جست‌وجوی استاد و پژوهشگر

Snigdha Panigrahiمشاهده پروفایل

استادیار

• Selective Inference
• Machine Learning
• Data Aggregation
• +۵ مورد دیگر

Snigdha Panigrahi is an Assistant Professor of Statistics at the University of Michigan, with a courtesy appointment in the Department of Biostatistics. She specializes in developing methods for replicable learning from complex data, focusing on quantifying uncertainties in machine learning outputs. Her work bridges selective inference, data aggregation, and non-parametric statistics, supported by NSF and NIH grants. Notable awards include the NSF CAREER Award (2023) and the Bernoulli Society’s New Researcher Award (2025). Education: Ph.D. in Statistics from Stanford University (2013-2018), Master’s and Bachelor’s in Statistics from Indian Statistical Institute (2008-2013). Research interests include selective inference methodologies, distribution-free inferences, and scalable statistical techniques for high-dimensional data. Her research applications span genomics, neuroimaging, and health trials. Key contributions include methods for post-selection inference in graphical models, distributed data analysis, and quantile regression. Current efforts address uncertainties in mobile health trials and malaria serology collaborations with NIH. Awards: Recognitions include the NSF CAREER Award, Bernoulli Society Award, and election to the International Statistical Institute (2021). She serves on editorial boards for Bernoulli , Journal of Computational and Graphical Statistics , and Journal of the Royal Statistical Society: Series B . Advising: Supervises PhD students including Judy Wu and Soham Bakshi, with over 20+ mentored undergraduates and master’s students. Leads a research team focused on adaptive data analysis and selective inference. Grants: Includes NSF DMS-2337882 ($450K) for uncertainty quantification in ML and NIH R01 ($1.2M) for moderation analysis in micro-randomized trials. Additional funding from NSF and institutional grants.

Jean Gallierمشاهده پروفایل

استاد

• Computer Vision
• Computer Graphics and Animation
• Differential Geometry
• +۷ مورد دیگر

Professor Jean Gallier is a faculty member at the University of Pennsylvania's Department of Computer and Information Science. His research focuses on Intelligent Systems, particularly Computer Vision, Computer Graphics, and Animation, alongside foundational work in Algebraic Topology, Differential Geometry, and Optimization Theory. His interdisciplinary contributions bridge mathematics and computer science, with notable publications in representation theory, harmonic analysis, and applications to machine learning. Education and Academic Background: While specific educational details are not explicitly stated, his work suggests deep expertise in mathematics and computer science. His affiliations include the Department of Computer and Information Science and cross-disciplinary collaborations with the Mathematics Department. Research Interests: Gallier’s work spans theoretical and applied domains. He explores geometric methods in computer science, spectral graph theory, and computational aspects of topology. His recent publications address topics like Riemannian manifolds, Lie groups, and optimization techniques such as Lasso regression. His contributions to formal languages and automata theory further demonstrate his versatility across theoretical computer science. Publications and Trends: His articles reflect a focus on foundational mathematical frameworks with practical applications. Recent work emphasizes differential geometry, algebraic structures, and their computational implications. Earlier publications delve into spectral clustering, graph theory, and algorithmic foundations. Grants and Collaborations: While specific grants are not listed, his extensive publication record suggests sustained research activity, likely supported by institutional and collaborative projects. He contributes to academic discourse through course materials and notes on topics ranging from CIS511 to CIS262. Labs and Teams: Affiliated with the Computer and Information Science department, his research likely involves interdisciplinary teams at the University of Pennsylvania, though specific lab affiliations are not detailed in the provided text.

Yufeng Liuمشاهده پروفایل

استاد

• High-dimensional Data Analysis
• Machine Learning
• Nonparametric Statistics
• +۳ مورد دیگر

Dr. Yufeng Liu is a Professor at the University of North Carolina at Chapel Hill, holding positions in the Department of Statistics and Operations Research, Department of Genetics, and Department of Biostatistics. He is affiliated with the Carolina Center for Genome Sciences and the UNC Lineberger Comprehensive Cancer Center. His research focuses on statistical machine learning, high-dimensional data analysis, and bioinformatics, with applications in cancer genomics and neuroimaging. Dr. Liu earned his Ph.D. in Statistics from The Ohio State University (2004), following an M.S. (2001) and B.S. (1999) from the same institution and Nankai University, respectively. He has received prestigious awards including the NSF CAREER Award (2008–2013) and election as a Fellow of the American Statistical Association (2013) and the Institute of Mathematical Statistics (2017). Research Interests: Machine Learning, High-dimensional Data Analysis, Nonparametric Statistics, Bioinformatics, Cancer Genomics Professional Service: Associate Editor roles in journals like the Journal of the American Statistical Association, and leadership in statistical societies His work spans methodological development for classification, regression, and clustering, with applications in genomics and medical data. Notable contributions include the development of robust classification methods and algorithms for high-dimensional datasets. Dr. Liu has been involved in significant collaborative projects, such as The Cancer Genome Atlas (TCGA), and holds grants focused on statistical methods for genomic data. His research has led to advancements in understanding cancer genetics and personalized medicine strategies.

Aniruddha Dattaمشاهده پروفایل

استاد

• Genomic Signal Processing and Control
• Adaptive Control Systems
• Parametric Robust Control
• +۳ مورد دیگر

Aniruddha Datta is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holding the J.W. Runyon, Jr. '35 Professor II title. He serves as Co-Director of Graduate Programs and leads the Genomic Signal Processing Laboratory. His expertise spans adaptive control, genomic signal processing, and systems biology. Education: Ph.D., Electrical Engineering, University of Southern California (1991) M.S., Applied Mathematics, University of Southern California (1991) M.S.E.E., Southern Illinois University (1987) B.Tech (Hons.), Electrical Engineering, Indian Institute of Technology (1985) Research Interests: Focuses on integrating control theory with genomic data analysis, including drug target identification, cancer therapy modeling, and plant stress response pathways. His work emphasizes systems biology approaches to understand complex biological networks and design interventions. Awards: Fellow, IEEE (2009) Eugene E. Webb Faculty Fellow (2006) National Science Foundation Panelist (2001, 2006, 2008, 2009) Advising & Grants: Leads interdisciplinary projects in genomics and control systems, collaborating across engineering, biology, and medicine. His lab develops algorithms for drug combination optimization and genomic data processing. Labs/Teams: Directs the Genomic Signal Processing Laboratory, which pioneers methods for analyzing transcriptional signaling, cancer pathways, and crop genetics using control theory principles.

Ingrid Kristine Gladمشاهده پروفایل

استاد

• Statistics and Data Science
• Machine Learning
• Anomaly Detection
• +۵ مورد دیگر

Ingrid Kristine Glad is a Professor at the Department of Mathematics, University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. She serves as co-director of the Integreat Centre of Excellence and BigInsight Centre for Research-Based Innovation, and chairs the Abel Board (2022–2026). Her research focuses on statistical and machine learning methodologies for high-dimensional data, particularly in genomics, sensor systems, and maritime applications. She has pioneered methods like monotone regression, tailored graphical lasso, and Shapley-value-based explainability frameworks. Research Interests: Glad’s work integrates theoretical statistics with practical applications in anomaly detection, change-point analysis, and predictive modeling. She develops novel algorithms for analyzing large-scale datasets from genomics (e.g., gene networks) and industrial sensor streams (e.g., battery degradation in maritime batteries). Her methods emphasize interpretability and scalability, addressing challenges in both supervised and semi-supervised learning contexts. Publications: Her recent work includes advancements in Shapley-value explanations, maritime battery health monitoring, and biofouling impact analysis. Key themes across her articles are statistical methodology development, machine learning applications in engineering, and computational tools for genomic data integration. Over 50 peer-reviewed papers span journals like Expert Systems with Applications , Journal of Machine Learning Research , and BMC Bioinformatics . Grants & Leadership: Leads interdisciplinary projects funded by the Norwegian Research Council and EU initiatives. Her roles in major centers highlight her influence in shaping statistical research agendas. Supervises active PhD students focused on topics like lifetime analysis models and maritime system analytics. Labs & Collaborations: Central to the Genomic HyperBrowser platform and collaborations with maritime industry partners. Active in both theoretical statistics (e.g., penalized regression) and applied domains (e.g., autonomous ship safety modeling).

Taiji Suzukiمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning Theory
• Deep Learning
• +۶ مورد دیگر

Prof. Taiji Suzuki is an Associate Professor at the University of Tokyo in the Department of Mathematical Informatics . He also serves as Team Leader of the "Deep Learning Theory" team at AIP-RIKEN , Japan. With a PhD in Information Science and Technology from the University of Tokyo (2009), he has held academic positions at the University of Tokyo (2009-2013) and Tokyo Institute of Technology (2013-2017) before returning to the University of Tokyo in 2017. University of Tokyo (2004: BEng in Mathematical Engineering) University of Tokyo (2006: MSc in Information Science and Technology) University of Tokyo (2009: PhD in Information Science and Technology) His research interests span statistical learning theory , deep learning , kernel methods , sparse estimation , and stochastic optimization . He investigates how neural networks adapt to function smoothness, avoid the curse of dimensionality, and achieve global optimization through mean-field dynamics. His work bridges theoretical guarantees (minimax optimality, convergence analysis) with practical implementations (transformers, graph neural networks, diffusion models). The 15 most recent articles focus on transformers' representation power, graph neural networks' limitations, diffusion models' convergence, and optimization theories for deep learning. Key themes include information-theoretic bounds , feature learning dynamics , and mean-field analysis . He explores applications in AI for medicine, federated learning, and biomedical modeling. Awards & Recognition: Outstanding Paper Award, ICLR 2021 MEXT Young Scientists’ Prize Outstanding Achievement Award, Japan Statistical Society 2017 Outstanding Achievement Award, Japan Society for Industrial and Applied Mathematics 2016 Best Paper Award, IBISML 2012 Best Paper Candidate, ICDM 2019 He has served as Area Chair for NeurIPS, ICML, ICLR, AISTATS, and as Program Chair for ACML. His scientific contributions include convergence theories for stochastic gradient methods, minimax analysis of deep learning vs kernel methods, and novel frameworks for distributional optimization in diffusion models.

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

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

Jiebiao Wangمشاهده پروفایل

استادیار

• Biostatistics
• Bioinformatics
• Computational Biology
• +۷ مورد دیگر

Jiebiao Wang is an Assistant Professor in the Department of Biostatistics at the University of Pittsburgh School of Public Health. He develops computational methods for omics data analysis, focusing on cellular deconvolution, statistical genetics, and proteomics modeling. Education : BSc/MSc in Statistics from Renmin University of China, PhD in Biostatistics from University of Chicago, Postdoc in Statistics/Data Science at Carnegie Mellon University. Research Interests include: Cellular deconvolution methods (EnsDeconv, HiDecon, scMD, MIND, bMIND) Proteomics data analysis with mvMISE models Statistical genetics applications in gene expression imputation (MixRF) and autism studies Scientific Awards : Research Poster Award, ICSA Applied Statistics Symposium (2023) Student Research Award, New England Statistics Symposium (2023) Teaching : Courses on causal inference (BIOST 2068) and omics data methods (BIOST 2154).

Lisha Chenمشاهده پروفایل

استادیار

• Data Mining
• Statistical Learning
• Dimension Reduction
• +۵ مورد دیگر

Lisha Chen is an Assistant Professor in the Department of Statistics at Yale University, located at 24 Hillhouse Avenue, New Haven. Her academic work focuses on developing statistical methodologies for high-dimensional data analysis, with particular emphasis on applications in machine learning and medical research. She maintains active research collaborations as evidenced by her publication record in top statistical journals. Her core research explores: Dimension reduction techniques including multidimensional scaling and sparse modeling Advanced regression methods such as reduced-rank regression and variable selection Statistical learning applications in autism research using eye-tracking data Novel algorithms for imbalance learning and multivariate testing Her work bridges theoretical statistics with practical applications in medicine and social sciences. Analysis of her 12 most recent publications (2008-2014) reveals three primary research streams: Development of dimension reduction frameworks for visualization and analysis Innovation in variable selection methodologies for regression and classification Application of statistical learning to autism spectrum disorder research Her publications demonstrate consistent focus on solving high-dimensional problems through novel statistical computing approaches. At Yale, she has taught multiple courses including: Data Analysis (Fall semesters: 2006-2008, 2011-2013) Introductory Statistics (Spring 2013) Data Mining and Machine Learning (Spring semesters: 2007-2009, 2011-2013) Unsupervised Learning: Dimension Reduction and Clustering Analysis (Spring 2009)

Xinwei Dengمشاهده پروفایل

استاد

• Statistical Learning
• Uncertainty Quantification
• High-Dimensional Data Analysis
• +۷ مورد دیگر

Xinwei Deng is a Professor of Statistics and Data Science Faculty Fellow at Virginia Tech, affiliated with the Department of Statistics in the College of Science. He also co-directs the VT Statistics and Artificial Intelligence Laboratory (VT-SAIL). He earned his Ph.D. from Georgia Institute of Technology in 2009 under Professors C.F. Jeff Wu and Ming Yuan, and joined Virginia Tech in 2011. Education: PhD in Statistics (Georgia Tech, 2009), B.S. in Mathematics (Nanjing University, China, 2003). Research Interests: Focuses on the interface between experimental design and machine learning, uncertainty quantification, covariance matrix estimation, high-dimensional data analysis, and applications in nanotechnology, manufacturing, and healthcare. His work bridges statistical theory with practical challenges in emerging technologies like tissue engineering and environmental science. Publications: Over 80 peer-reviewed articles in top journals such as Journal of the American Statistical Association , Technometrics , and Proceedings of the National Academy of Sciences . Recent work emphasizes scalable algorithms, AI resilience in manufacturing, and uncertainty quantification in complex systems. Awards/Honors: Includes Data Science Faculty Fellowships, ISI election (2017), and multiple best paper awards. Recognized for contributions to statistical methodologies in quality engineering and AI robustness. Teaching & Advising: Teaches advanced courses like Data Analytics, Experimental Design, and Multivariate Analysis. Mentors students in interdisciplinary projects spanning statistics, computer science, and engineering. Labs/Teams: Leads VT-SAIL, fostering AI-driven statistical solutions for real-world problems. Collaborates with industry partners (e.g., P&G, Deloitte) and government agencies (NSF, AFRL) on applied research.

• Econometrics
• Financial Economics
• Machine Learning in Finance
• +۳ مورد دیگر

Dr. Maria Grith is an Assistant Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. Her research focuses on econometric methodologies applied to financial markets, with particular emphasis on machine learning techniques in finance, risk management, and time series analysis. She actively contributes to academic supervision, as evidenced by her involvement in bachelor thesis projects in Econometrics and Economics. Her research interests span quantitative finance, including applications of neural networks to volatility forecasting, cryptocurrency risk analysis, and reinforcement learning in high-dimensional settings. She also explores traditional econometric challenges such as delta-hedged options pricing, sovereign default risk, and macroeconomic persistence heterogeneity. Her publication record includes over 20 peer-reviewed articles, with recent work addressing cutting-edge topics like neural tangent kernels in finance and graphical models for multivariate time series. While no scientific awards are explicitly mentioned, her prolific output reflects a strong research trajectory in financial econometrics and computational methods.

• High Dimensional Statistical Inference
• Machine Learning
• Data Science
• +۱۱ مورد دیگر

Professor Bala Rajaratnam is a leading academic in Business Analytics at the University of Sydney Business School . His research spans high-dimensional statistical inference , machine learning , and data science , with applications in economics , finance , biomedical sciences , and environmental modeling . Key focus areas include graphical models , network analysis , and Bayesian methodology . His recent work examines partial correlation screening , covariance estimation , and climate field completion . Publications highlight interdisciplinary collaborations across climate science , financial engineering , and biomedical research . He received a 2019 Australian Research Council Discovery Project grant for Principled statistical methods for high dimensional correlation networks .

Mark Benderمشاهده پروفایل

دانشیار

• Graphic Design
• Illustration
• Traditional Art
• +۲ مورد دیگر

Mark Bender is an Associate Professor of Graphic Design at Chatham University in Pittsburgh, where he has taught for the past 8 years. Previously, he spent 16 years nurturing artists at the Art Institute of Pittsburgh. He holds a Master’s in Illustration from Syracuse University, part of the 'Syracuse Syndicate' cohort. His professional career spans over 30 years as an illustrator, with work recognized by major industry publications and societies including Communication Arts Magazine, 3X3 Magazine, and the New York Society of Illustrators. Affiliations: Member of the New York Society of Illustrators, Los Angeles Society of Illustrators, and founding member of the Pittsburgh Society of Illustrators. Education: Master’s in Illustration from Syracuse University. His research interests bridge traditional and digital art practices, emphasizing handcrafted techniques combined with contemporary technology. Bender’s work often focuses on portraiture, editorial illustration, and commercial design, with projects for clients like the Pittsburgh Steelers, Commonwealth Press, and TED LASSO. Awards & Recognition: Gold and Silver Addy Awards Golden Quill Awards Silver Medals from NY and LA Societies of Illustrators Inclusion in Original Art (children’s book illustrations) Professional Work: Available for freelance projects, with a Behance portfolio showcasing over 65,000 project views. Maintains a personal website ( benderillustration.com ) and active creative presence.

John Ormerodمشاهده پروفایل

دانشیار

• Statistics
• Bayesian Inference
• Data Mining
• +۴ مورد دیگر

Associate Professor John Ormerod is affiliated with the Statistics Research Group at the School of Mathematics and Statistics , University of Sydney . His research focuses on advanced statistical methodologies, including Variational Bayes , Generalized Linear Mixed Models , Splines , and Missing Data analysis. Current research students: Rajan Shankar and Jackson Zhou Key research strengths: Precision and Digital Health , Data and Decisions , National Security His work spans computational statistics, bioinformatics, and applications in health and biological data. Recent publications emphasize scalable Bayesian inference, variable selection, and single-cell data analysis frameworks. Grants include multiple Australian Research Council (ARC) Discovery Projects (2021, 2017, 2012, 2010) supporting research in feature selection, network modeling, and computational efficiency. He has supervised projects on modern regularization techniques and fast expectation propagation , contributing to BMC Bioinformatics , Bioinformatics , and Journal of Computational and Graphical Statistics .

Matthias Schonlauمشاهده پروفایل

استاد

• Survey Methodology
• Natural Language Processing
• Data Visualization
• +۳ مورد دیگر

Matthias Schonlau is a Professor in the Department of Statistics at the University of Waterloo. He previously worked as a statistician at the RAND Corporation (1999-2011), where he led the RAND Statistical Consulting Service. He holds a PhD from the University of Waterloo (1997) and a Master's from Queen's University (1993). His research focuses on survey methodology, natural language processing for open-ended questions, data visualization, and statistical software development. Key contributions include the Hammock Plot for mixed data visualization and automated classification algorithms for open-ended survey responses. His work spans algorithmic innovation (e.g., occupation coding, multi-label classification) and statistical software tools (e.g., HAMMOCK and RFOREST modules for Stata). Recent projects address semi-automated classification, one-shot learning, and text dataset distillation. He has held sabbaticals at the University of Auckland (2015-2016) and DIW Berlin (2009-2010), collaborating with the Max Planck Institute. Major awards include the Humboldt Research Prize (2022) and ASA Fellowship. His publications emphasize bridging statistical methods with practical applications, including books like Applied Statistical Learning (2023) and peer-reviewed articles in computational statistics and machine learning.