جست‌وجوی استاد و پژوهشگر

Yaning Liuمشاهده پروفایل

دانشیار

• Machine Learning
• Uncertainty Quantification
• Monte Carlo Methods
• +۵ مورد دیگر

Yaning Liu is an Associate Professor and Director of Statistical Programs at the Department of Mathematical & Statistical Sciences within the College of Liberal Arts and Sciences at the University of Colorado Denver. Their research focuses on surrogate modeling with machine learning, uncertainty quantification, Monte Carlo methods, hydrological modeling, and subsurface energy storage. Education: PhD in Applied and Computational Mathematics from Florida State University (2013), MS in same field (2011), BS in Information and Computational Science from Zhejiang University (2006) Yaning develops and teaches advanced courses including Monte Carlo Methods , Machine Learning Methods , and Uncertainty Quantification . Their work combines computational mathematics with energy system modeling, particularly for compressed air and CO2 storage applications in aquifers. Publications cover: Subsurface energy storage dynamics Multivariate sensitivity analysis Hybrid energy system modeling Environmental impact assessments Computational grouting materials Bayesian parameter inversion techniques They actively contribute to Open Educational Resources (OER) through co-authored textbooks like First Semester in Numerical Analysis with Python and Probability and Simulation: A Python Companion .

Aritra Halderمشاهده پروفایل

استادیار

• Bayesian Modeling
• Spatial and Spatiotemporal Analysis
• Optimization
• +۵ مورد دیگر

Aritra Halder is an Assistant Professor of Biostatistics in the Department of Epidemiology and Biostatistics at Drexel University's Dornsife School of Public Health. He holds a PhD and MS in Statistics from the University of Connecticut, and an MS in Applied Mathematics from the Chennai Mathematical Institute, alongside a BS in Statistics from Presidency College. His research focuses on Bayesian modeling, spatial and spatiotemporal statistical methods, and optimization techniques applied to public health policy, environmental science, and biomedical domains. Recent work includes developing R packages for Bayesian wombling (boundary detection), analyzing spatial heterogeneity in air pollution effects on pediatric health, and modeling pandemic dynamics during India's 2020 lockdown. Education: PhD, Statistics, University of Connecticut (2020) MS, Statistics, University of Connecticut MS, Applied Mathematics, Chennai Mathematical Institute BS, Statistics, Presidency College Dr. Halder's applied research spans epidemiological modeling, spatial omics analysis, and infrastructure policy evaluation. His 2023 work on rural broadband efficacy and 2021 pandemic modeling contributions highlight his interdisciplinary approach to solving complex societal challenges through statistical innovation. His academic contributions include over 15 peer-reviewed articles in journals like Scandinavian Actuarial Journal and Methodology and Computing in Applied Probability , alongside conference proceedings on linguistic perception and pharmaceutical engineering.

Lijing Wangمشاهده پروفایل

استادیار

• Geostatistics
• Hydrogeology
• Hydrologic Modeling
• +۵ مورد دیگر

Lijing Wang is an Assistant Professor at the University of Connecticut, specializing in geostatistics and hydrogeology. Her research focuses on integrating hydrologic modeling with multiscale datasets to explore watershed and aquifer dynamics, quantifying uncertainties through machine learning and Bayesian methods. She co-authored the textbook Data Science for the Geosciences , emphasizing data science education for geoscientists. Education: Ph.D. in Geological Sciences from Stanford University (with a minor in Computer Science) and B.S. in Space Physics and Applied Mathematics from Peking University. Her research interests include subsurface heterogeneity, snow dynamics in mountainous regions, and climate impacts on hydrology. She develops advanced statistical and computational tools for geophysical inversion and uncertainty quantification. Her work also explores the application of surrogate models for environmental decision-making under climate change scenarios. Key contributions include the GStatSim software package for geostatistical analysis and collaborations on Antarctic ice shelf bathymetry modeling. Her research bridges geoscience and data science, addressing challenges in mineral exploration, groundwater contamination, and climate resilience.

Xinqiang Dingمشاهده پروفایل

استادیار

• Computational Drug Design
• Molecular Simulations
• Statistical Mechanics
• +۳ مورد دیگر

Xinqiang Ding is an Assistant Professor of Chemistry at Tufts University, holding the Dr. Charles W. Fotis A37, AG39 Junior Professorship. He leads the Ding Group, focusing on computational methods integrating statistical mechanics, molecular dynamics, and machine learning. His research addresses challenges in computational drug design, force field development, and biomolecular simulations. He earned a Ph.D. in Chemistry from the University of Michigan (2018) and dual B.S. degrees in Pharmaceutical Science and Mathematics from Peking University (2012). Research Interests : Computational drug design via protein-ligand binding free energy calculations Coarse-grained force field development for biophysical systems Multiscale modeling of biomolecular condensates Molecular simulation techniques enhanced by machine learning Teaching : Teaches courses including Biophysical Chemistry and special topics in Machine Learning & Molecular Dynamics. Advises graduate students and postdoctoral researchers in computational chemistry and biophysics. Labs/Teams : Directs the Ding Group at Tufts, collaborating on software tools like BayesMBAR and OpenABC for free energy calculations and biomolecular simulations.

Martin Radfarمشاهده پروفایل

استادیار

• Machine Learning
• Computational Biology
• Bio/Health
• +۵ مورد دیگر

Martin Radfar is an Assistant Professor in the Department of Computer Science at Stony Brook University, affiliated with the Institute for AI-Driven Discovery and Innovation. He holds a Ph.D. in Machine Learning and Signal Processing from the University of Toronto (2014). His academic roles include teaching courses such as CSE215 (Foundations of Computer Science), CSE351 (Introduction to Data Science), and ISE316 (Introduction to Networking). He has advised PhD student Xuan Xu and MS student Pranavi Meda. Radfar's research focuses on voice-based human-machine interfaces, auditory scene analysis, Bayesian networks, and cancer drug target prediction using machine learning. His expertise spans signal processing, computational biology, and healthcare applications. Notable contributions include developing the PGMLAB library for Bayesian networks and pioneering work in microRNA target prediction. His academic achievements include awards such as the NSERC Postdoctoral R&D Fellowship (2014) and the Edward S. Rogers Graduate Scholarship (2008). His teaching emphasizes foundational computer science, data science tools (Python, Jupyter notebooks), and network architecture. He has published widely in venues like IEEE Signal Processing Society journals and ACM Transactions, addressing topics like speech separation, genomic data analysis, and network protocols.

Neil Ernstمشاهده پروفایل

دانشیار

• Software Engineering
• Software Architecture
• Requirements Engineering
• +۶ مورد دیگر

Neil Ernst is an Associate Professor in the Department of Computer Science at the University of Victoria and Director of the Matrix Institute for Applied Data Science. His research focuses on software architecture, requirements engineering, technical debt management, and the integration of AI into software development practices. He previously worked at the Software Engineering Institute (CMU) and holds a PhD in Computer Science from the University of Toronto. Education: PhD in Computer Science, University of Toronto, 2011 M.Sc. in Computer Science, University of Victoria, 2004 Bachelor’s degree with research in Geography and GIS, University of Victoria Research Interests: Software engineering methodologies, empirical studies, technical debt, AI-driven development, and interdisciplinary applications of computing sciences. His work emphasizes human-centered approaches to software architecture and the ethical implications of AI in development practices. Professional Roles: Senior Associate Editor, Journal of Systems and Software Associate Editor, Journal of Empirical Software Engineering Advisor for International Conferences (e.g., ICSE, RE, XP) Current Projects: Technical debt in scientific software, climate informatics, and the societal impacts of AI in software engineering. He advocates for open science and registered reports to improve research rigor. Labs & Teams: Leads the Matrix Institute for Applied Data Science, focusing on transdisciplinary data science applications.

Jackie Wong Siaw Tzeمشاهده پروفایل

مدرس

• Actuarial Science
• Bayesian Statistics
• Mortality Modeling
• +۳ مورد دیگر

Dr. Jackie Wong Siaw Tze is a Lecturer in Actuarial Science at the University of Essex's Department of Mathematical Sciences within the School of Mathematics, Statistics and Actuarial Science (SMSAS). He holds a PhD and BSc in Mathematics/Actuarial Science from the University of Southampton. His research focuses on Bayesian methodology applications in mortality forecasting with overdispersion, Bayesian computation (model selection and MCMC methods), and machine learning in actuarial science. He has taught at the University of Leeds and Southampton prior to his current role. Research interests include addressing mortality heterogeneity through statistical models, with projects employing MCMC techniques. He explores computational Bayesian methods and their practical use in actuarial contexts. His publications span mortality forecasting, MCMC efficiency, and time-series population projections. No scientific awards are listed, but his work contributes to advancing statistical methodologies in actuarial science. His teaching involves actuarial science and statistics at both undergraduate and postgraduate levels. Grants and funding details are not specified in the provided text.

• Pharmacometrics
• Clinical Pharmacokinetics
• Drug Development
• +۳ مورد دیگر

Aristeidis Dokoumetzidis is an Associate Professor in the Department of Pharmacy at the National and Kapodistrian University of Athens. His research focuses on pharmacometrics, clinical pharmacokinetics, and model-informed drug development. He contributes to the European Medicines Agency’s Modelling and Simulation Working Party and serves on the editorial board of the Journal of Pharmacokinetics and Pharmacodynamics. Education: Bachelor of Science in Physics (University of Athens) Master of Science in Physics (University of Athens) Doctor of Philosophy in Pharmacy (University of Athens) Teaching: Undergraduate: General Mathematics, Introduction to Programming, Biopharmaceutics Labs Graduate: Pharmacometrics (MSc Biostatistics), Applications of Statistics in Pharmaceutical Sciences His recent work emphasizes optimizing drug delivery systems, particularly for pediatric tuberculosis treatments, and advancing in silico clinical trial methodologies. Collaborations include studies on hyperhydration effects on doping detection and PBPK modeling for drugs like diazepam.

Rafael Ballester-Ripollمشاهده پروفایل

استادیار

• Tensor Decompositions
• Sensitivity Analysis
• Uncertainty Quantification
• +۳ مورد دیگر

Rafael Ballester-Ripoll is an Assistant Professor at IE University's School of Science & Technology, specializing in Data Science and Machine Learning. Before joining IE in 2019, he held postdoctoral positions at ETH Zurich and the University of Zurich (UZH). He earned his PhD in Computer Science from UZH (2017), and BSc/MSc degrees in Mathematics and Computer Science from the Technical University of Catalonia-UPC (2012). His research focuses on low-rank tensor decompositions, explainable AI, sensitivity analysis, and scientific visualization. Key contributions include advancements in large-scale visualization, data compression, and uncertainty quantification. Recent work (2025) explores CSR authenticity in AI-era social media, hexagonal A-Star algorithms for weather routing, and entropy coding for tensor networks. Publications span journals like Journal of Machine Learning Research, IEEE Transactions on Visualization and Computer Graphics, and SIAM/ASA Journal on Uncertainty Quantification. He actively participates in conferences such as IEEE VIS and EuroVis, and co-developed tools like tntorch and VIAN. His research trends emphasize tensor-based methods applied to interdisciplinary domains, including quantum computing, Bayesian networks, and probabilistic graphical models. Collaborative projects highlight applications in ocean engineering, film analysis, and recommender systems. Ballester-Ripoll has contributed to IE's Research Datalab initiatives, though no formal awards or grants are explicitly noted in the provided texts. His work bridges theoretical foundations with practical tools for data-driven decision making.

Stephen P. Robertsمشاهده پروفایل

استاد

• Comparative Physiology
• Insect Flight Aerodynamics
• Stress Tolerance
• +۵ مورد دیگر

Dr. Stephen P. Roberts is a **Professor of Biological Sciences** at Missouri University of Science and Technology and currently serves as the **Vice Chancellor of Strategic Initiatives**, advising leadership on strategic planning to achieve institutional goals such as Carnegie R1 status, Top-100 USN&WR ranking, and enrollment growth. His research focuses on **comparative and integrative physiology of insects**, exploring flight aerodynamics, stress tolerance, aging, and locomotion biomechanics across global biomes. He has conducted fieldwork on all continents except Australia and has authored a book on ecological and environmental physiology of insects. **Education**: He earned a B.S. (1990) and M.S. (1992) in Biology from Illinois State University, followed by a Ph.D. in Zoology (1998) from Arizona State University. His postdoctoral training was at the University of Chicago in the 1990s. **Research Interests**: Roberts’ work spans **insect physiology**, **machine learning applications** in environmental science, and **climate modeling**. His recent projects include developing the **RTide** software package for tidal analysis and storm surge prediction, as well as studying the effects of environmental stressors on insect behavior and aging. **Awards and Grants**: While specific awards are not listed, his extensive publication record and leadership roles suggest significant recognition in his field. His grants include major projects on insect physiology and environmental modeling. **Advising and Labs**: Though students are not explicitly listed, his research involves collaborative projects in entomology, bioenergetics, and computational ecology. He leads strategic initiatives that likely involve interdisciplinary teams focused on institutional growth and research excellence.

Haipeng Xingمشاهده پروفایل

استاد

• Financial Statistics
• Time Series Analysis
• Change-Point Detection
• +۲ مورد دیگر

Haipeng Xing is Professor of Applied Mathematics and Statistics at Stony Brook University, specializing in financial statistics and time series analysis. His research develops statistical methods for change-point detection, financial market modeling, and risk analytics with applications in quantitative finance and econometrics. His publications demonstrate expertise in: High-frequency financial data modeling and volatility measurement Structural break detection in credit markets Stochastic control approaches for trading strategies Multivariate GARCH models with regularization Statistical methods for financial risk management He has authored influential textbooks on statistical models for financial markets and time series analysis.

Kalanka Jayalathمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Spatial Statistics
• Survival Analysis
• +۳ مورد دیگر

Kalanka Jayalath is an Associate Professor and Program Director of Statistics at the University of Houston-Clear Lake (UHCL), within the College of Science and Engineering. He holds a Ph.D. in Statistical Science from Southern Methodist University (SMU), an M.S. in Statistics from Sam Houston State University, and a BSc in Mathematics from the University of Peradeniya, Sri Lanka. His research focuses on Bayesian statistics, spatial statistics, survival analysis, and applications of statistical methods in engineering, archaeology, and sports analytics. Dr. Jayalath has authored books and peer-reviewed articles, including Statistics and Probability with Applications for Engineers and Scientists and contributions to spatial analysis of archaeological sites. His work bridges theoretical statistics with practical applications, such as analyzing cricket performance using machine learning and improving variance homogeneity tests. He has secured grants from the University of Houston-Clear Lake’s Faculty Development Fund (FDF) for research on spatial data modeling and graphical statistical tests. His presentations span international conferences, including the Conference of Texas Statisticians and the International Conference on Statistical Distributions and Applications (ICOSDA). Dr. Jayalath serves on editorial boards for journals like Austin Statistics Journal and reviews for Journal of Statistical Computation and Simulation . His professional affiliations include the American Statistical Association and the International Statistical Engineering Association.

Dongfeng Wuمشاهده پروفایل

استاد

• Probability Modeling in Cancer Screening
• Bayesian Inference
• Statistical Decision Theory
• +۵ مورد دیگر

Dongfeng Wu, PhD is a tenured Professor in the Department of Bioinformatics and Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS). He holds positions in both academic and research capacities, focusing on probability modeling and statistical inference in cancer screening. His work integrates Bayesian methods, statistical decision theory, and epidemiological analysis to address challenges in medical research. Education: B.S. in Probability and Statistics, Peking University (1986–1990) M.S. in Probability and Statistics, Peking University (1990–1993) M.S. in Computer Science, University of California, Santa Barbara (1997–1999) Ph.D. in Statistics, University of California, Santa Barbara (1995–1999) Research Interests: Dr. Wu specializes in developing statistical methodologies for optimizing cancer screening protocols, including dynamic scheduling algorithms, lead time estimation, and over-diagnosis analysis. His research extends to Bayesian inference, time series analysis, and applications in lung, breast, and colorectal cancer screening. Key contributions include a published book on probability modeling in screening and collaborative work with institutions like the National Lung Screening Trial (NLST). Funding: He leads the NIH/NCI-funded project R15CA242482 (2019–2023), totaling $435,309, focused on dynamic screening scheduling using statistical methods and software development. Teaching: At SPHIS, Dr. Wu teaches advanced courses such as Bayesian Inference, Clinical Trials, Survival Analysis, and Biostatistical Methods. Recent courses include PHST691-Bayesian Inference and PHST683-Survival Analysis. Advising: He has mentored multiple graduate students, including Farhin Rahman (Ph.D. expected 2022), Ruiqi Liu (Ph.D. 2017), and Xiaohong Li (Ph.D. 2017), focusing on topics like parameter estimation in screening and RNA-seq data analysis.

Julie Zhouمشاهده پروفایل

استاد

• Robust Statistics
• Optimal Design of Experiments
• Fractional Factorial Design
• +۶ مورد دیگر

Dr. Julie Zhou is a Professor in the Department of Mathematics and Statistics at the University of Victoria. Her research focuses on robust statistics and optimal design of experiments, with applications in medical, natural, and social sciences. PhD in Statistics, University of Alberta B.Sc. and M.Eng. from Nanjing University and Nanjing University of Science and Technology Her research spans robust statistical procedures , optimal design theories , and statistical computing . Recent work includes robust designs for heteroscedastic errors, multi-response regression models, and computational tools using CVX and SeDuMi. Her 15 most recent publications highlight advancements in minimax D-optimal designs , Bayesian optimization , and biomedical applications . Collaborators include researchers from the University of Alberta, Simon Fraser University, and international institutions. She has supervised over 20 graduate students and serves on editorial boards of the Canadian Journal of Statistics and Statistical Papers . Her work emphasizes cost-effective experimental designs and accurate statistical inferences.

Frank van der Meulenمشاهده پروفایل

استاد

• Statistical Inference for Stochastic Processes
• Bayesian Computation
• Filtering Methods
• +۴ مورد دیگر

Frank van der Meulen is a Professor of Mathematical Statistics at the Vrije Universiteit Amsterdam (VU), where he leads the Department of Mathematics. He holds leadership roles including Director of the VU Bachelor Programme in Business Analytics, Chair of the Mathematical Statistics section at the Netherlands Society for Statistics and Operations Research (VVSOR), and Organizer of the nationwide Van Dantzig seminar. His research focuses on statistical inference for stochastic processes, Bayesian computation, and applications in fields like sports engineering, climate science, and maritime engineering. He has developed influential methods such as 'backward filtering forward guiding' for simulating conditioned Markov processes. Frank obtained his PhD in 2005 from VU Amsterdam and has held academic positions at TU Delft (2007–2022) before returning to VU as a Full Professor in 2022. He teaches courses in statistics, probability, and data science, and has authored packages like BridgeLandmarks and BayesianDecreasingDensity. His work bridges theoretical advancements with practical applications, emphasizing collaboration across disciplines. Research interests include Bayesian computational methods (MCMC, SMC), stochastic differential equations, and inference for diffusions on manifolds. Recent work explores stochastic phylogenetic models and fatigue calculations for maritime structures. He consults on statistical and machine learning projects, offering expertise through his GitHub repositories and published software tools.