جست‌وجوی استاد و پژوهشگر

Gemechis Djiraمشاهده پروفایل

استاد

• Biostatistics
• Simultaneous Statistical Inferences
• Structural Equation Modeling
• +۴ مورد دیگر

Gemechis Djira is a Professor of Biostatistics at South Dakota State University (SDSU) and serves as co-director of the SDSU Statistical Consulting Center. He holds a Ph.D. in Biostatistics from Leibniz University Hannover, along with multiple master's degrees in statistics and biostatistics from institutions including Addis Ababa University and Limburgs Universitair Centrum. His research focuses on simultaneous statistical inferences, structural equation modeling, and methodological advancements in biostatistical applications. He has extensive professional experience including over 20 years at SDSU, previously as Associate and Assistant Professor. His academic responsibilities include teaching advanced biostatistics courses (e.g., Biostatistics I/II, Regression Analysis) and advising graduate students. He has led research projects funded by USDA ($150k) and OSHA ($110k), analyzing agricultural education trends and occupational safety programs. Professional memberships include the International Biometric Society (ENAR region). Recent research emphasizes spatial panel autoregressive models, quantile-based statistical methods, and healthcare data analytics. Djira has published extensively in top journals like Biometrical Journal and Communications in Statistics, with a focus on developing novel statistical methodologies for real-world applications.

Theodore Karrisonمشاهده پروفایل

استاد

• Biostatistics
• Clinical Trial Design
• Oncology Research
• +۵ مورد دیگر

Dr. Theodore Karrison is a Professor in the Department of Public Health Sciences at the University of Chicago. He serves as Director of the Biostatistics Laboratory and Technical Director of the Biostatistics Core for the Comprehensive Cancer Center. His work focuses on clinical trial design, oncology research, and survival analysis methodologies. Dr. Karrison has contributed to pivotal studies in areas such as genitourinary cancers, phase II trial endpoints, and response-adaptive designs. Education: B.S. in Mathematics & Computer Science (University of Illinois at Chicago, 1974), M.S. and Ph.D. in Statistics (University of Chicago, 1975 and 1985). Research Interests: Development of statistical methods for clinical trials, restricted mean survival analysis, and collaborative oncology studies. Notable contributions include estimating insulin secretion rates, analyzing aneurysm rupture risks, and designing phase II cancer trials. Current Projects: Leading statistical work for NRG Oncology genitourinary trials, validating metformin in ovarian cancer, and studying azithromycin's effects on asthma microbiomes. Collaborates with multiple institutions on trial design and data analysis. Grants & Labs: Principal investigator on NIH-funded studies including cancer center support grants and early therapeutics development projects. Oversees Biostatistics Core operations supporting over 200 investigators.

Aaditya Ramdasمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Post-selection inference
• +۹ مورد دیگر

Aaditya Ramdas is an Associate Professor in the Department of Statistics and Data Science and the Machine Learning Department at Carnegie Mellon University. He is also a visiting academic at Amazon Research. His work bridges theoretical statistics and practical machine learning, focusing on algorithms with strong guarantees for post-selection inference, game-theoretic statistics, and predictive uncertainty quantification. Applied domains include privacy, neuroscience, genetics, and auditing. He has received prestigious awards such as the Sloan Fellowship, IMS Peter Gavin Hall Prize, and NSF CAREER Award. His research spans foundational areas like sequential testing, multiple hypothesis testing, and conformal prediction, with recent work emphasizing anytime-valid inference and robust statistical methods. Affiliations: Dietrich College of Humanities and Social Sciences, Amazon Research Labs/Teams: DELPHI Lab Group (specializing in public health forecasting), CSAFE Lab Group (computational statistics). Key contributions include advancing e-value based methods for hypothesis testing and developing scalable techniques for uncertainty quantification in machine learning models. His work often intersects interdisciplinary fields, such as auditing AI systems for fairness and privacy compliance.

Larry Wassermanمشاهده پروفایل

استاد

• nonparametric inference
• causal inference
• machine learning
• +۶ مورد دیگر

Larry Wasserman is a UPMC University Professor at Carnegie Mellon University, jointly appointed in the Department of Statistics and Data Science and the Machine Learning Department. He received his Ph.D. from the University of Toronto in 1988 and is recognized as one of the leading statisticians of his generation. His research spans theoretical and applied statistics, with core interests in: Foundational inference : Nonparametric methods, asymptotic theory, causal frameworks Modern applications : Machine learning, high-dimensional statistics, astrostatistics Interdisciplinary domains : Bioinformatics, genomics, physical sciences via the STAMPS group His recent publications demonstrate strong emphasis on causal methodology, optimal transport, and robust inference, with applications ranging from particle physics to genomic analysis. Articles frequently develop novel nonparametric techniques with minimax optimality guarantees. Award highlights include: COPSS Presidents' Award (1999) - Top honor for statisticians under 40 CRM-SSC Prize (2002) - Landmark contributions to statistics Fellowships: American Statistical Association, Institute of Mathematical Statistics, AAAS He leads the Statistical Machine Learning Theory Group and founded STAMPS (Statistical Methods for Physical Sciences). His textbooks All of Statistics and All of Nonparametric Statistics are widely used in graduate programs globally.

Peter Freemanمشاهده پروفایل

عضو هیئت علمی

• Astrostatistics
• Astronomical Data Analysis
• Machine Learning
• +۳ مورد دیگر

Peter Freeman is an Associate Teaching Professor and Director of the Undergraduate Program in the Department of Statistics & Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. He holds a B.A. in Physics (1989, University of California) and a Ph.D. in Astronomy and Astrophysics (1997, University of Chicago). Before joining CMU in 2004, he worked as a scientific programmer for the Chandra X-ray Telescope mission, specializing in data analysis methodologies. His research focuses on astrostatistics, developing advanced statistical techniques for analyzing complex astronomical datasets. Key areas include source detection algorithms, cosmic microwave background mapping, photometric redshift estimation, and galaxy morphology analysis. He has collaborated with faculty and students from CMU and the University of Pittsburgh on interdisciplinary projects, integrating machine learning and computational methods into astrophysical research. Freeman is actively involved in educational initiatives such as the STAMPS (Statistical Pedagogy & Educational Research) group and the Carnegie Mellon Sports Analytics Camp (CMSAC). His work emphasizes authentic, community-engaged learning experiences, particularly in undergraduate laboratory courses and data science curricula. His recent publications span astrostatistics, ecological studies (e.g., Coqui frog acoustic analysis), and pedagogical innovations in STEM education. He has contributed to major projects like the Rubin Observatory Legacy Survey of Space and Time (LSST) and the CANDELS galaxy structure classification effort.

Xin Tongمشاهده پروفایل

دانشیار

• Asymmetric Statistical Learning
• Neyman-Pearson Classification
• High-dimensional Statistics
• +۵ مورد دیگر

Xin Tong is an Associate Professor in the Department of Data Sciences and Operations at the Marshall School of Business, University of Southern California. His research focuses on asymmetric statistical learning, Neyman-Pearson classification, high-dimensional statistics, and network analysis. He holds a PhD in Operations Research from Princeton University (2012) and a B.S. in Mathematics from the University of Toronto (2007). Key contributions include developing Neyman-Pearson classification frameworks for imbalanced datasets and advancing spectral clustering techniques. His work has been supported by NSF grants and NIH funding. Tong serves as an Associate Editor for the Journal of the American Statistical Association and Journal of Business and Economic Statistics. Recent research highlights include studies on label-noise-adjusted classification algorithms, citation network analysis, and fairness-adjusted machine learning. He teaches courses on modern statistical learning and GenAI/ML strategies for business transformation. Notable awards include the Zellner Thesis Award (2013) and multiple NSF grants. His lab's work bridges statistical theory with applications in genomics, social networks, and healthcare risk assessment.

• Uncertainty Quantification
• Causal Inference
• Conformal Prediction
• +۳ مورد دیگر

Ying Jin is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. She received her PhD in Statistics from Stanford University in 2024, advised by Emmanuel Candès and Dominik Rothenhäusler, and holds a B.S. in Mathematics and B.A. in Economics from Tsinghua University. Current faculty at UPenn Wharton Former postdoctoral fellow at Harvard Data Science Initiative Her research focuses on Uncertainty Quantification and Generalizability in AI models, particularly through conformal prediction , causal inference , and multiple testing . Recent work explores distribution shifts in large-scale replication studies and methods for trustworthy AI in drug discovery and medical applications. Key article trends show expertise in: Conformal prediction methods Causal inference under distribution shifts LLM-driven scientific discovery Replicability analysis AI uncertainty quantification High-stakes AI validation Scientific awards include: 2025 IMS Lawrence D. Brown PhD Student Award 2024 Jack Youden Prize for best expository paper in Technometrics She organizes the Online Causal Inference Seminar and contributes to open science through the awesome-replicability-data GitHub repository containing curated replication datasets.

Xiaohong Chenمشاهده پروفایل

استاد

• Econometrics
• Penalized Sieve Estimation
• Semiparametric Models
• +۷ مورد دیگر

Xiaohong Chen is the Malcolm K. Brachman Professor of Economics at Yale University, previously holding positions at the University of Chicago, London School of Economics, and New York University. She earned her PhD in Economics from the University of California, San Diego. Her research focuses on econometrics, particularly penalized sieve estimation and nonparametric/semiparametric models, with contributions to time series analysis and causal inference. Chen has authored influential papers in top journals like Econometrica and Annals of Statistics , and her work has won several awards, including the 2017 China Economics Prize and the Econometric Theory Multa Scripsit Award (2012). She serves as editor of the Journal of Econometrics and has held editorial roles at numerous top journals. Her research interests include semiparametric models, empirical asset pricing, copula methods, and measurement error analysis. Recent work addresses scalable algorithms like stochastic GMM and neural network applications in treatment effect estimation. Chen’s contributions span theoretical econometrics and applied methods, with applications in finance, macroeconomics, and climate modeling. Awards: Fellowships from the Econometric Society, American Academy of Arts and Sciences, and multiple best paper awards. Grants/Editorial Roles: Editor of Journal of Econometrics (2019–present), associate editorships across leading journals, and grants supporting methodological innovations.

Chan Hock Pengمشاهده پروفایل

استاد

• Sequential analysis
• Change-point detection
• Monte Carlo methods
• +۲ مورد دیگر

Chan Hock Peng is a Professor at the National University of Singapore (NUS), affiliated with the Institute of Operations Research and Analytics (IORA), part of NUS’s Smart Nation Research Cluster. His academic role focuses on advancing statistical methodologies and their applications in complex systems. His research interests include: Sequential analysis and change-point detection Monte Carlo methods and particle filtering Multi-armed bandit problems and decision optimization Notable contributions span theoretical advancements in sequential detection, bandit algorithms, and computational statistics. His work addresses challenges in detecting changes in multi-stream data, optimizing decision-making under uncertainty, and developing efficient methods for sparse signal analysis. Scientific Awards: No awards explicitly mentioned in the provided information. Advising and Grants: Details on his advising of students or involvement in grants are not available in the current data. Labs/Teams: Affiliated with IORA, contributing to research in operations research and analytics.

Chunming Zhangمشاهده پروفایل

استاد

• Neuroinformatics
• Bioinformatics
• Machine Learning
• +۷ مورد دیگر

Chunming Zhang is a Professor of Statistics at the University of Wisconsin-Madison, affiliated with the Department of Statistics within the School of Computer, Data & Information Sciences. He holds academic leadership roles and has been a faculty member since 2000. His research focuses on statistical learning theory and applications in neuroinformatics, bioinformatics, financial econometrics, and imaging data analysis. Key areas include high-dimensional inference, robust statistics, and functional data analysis. Education: Ph.D. in Statistics (University of North Carolina-Chapel Hill, 2000), M.S. in Computational Mathematics (Academia Sinica, 1995), B.S. in Mathematical Statistics (Nankai University, 1992). His research interests span computational neuroscience, medical imaging, and financial time series analysis. Notable contributions include methodologies for neuroimaging data analysis, robust statistical estimation, and large-scale multiple testing. He has authored over 50 peer-reviewed publications and serves on editorial boards of leading journals such as the Annals of Statistics and Journal of the American Statistical Association. Professional service includes roles as Associate Editor for multiple top-tier journals and leadership in statistical societies. Teaching focuses on advanced statistical theory, including courses on Mathematical Statistics and Nonparametric Methods.

Zheng-Zheng Tangمشاهده پروفایل

دانشیار

• Statistical Genetics
• Microbiome Data Analysis
• High-Dimensional Omics
• +۲ مورد دیگر

Zheng-Zheng Tang is an Associate Professor in Biostatistics and Medical Informatics at the University of Wisconsin–Madison, with joint appointment in the Department of Statistics. She serves as Associate Director for Biostatistics at the Center for Health Disparities Research and is affiliated with the Wisconsin Institute for Discovery and Center for Demography of Health and Aging. Her research develops statistical methods and computational tools for high-dimensional omics data, focusing on microbiome analysis, genetic association studies, and functional genomics. Current projects include protein-structure-guided association tests, microbiome mediation analysis, and meta-analysis methods for sequencing studies. She leads the Tang Lab developing open-source software including PSCAN, MTAR, and miMediation. Dr. Tang mentors graduate students in statistics and biostatistics programs. Her lab's methodological innovations have addressed microbiome clustering challenges, genetic risk prediction fairness, and phylogeny-based mediation tests. Recent applied collaborations examine gut microbiome's role in multiple sclerosis, dietary fiber metabolism, and preterm birth prediction. Her research is funded by NIH and NSF grants. She contributes to scientific consortia including the Microbiome Preterm Birth DREAM Challenge and leads development of reproducible analysis pipelines for genomic and metagenomic studies.

Jonathan Taylorمشاهده پروفایل

استاد

• Gaussian processes
• Smooth stochastic processes
• Differential geometric methods in statistics
• +۶ مورد دیگر

Jonathan Taylor is a Professor of Statistics at Stanford University , with a joint appointment in Data Science . His research focuses on Gaussian processes , stochastic processes , differential geometric methods , neuroimaging , and HIV protein sequence analysis . He is known for his contributions to BrainSTAT/NiPy software and teaches courses like STATS 191 , STATS 202 , and STATS 306B . Education: Details not explicitly provided in the text, but his role as a Professor suggests advanced degrees in Statistics or related fields. Research Interests: His work spans Gaussian processes and smooth stochastic processes Differential geometric methods in statistics Integral geometry and geometric probability Multiple comparisons and False Discovery Rate Neuroimaging data modeling and analysis HIV protein sequence analysis and mutation patterns Awards: Invited speaker at the International Congress of Mathematicians (ICM) 2018 in the Probability and Statistics track. Contact: jonathan.taylor@stanford.edu , Office: Sequoia Hall 137, Stanford University.

Charles Connerمشاهده پروفایل

استاد

• Digital Signal Processing
• Image Processing
• Linux Systems
• +۳ مورد دیگر

Dr. Charles Conner is a Professor of Engineering at Capitol Technology University, where he has held a full-time faculty position since transitioning from part-time teaching in 1984. He specializes in digital signal and image processing, with expertise in DSP Assembly/C++, MATLAB/Octave, and Linux systems development. Dr. Conner has also been instrumental in securing ABET accreditations for the university and held adjunct roles earlier in his career. His career spans over four decades in academia and industry, including roles as a Staff Scientist at Morehead State University’s Space Science Center, contributing to small satellite control software and the Lunar IceCube mission. He has developed critical systems such as the core Flight System (cFS) and AMMOS Instrumentation Toolkit (AIT). His technical skills include embedded systems design, Verilog/VHDL, and web development using Java/XML/Perl/Python. Research interests focus on signal/image processing, Linux systems, and aerospace applications. Notable projects include thermographic defect detection algorithms and free-space optical communication synchronization using Haar wavelets. His work bridges theoretical research and practical engineering, with contributions to both academic conferences and industry projects. Awarded multiple Esteemed Professor titles from Alpha-Chi Honor Society and recognized in Who’s Who publications, Dr. Conner emphasizes service, including Outstanding Service accolades at Capitol College and the Space Operations Institute. He advises IEEE student chapters and fosters interdisciplinary collaboration between engineering disciplines. Dr. Conner holds a B.S.E.E. (magna cum laude) and M.S.E.E. from the University of Maryland, and a Ph.D. in Electrical Engineering from Catholic University of America. He remains active in technical communities, including the Laurel Linux Users’ Group and Alpha-Chi Honor Society.

Xuhong Zhangمشاهده پروفایل

استادیار

• Machine Learning
• Medical Image Analysis
• Natural Language Processing
• +۵ مورد دیگر

Xuhong Zhang is an Assistant Professor in the Department of Computer Science at Indiana University Bloomington, affiliated with the Luddy School of Informatics. He participates in the Precision Health Initiative and the Data Science Program. His research focuses on computational methods for medical image analysis, NLP, network analysis, and Bayesian techniques for high-dimensional biological data. Previously, he held postdoctoral roles at the University of Colorado and a PhD from UC Irvine under Prof. Carter Butts. Education: PhD in EECS, University of California, Irvine (2011-2017) Postdoctoral Fellow, University of Colorado Anschutz Medical Campus (2017-2020) Research Interests: Develops machine learning models for medical imaging (e.g., tumor cell biomarker prediction), applies NLP to medical text mining, analyzes large-scale human behavior networks, and uses Bayesian methods for single-cell cytometry analysis. Prior work includes spatial network analysis and communication data spectral analysis. Teaching & Mentorship: Instructor for CSCI-B 659 (Medical Image Analysis) and CSCI-P 556 (Applied Machine Learning) Mentored hackathons at UCI and CSPH Labs & Collaborations: Part of the Networks, Computation and Social Dynamics (NCASD) lab at UCI and contributes to the Precision Health Initiative's interdisciplinary research.

Mark Hallahanمشاهده پروفایل

دانشیار

• Social psychology
• Person perception
• Impression formation
• +۴ مورد دیگر

Mark Hallahan is an Associate Professor and Department Chair in the Psychology Department at College of the Holy Cross. He holds editorial roles including Associate Editor for Frontiers in Personality and Social Psychology . With a Ph.D. from Harvard University (1995), his expertise spans social psychology, person perception, and causal attribution. His research emphasizes judgment accuracy, cultural influences on attribution, and nonverbal behavior analysis. Education: Ph.D. and MA in Social Psychology from Harvard University; BA in Psychology from Amherst College. Awards include the Arthur J. O'Leary Faculty Recognition Award (2006) and multiple fellowships from Harvard and Amherst. Research focuses on cross-cultural psychology, statistical methodology in social sciences, and the interplay between nonverbal cues and social perception. Notable contributions include studies on sexual orientation perception through thin slices of behavior (1999) and cultural effects on event explanations (1996). Publications span 1995–2013, emphasizing methodological rigor, judgment accuracy, and cultural psychology. His work frequently appears in journals like Journal of Personality and Social Psychology and Personality and Social Psychology Bulletin . Labs/Teams: While not explicitly named, his research collaborations involve experimental social psychology setups focusing on judgment accuracy and cultural comparisons. Grant involvement includes NSF-related funding and institutional grants.