جستجوی استاد راهنمای دانشگاهی

• Statistical Machine Learning
• Algorithmic Statistics
• Robust Statistics
• +۱۱ مورد دیگر

Sivaraman Balakrishnan is a Professor at Carnegie Mellon University with joint appointments in the Department of Statistics and Data Science and the Machine Learning Department. His research bridges statistical machine learning, algorithmic statistics, and robust inference. Education: Ph.D. in Computer Science from Carnegie Mellon University (Language Technologies Institute, advised by Jaime Carbonell); postdoctoral work at UC Berkeley (Department of Statistics, advised by Martin Wainwright and Bin Yu). Research Interests: Spanning robust statistics, domain adaptation, minimax hypothesis testing, assumption-light inference, causal inference, statistical optimal transport, non-parametric statistics, ranking, crowdsourcing, optimization, and topological data analysis. Key Research Trends: Recent work focuses on domain adaptation under label/misingness shifts, robust gradient estimation, smooth optimal transport maps, and conditional independence testing. He explores minimax optimal methods, univariate mean estimation, and high-dimensional regression with missing data. Scientific Awards: IMS Lawrence D. Brown Student Award (2021, 2020) NVIDIA Pioneer Award (2018) Franklin V. Taylor Memorial Best Paper Award (2018) Grants and Editorial Roles: NSF grants (CCF-1763734, DMS-1713003, DMS-2113684, DMS-2310632), Amazon Research Award (2021), Google Research Scholar Award (2021). Associate Editor for JASA and JRSSB ; Editorial Board member for Foundations and Trends in Statistics . Collaborative Groups: Co-organizes the Statistics and Machine Learning Reading Group and participates in the Causal Inference Working Group at CMU.

• Biostatistics
• Longitudinal Studies
• Public Health
• +۴ مورد دیگر

Denise Esserman is a Professor of Biostatistics at the Yale School of Public Health, where she joined the faculty in 2014. She is a member of the Yale Center for Analytical Sciences and collaborates with multiple departments at the Yale School of Medicine, including the Clinical and Translational Science Award Program, Patient-Centered Outcomes Research Institute, and the Cancer Center. Her research focuses on methodological aspects of clustered randomized trials and sample size calculations. Education: PhD in Biostatistics from Columbia University (2006) MS in Statistics from University of Georgia (2001) Dr. Esserman's research interests span several critical areas in biostatistics and public health methodology. She specializes in clustered randomized trials, with particular expertise in understanding how intraclass correlation coefficients (ICC) and other factors impact sample size calculations. Her work extends to longitudinal studies methodology, randomized controlled trial design, and sampling techniques. She has contributed significantly to statistical methods for clinical trials, healthcare data analysis, and public health research. Her interdisciplinary approach bridges theoretical statistics with practical applications in healthcare settings. Analysis of Dr. Esserman's recent publications reveals a strong focus on methodological innovations in clinical trial design and analysis, particularly for cluster-randomized trials. Her work spans healthcare applications including fall injury prevention in elderly populations, opioid use disorder treatment in international settings, pain management for hemodialysis patients, and validation of medical coding algorithms. She frequently employs advanced statistical techniques including Bayesian methods, mediation analysis, and methods for handling clustered data. Her research demonstrates a consistent commitment to improving the rigor and applicability of statistical methods in public health and clinical research. Dr. Esserman serves as a reviewer for several prestigious journals including the American Journal of Epidemiology, Arteriosclerosis, Thrombosis and Vascular Biology; Statistics in Biopharmaceutical Research; Clinical Trials; and Obesity. As a member of the Yale Center for Analytical Sciences, Dr. Esserman collaborates with numerous researchers across Yale University. Her current projects include the EQuIP trial (HIC ID 2000033355), where she serves as Sub Investigator with primary completion date of 08/31/2027, focusing on mental health and behavioral research for sexual minority women.

Ali Shojaieمشاهده پروفایل

استاد

• Biostatistics
• Statistics
• Machine Learning
• +۴ مورد دیگر

Ali Shojaie is a Professor of Biostatistics and Statistics at the University of Washington, serving as Associate Chair for Strategic Research Affairs in the Department of Biostatistics. He leads the Summer Institute for Statistics in Big Data (SISBID) and the Data Management and Statistics (DMS) Core for the UW Alzheimer's Disease Research Center. His research focuses on developing statistical and machine learning methods for high-dimensional data, with applications in genomics, neuroscience, and public health. Shojaie's work includes advancements in graphical models, Granger causality, and spatial statistics. He has contributed to methodologies for analyzing networks from time series and spatial data, with applications in understanding gene regulatory networks and brain connectivity. His recent projects involve NIH-funded grants exploring gene-phenotype associations using omic data and explainable machine learning for brain stimulation research. Scientific awards include the 2022 Leo Breiman Award from ASA's Statistical Learning and Data Science section, and election as a Fellow of the Institute of Mathematical Statistics (IMS) and American Statistical Association (ASA). He serves on editorial boards for journals like the Journal of the American Statistical Association and Biometrika. Shojaie advises numerous PhD students and postdocs, many of whom have secured academic and industry positions. His lab develops open-source software tools, including the netgsa and ngc packages for network analysis and Granger causality estimation.

Daniela M Wittenمشاهده پروفایل

استاد

• Statistical Machine Learning
• High-Dimensional Statistics
• Unsupervised Learning
• +۵ مورد دیگر

Daniela M Witten is a Professor of Statistics and Biostatistics at the University of Washington, holding the Dorothy Gilford Endowed Chair in Mathematical Statistics. Her research focuses on developing statistical machine learning methods for high-dimensional data, with a particular emphasis on unsupervised learning and theoretical foundations. Witten earned her BS in Math and Biology with Honors and Distinction from Stanford University in 2005 and her PhD in Statistics from Stanford University in 2010 under Robert Tibshirani. Her academic journey established her expertise in bridging mathematical theory with biological applications. Her research program centers on high-dimensional statistical learning , where she develops methods for unsupervised learning and graphical modeling when features outnumber observations. She pioneers statistical models for neural activity through collaborations with the Allen Institute for Brain Science and Princeton University, addressing functional connectivity and neuron sub-population identification. Her groundbreaking work on selective inference solves the "double-dipping" problem in hypothesis generation and testing, enabling valid inference after hierarchical clustering and regression trees. Additionally, she advances multi-view data analysis to integrate complementary data sources like clinical and genomic measurements. Applications span genomics, neuroscience, microbial ecology, and pathology, demonstrating her commitment to solving real-world biomedical challenges. Her 2025 publications reveal a cohesive trend toward developing theoretically rigorous inference frameworks for high-dimensional settings, with emphasis on linear regression validity, semi-supervised efficiency, Gaussian decomposition, and PCA variance quantification—showcasing her signature blend of methodological innovation and practical applicability. Witten's exceptional contributions are recognized through extensive honors: Presidents’ Award, Committee of Presidents of Statistical Societies (COPSS) (2022) Mortimer Spiegelman Award, American Public Health Association (2019) Simons Investigator Award (2018-2023) Sloan Research Fellowship (2013-2015) NSF CAREER Award (2013-2018) NIH Director’s Early Independence Award (2011-2016) 23 major awards including named lectureships, fellowships, and editorial leadership As a dedicated mentor, she has guided students like Olivia McGough (NSF GRFP winner), Dwight (Zichun) Xu (ASA Nonparametrics Student Paper Award winner), Yiqun Chen (Hopkins Biostat faculty), and Anna Neufeld (Williams College faculty). Her research is sustained by major grants from NIH, NSF, and Simons Foundation. Witten co-authored the seminal textbook "Introduction to Statistical Learning" and currently serves as Joint Editor of the Journal of the Royal Statistical Society, Series B (2023-2025), shaping the field through both scholarship and community leadership.

Ruohui Chenمشاهده پروفایل

استادیار

• Biostatistics
• Data Science
• Large-scale Healthcare Data Analysis
• +۱۳ مورد دیگر

Ruohui Chen, PhD, is an Assistant Professor in the Department of Preventive Medicine (Biostatistics and Informatics) at Northwestern University's Feinberg School of Medicine, where he develops advanced biostatistical methodologies and leads collaborative research in oncology and chronic disease. His educational background includes: PhD from University of California San Diego (2023) Dr. Chen specializes in large-scale healthcare data analysis, functional/longitudinal data methods, predictive modeling, and causal inference. His collaborative work addresses cancer treatment disparities, Alzheimer's disease mechanisms, kidney disease progression, and activity pattern impacts on health outcomes through rigorous statistical frameworks. His 2025 publications demonstrate cross-disciplinary applications: analyzing socioeconomic factors in bone cancer prognosis, testing physical activity interventions for cardiovascular health in postmenopausal women, evaluating novel lymphoma immunotherapies, and optimizing aspirin dosing for colorectal cancer prevention. These studies consistently bridge methodological innovation with clinical oncology and public health challenges. No major scientific awards are documented in the current profile. Professional activities include editorial board service for Taylor & Francis (2024-present) and American Statistical Association membership (2016-present), with prior leadership as UC San Diego chapter president (2020-2023). Research grant details and student mentorship information are not provided. Dr. Chen operates within collaborative oncology research networks at Feinberg, though specific laboratory structures are not detailed in available materials.

• Antenna Design
• Microwave Engineering
• Dielectric Materials
• +۲ مورد دیگر

Wolfgang Bösch is a Professor at Graz University of Technology's Institute of Microwave and Photonic Engineering, specializing in advanced RF components and measurement techniques. His research advances high-frequency systems through innovations in antenna technology and electromagnetic theory. Research domains include: metamaterial-based antennas, precision measurement calibration, microwave filter optimization, and 3D-printed RF components. Recent work demonstrates strong focus on millimeter-wave systems and reconfigurable antenna arrays. Publications highlight expertise in: machine learning for filter design, metasurface applications, PCB transitions for high-frequency systems, and uncertainty quantification in RF engineering. Research consistently addresses miniaturization and performance optimization challenges. Awards recognize contributions to measurement science and antenna design: Fellow of IET, Houska Prize, and best paper awards. Current laboratories investigate liquid crystal antenna systems and error calibration methodologies for next-generation wireless systems.

• Ocean Renewable Energy
• Robotics
• Biomechanics
• +۲ مورد دیگر

Dr. Boyin Ding is an Associate Professor at the University of Adelaide , serving as Academic Director at Haide College and researcher in the Mechanical Engineering department within the Faculty of Sciences, Engineering and Technology. He leads the Wave Energy Research initiative established in 2014, while also contributing to Robotics and Biomechanics through his work with the Flinders Medical Device Research Institute. Research Areas: Ocean Wave Energy Harvesting Control Systems for Renewable Energy 6DOF Robotic Testing Spine Biomechanics Transnational Education Programs Key Collaborations: Australia-China Joint Research Centre for Offshore Wind & Wave Energy Acoustics, Vibration and Control Research Group Scientific Awards: Australian Endeavour Fellowship Malcolm Kinnaird Engineering Excellence Award (2012) His recent publications focus on hybrid offshore energy systems, nonlinear hydrodynamics in wave energy converters, and biomechanical testing technologies. He has developed control algorithms for floating offshore wind-wave systems and pioneered 6DOF robotic platforms for medical applications. As an eligible PhD supervisor, he actively collaborates with global industries and academic institutions.

Nadine Walterمشاهده پروفایل

استاد

• Branding
• Sustainability Marketing
• Influencer Marketing
• +۳ مورد دیگر

Prof. Nadine Walter is a Professor of International Marketing at Pforzheim University’s Business School. She holds an MBA from the University of Massachusetts Dartmouth and a Ph.D. from the University of Hamburg. Since 2008, she has taught marketing and directed the double-degree Business/International Marketing program. Her research focuses on branding (brand purpose, international branding), sustainability marketing (femvertising, social/ecological advertising), influencer marketing (micro/macro-influencers, virtual influencers), and technology adoption (payment methods, IVAs). She advises companies in marketing and strategy, and serves as a visiting professor at Tilburg University and Pennsylvania State University. She has been recognized with a 2014 Teaching Award from Pforzheim University. Her work bridges academic research and industry applications, emphasizing authentic brand strategies and consumer behavior insights. Education: Ph.D. (2004, Universität Hamburg), MBA (1998, UMass Dartmouth), Diplom-Kauffrau (2000, Universität Mannheim) Roles: Program Director, Visiting Professorships (Netherlands, USA), Executive Education (St. Gallen) Research: Over 30 peer-reviewed articles in journals like Journal of Business Research and Journal of Brand Management , with a focus on sustainability, digital marketing, and consumer psychology. Her research highlights include analyzing femvertising’s impact on brand attitudes, influencer effectiveness across micro/macro scales, and the role of IVAs in customer journeys. She emphasizes ethical marketing practices and authentic brand communication to build consumer trust.

• Econometrics
• Applied Economics
• Causal Inference
• +۳ مورد دیگر

Dr. Liyang Sun is a Lecturer in Economics and Deputy Graduate Tutor at the University of College London's Department of Economics, and an Untenured Associate Professor (on leave) at CEMFI in Madrid. She holds a PhD in Economics and Statistics from MIT (2021) and a BA in Economics and Mathematics from Wellesley College (2014). Her research focuses on causal inference methodologies under treatment effect heterogeneity and weak identification with many instruments. Prior to her current roles, she was a Postdoctoral Research Fellow at UC Berkeley. Her academic positions include: Lecturer in Economics, University College London (current) Untenured Associate Professor, CEMFI, Madrid (on leave) Postdoctoral Research Fellow, UC Berkeley (previous) Research interests span econometric method development, applied economics, and policy analysis. Her work emphasizes improving causal inference techniques in realistic economic settings. Recent publications explore synthetic control methods, instrumental variables with many weak instruments, and machine learning applications in structural reforms analysis. Her scholarly contributions address core econometric challenges such as: Policy learning and confidence estimation Temporal aggregation in synthetic control frameworks Adaptive methods for model misspecification No specific grants or advising activities are documented here. She contributes to the department's teaching and graduate training programs as Deputy Graduate Tutor.

Soumik Purk3ayasthaمشاهده پروفایل

استادیار

• Biostatistics
• Machine Learning
• Causal Inference
• +۷ مورد دیگر

Soumik Purkayastha is an Assistant Professor in the Department of Biostatistics and Health Data Science at the University of Pittsburgh School of Public Health. He also serves as a Research Biostatistician at the Center for Healthcare Evaluation, Research, and Promotion (CHERP) within the Department of Veterans Affairs, focusing on improving healthcare outcomes for veterans. B.Sc. (Hons.), St. Xavier's College, Kolkata, 2014-17 M.Stat. (Biostatistics), Indian Statistical Institute, 2017-19 M.S. in Biostatistics, University of Michigan, 2019-21 Ph.D. in Biostatistics, University of Michigan, 2019-24 His research develops scalable statistical and machine learning methods for biomedical studies, emphasizing information-theoretic frameworks for association and causality without traditional causal inference assumptions. Applications include mediation analysis , instrumental variables , and spatiotemporal forecasting of infectious diseases like SARS-CoV-2. He integrates Bayesian and semi/non-parametric approaches with computational challenges in statistical modeling. His publications focus on asymmetric association methods, infectious disease compartmental models (e.g., SEIR-fansy), and data-driven pandemic resilience strategies. Key themes include causal discovery , collider detection , and patient-reported outcome correlation analysis in clinical studies. Prior to joining Pitt, he worked with the Abecasis Group and Diabetic Foot Consortium at the University of Michigan. He has developed open-source software tools like SEIRfansy , fastMI , and comet , contributing to epidemiological and statistical methodology.

Michael Danielsمشاهده پروفایل

استاد

• Biostatistics
• Bayesian methodology
• Methodology for longitudinal data
• +۱ مورد دیگر

Michael Daniels is a Professor and Chair of the Department of Statistics at the University of Florida, holding the Andrew Banks Family Endowed Chair. He previously held faculty positions at the University of Texas at Austin, Iowa State University, and Carnegie Mellon University. He earned his Sc.D. in Biostatistics from Harvard University (1995) and A.B. in Applied Mathematics from Brown University (1991). University: University of Florida College: College of Liberal Arts and Sciences (CLAS) Department: Department of Statistics His research focuses on Biostatistics , Bayesian methodology , and methodologies for longitudinal and causal inference , with applications in cardiovascular health, muscular dystrophy, and healthcare analytics. He has authored influential books like Bayesian Nonparametrics for Causal Inference and Missing Data (2023) and Missing Data in Longitudinal Studies (2008). Key awards include the Lagakos Distinguished Alumni Award (Harvard) and L. Adrienne Cupples Award (Boston University). He has been funded by NIH grants since 2001 and leads collaborative research in chronic disease management, including studies on opioid use in elderly populations and Duchenne muscular dystrophy progression modeling. Grants & Collaborations: NIH-funded studies on muscle degeneration biomarkers Telemedicine and chronic pain management trials Development of clinical trial simulation tools for neuromuscular diseases His work emphasizes Bayesian approaches to handle missing data and causal inference, with applied focus on improving healthcare outcomes through rigorous statistical methods.

Gholamreza Aminمشاهده پروفایل

دانشیار

• Performance Measurement and Management
• Mathematical Programming
• Productivity and Efficiency Analysis
• +۵ مورد دیگر

Gholamreza Amin is an Associate Professor of Management Science in the Faculty of Business at the University of New Brunswick (UNB), Canada. Previously, he taught at Sultan Qaboos University (Oman), where he received the Distinguished Research Award. He holds a Ph.D. in Operations Research from Azad University (2003), Iran. His roles include Associate Editor of the IMA Journal of Management Mathematics and SN Business and Economics , and participation in organizing international conferences. Dr. Amin's research focuses on Performance Measurement, Mathematical Programming, and Productivity Analysis using Data Envelopment Analysis (DEA), Ordered Weighted Averaging (OWA) Operators, and Inverse Optimization. He has published over 90 articles in top journals like European Journal of Operational Research and Annals of Operations Research , with over 3,200 citations. His recent work emphasizes DEA applications in mergers, efficiency evaluation, and environmental sustainability. Awards: UNB Merit Award (2019-2020), Teaching Excellence Award (2024), and Distinguished Research Award (Sultan Qaboos University). His teaching spans Operations Research courses including Management Science, Quantitative Methods, and Operations Management. He contributes to editorial work and conference organization, advancing the field's theoretical and applied dimensions.

Shan Yuمشاهده پروفایل

استادیار

• Statistical Methodology
• Machine Learning
• Neuroimaging Analysis
• +۷ مورد دیگر

Shan Yu is an Assistant Professor in the Department of Statistics at the University of Virginia. His research focuses on developing statistical and machine learning methods for large-scale, complex data, with applications in neuroimaging, genomics, spatial epidemiology, and health disparities. He employs advanced techniques including non/semi-parametric regression, functional data analysis, and distributed learning while emphasizing data privacy. Yu received his Ph.D. in Statistics from Iowa State University (2020), advised by Professors Lily Wang and Dan Nettleton, following a B.S. from the University of Science and Technology of China. His work bridges statistical methodology and real-world problems, addressing challenges in environmental science (e.g., nitrogen dioxide inequalities), public health (e.g., pandemic forecasting), and computational biology (e.g., genotype-environment interactions). He collaborates on tools like the GgAM R package for generalized geoadditive models and contributes to open-source projects such as fFLM for functional linear regression. Key research trends include spatially varying coefficient models, fusion learning for heterogeneous data, and integration of satellite data with environmental health studies. His publications span journals in statistics, epidemiology, and environmental science, reflecting interdisciplinary impact.

Elisenda Feliuمشاهده پروفایل

استاد

• Applied Algebraic Geometry
• Chemical Reaction Network Theory
• Semi-Algebraic Geometry
• +۳ مورد دیگر

Elisenda Feliu is a Professor at the Department of Mathematical Sciences, University of Copenhagen. She holds an ERC Consolidator Grant (2023-2027) and is an associate editor of Mathematical Biosciences . Her research focuses on applied algebraic geometry and chemical reaction network theory, emphasizing positive solutions to polynomial systems. She has organized workshops like the Mathematical Trends in Reaction Network Theory (2015) and contributed to conferences globally. She has advised multiple PhD students and postdocs, including Joan Ferrer Rodríguez and M. Telek. Her academic journey includes a PhD in Mathematics (2007, UB), a Master in Bioinformatics (2008, UB/UPF), and postdoctoral positions in Barcelona, Aarhus, and Copenhagen. She is a former member of the Danish Young Academy and has held roles in committees such as the ESMTB Board (2021–2026) and the diversity committee at her department (2018–present). Education: Llicenciatura (BSc) in Mathematics (2000, UB), DEA (MSc) in Mathematics (2002, UB), Master in Bioinformatics (2008, UB/UPF), PhD in Mathematics (2007, UB). Research Interests: Her work bridges algebraic geometry and systems biology, addressing challenges such as multistationarity, Hopf bifurcations, and model reduction in biochemical networks. She explores how algebraic methods can elucidate network properties like steady states and bistability. Awards & Grants: Sapere Aude Starting Grant (2015–2020), ERC Consolidator Grant (2023–2027), Josep Teixidor Prize (2010). Her projects include Signs, Polynomials, and Reaction Networks (ERC-funded) and collaborations with institutions like MPI Leipzig and the Novo Nordisk Foundation. Advisees & Collaborations: Supervised 5 PhD students and multiple postdocs. Her team hosts the Mathematics of Reaction Networks seminar and maintains active collaborations internationally. She co-organized the 2015 workshop on reaction network theory and chairs committees promoting diversity in STEM.

• Econometrics
• Agricultural Economics
• Resource Economics
• +۳ مورد دیگر

Zheng Li is an Assistant Professor in the Department of Agricultural and Resource Economics at North Carolina State University. His research focuses on econometric methodologies with applications in agricultural economics, resource management, and policy analysis. He holds expertise in nonparametric estimation, quantile regression, and structural econometric modeling. Key research interests include analyzing agricultural production risks, evaluating policy impacts on housing markets, and developing advanced statistical techniques for mixed data types. His work bridges econometric theory with practical applications in environmental, urban, and transportation sectors. Recent publications explore topics such as lung cancer detection via biomedical sensing technologies, ridesharing platform incentives, and pandemic effects on real estate markets. Methodologically, his contributions span kernel-based specification tests, bootstrap methods for heavy-tailed data, and monotonicity-constrained estimation techniques. No scientific awards or formal advisees are listed. His research often intersects with interdisciplinary challenges, reflecting a commitment to innovative solutions in applied economics and data science.