جست‌وجوی استاد و پژوهشگر

Yarema Okhrinمشاهده پروفایل

استاد

• Statistics
• Data Science
• Machine Learning
• +۷ مورد دیگر

Prof. Dr. Yarema Okhrin serves as Full Professor at the Department of Statistics and Data Science within the Faculty of Economics at the University of Augsburg. He leads the Chair of Statistics and Data Science, which is part of the Business Analytics & Operations and Finance, Accounting, Controlling & Taxation clusters. His research group includes Dr. Sebastian Heiden, Dr. Rui Ren, and several research assistants working on advanced statistical methods and their applications in business and economics. Professor Okhrin's research spans mathematical and statistical models for data-driven problems in business and economics, with particular expertise in statistics, data science, and machine learning. His work focuses on developing methodologies for time series analysis, portfolio optimization, risk management, and statistical process control. Recent research has expanded into high-dimensional statistics, image analysis, and network monitoring applications. Analysis of his recent publications (2023-2025) reveals a strong trend toward interdisciplinary applications of statistical methods, particularly in finance and process monitoring. His work demonstrates increasing integration of machine learning techniques with traditional statistical approaches, especially in portfolio optimization and risk assessment. A notable pattern is the development of specialized statistical methods for high-dimensional data analysis across multiple domains. Professor Okhrin supervises bachelor's and master's theses at the Department of Statistics, with research topics including data mining, time series modeling, forecasting methods, regression analysis, and statistical data analysis. His department offers thesis opportunities in specialized areas such as asymmetric dependencies in financial markets, forecasting, investor sentiment analysis, medical statistics, volatility modeling, multivariate distributions, sustainable investing, and portfolio optimization. The Chair of Statistics maintains active research collaborations and offers academic consulting on statistical issues and data analysis. The department hosts regular research seminars to facilitate exchange of ongoing research projects and maintains connections with industry partners for practical applications of statistical methods.

• Incentive-Aware Machine Learning
• Social Computing
• Online Learning
• +۷ مورد دیگر

Chara Podimata is an Assistant Professor of Operations Research and Statistics at the MIT Sloan School of Management and a Lead Researcher at Archimedes/Athena RC. She holds the Class of 1942 Career Development Professorship and focuses on the intersection of Theoretical Computer Science, Economics, and Machine Learning, particularly in incentive-aware machine learning, social computing, online learning, and mechanism design. PhD in Computer Science from Harvard FODSI Postdoctoral Fellow at UC Berkeley Diploma from National Technical University of Athens Her research explores the social aspects of computing, including how humans adapt to machine learning algorithms used in consequential decision-making. Recent work investigates policy questions related to AI and recommendation systems, adversarial robustness, and fairness in revenue management. She has received funding from Amazon, MacArthur Foundation, Google, and MIT GenAI Consortium. Key trends in her publications include strategic classification, contextual search, multi-armed bandits with evolving preferences, and incentive-compatible mechanism design. These works bridge theoretical foundations with practical applications in responsible AI and user behavior modeling. Amazon Research Award (2023) Google Research Scholar Award (2025) MacArthur x-grant Microsoft Dissertation Grant Siebel Scholarship As an advisor, Podimata collaborates with PhD students at MIT ORC, EECS, and MBAn capstone students. She is affiliated with the MIT Operations Research Center and previously worked with Microsoft Research and Google. Her personal page details her advising philosophy and research collaborations.

Lu Tangمشاهده پروفایل

دانشیار

• Biostatistics
• Machine Learning
• Health Data Science
• +۷ مورد دیگر

Lu Tang is an Associate Professor and Vice Chair for Education in the Department of Biostatistics and Health Data Science at the University of Pittsburgh School of Public Health. Her research bridges biostatistics and machine learning to advance health data science through integrative analysis of distributed and high-dimensional datasets, with significant applications in precision medicine, opioid use disorder, sepsis treatment, and public health policy. Dr. Tang's educational journey includes foundational training at Sun Yat-sen University followed by advanced degrees in quantitative fields: PhD in Biostatistics, University of Michigan (2018) MS in Statistics, University of Virginia (2013) BA in Mathematics, University of Virginia (2012) Her research program develops statistical methodologies for data integration, causal inference, and decision-making in healthcare settings. She pioneers techniques for federated learning, robust individualized treatment rules, and high-dimensional data analysis, directly addressing challenges in health disparities, electronic health records, and clinical trial heterogeneity. Her work consistently translates methodological innovations into practical tools for biomedical researchers. Analysis of Dr. Tang's publication trajectory reveals a strategic evolution toward solving real-world healthcare data challenges through distributed learning frameworks. Her recent work emphasizes robust decision rules for heterogeneous populations (2022-2025), transfer learning for federated systems (2024), and methodological innovations for longitudinal and high-dimensional data (2019-2023), demonstrating consistent contributions across biostatistics, machine learning, and public health domains. Dr. Tang has been recognized with: IMS New Researchers Travel Award (2023) She actively mentors 6 current graduate students across PhD and MS programs while supervising 8 alumni now in industry and academia. Her research program is sustained by major funding including NIH R21DA055672 (PI, 2023-2025) on federated Medicaid data analysis, NSF DMS 2310217 (PI, 2023-2026) for longitudinal missing data methods, and multiple NIH R01 grants as Co-I addressing opioid treatment equity and precision medicine in sepsis. Dr. Tang leads the development of critical open-source software including RISE (Python) for robust individualized decisions, ifedtree (R) for tree-based federated learning, and metafuse (R/CRAN) for data integration, establishing infrastructure widely adopted in health data science research communities.

Dan Nettletonمشاهده پروفایل

استاد

• Statistical Methods for Biological Data
• Transcriptomics (RNA-seq, Microarrays)
• Genome-Wide Association Studies
• +۵ مورد دیگر

Dan Nettleton is a Professor at Iowa State University 's Department of Statistics , where he serves as the Laurence H. Baker Endowed Chair and Department Chair . His academic career focuses on statistical methods for high-dimensional biological datasets, including transcriptomic data (microarrays, RNA-seq), microbiome data, and genome-wide association studies, with applications in plant and animal sciences. He also contributes to statistical learning methodology and sports analytics. PhD , Statistics, University of Iowa (1996) MS , Statistics, University of Iowa (1993) BA , Mathematics, Wartburg College (1991) Nettleton's research addresses statistical challenges in analyzing complex biological datasets, including methods for genome-wide association studies, RNA-seq analysis, and genotype-by-environment interactions. He applies these methods to problems in plant genetics, microbiome studies, and sports prediction models. His work on random forests and predictive analytics has broad implications in both life sciences and sports statistics. His recent publications highlight interdisciplinary applications of statistics, including RNA-seq methodology, plant phenotyping with CNN models, and sports analytics using conformal prediction. Collaborations with plant and animal scientists underscore his impact on agricultural research and biological systems analysis. Advising : Nettleton actively advises graduate students, serves on doctoral committees, and contributes to student education in statistics and interdisciplinary research. No explicit grants are listed in the provided text. Labs/Teams : Affiliated with the Laurence H. Baker Center for Bioinformatics and Biological Statistics at Iowa State University, integrating computational and biological research.

• Econometric Methods
• Machine Learning
• Causal Inference
• +۴ مورد دیگر

Jeremy L'HOUR is a quantitative research specialist at Capital Fund Management (CFM) in Paris, France, and an affiliated researcher in the Economics department of CREST (Center for Research in Economics and Statistics, Université Paris-Saclay). His work focuses on econometric methods and the application of machine learning to causal inference.

Joe Haleyمشاهده پروفایل

استاد

• Experimental High Energy Physics
• Vector-like Quarks
• Standard Model Extensions
• +۸ مورد دیگر

Joe Haley is a Professor in the Department of Physics at Oklahoma State University, where he has been a faculty member since 2013 (tenured 2018, full professor 2023). His research focuses on experimental high energy physics through the ATLAS experiment at CERN, particularly searching for vector-like quarks and new fundamental particles that could address Standard Model limitations. Dr. Haley earned a B.S. in Physics and Astronomy from the University of Washington (2003), followed by a Ph.D. in Physics from Princeton University (2009) for DZero experiment research at Fermilab. He conducted postdoctoral work at Northeastern University (2009-2013) with the CMS experiment at CERN. His research spans collider phenomenology, including Higgs boson studies, B meson lifetime measurements, and lepton universality tests in W boson decays. He employs neural simulation-based inference techniques and advanced τ-lepton reconstruction methods to analyze data from the Large Hadron Collider. Dr. Haley actively contributes to educational initiatives through multiple grants, including the US ATLAS Summer Undergraduate Program (2012-2025) and QuarkNet programs (2012-2024). He teaches core physics courses such as University Physics I-III and specialized topics in General Relativity and Particle Physics. As a strong advocate for equity in academia, Dr. Haley engages in inclusion programs and public outreach. His work intersects with Sustainable Development Goals in quality education, reduced inequalities, and clean energy research.

Jennifer Castleمشاهده پروفایل

دانشیار

• Climate Econometrics
• Econometric Modeling
• Model Selection
• +۷ مورد دیگر

Jennifer Castle is the Director of Climate Econometrics at the University of Oxford and a Titular Associate Professor and Tutorial Fellow in Economics at Magdalen College. Her work bridges econometrics and climate-economic modeling, focusing on robust forecasting methodologies and empirical analysis of macroeconomic variables like inflation, unemployment, and climate impacts. Affiliation: University of Oxford, Magdalen College Research Themes: Climate econometrics, structural break analysis, general-to-specific modeling, and non-linear time series Research Interests center on disentangling complex climate-economic relationships and improving forecasting accuracy through rigorous model evaluation. She applies these methods to inflation dynamics, energy policy, and long-term climate transition scenarios. Recent Publications highlight her work on UK inflation forecasting challenges, cryptocurrency market stability, income inequality projections, and climate policy intervention points. Her research emphasizes model robustness in the face of structural shifts and data uncertainty.

Andreas Asp Bockمشاهده پروفایل

پژوهشگر

• Scientific Computing
• Numerical Linear Algebra
• Bayesian Inversion
• +۳ مورد دیگر

Andreas Asp Bock is a postdoctoral researcher at the Department of Applied Mathematics and Computer Science within the Technical University of Denmark . His work focuses on scientific computing and numerical linear algebra, with particular emphasis on matrix approximation techniques, preconditioning strategies, and optimization methods. Current affiliation: Technical University of Denmark (College of Engineering) Academic role: Researcher (postdoctoral level) Research interests include: Matrix factorization and truncation Bregman divergence applications Baysian inversion frameworks Curve registration algorithms High-dimensional data analysis Preconditioning for iterative solvers Recent publications demonstrate expertise in improving approximate factorization preconditioners, geometric curve registration, and divergence-based preconditioner design. Collaborations with Martin S. Andersen and others indicate ongoing contributions to sparse linear algebra and computational statistics. Supervision : Currently mentoring J. V. Galvão da Mata in a PhD project focused on Optimization methods for data-sparse models , active from 2022-2025.

Luis Secoمشاهده پروفایل

استاد

• Harmonic Analysis
• Mathematical Physics
• Mathematical Finance
• +۳ مورد دیگر

Luis Seco is a Professor in the Department of Mathematics at the University of Toronto, with a focus on Mathematical and Computational Sciences. He is based at the Bahen Centre for Information Technology (Room BA-7262) and maintains a presence at the University of Toronto Mississauga (UTM) campus. He serves as Director of the Mathematical Finance Program and is affiliated with RiskLab Toronto, housed at the Bahen Centre. Dr. Seco's research spans multiple disciplines at the intersection of mathematics and finance: Harmonic Analysis Mathematical Physics Mathematical Finance Quantitative Risk Management His scholarly work demonstrates a sophisticated integration of advanced mathematical techniques with practical financial applications. Recent publications (2022-2025) reveal a strong emphasis on machine learning approaches to financial problems, with particular focus on algorithmic trading, risk modeling, and ESG (Environmental, Social, and Governance) investing. His research has evolved from theoretical mathematical foundations to cutting-edge applications in quantitative finance. Dr. Seco's publication record shows significant scholarly productivity with a clear trajectory from pure mathematics toward increasingly applied financial mathematics. His work bridges theoretical rigor with practical financial applications, making substantial contributions to both academic research and industry practices. As Director of the Mathematical Finance Program, Dr. Seco provides leadership in shaping education and research in quantitative finance at the University of Toronto. His RiskLab affiliation indicates active engagement with real-world risk management challenges, connecting academic theory with industry applications.

Spiridon Penevمشاهده پروفایل

استاد

• Wavelet Methods in Non-Parametric Curve Estimation
• Edgeworth Expansions and Saddlepoint Approximation Methods
• Structural Equation Models
• +۴ مورد دیگر

Professor Spiridon Penev is a faculty member at the School of Mathematics & Statistics , University of New South Wales (UNSW), Sydney. After completing his PhD in Mathematical Statistics at Humboldt University, Berlin, he worked at Technical University of Sofia (Bulgaria) for 10 years, becoming Associate Professor in 1991. He joined UNSW in 1992, progressing from Lecturer to Professor in 2019. His teaching focuses on Statistical Inference , Multivariate Analysis , Longitudinal Data Analysis , and related advanced courses. Research Interests: Wavelet Methods in Non-Parametric Curve Estimation, Edgeworth Expansions, Saddlepoint Approximation, Structural Equation Models, Inference in Semiparametric Models, and Stochastic Risk Modelling. Article Trends: His work spans from foundational wavelet methods (pre-2010) to modern applications in climate model ensembles , portfolio optimization , marine engineering , and machine learning . Keywords include Statistics, Finance, Climate Science, Structural Health Monitoring, and Optimization. Awards: DAAD Award, Elected Member of the International Statistical Institute (ISI). Grants: Led ARC Discovery Project (2016–2018), ARC Linkage Project (2018–2022), and industry collaborations like SCA water quality analysis (2014–2019). Location: School of Mathematics and Statistics, UNSW Sydney, Room 1038, The Red Centre.

Peter Michael Robinsonمشاهده پروفایل

استاد

• Econometrics
• time series analysis
• spatial analysis
• +۲ مورد دیگر

Peter Michael Robinson holds the Tooke Professorship of Economic Science and Statistics at the London School of Economics and Political Science (LSE), where he maintains an active research profile in advanced econometric theory. His institutional affiliation centers on LSE's Department of Economics, though the departmental structure is not explicitly detailed in available materials. Robinson's research program focuses on cutting-edge econometric methodologies, particularly long-memory processes, spatial dependence structures, and nonstationary time series analysis. His work bridges theoretical rigor with practical applications in panel data and spatial econometrics, emphasizing semiparametric and nonparametric approaches for complex dependency patterns. Key innovations include refined inference techniques for spatial autocorrelation and fractional integration models. Analysis of his recent publications (2012-2018) reveals a sustained emphasis on spatial econometrics and time-series methodology, with significant contributions to panel data modeling, long-range dependence theory, and nonparametric estimation under cross-sectional dependence. His work consistently advances asymptotic theory while addressing real-world data challenges in economics and statistics. No scientific awards or honors were explicitly documented in the source materials. Available records contain no information regarding doctoral students, grant funding, or academic advising activities. Research infrastructure details such as laboratories or collaborative teams were not specified in the provided documentation.

• Statistics
• Epidemiology
• Genetics
• +۷ مورد دیگر

Maria De Iorio is a Professor at the Yong Loo Lin School of Medicine and holds a joint appointment at the Faculty of Science , both at the National University of Singapore. Her work bridges statistics, epidemiology, and clinical sciences through advanced Bayesian modeling and computational methods. Key research areas: Statistics, Epidemiology, Genetics, Clinical sciences, Medical biochemistry Primary affiliations: Paediatrics department (YLLSoM) and interdisciplinary projects in Science faculty Recent publications focus on extreme value theory, digital numismatics, metabolomic simulations, and maternal-child health Her statistical innovations include: Repulsive mixture models for interpretable clustering Bayesian semi-parametric approaches for longitudinal studies Novel prior distributions for graphical models Application domains span: Pediatric oncology (ALL treatment response modeling) Metabolic syndrome analysis (obesity, diabetes, bone health) Digital humanities (coin distribution patterns, ceramic artifact analysis) Financial forecasting (discrete survival methods for invoice prediction)

Sara Mostafaviمشاهده پروفایل

دانشیار

• Computational Biology
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Sara Mostafavi is an Associate Professor at the Paul Allen School of Computer Science & Engineering at the University of Washington (UW), with a focus on Artificial Intelligence and Computational Biology. She is currently on leave (2024-2025) leading the Computational Biology and Translation Organization at Genentech. Mostafavi previously held faculty roles at the University of British Columbia (UBC) and the Vector Institute, where she received prestigious Canada Research Chair (CRC II) and Canada CIFAR Chair in AI (CIFAR-AI) awards. PhD in Computer Science, University of Toronto (2011) Postdoctoral Researcher, Stanford University Her research develops machine learning and statistical methods to study gene regulation, disease susceptibility, and molecular networks, with applications in immunology, genetics, and neuroscience. She has pioneered sequence-to-function models for understanding gene regulatory grammar and predicting disease mechanisms. Her publications highlight expertise in genomic data integration, interpretable AI, and cross-modality learning. Recent work includes analyzing mosaic chromosome loss in aging microglia, immune cell differentiation, and Alzheimer's disease networks. Scientific Awards Canada Research Chair (CRC II) in Computational Biology (2015–2020) Canada CIFAR Chair in Artificial Intelligence (CIFAR-AI) Co-founder of the Machine Learning for Computational Biology (MLCB) Conference Mostafavi has mentored numerous PhD and Master’s students, including Xinming Tu, Anna Spiro, Gherman Novakovsky (PhD 2023), and Elijah Willie (MSc 2020). Her lab collaborates with institutions like the Immunological Genome (ImmGen) Consortium and Canadian Institute for Advanced Research (CIFAR). She leads the Mostafavi Lab, which specializes in combining association evidence across genomics datasets and modeling biological pathways to disentangle spurious correlations. Her team develops tools like CEWAS, AI-TAC, and Brain xQTL web server.

Xiaolin Huمشاهده پروفایل

استاد

• Modeling and Simulation
• Complex Systems Science
• Agent-Based Systems
• +۳ مورد دیگر

Xiaolin Hu is a Professor in the Department of Computer Science at Georgia State University within the College of Arts & Sciences. His research focuses on modeling and simulation theory with applications in complex systems science, agent-based systems, and advanced computing environments. Ph.D. in Electrical and Computer Engineering, University of Arizona (2004) NSF CAREER Award recipient Senior member of IEEE and Society for Modeling and Simulation International (SCS) Associate editor for ACM Transactions on Modeling and Computer Simulation (TOMACS) and other journals SIMULATION: Transactions journal and International Journal of Modeling, Simulation, and Scientific Computing (IJMSSC) Dr. Hu's research spans three major areas: Dynamic Data-Driven Simulation (DDDS): Development of simulation frameworks that assimilate real-time data, particularly applied to wildfire spread prediction and autonomous systems. Multi-Agent Systems: Study of adaptive behaviors and coordination mechanisms for unmanned aircraft and social/public health systems. Advanced Computing: Implementation of parallel and cloud computing solutions for large-scale simulations. His recent publications (2025-2023) demonstrate a strong focus on integrating real-time data from UAVs and satellite systems into wildfire simulations, utilizing Bayesian methods for sequential data assimilation, and developing cloud-based simulation services. These works bridge computational modeling with environmental science applications. Scientific recognitions include: National Science Foundation (NSF) CAREER Award Dr. Hu has chaired multiple international conferences in modeling and simulation and leads the SIMS Lab at Georgia State University, which specializes in simulation technologies and their applications in diverse domains.

Xin Xingمشاهده پروفایل

استادیار

• Bioinformatics
• Metagenomics
• Single-cell analysis
• +۷ مورد دیگر

Overview Xin Xing is an Assistant Professor in the Department of Statistics at Virginia Tech. He holds a PhD from the University of Georgia (2018), an M.S. and B.S. from the University of Science and Technology of China (2013, 2010). His research focuses on statistical methodology development in bioinformatics, machine learning, and computational biology. He has received grants including the NSF ATD (2022-2024) and NVIDIA GPU Grant (2019). Education Ph.D. – Statistics – University of Georgia (2018) M.S. – Statistics – University of Science and Technology of China (2013) B.S. – Statistics – University of Science and Technology of China (2010) Research Interests Dr. Xing's work spans three core areas: Bioinformatics: Metagenomics, single-cell analysis, epigenomics, neuroimaging. Statistics: Minimax nonparametric testing, smoothing splines, dimension reduction, controlled variable selection, causal inference. Machine Learning: Deep learning, generative models, transfer learning, neural network compression. Key Awards New Faculty Mentoring Grant, Virginia Tech (2020-2022) Best Senior PhD Student Award, University of Georgia (2018) Outstanding Dissertation Award, USTC (2010) Teaching Recent courses include CMDA 3654 (Intro to Data Analytics & Visualization) and CMDA 4654 (Deep Learning). Office: D&DS 308.