جست‌وجوی استاد و پژوهشگر

José Mouraمشاهده پروفایل

استاد

• Data Science
• Signal Processing
• Graph Signal Processing
• +۹ مورد دیگر

Prof. José M. F. Moura is the Philip L. and Marsha Dowd University Professor at Carnegie Mellon University's Electrical and Computer Engineering Department. He holds a courtesy appointment in BioMedical Engineering. A member of the US National Academy of Engineering and Portugal Academy of Sciences, he is an IEEE Fellow and AAAS Fellow. His research focuses on signal processing, graph signal processing, and data science, with contributions to projects like SPIRAL and cognitive networks. He co-founded SPIRALGEN and led the CMU-Portugal Program, fostering collaboration between CMU and Portuguese institutions. Moura has received prestigious awards, including the IEEE Jack S. Kilby Medal and the 2023 IEEE Haraden Pratt Award. His work spans sensor networks, distributed algorithms, and urban science, with over 40 PhD students supervised and numerous grants from DARPA, NSF, and industry. Education: DSc (MIT, 1975), M.S./EE (MIT, 1973), Licenciado EE (Technical University of Lisbon, 1969). Research Interests: Signal processing on graphs, distributed decision systems, SPIRAL library, time reversal imaging, bioimaging, and urban science. Key projects include the DARPA-funded SPIRAL and SMART initiatives. Recent Articles: Focus on machine learning, graph signal processing, and distributed systems. Notable works include 'Natural Language Does Not Emerge Naturally' (2017) and 'SPIRAL: Extreme Performance Portability' (2018). Awards: IEEE Jack S. Kilby Medal (2023), US National Academy of Engineering membership, and multiple fellowships. Recognized for contributions to statistical and graph signal processing. Grants & Funding: Over $100M in programs like CMU-Portugal, DARPA's BRASS, and NSF grants. SPIRAL's commercialization via SPIRALGEN reflects his translational impact. Labs/Teams: Leads the Time Reversal Imaging Lab (TriLab), collaborates with CenSCIR (Critical Infrastructure Research), and directs the CMU-Portugal Program's dual degree initiatives.

Rohit Negiمشاهده پروفایل

استاد

• Wireless communications
• Networking
• Signal processing
• +۷ مورد دیگر

Rohit Negi is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), affiliated with the College of Engineering. He holds a Ph.D. (2000) and M.S. (1996) in Electrical Engineering from Stanford University, and a B.Tech. from the Indian Institute of Technology Bombay (1995). His research focuses on wireless communications, networking, information theory, and cyberphysical systems, with emphasis on ad hoc networks, sensor networks, and power systems. Key roles include leading research on capacity of ultra-wideband (UWB) networks and joint optimization frameworks for physical/MAC/network layers. He has advised numerous Ph.D. and M.S. students, with notable alumni in industry (e.g., Qualcomm, Samsung) and academia (e.g., University of Florida). Research sponsors include NSF (including a Career Award), Texas Instruments, and industry partnerships. He has published extensively in IEEE journals/conferences, focusing on scheduling algorithms, secure communication, and power systems optimization. Notable awards include a 2012 IEEE Smart GridComm Best Paper Award and the President of India Gold Medal (1995). Teaching contributions include developing courses like 18-450 (Digital Wireless Communications) and co-designing the Intel Instructional Wireless Lab. His professional activities include editorial roles for IEEE Transactions and NSF review panels. Current research explores cyberphysical systems, smart grid state estimation, and reconfigurable RF-FPGA systems. Collaborations span disciplines like machine learning and network science, reflecting his holistic approach to engineering challenges.

• Combinatorics
• Probability Theory
• Computer Science
• +۳ مورد دیگر

Po-Shen Loh is a Professor of Mathematics at Carnegie Mellon University, affiliated with the Mellon College of Science's Department of Mathematical Sciences since 2010. He is renowned for his dual focus on academic research and innovative educational outreach. Loh’s research spans combinatorics, probability theory, and extremal networks, with notable contributions to understanding network properties and their applications. He led the U.S. International Mathematical Olympiad (IMO) team to four first-place finishes between 2015–2023, emphasizing collaborative problem-solving and global peer engagement. Beyond academia, Loh developed the NOVID app for pandemic control, leveraging network science to empower users in avoiding infection. He also founded Expii.com, a free platform offering diverse explanations for math and science concepts, and launched the LIVE learning platform blending math education with performing arts. His work bridges disciplines, aiming to democratize education and prepare future leaders in an AI-driven world. Education & Background : Grew up in a math-centric household in Madison, Wisconsin, with parents in academia. Earned his Ph.D. in Algorithms, Combinatorics, and Optimization from Carnegie Mellon (though exact dates not specified in texts). Research & Leadership : Specializes in extremal combinatorics, including network structure analysis. His research on probabilistic network existence proofs has advanced graph theory. As IMO coach, he pioneered cross-national training programs, inviting international teams to U.S. camps. Awards & Recognition : Recipient of the 2019 Presidential Early Career Award (PECASE). Recognized for pandemic control innovations and educational initiatives. Outreach : Conducted a record-breaking 40-city math tour in 2021, developed the Daily Challenge math curriculum, and launched Moderate Zoom Chat to improve virtual engagement. Active in mentorship programs like LEAD NATURALLY, targeting culturally diverse leaders. Notable Projects : NOVID app (pandemic control), Expii (free educational resource), and LIVE platform (math education via live-streaming).

Geoffrey J. Gordonمشاهده پروفایل

استاد

• Multi-agent planning
• Reinforcement learning
• Decision-theoretic planning
• +۷ مورد دیگر

Geoffrey J. Gordon is a Professor in the Machine Learning Department at Carnegie Mellon University and affiliated with the Robotics Institute. His research spans multi-agent planning, reinforcement learning, decision-theoretic planning, statistical models of complex data, computational learning theory, and game theory. He leads the SELECT lab (SEnse, LEarn, and aCT), focusing on predictive state representations, spectral learning, and applications in robotics. His recent work integrates deep learning with controlled dynamical systems and optimization, as seen in publications at AAAI and AISTATS. Research Interests: Multi-agent systems and game theory Reinforcement learning and dynamical systems Statistical models for high-dimensional data Spectral learning and quantum Markov models Scientific Awards: Best paper award at ICML 2010 Teaching: 10-405/605: Machine Learning with Large Datasets (2023) 10-606/607: Mathematical/Computational Background for ML (2022, 2017) 10-701: Intro to Machine Learning (2021, 2014) Labs & Teams: SELECT Lab (SEnse, LEarn, and aCT) Collaborations with Stanford Robotics Lab, AUTON Lab, and others

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +۱۲ مورد دیگر

Bryon Aragam is an Associate Professor and Topel Faculty Scholar at the Booth School of Business , University of Chicago . His work bridges causality , statistical machine learning , and probabilistic modeling , with applications in AI systems like ChatGPT and DALL-E. Key research themes include: Causal Structure Learning : Extracting latent causal graphs from multimodal data using nonparametric methods. Deep Generative Models : Analyzing overparametrization and variational inference for representation learning. Latent Variable Discovery : Using Markov boundaries and convex subset lattices to uncover hidden dependencies. Algorithm Design : Developing scalable methods like DAGMA for DAG learning and theoretical guarantees for GES/PC algorithms. His paper trends reveal a focus on nonparametric statistics , graphical models , and neural network theory , with recent work on transformer memory dynamics and identifiability in deep latent models . Papers frequently appear in top venues like NeurIPS , JMLR , and AOS , emphasizing theoretical rigor and practical validation.

Cosma Shaliziمشاهده پروفایل

دانشیار

• Nonparametric prediction of time series
• Learning theory
• Nonlinear dynamics
• +۱۵ مورد دیگر

Cosma Shalizi is an Associate Professor in the Statistics Department and Machine Learning Department at Carnegie Mellon University, and an External Professor at the Santa Fe Institute. His work bridges statistics, machine learning, and complex systems theory, with applications spanning neuroscience, statistical mechanics, and social networks. Shalizi's research focuses on nonparametric prediction of time series, learning theory, information theory, and causal inference. He has made significant contributions to computational mechanics, developing algorithms like CSSR (Causal State Splitting Reconstruction) for identifying optimal predictive states in complex systems. His work extends to heavy-tailed distributions, network analysis, and the statistical foundations of complex systems. He has pioneered methods for quantifying self-organization and developing nonparametric approaches to spatio-temporal prediction. His recent publications reveal a trend toward increasingly interdisciplinary work, connecting network science with causal inference, statistical learning theory with macroeconomic forecasting, and information theory with biomedical applications. His work consistently emphasizes rigorous statistical methodology applied to complex, dependent data structures across diverse scientific domains. Winner of the Best Student Paper and Best Poster awards Shalizi has advised students including Georg Goerg, who extended spatio-temporal prediction techniques to continuous-valued fields, and George Montañez, who developed fast approximate algorithms for prediction and explored information-theoretic explanations for machine learning. His collaborative network spans statistics, physics, neuroscience, and social sciences, reflecting his interdisciplinary approach to complex systems. His work with collaborators has led to significant contributions in network analysis, causal inference in social networks, and the development of nonparametric methods for complex data structures. He maintains active research programs in statistical network modeling, time series analysis, and the application of information-theoretic approaches to diverse scientific problems.

Aditya Prakashمشاهده پروفایل

دانشیار

• Data Science
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Aditya Prakash is the Associate Chair for Academic Affairs and an Associate Professor at the School of Computational Science and Engineering within the College of Computing at Georgia Institute of Technology, Atlanta. He received his PhD in Computer Science from Carnegie Mellon University in 2012 and a BTech in Computer Science and Engineering from the Indian Institute of Technology - Bombay in 2007. His research spans Data Science, Machine Learning, and AI with emphasis on solving big-data problems in networks and sequences. Dr. Prakash focuses on understanding and managing dynamical mechanisms (like propagation) on networks occurring across natural, social and technological systems. His work addresses problems motivated by public health, computational epidemiology, cybersecurity, urban computing, and the web. His research methodology combines theoretical analysis of models, developing efficient algorithms, and empirical studies on large-scale data. Dr. Prakash leads multiple significant research projects including the NSF BEHIVE project for developing the science of pandemic prevention and prediction, collaborating with members from Georgia Tech, MIT, Michigan, Mayo Clinic, and UGA. His group has been actively involved in numerous efforts as part of the COVID-19 response, with work highlighted in Nature Biotechnology and IEEE Spectrum. His research output shows strong trends in time-series forecasting, epidemic modeling, and network analysis. Recent publications demonstrate increasing integration of large language models with traditional forecasting methods, development of specialized architectures for epidemiological modeling, and innovative approaches to handling uncertainty in public health forecasting. Facebook Faculty Award on Statistics for Improving Insights, Models, and Decisions (2021) Best Paper Award at AI4ABM workshop at ICML 2022 Alex Rodriguez (his PhD student) recognized as a 'Rising Star in Data Science' (2021) First place in the COVID-19 Symptom Data Challenge (organized by Facebook/Catalyst Health/CMU) Second place in the C3AI COVID-19 Grand Challenge Dr. Prakash has successfully advised numerous PhD students who have gone on to faculty positions at institutions like University of Michigan, University of Iowa, and Virginia Tech, as well as positions at major technology companies including Google and Pinterest. His research has been supported by multiple NSF grants including NSF IIS Medium, NSF CISE Expeditions, NSF CAREER, and CDC MIND funding. He leads the BEHIVE project and has been involved in multiple pandemic response initiatives through the MIDAS network. He teaches courses including 'CSE 8803: EPI: Data Science for Epidemiology' and 'CSE 8803 IUC: Introduction to Urban Computing,' demonstrating his commitment to training the next generation of researchers in these critical interdisciplinary areas.

Jian Maمشاهده پروفایل

استاد

• Computational Biology
• AI/ML in Genomics
• Nuclear Organization
• +۴ مورد دیگر

Jian Ma is the Ray and Stephanie Lane Professor of Computational Biology in the Ray and Stephanie Lane Computational Biology Department at Carnegie Mellon University's School of Computer Science, based in the Gates Hillman Center. His research focuses on developing AI/ML methods to study human genome structure, cellular organization, and their implications for health and disease. Primary research areas include nuclear architecture, single-cell epigenomics, spatial omics, and molecular interactions. The lab employs probabilistic modeling and deep learning techniques (especially graph representation learning) to analyze multi-scale biological systems, with recent exploration of large language models for decoding gene regulatory mechanisms. Dr. Ma leads an NIH Center in the 4D Nucleome (4DN) Program and participates in the NIH SenNet and IGVF Consortia. He advises multiple PhD students and researchers working on computational biology and machine learning projects.

Osman Yaganمشاهده پروفایل

استاد پژوهشی

• Communication Systems
• Network Science
• Cyber-Physical Systems
• +۱۶ مورد دیگر

Osman Yagan is a Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with affiliate roles in the School of Computer Science and core membership in CyLab Security and Privacy Institute, IDeaS, and Block Center. His research spans modeling, analysis, and optimization of computing systems using tools from applied probability, network science, and machine learning. PhD in Electrical and Computer Engineering, University of Maryland (2011) BS in Electrical and Electronics Engineering, Middle East Technical University (2007) Research Interests: Dr. Yagan focuses on statistical inference , resilient distributed machine learning , robustness of cyber-physical systems , secure IoT networks , and contagion processes in complex networks, including misinformation spread and epidemic modeling. His work integrates multi-armed bandit algorithms , reinforcement learning , and stochastic geometry . Scientific Contributions: He has received multiple fellowships (CIT Dean’s Early Career, IBM Academic Award), best paper awards (ICC 2021, IPSN 2022, ASONAM 2023), and co-authored 15+ recent articles on topics like federated learning, contagion dynamics, and network robustness. His research is funded by ONR, NSF, ARO, AFOSR, and industry grants. Advising: Dr. Yagan has mentored 23 PhD and Master’s students, including junior faculty at Nanyang Technological University and researchers at Meta, Intel, Bloomberg, and MIT. He has taught courses such as Advanced Probability & Statistics for Engineers and Applied Stochastic Processes. Collaborations: He works with institutions including Princeton, MIT, and industry partners like IBM and Intel, and is a Senior Member of IEEE.

Seth Copen Goldsteinمشاهده پروفایل

دانشیار

• Programmable Matter
• Claytronics
• Reconfigurable Computing
• +۶ مورد دیگر

Seth Copen Goldstein is an Associate Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research spans multiple interdisciplinary domains focusing on ensembles - large collections of interacting agents. He has made significant contributions to reconfigurable computing, molecular-scale devices, programmable matter, and more recently, the impact of technology on labor markets and alternative monetary systems. Education: PhD in Computer Science, University of California at Berkeley BS, Princeton University Goldstein's research agenda has evolved from reconfigurable computing to molecular-scale devices and programmable matter, and most recently to social technology and alternative monetary systems. His early work focused on compiling high-level programming languages directly into configurations for computing ensembles of gates. He then investigated molecular-scale circuits and programmable matter - ensembles of computing elements that can be programmed to change physical properties. Since returning from a startup, he has shifted focus to ensembles of people, studying technology's impact on labor markets and innovation, and developing social technologies to reduce poverty and inequality. His publication trends show a consistent focus on modular robotics, distributed systems, and programmable matter. The research spans theoretical foundations, hardware implementation, and programming languages for large-scale ensembles. Recent work emphasizes energy efficiency, distributed algorithms, and novel programming paradigms for heterogeneous systems. The interdisciplinary nature of his work connects computer science, electrical engineering, and social sciences. Research Leadership: Claytronics project - creating ensembles of cooperating submillimeter robots Development of Meld and LDP programming languages for ensemble systems Phoenix project - computing without processors Building on Local Trust (BoLT) initiative Goldstein has advised numerous PhD students who have gone on to positions at major technology companies including Microsoft Research, Sun Microsystems, and NEC Labs. His work has been supported by significant research grants focusing on modular robotics, programmable matter, and distributed computing systems. He has taught core computer science courses including Compiler Design, Introduction to Computer Systems, and Cloud Computing.

Mary Shawمشاهده پروفایل

استاد

• Software Architecture
• Value-Based Software Engineering
• End-User Software Engineering
• +۶ مورد دیگر

Mary Shaw is the A.J. Perlis Professor of Computer Science at Carnegie Mellon University's School of Computer Science (SCS), with a joint appointment at the Software Engineering Institute (SEI). She co-founded the SEI and served as Chief Scientist (1984-1987). Research Focus: Software architecture, value-based software engineering, end-user software engineering, and software reliability modeling. Education: Pioneered software engineering education frameworks and graduate curriculum guidelines. Grants: Involved in NSF-CCF-0438929, NSF-CCR-0325273, and Sloan Software Industry Center projects. Scientific Awards: National Medal of Technology and Innovation Stibitz Computer Pioneer Award Warnier Prize (1993) Fellow of IEEE Computer Society and AAAS Publications Trends: Recent work spans AI ethics in software, design space theory, adaptive systems, programming language philosophy, and empirical software research. She emphasizes bridging theoretical research with practical applications through projects like Vitruvius for software architecture and EUSES for end-user programming tools.

• Combinatorial Optimization
• Distributed and Parallel Computing
• Coding Theory
• +۴ مورد دیگر

Bernhard Haeupler is a Professor at INSAIT (Institute for Computer Science, Artificial Intelligence and Technology) under Sofia University "St. Kliment Ohridski" since 2024. He is also a Post-Professor and Researcher at ETH Zurich 's Computer Science Department since 2020 and an Adjunct Professor at Carnegie Mellon University 's Computer Science Department since 2022. His roles span theoretical computer science, algorithm design, and distributed/parallel systems. Research Interests : Haeupler focuses on algorithms for combinatorial optimization, distributed and parallel computing, coding theory, graph/network algorithms, and information theory. His work bridges theoretical foundations with practical applications in network resilience, synchronization, and quantum/parallel optimization. Article Trends : His 15 most recent articles (2023-2025) emphasize distributed algorithms (e.g., dynamic routing, fault-tolerant spanners), parallel computing (e.g., low-congestion shortcuts, near-linear work), and coding theory (e.g., synchronization strings, insertion-deletion codes). Topics include graph theory , network design , and quantum shortest paths . Scientific Awards : ERC Starting Grant (2020) Sloan Research Fellow (2019) NSF CAREER Award (2018) ACM-EATCS Doctoral Dissertation Award (2014) George Sprowls Award for MIT PhD Thesis Advising & Grants : Haeupler has advised eight PhD students, including Jason Li (CMU, now at Google Research Zurich) and Nicolas Resch (University of Amsterdam). He has secured multiple NSF grants for projects like Distributed Optimization Beyond Worst-Case Topologies and New Coding Techniques for Synchronization Errors .

Anıl Adaمشاهده پروفایل

استاد آموزشی

• Theoretical Computer Science
• Communication Complexity
• Circuit Complexity
• +۴ مورد دیگر

Anıl Ada is an Associate Teaching Professor in the Computer Science Department at Carnegie Mellon University and has previously taught at McGill University . He holds a Ph.D. in Computer Science from McGill, advised by Denis Thérien and Hamed Hatami. Research Interests: His work bridges theoretical computer science and education , focusing on Communication complexity Circuit complexity Analysis of boolean functions and matrices Pseudorandomness Additive combinatorics Notable Contributions: He developed Panda Notes , a web application for academic collaboration and teaching, combining document hosting with interactive discussion features. His publications span conferences like ICALP , RANDOM , and journals such as International Journal of Foundations of Computer Science , addressing foundational questions in computational theory.

Peter Manoharمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Algorithms
• Coding Theory
• +۴ مورد دیگر

Peter Manohar is a postdoctoral researcher in the Computer Science and Discrete Math group at the Institute for Advanced Study , focusing on Theoretical Computer Science with emphasis on algorithms, coding theory, and cryptography. His work explores spectral algorithms for semirandom and smoothed instances of NP-hard constraint satisfaction problems, linking these methods to coding theory, extremal combinatorics, and cryptography. Education: PhD in Computer Science from Carnegie Mellon University, advised by Venkatesan Guruswami and Pravesh K. Kothari B.S. in EECS from UC Berkeley, advised by Alessandro Chiesa and Ren Ng Research Trends: His recent publications highlight advancements in spectral refutation techniques, locally decodable/correctable codes, and connections between complexity theory and coding. Articles span venues like FOCS, STOC, APPROX, and arXiv, reflecting his interdisciplinary approach. Awards: He has received prestigious NSF and Cylab Presidential Fellowships, along with ARCS scholarships during his PhD. His work on quantum proofs (TCC 2019) and constraint satisfaction problems has been recognized in invited journal special issues. Teaching & Collaboration: Peter has taught courses at Carnegie Mellon, including Quantum Computing and Computer Graphics. He interned at TTIC in Summer 2023 and co-organized CMU's Theory Club, demonstrating active engagement in academic communities.

Charles Joseph Argueمشاهده پروفایل

مدرس

• Algorithms
• Combinatorics
• Optimization
• +۴ مورد دیگر

Charles Joseph Argue is a Lecturer in Mathematics at Yale University since July 2022, specializing in Algorithms, Combinatorics, and Optimization. He received his PhD in Mathematics from Carnegie Mellon University, where he was advised by Anupam Gupta. Specialties: Online algorithms, convex geometry, convex optimization, combinatorial optimization Current research focuses on information-theoretic questions in algorithms, convex optimization, and their applications to theoretical computer science. The trends in his publications emphasize algorithmic design for convex and combinatorial optimization problems, with significant contributions to competitive algorithms for convex body chasing and packing integer programs. His work spans both theoretical foundations and practical applications. Scientific awards include: SODA 2020 Best Paper SODA 2019 Best Paper At Carnegie Mellon University, he taught courses in multivariate analysis, matrix algebra, discrete mathematics, and calculus. Since 2016, he has coached the Western Pennsylvania ARML Team, mentoring high school and middle school students in mathematics competitions.