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Yihan SunView profile

Assistant Professor

• Parallel Computing
• Algorithms
• Data Structures
• +12 more

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.

• Algorithms
• Data Streams
• Machine Learning
• +3 more

David P. Woodruff is a Professor in the Department of Computer Science at Carnegie Mellon University, part of the Theory Group within the School of Computer Science. He is actively involved in academic leadership roles, including chairing the CATCS (Conference on Theoretical Computer Science) and serving as PC chair for SODA 2024 and ICALP 2022. His research focuses on algorithms, data streams, machine learning, numerical linear algebra, sketching, and sparse recovery. He has been recognized with awards such as the Herbert Simon Award for teaching and the PODS Best Paper Award. Woodruff has advised numerous students and postdocs, including notable scholars like Ainesh Bakshi, Rajesh Jayaram, and Hongyang Zhang. His work often addresses foundational challenges in theoretical computer science, with contributions to distributed computing, streaming algorithms, and privacy-preserving techniques. He has published extensively in top conferences like NeurIPS, ICML, FOCS, and STOC, covering topics ranging from low-rank approximation to adversarial robustness in data streams. His teaching includes courses like Algorithms for Big Data and core algorithms courses, reflecting his commitment to both research and education. Collaborations span academia and industry, with applications in genomics and secure computation. Woodruff is a key contributor to the Foundations of Data Science program at the Simons Institute.

• Operations Research
• Optimization
• Combinatorial Optimization
• +6 more

Michael Trick is the Senior Associate Dean for Faculty and Research and Higgins Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University. His research focuses on combinatorial optimization, sports scheduling, constraint programming, and operations research applications. He has contributed to seminal work on sports timetabling (e.g., scheduling college basketball conferences) and the traveling tournament problem. Trick's work bridges theoretical advancements with practical applications, including integer programming, branch-and-price methods, and stochastic dynamic programming. He has led initiatives in operations research education and served as President of INFORMS, emphasizing the field's societal impact. His research also spans voting systems, optimization for newspaper zoning, and algorithmic design for complex scheduling problems. Trick's expertise spans academic leadership, research methodology, and cross-disciplinary applications. His articles address topics like auction design for spectrum allocation, bike-sharing logistics, and robust scheduling strategies. He collaborates across academia and industry, contributing to both theoretical advancements and real-world operational challenges. His work often highlights the practical utility of mathematical programming techniques in diverse domains.

Gary L MillerView profile

Professor

• Spectral Graph Theory
• Algorithms
• Computational Geometry
• +4 more

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 LiView profile

Assistant Professor

• Econometrics
• Statistics
• Machine Learning
• +5 more

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.

Will TownesView profile

Assistant Professor

• Biostatistics
• Epidemiology
• Infectious Disease Forecasting
• +7 more

Will Townes is an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. He joined CMU in 2022 after completing a postdoctoral fellowship in computer science at Princeton University with Barbara Engelhardt. His academic journey includes a Ph.D. in biostatistics from Harvard University under Rafael Irizarry's supervision, an M.S. in math and statistics from Georgetown, and earlier work in tropical ecology fieldwork in the Philippines. Dr. Townes specializes in applied statistics with primary focus areas in biomedical and public health domains. His research centers on wastewater-based epidemiology, wearable devices, and auxiliary signals for infectious disease tracking and forecasting as part of the Delphi research group. He has developed normalization, feature selection, and dimension reduction methods for single cell RNA-Seq and spatial transcriptomics data analysis. His broader research interests span biostatistics, epidemiology, genomics, time series forecasting, and theoretical aspects of Tweedie distributions. His recent publications reveal a strong emphasis on wastewater surveillance methodologies, single-cell data analysis techniques, and infectious disease forecasting models. The research demonstrates a consistent focus on computational scalability and efficiency through approximate inference techniques. Dr. Townes approaches statistical problems with a pragmatic perspective, comfortable with probabilistic (Bayesian) models while drawing inspiration from diverse statistical perspectives. Member of DELPHI Lab Group at CMU Active contributor to genomics and biostatistics research Focus on computational efficiency in statistical methods Dr. Townes mentors several students including Gabrielle Thivierge (PhD candidate working on infectious disease forecasting methods), Julia Elrod, and Anna Rosengart. He teaches data science courses at CMU, including a field course in Costa Rica where students work with community partners on real-world data projects involving water quality indicators, spring flow rates, and ecological monitoring.

Reza ZadehView profile

Adjunct Professor

• Machine Learning
• Distributed Computing
• Discrete Mathematics
• +7 more

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.

• Machine Learning
• Generative Modeling
• Scientific Computing
• +3 more

Tanya Marwah is a Research Fellow at the Simons Foundation , collaborating with Polymathic AI . She earned her PhD from Carnegie Mellon University's Machine Learning Department, co-advised by Prof. Andrej Risteski and Prof. Zachary Lipton, and holds a master's degree from CMU's Robotics Institute. Her research bridges Machine Learning and Scientific Computing , focusing on generative modeling , inverse problems , and building scientific agents . Her work explores theoretical and empirical foundations for applying ML to differential equations, with key contributions in neural operators , memory mechanisms , and edge embeddings in GNNs . Recent publications highlight trends in PDE solvers via LLMs , cross-modal adaptation , and implicit regularization in SGD . She has received the prestigious Siebel Scholar award and actively contributes to top ML venues (NeurIPS, ICML, ICLR, TMLR). Her collaborations span institutions including Carnegie Mellon University, Polymathic AI, and CMU's Robotics Institute.

• Transportation Engineering
• Intelligent Transportation Systems
• Infrastructure Resilience
• +7 more

Sean Qian is a Professor at Carnegie Mellon University (CMU), jointly appointed in the Department of Civil and Environmental Engineering, Heinz College of Information Systems and Public Policy, and the Department of Electrical and Computer Engineering. He directs the Mobility Data Analytics Center (MAC) and co-founded TraffiQure Technologies to commercialize AI-driven infrastructure solutions. His research focuses on dynamic network modeling, intelligent transportation systems, climate resilience, and infrastructure interdependency. Supported by NSF, U.S. DOT, and industry partners, his work integrates AI, big data, and policy analysis to address urban mobility challenges. Education: PhD in Civil Engineering (UC Davis, 2011), MS in Statistics (Stanford, 2012), and dual MS/BS in Civil Engineering (Tsinghua University, 2006/2004). Research emphasizes smart cities, EV integration, cybersecurity for infrastructure, and equity in mobility policies. He serves on editorial boards for Transportation Research journals and TRB committees. Awards include the NSF CAREER Award (2018) and Greenshields Prize (2017). Grants and collaborations include projects with Fujitsu, IBM, and state agencies, addressing curbside management, climate adaptation, and rural mobility. His lab develops tools like the Rural Access Mobility Platform and social digital twin technologies for infrastructure resilience.

R RaviView profile

Professor

• Discrete Optimization
• Operations Research
• Computer Science
• +4 more

R Ravi is the Vasantrao Dempo Professor of Operations Research and Computer Science at Carnegie Mellon University's Tepper School of Business. He holds a B.E. from IIT Madras and M.S./Ph.D. from Brown University. Active since 1995, he served as Associate Dean for Intellectual Strategy and Chair of the Future Educational Delivery Committee. He currently directs Analytics Strategy and leads the Center for Intelligent Business. His research focuses on discrete optimization, network optimization, and applications in supply chain logistics and online advertising, supported by NSF, Google, and others. He has advised over two dozen doctoral students and developed graduate courses like Network Optimization. He received the 30-year Test of Time Award (2023) and is an INFORMS Fellow. His academic roles include editorial leadership in Operations Research and FOCS chairmanship.

Ralf BrownView profile

Researcher

• Machine Translation
• Computational Linguistics
• Digital Forensics
• +5 more

Ralf Brown is a Principal Systems Scientist at Carnegie Mellon University's School of Computer Science, affiliated with the Language Technologies Institute (LTI). His research focuses on machine translation , language identification , and digital forensics , with notable contributions like the open-source CMU-EBMT system and the LTI LangID Corpus for 2000+ languages. Academic Roles: Research faculty since 1993, teaching courses like Coding & Algorithms Bootcamp and MIIS Capstone Project . Research Interests: Multilingual processing, example-based translation, low-resource languages, and text mining. Scientific Contributions: Authored 15+ publications on EBMT, corpus indexing, and context-sensitive translation. Developed tools for digital forensics (corrupted ZIP recovery) and language identification at scale. Awards: IJCAI Distinguished Paper Award Allen Newell Award for Research Excellence Open-Source Contributions: Key developer of darktable image editor Maintainer of the Interrupt List and other technical resources

Chad M. SchaferView profile

Associate Professor

• Statistical Methodology
• Cosmology
• Astronomy
• +7 more

Chad M. Schafer is an Associate Professor in the Department of Statistics & Data Science at Carnegie Mellon University , specializing in statistical methodology for astronomy and cosmology. He co-chairs the LSST Informatics and Statistics Science Collaboration and is affiliated with the McWilliams Center for Cosmology at CMU. His research focuses on rigorous handling of complex models and high-dimensional data in the sciences, particularly astronomy. Ph.D. in Statistics, University of California, Berkeley (2004) M.S. in Statistics, University of Illinois at Urbana-Champaign B.S. in Statistics, Western Michigan University Former staff at Argonne National Laboratory (Mathematics and Computer Science Division) His research spans topics such as likelihood-free inference, Bayesian computation, photometric redshift estimation, and semi-supervised learning for supernova classification. He has applied statistical methods to cosmological surveys like SDSS and LSST, as well as climate modeling and hurricane track analysis. Recent publications highlight applications of statistical techniques to astrophysics, including Approximate Bayesian Computation for supernovae, SCA-based photometric redshift estimation , and high-dimensional density modeling . His work intersects astronomy, data science, and computational statistics. He has served in multiple educational roles, including: Teaching data science courses for CMU's Master of Science in Computational Finance (MSCF) program Steering Committee member for MSCF Instructor for the Summer School in Statistics for Astronomers at Penn State's Center for Astrostatistics Moderator of the methodology subsection of the arXiv Statistics area (2007-2018) Director of CMU's Summer Undergraduate Research Experience in Statistics program (2015-2018) His departmental affiliations and committee roles underscore his interdisciplinary approach, bridging statistical theory with practical applications in astronomy and finance.

Guillaume MarçaisView profile

Researcher

• Computational Biology
• Algorithm design
• Graph theory
• +4 more

Guillaume Marçais is a System Scientist in Dr. Kingsford's group at the Computational Biology Department of Carnegie Mellon University. His research focuses on algorithm development and improvement in Bioinformatics, with particular emphasis on genome assembly, RNA transcript assembly, sequence alignment, and biological data storage, search and retrieval. Education: PhD in Applied Mathematics, 2011, University of Maryland Maitrise de Mathématiques, 2001, Université Paris VII René Diderot Diplôme d'Ingénieur en Informatique et Télécommunications, 1999, Institut National des Télécommunications Marçais' research interests center on computational biology with a strong focus on algorithm design and graph theory. His work has significantly advanced sequence sketching methods, particularly minimizers and k-mer based approaches for handling large-scale genomic data. He has made important contributions to understanding the theoretical properties of these methods while developing practical implementations that improve efficiency in genomic analysis. His research bridges theoretical computer science with practical applications in bioinformatics, addressing computational challenges in modern genomic data analysis. Analysis of Marçais' publication record reveals a consistent focus on sequence sketching methods and k-mer selection techniques. His work has evolved from foundational theoretical work on minimizers to more complex applications involving reverse complements and generative models. The research demonstrates a progression from basic algorithm development to addressing specific challenges in genomic data analysis, with applications spanning sequence alignment, genome assembly, and large-scale data processing. His recent work on k-nonical space represents a significant advancement in handling double-stranded DNA sequences within sketching frameworks. Marçais maintains an extensive collaborative network, primarily with Carl Kingsford and other researchers at Carnegie Mellon University. His work often appears in high-impact computational biology journals and conferences, demonstrating the significance of his contributions to the field. While specific grant information isn't detailed in the provided texts, his consistent publication record suggests sustained research funding for his work in computational genomics. As part of Dr. Kingsford's research group at CMU, Marçais contributes to a vibrant research environment focused on developing innovative computational methods for biological data analysis. His work forms an integral part of the group's efforts to address computational challenges in modern genomics, particularly those arising from the increasing scale and complexity of sequencing data.

Brian QuinnView profile

Professor

• Nuclear Physics
• Particle Physics
• Quark Interaction Physics
• +6 more

Brian Quinn is a Professor of Physics at Carnegie Mellon University's Mellon College of Science, where he has served on the faculty since 1988, achieving indefinite tenure in 1996 and full professorship in 2001. His research focuses on the intersection of nuclear and particle physics through quark interaction studies. Education Ph.D. in Physics, Massachusetts Institute of Technology (1984) B.Sc. in Physics, McGill University, Canada (1978) Research Focus Professor Quinn's work explores non-perturbative quantum chromodynamics through precision experiments at Jefferson Laboratory. His research program investigates hadron structure, strangeness content of nucleons, parity violation effects, and neutron radius measurements using electron and photon beams. Key contributions include leadership in the G0 and HAPPEX-III parity violation experiments and development of advanced instrumentation for nucleon form factor studies. Publication Trends His 2006-2016 publications reveal a consistent focus on precision measurements at Jefferson Lab, particularly through parity-violating electron scattering. Major themes include determination of strange quark contributions to the proton, neutron radius measurements in lead-208, and high-precision nucleon form factor studies. Instrumentation development for polarimetry and calorimetry constitutes approximately 40% of his recent work, reflecting his dual expertise in physics analysis and detector design. Scientific Awards No specific awards mentioned in source material Advising and Research Leadership Professor Quinn has advised graduate students including Megan Friend (thesis on Compton polarimetry for HAPPEX-III). His research is conducted through major Jefferson Lab collaborations including G0, HAPPEX, MOLLER, and nucleon form factor experiments. He led CMU's design and prototyping of the hadron calorimeter for high-precision form factor measurements, securing significant experimental resources through these collaborations. Research Group He leads the Carnegie Mellon Quark Interaction group, which has contributed to diverse experiments using anti-protons, kaons, pions, photons, and electrons. The group specializes in detector development, data acquisition systems, and complex data analysis, with CMU graduate students consistently taking leadership roles in experimental execution and interpretation.

• Process Systems Engineering
• Large-Scale Optimization
• High-Performance Computing
• +2 more

Carl D. Laird is the John E. Swearingen Professor and Department Head of Chemical Engineering at Carnegie Mellon University. He leads an internationally recognized research program in process systems engineering, known for high-performance computing techniques in large-scale nonlinear optimization, parallel scientific computing, and open-source software development. Education: Ph.D. in Chemical Engineering, Carnegie Mellon University (2006) B.S. in Chemical Engineering, University of Alberta (2000) Research Focus: His work solves problems in non-traditional domains including public health, homeland security, critical infrastructure, and energy systems through advanced optimization methodologies. Current research integrates machine learning with optimization for improved decision-making in complex systems. Publication Trends: Recent work focuses on mathematical optimization frameworks, decomposition methods for large-scale problems, integration of machine learning surrogates, and applications in energy systems and chemical manufacturing. Research demonstrates consistent innovation in computational methods for engineering challenges. Awards and Honors: Steven J. Fenves Award for Systems Research INFORMS Computing Society Prize CAST Division Outstanding Young Researcher Award NSF CAREER Award Montague Center Teaching Excellence Award Wilkinson Prize for Numerical Software (for IPOPT development) Leadership and Funding: As director of the Center for Advanced Process Decision-Making, he oversees collaborative research with industry partners. His research has been supported by NSF, DOE, and industrial consortia.