seven states and testified before Congress multiple times. His work uniquely integrates legal expertise with technical knowledge, especially regarding intellectual property in the digital age. He directs the M.S. in Artificial Intelligence and Innovation program at CMU, advising 74 students in this cutting-edge field. His publication trajectory reveals an evolution from foundational computational geometry (co-authoring the seminal "Computational Geometry: An Introduction") to contemporary issues in voting security and experimental mathematics. The consistent thread is applying computational methods to solve real-world problems with careful attention to legal and societal implications. His notable recognitions include: Industry Service Award of the Billiard and Bowling Institute of America (1996) Black and White Scotch Achiever's Award for contributions to bagpipe musicography (1991) As an educator and mentor, Dr. Shamos directs the M.S. in Artificial Intelligence and Innovation program and teaches courses including "AI & Future Markets" and "The Law of Computer Technology." He has served as an expert witness in over 360 legal cases involving computer technology and has been a statutory examiner of computerized voting systems for Pennsylvania since 1980. His extensive industry experience includes founding technology companies and serving as General Counsel for an AI company. Dr. Shamos is actively involved with the Universal Library project, which has scanned over 1.5 million books. He also serves as faculty advisor to the Carnegie Mellon Pool Team and is Curator of The Billiard Archive, reflecting his deep commitment to preserving billiards history alongside his academic pursuits.

• Causal Inference
• Machine Learning
• Nonparametric Methods
• +۳ مورد دیگر

Edward H. Kennedy is an Associate Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. He joined CMU in 2016 as an Assistant Professor after earning his PhD in Biostatistics from the University of Pennsylvania, alongside an MA in Statistics from the Wharton School, an MS in Biostatistics from the University of Michigan, and a BA in Mathematics from the University of Pennsylvania. His research focuses on causal inference, missing data problems, nonparametric methods, and machine learning, with applications in criminal justice, health services, medicine, and public policy. Key areas include incremental treatment effects, counterfactual analysis, and semiparametric sensitivity frameworks. He has developed methodologies for causal clustering, functional average treatment effects, and robust population size estimation. Edward's work bridges theoretical statistics and practical applications, addressing challenges in high-dimensional and complex data. His contributions span methodological advancements in instrumental variables, doubly robust estimation, and counterfactual fairness in algorithmic decision-making. His research also extends to policy-relevant domains such as healthcare resource allocation, criminal justice reform, and pandemic response analysis. Edward is actively involved in interdisciplinary collaborations, mentoring students in statistical theory and applied data science. His lab emphasizes rigorous methodological innovation while addressing real-world societal challenges through data-driven approaches.

Francesca Zaffora Blandoمشاهده پروفایل

استادیار

• Logic
• Formal Epistemology
• Philosophy of Science
• +۵ مورد دیگر

Francesca Zaffora Blando serves as an Assistant Professor in the Department of Philosophy at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. Her academic profile bridges rigorous formal methods with foundational questions in epistemology and scientific methodology. Her educational trajectory includes: Ph.D. in Philosophy and Symbolic Systems, Stanford University (2020) M.Sc. in Logic, Institute for Logic, Language and Computation, University of Amsterdam M.A. in Philosophy, University of Edinburgh Zaffora Blando's research centers on algorithmic randomness —a computability-theoretic framework for patternless sequences—and its implications for inductive learning and Bayesian inference . She investigates how algorithmically random data streams constrain the learning performance of computationally bounded agents, revealing deep connections between randomness, convergence to truth, and probabilistic reasoning. Her work spans modal logic applications in dynamic epistemic scenarios and historical analyses of probability theory from von Mises to contemporary formalizations. Her publication record (2015-2025) demonstrates sustained innovation at the intersection of computability and epistemology, with increasing focus on Schnorr randomness, Bayesian consistency, and learning-theoretic characterizations. Key themes include the role of randomness in merging opinions, disintegration of measures, and historical evolution of randomness concepts. She actively contributes to the Center for Formal Epistemology through event organization including the Pittsburgh Formal Epistemology Workshop (PFEW), the September 2024 Workshop on Chance, Credence, Computation, and Progic 2025—the Twelfth Workshop on Combining Probability and Logic with special focus on theoretical learning approaches.

Alex John Londonمشاهده پروفایل

استاد

• Ethics of Artificial Intelligence
• Bioethics
• Medical Ethics
• +۳ مورد دیگر

Alex John London is the K&L Gates Professor of Ethics and Computational Technologies at Carnegie Mellon University, where he directs the Center for Ethics and Policy and serves as Chief Ethicist at the Block Center for Technology and Society . He co-leads the K&L Gates Initiative in Ethics and Computational Technologies and is an elected Fellow of the Hastings Center . Research Focus: Ethical and policy challenges in AI, medicine, and biotechnology; cross-national justice in research; methodological rigor in theoretical and practical ethics. Key Contributions: Author of For the Common Good: Philosophical Foundations of Research Ethics (Oxford University Press, open access) and co-editor of the widely used textbook Ethical Issues in Modern Medicine . Served on WHO expert panels for AI governance, U.S. National Academy of Medicine committees, and contributed to international guidelines like the CIOMS Ethical Guidelines and Declaration of Helsinki revisions. Recent Awards: Fellow of the Hastings Center Leadership Roles: Ethics core co-leader for the NSF AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING); program committee member for major AI ethics conferences (FAccT, AIES). Public Engagement: Active in shaping biosecurity policy through the U.S. National Science Advisory Board for Biosecurity (NSABB) and consulted for NIH, WHO, and World Bank.

Guy E. Blellochمشاهده پروفایل

استاد

• Parallel Computing
• Programming Languages
• Algorithms and Complexity
• +۵ مورد دیگر

Guy E. Blelloch is the U.A. and Hellen Whitaker University Professor at Carnegie Mellon University , affiliated with the Computer Science Department . He focuses on the intersection of algorithms and programming languages within parallel computing , with significant experimental and theoretical contributions. His recent work includes the PSCICO project (with Gary Miller, Bob Harper, Peter Lee) exploring high-level programming constructs in geometric algorithms, and continued development of the NESL programming language for parallel computation. Research spans Parallel Garbage Collection Work-Efficient Scheduling Deterministic Parallel Algorithms Processing-in-Memory Models Key publications since 2022 show trends in dynamic data structures , cache-oblivious algorithms , and concurrent memory management , with applications in graph processing (PIM-tree), geometric computation (tree-based sorting), and multiversioning systems. His work often bridges theory and practice, demonstrated in frameworks like Ligra , GBBS , and Aspen for large-scale graph analysis. Scientific awards include ACM Paris Kanellakis Theory and Practice Award (2023) IEEE Charles Babbage Award (2021) Best Paper Awards at PPoPP 2022, SPAA 2021 SCS Doctoral Dissertation Award (Honorable Mention) He has advised numerous PhD students including Magdalen Dobson (current student on nearest-neighbor problems), Daniel Anderson (parallel batch-dynamic algorithms), and Julian Shun (graph processing frameworks). His teaching includes advanced courses on Parallel and Sequential Data Structures and Parallel and Concurrent Algorithms .