جستجوی استاد راهنمای دانشگاهی

Kathryn Roeder is the UPMC University Professor of Statistics and Life Sciences at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences and the Departments of Statistics & Data Science and Computational Biology. Her research focuses on developing statistical methods for genetic and genomic data, particularly in identifying autism risk genes and analyzing single-cell multi-omic data. She earned her Ph.D. in Statistics from Penn State University and has been at CMU since 1994, previously serving as Vice Provost for Faculty (2015–2019). Education: Ph.D. in Statistics, Penn State University (1988) B.S. in Wildlife Resources, University of Idaho (1982) Research Interests: Her work integrates modern statistical techniques (high-dimensional statistics, machine learning, networks) to study complex diseases like autism and schizophrenia. Recent efforts include tools for analyzing single-cell RNA-seq and proteomic data, such as UNICORN, DAWN, and SCEPTRE. Key Awards: COPSS Distinguished Achievement Award (2020) National Academy of Sciences Member (2019) COPSS Presidents’ Award (1997) AAAS Fellow (2020) Advising & Grants: She has advised over 20 Ph.D. students, many contributing to landmark studies in autism genetics. Her grants include NIH funding for projects like the Autism Sequencing Consortium. Current research teams focus on computational biology and statistical genetics. Labs & Collaborations: Her lab develops software tools (e.g., TADA, MIND) and collaborates with the Autism Sequencing Consortium and iPSYCH-BROAD Consortium on large-scale genomic studies.

• Statistical Genetics
• Genomics
• Biostatistics
• +۵ مورد دیگر

Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.

• Scientific Machine Learning
• Trust-Worthy UQ
• Likelihood-Free Inference
• +۱ مورد دیگر

Eric P. Xing is a Professor at the Language Technologies Institute of Carnegie Mellon University , and currently serves as President of the Mohamed bin Zayed University of Artificial Intelligence . His work bridges machine learning methodology with computational biology and large-scale AI systems . Research Focus: Developing machine learning theory for high-dimensional, dynamic data Building foundation models for biology (AIDO, scLong, ProteinAligner) Designing scalable AI architectures (Pollux, LLM360, PAN) Advancing interpretable and controllable NLP systems Scientific Leadership: Founded the SAILING Lab at CMU Co-chaired ICML 2014 and ICML 2019 Recipient of the Jay Lepreau Best Paper Award (OSDI 2021) Education & Mentorship: Advises PhD students across machine learning and computational biology Alumni include faculty at ETH Zurich, University of Chicago, and UC San Diego

• Machine Learning
• Statistical Methodology
• Computational Biology
• +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.

• Econometrics
• Statistics
• Machine Learning
• +۵ 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.

• Statistical Genetics
• Spatial Statistics
• Machine Learning
• +۳ 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.

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

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.

• Statistical Methodology
• Cosmology
• Astronomy
• +۷ مورد دیگر

Ron Yurko is an Assistant Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University , where he focuses on methods at the intersection of statistical inference and machine learning. He serves as Director of the Carnegie Mellon Sports Analytics Center (CMSAC) , overseeing initiatives like the sports analytics research lab, CMSACamp , and the annual Sports Analytics Conference. Research Interests His work bridges Sports Analytics and Natural Language Processing , with applications in Biostatistics , Public Policy , and Statistical Pedagogy . Recent projects include evaluating NFL player positioning ( NFL Ghosts ), analyzing language patterns in LLM outputs, and developing fractional tackle metrics for defensive evaluation. Google Scholar articles reveal deeper methodological contributions to Bayesian modeling for directional movement analysis, multilevel models for snap timing, and spatio-temporal frameworks in football event modeling. Academic Background PhD in Statistics (2022) from Carnegie Mellon University BS in Statistics with University Honors (2015) from Carnegie Mellon Additional Contributions He is developing a textbook Statistical Methods in Sports Analytics (2027), maintains the Statistical Thinking in Sports Analytics newsletter, and teaches course 36-460/660 at CMU.

• Sports Analytics
• Natural Language Processing & Large Language Models
• Biostatistics & Epidemiology
• +۴ 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.

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

Haohan Wang is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, with affiliations to the Carl R. Woese Institute for Genomic Biology and the National Center for Supercomputing Applications. His research focuses on developing trustworthy machine learning methods for computational biology and healthcare applications , emphasizing robustness , causality , and interpretability in vision-based models. Recent research trends in his work include: Large Language Model interactions with biomedical challenges (GenoAgent, GenoTex) Adversarial security in language and vision models (Guard, Jailbreakzoo) Genomic data analysis through robust machine learning frameworks (Precision Lasso, Kernel Mixed Models) Interactive toolkits like Robustar for data annotation and model training Scientific awards include recognition as Baidu's Top 50 AI+X Rising Young Scholars (2022), Best Paper Honorable Mention at WSDM 2023, and Broad Institute's Next Generation status (2019). Current projects explore AI-made scientists for biomedical discovery and Robustar development for GUI-based robust vision learning.

• Trustworthy Machine Learning
• Computational Biology
• Healthcare Applications
• +۵ مورد دیگر

Jay D. Aronson is a Professor of Science, Technology, and Society in the Department of History at Carnegie Mellon University. He is also the founder and director of the Center for Human Rights Science at CMU. His research and teaching focus on the intersections of science, technology, law, media, and human rights, with particular emphasis on forensic science, digital evidence, and post-conflict justice. Ph.D. in History of Science and Technology, University of Minnesota (2004) Pre- and post-doctoral fellow, Harvard University’s John F. Kennedy School of Government Aronson’s work explores how technological innovations—especially digital evidence and DNA profiling—shape human rights practices, legal accountability, and public policy. He has authored or co-authored several books, including Death in Custody: How America Ignores the Truth and What We Can Do About It (2023), Who Owns the Dead? The Science and Politics of Death at Ground Zero (2016), and Genetic Witness: Science, Law, and Controversy in the Making of DNA Profiling (2007). He co-edited New Technologies for Human Rights Law and Practice (2018) and Counting Civilian Casualties (2013). His recent research delves into the use of computer vision and machine learning for analyzing human rights media, shooter localization via social media videos, and ethical challenges in forensic DNA databases. He collaborates with computer scientists and statisticians to improve tools for civilian casualty recording and digital evidence authentication. Aronson also investigates the role of user-generated content in transitional justice and the politics of memorialization after mass disasters.

• Science and Technology Studies
• Human Rights
• Forensic Science
• +۳ مورد دیگر

Jiashun Jin is a Professor of Statistics and Affiliated Professor of Machine Learning at Carnegie Mellon University, where he leads research in statistical inference for Big Data with a focus on 'Rare and Weak' signal regimes. He received his Ph.D. in Statistics from Stanford University in 2003. His research interests span: Development of novel methods like Higher Criticism, Graphlet Screening, IF-PCA, and SCORE for high-dimensional data Applications in genomics (cancer classification, SNP analysis), cosmology (non-Gaussian detection in CMB), and network science Theoretical frameworks including phase diagrams to characterize statistical detectability boundaries Recent publications (2021-2023) demonstrate strong emphasis on network community detection, text analysis, genetic association studies, and COVID-19 resource allocation, with consistent focus on statistical optimality in high-dimensional settings. Honors and awards include: NSF CAREER Award (2007) IMS Tweedie Award (2009) IMS Fellow (2011) ASA Fellow (2019) ICCM Distinguished Paper Award (2020) He has developed a significant dataset of 83,381 statistical publications for coauthorship/citation network analysis and has delivered invited lectures including the IMS Medallion Lecture (2015).

• Statistics
• Big Data Analytics
• Network Analysis
• +۴ مورد دیگر

Weijing Tang is an Assistant Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. His research focuses on developing statistical methodologies for network analysis, machine learning, and survival analysis, addressing challenges in large-scale interdisciplinary datasets across health and social sciences. He holds a Ph.D. in Statistics from the University of Michigan (2022) and a B.S. in Mathematics from Tsinghua University (2016). Prior to CMU, he was a Postdoctoral Research Fellow in Biostatistics at Harvard University (2022-2023). Education: Ph.D. in Statistics, University of Michigan, 2022 B.S. in Mathematics, Tsinghua University, 2016 Research Interests: Network analysis and signed network dynamics Survival analysis and Cox models Machine learning applications in biostatistics and public policy Optimization and scalable statistical methods Nonparametric methods and subgroup heterogeneity analysis Key Awards: ProQuest Distinguished Dissertation Award (2023) IMS Hannan Graduate Student Travel Award (2022) Multiple awards from ASA and ENAR (2021-2020) Grand Prize in American Heart Association's COVID-19 Data Challenge (2020) Funding: NSF Grant DMS-2412853 (sole PI) CMU Dietrich College Seed Grant (co-PI)

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

Bernie Devlin is a Professor of Psychiatry and Clinical and Translational Science at the University of Pittsburgh School of Medicine. He holds a Ph.D. in Botany from Penn State University (1986) and a B.S. in Biology from Indiana University of Pennsylvania (1976). His research focuses on statistical methods for genetic analysis and their application to neurodevelopmental disorders like autism and schizophrenia. He has been a faculty member at the University of Pittsburgh since 1997, previously holding roles at Yale University and Carnegie Mellon University. Dr. Devlin’s work bridges computational genetics and clinical psychiatry, emphasizing methodological innovation in large-scale genomic studies. His lab collaborates with Kathryn Roeder’s group at Carnegie Mellon University, focusing on transcriptomics, rare variant analysis, and neurobiological mechanisms. Key contributions include identifying risk loci for OCD, schizophrenia, and Alzheimer’s disease, with over 30 years of experience in psychiatric genetics. His awards include AAAS Fellowship (2009), NIMH MERIT Award (2013), and recognition from the FBI for DNA analysis contributions (1988–1998). He directs the Program in Computational Genetics at the Western Psychiatric Institute and Clinic, leading a team including Lambertus Klei and Haeun Moon. His research also addresses health equity in polygenic risk scoring and cross-population genomic analysis. Dr. Devlin’s lab integrates statistical genetics and neurobiology, with active projects on autism’s allelic architecture, OCD transcriptomics, and neurodegenerative disease mechanisms. Current efforts emphasize refining methods for analyzing rare variants and improving diagnostic workflows using genomic data.

• Human Genetics
• Psychiatric Genetics
• Statistical Genetics
• +۵ مورد دیگر

Ankur Parikh is a staff research scientist at Google NYC and an adjunct assistant professor at New York University. His research focuses on natural language processing, machine learning, and spectral learning methods with applications to graphical models and biological networks. He holds a PhD from Carnegie Mellon University’s Machine Learning Department (2014), advised by Eric Xing, and an undergraduate degree in Computer Science and Applied Math from Princeton University. His work bridges theoretical advancements in machine learning with practical applications, including decomposable attention models for NLP, kernel graphical models, and network analysis of biological systems. Notable contributions include the TREEGL algorithm for gene network inference and spectral methods for latent structure discovery. He has been recognized with best paper awards at EMNLP and ISMB conferences. Education: PhD, Machine Learning, Carnegie Mellon University (2014) BS, Computer Science & Applied Math, Princeton University Parikh has taught advanced courses on spectral learning and probabilistic graphical models, including guest lectures at CMU and tutorials at Microsoft Research. His research is supported by an NSF Graduate Fellowship and has led to open-source implementations like the PLRE language modeling toolkit. Current affiliations include NYU’s machine learning community and Google’s NYC research team, with ongoing collaborations in NLP, computational biology, and statistical network analysis.

• Natural Language Processing
• Machine Learning
• Spectral Learning
• +۳ مورد دیگر