جست‌وجوی استاد و پژوهشگر

Oscar L. Olvera Astiviaمشاهده پروفایل

استادیار

• Psychometrics
• Educational Measurement
• Quantitative Methodology
• +۴ مورد دیگر

Oscar L. Olvera Astivia is an Assistant Professor in the Department of Measurement and Statistics at the University of Washington's College of Education . He also holds an affiliation with the Center for Statistics and the Social Sciences . His research focuses on advancing quantitative methodologies in psychometrics and educational measurement through theoretical and mathematical approaches. Research Interests: Monte Carlo simulations and their methodological implications in social sciences. Statistical heuristics and their underlying assumptions. Non-Gaussian data analysis and distribution theory. Meta-science and research methodology critique. Education: Ph.D. in Measurement, Evaluation & Research Methodology, University of British Columbia (2017) M.A. in Measurement, Evaluation & Research Methodology, University of British Columbia (2013) B.A. in Mathematics, University of the Fraser Valley Publications: Over 20 peer-reviewed articles, including work on multivariate outliers, simulation algorithms, and psychometric evaluation. Recent trends emphasize methodological rigor and reproducibility in quantitative research. Awards: None explicitly listed. Grants/Advising: Focus on statistical software development (e.g., a shiny app for power analysis) and mentorship in quantitative methods. Active in promoting open science practices. Labs/Teams: Collaborates with interdisciplinary groups on statistical methodology and applications in education and healthcare.

Hongwei Xiمشاهده پروفایل

دانشیار

• Programming Languages
• Type Theory
• Dependent Types
• +۴ مورد دیگر

Hongwei Xi is an Associate Professor in the Department of Computer Science at Boston University, affiliated with the College of Arts & Sciences. He holds a PhD in Pure and Applied Logic from Carnegie Mellon University (1998). His primary research focuses on applying advanced type theory to programming language design, particularly through the development of the ATS programming language. This language emphasizes safe software construction via a paradigm combining programming with theorem proving. Dr. Xi's work includes pioneering contributions to dependent and linear types for practical programming, as well as serving on program committees for major ACM conferences like POPL and PLDI. His research interests span type systems, formal verification, and concurrency models. He has authored numerous publications on topics such as session types, multirole logic, and dependently typed programming languages. Dr. Xi’s academic contributions also include the design of the ATS language and its implementation, which demonstrates practical applications of type-theoretic principles. His recent work explores two-level linear dependent type theories and multirole logic frameworks for multiparty systems. His professional activities include advising on language design and verification methodologies, though specific grant details are not detailed in the provided materials. Dr. Xi is actively involved in advancing the theoretical foundations of programming languages while ensuring practical applicability in real-world software development.

Dimitris N. Politisمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Statistics
• Bootstrap Methods
• +۶ مورد دیگر

Dimitris N. Politis is a Distinguished Professor in the Department of Mathematics and the Halicioglu Data Science Institute at the University of California, San Diego. He holds the prestigious Halicioglu Data Science Institute Chancellor's Endowed Chair II position and has been affiliated with UCSD since 1997, progressing from Associate Professor to his current distinguished position. His educational background includes a Ph.D. in Statistics from Stanford University (1990), along with multiple master's degrees in Statistics, Mathematics, and Computer and Systems Engineering from Stanford and Rensselaer Polytechnic Institute. Professor Politis's research focuses on advanced statistical methodologies, with particular expertise in: Time Series and Random Fields analysis Computer-Intensive Methods in Statistics Resampling and Subsampling for Dependent Observations Spatial Statistics and Point Processes Nonparametric Spectral and Probability Density Estimation Model-free Prediction and Regression Information Theory and Signal Processing Econometric Analysis of Financial Time Series His scholarly output includes over 100 journal papers and several influential books, most notably "SUBSAMPLING" (1999), "MODEL-FREE PREDICTION AND REGRESSION" (2015), and "TIME SERIES: A FIRST COURSE WITH BOOTSTRAP STARTER" (2020), which has become a key educational resource in the field. Professor Politis has received numerous prestigious awards and honors: Guggenheim Fellowship (2011) Fellow of the American Statistical Association (2011) Fellow of the Institute of Mathematical Statistics (2004) Distinguished Author Award from the Journal of Time Series Analysis (2020) Econometric Theory Multa Scripsit Award (2013) Tjalling C. Koopmans Econometric Theory Prize (2012) He has been principal investigator on numerous NSF and NIH grants, including current funding for "Computer-intensive methods for dependent and complex data" (NSF DMS 24-13718, 2024). Professor Politis has held significant leadership roles, including serving as Chair of the Faculty Council of the Halicioglu Data Science Institute (2019-2023) and Associate Director (Founding) of the Institute (2018-2024). As a co-founder of the International Society for NonParametric Statistics, Professor Politis has made substantial contributions to the organization of major conferences and workshops in his field, including the First Conference of the International Society for NonParametric Statistics in 2012. His editorial work includes serving as Co-Editor of the Journal of Time Series Analysis since 2013 and Senior Editor for the ACM/IMS Journal of Data Science since 2022.

Changbo Zhuمشاهده پروفایل

استادیار

• Optimal Transport
• Functional and Object Data Analysis
• High-Dimensional Statistics
• +۶ مورد دیگر

Changbo Zhu is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, part of the College of Science. He is also a Fellow of the Lucy Family Institute for Data & Society. His office is located at 101H Crowley Hall, and he can be reached at czhu4@nd.edu. Dr. Zhu holds a Ph.D. from the University of Illinois at Urbana-Champaign (2020), an M.S. and B.S. from the National University of Singapore (2016 and 2014, respectively). He completed a postdoctoral fellowship at the University of California, Davis from 2020 to 2022. His research focuses on the intersection of statistics, geometry, and optimization, with key interests in optimal transport, functional and object data analysis, time series analysis, high-dimensional statistical inference, and statistical machine learning. His work addresses challenges such as longitudinal data analysis, change point detection, and distance covariance methodologies. Zhu’s recent publications emphasize advancements in optimal transport theory, including barycenter optimization and autoregressive models, as well as applications in neuroimaging and high-dimensional data analysis. He collaborates with leading researchers such as Hans-Georg Müller and Jane-Ling Wang on topics ranging from spherical autoregressive models to brain volume trajectory studies. He advises Kaheon Kim, a Ph.D. candidate focusing on statistical optimal transport. His teaching portfolio includes courses on optimization algorithms for machine learning, computational statistics with R, and statistical computing methods. Zhu is affiliated with the Lucy Family Institute for Data & Society, contributing to interdisciplinary data science initiatives.

Yuke Wangمشاهده پروفایل

استادیار

• Deep Learning Systems
• High-Performance Computing
• Parallel Programming
• +۳ مورد دیگر

Yuke Wang is an incoming Assistant Professor at Rice University's Department of Computer Science starting Fall 2025. He earned his Ph.D. in Computer Science from the University of California, Santa Barbara (2024) and B.E. in Software Engineering from the University of Electronic Science and Technology of China (2018). His research focuses on optimizing deep learning systems through compiler and hardware co-design, with expertise in GPU acceleration, parallel computing, and distributed training. Education: Ph.D., UC Santa Barbara (2024); B.E., UESTC (2018). Professional experience includes postdoctoral research at Amazon AWS AI and internships at NVIDIA, Microsoft Research, and Alibaba DAMO Academy. Research interests include accelerating graph neural networks (GNNs), recommendation systems, and large language models (LLMs). He has developed frameworks like GNNAdvisor, MGG, and ZEN to enhance efficiency and scalability in deep learning workloads. His work has been recognized with awards such as the NVIDIA Graduate Fellowship and ACM PACT Student Research Competition. Awards include NVIDIA Graduate Fellowship (2022-2023), UCSB Dissertation Fellowship (2023), and multiple best paper nominations. He actively serves on program committees for top conferences like OSDI, ASPLOS, and ICS. Current hiring: Seeking graduate/undergraduate students for projects in deep learning systems, compiler optimization, and GPU acceleration. Collaborates closely with industry partners including NVIDIA and Amazon.

Michael McDermottمشاهده پروفایل

استاد

• Biostatistics
• Clinical Trials Methodology
• Statistical Inference
• +۳ مورد دیگر

Michael McDermott, PhD, is a Professor of Biostatistics, Neurology, and the Center for Health + Technology at the University of Rochester Medical Center. He holds an academic appointment in the Department of Biostatistics and Computational Biology. His research focuses on statistical methodology including order-restricted inference, clinical trial design, diagnostic test evaluation, and meta-analysis. Dr. McDermott has collaborated extensively on neurological disorders such as Parkinson’s, Huntington’s, and multiple sclerosis, and has a joint appointment with the Department of Neurology. Educated at the University of Rochester (PhD, 1989), his work bridges statistical theory and clinical application. He has authored influential papers on hypothesis testing under order constraints and developed methods for verification bias correction in diagnostic studies. McDermott serves on editorial boards for journals like International Statistical Review and Movement Disorders , and has held leadership roles in professional societies. His research interests span statistical theory (e.g., multivariate analysis, ROC curves) and applied biostatistics (e.g., dose-response modeling, clinical trial optimization). McDermott’s methodological contributions address challenges in medical research design, including adaptive trial phases and missing data imputation techniques. He has been recognized as a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. Collaborative efforts include leading national/international groups studying neurological diseases and serving as an advisor for the T32 Training Grant in Biostatistics. His lab focuses on advancing statistical tools for translational medical research while maintaining active involvement in neurology-related clinical studies.

Mingtao Xiaمشاهده پروفایل

مدرس

• Mathematical Modeling
• Numerical Methods
• Kinetic Theory
• +۳ مورد دیگر

Mingtao Xia is a Courant Instructor at the Courant Institute of Mathematical Sciences, New York University (2023–present). He holds a PhD in Applied Mathematics from UCLA (2019–2023) and a B.S. in Information and Computing Science from Peking University’s School of Mathematical Sciences (2015–2019). His research focuses on mathematical modeling, numerical methods, and machine learning applications in kinetic theories, stochastic systems, and unbounded-domain differential equations. He has contributed to interdisciplinary areas including biological systems, environmental science, and epidemic control through computational methods. Education: PhD in Applied Mathematics, UCLA, 2019–2023 B.S. in Information and Computing Science, Peking University, 2015–2019 His work integrates advanced numerical techniques like spectral methods and Wasserstein-distance frameworks with real-world problems such as gene regulation dynamics, solar wind analysis, and ozone pollution impacts. Recent publications emphasize efficient algorithms for high-dimensional systems and uncertainty quantification in stochastic models. While no awards are listed, his research demonstrates strong contributions to mathematical modeling across diverse domains. Advising and grant details are currently unavailable. His affiliation with the Courant Institute positions him at the forefront of computational mathematics and its applications in interdisciplinary research.

Daigo Shishikaمشاهده پروفایل

استادیار

• Robotics
• Autonomy
• Dynamics and Controls
• +۷ مورد دیگر

Daigo Shishika is an Assistant Professor in the Department of Mechanical Engineering at George Mason University's College of Engineering. His research focuses on autonomy, dynamics, controls, and robotics, particularly in multi-agent systems and cooperative control of robot teams in adversarial environments. PhD, Aerospace Engineering, University of Maryland, College Park Master of Science, Aerospace Engineering, University of Maryland, College Park Bachelor of Science, Aerospace Engineering, University of Tokyo Dr. Shishika's research interests lie at the intersection of robotics and game theory, with a strong emphasis on multi-robot systems , cooperative control , and adversarial engagement . He explores bio-inspired swarming behaviors—such as those observed in mosquitoes—to design scalable and robust distributed algorithms. His work also investigates deception through motion , dynamic resource allocation , and perimeter defense games , aiming to enable robot teams to operate effectively under uncertainty and limited sensing. The recent publications reflect a strong trend in applying game-theoretic models to multi-robot coordination, especially in security and defense contexts. Themes include dynamic games, adversarial resource allocation, perimeter defense, and distributed decision-making on graphs. These works combine control theory, artificial intelligence, and robotics to solve real-world challenges in autonomous teaming. Scientific recognition includes: Nominated for Best Paper in Cognitive Robotics at IROS 2023 Dr. Shishika advises several PhD students including Vi Rostobaya, Goutam Das, James Berneburg, Manshi Limbu, and Sara Oughourli. His research is supported by funding from DCIST and involves collaborative projects with institutions such as Georgia Tech, University of Pennsylvania, and UC Santa Barbara. He actively recruits motivated students for research in robotics, control, and game theory. He leads the Collaborative Robotics, Autonomy, and Dynamics Lab at George Mason University, where his team develops autonomous blimps and quadrotors for competitive and research purposes, including participation in the DTR autonomous blimp competition. The lab emphasizes both theoretical advances and practical implementations in robotics.

Ravi Tandonمشاهده پروفایل

استاد

• Information Theory
• Machine Learning
• Security and Data Privacy
• +۱۲ مورد دیگر

Ravi Tandon is a Professor in the Department of Electrical and Computer Engineering at the University of Arizona, where he holds a Craig M. Berge Faculty Fellowship and a courtesy appointment in Applied Mathematics. He joined the university in 2015 after serving as a Research Assistant Professor at Virginia Tech. Education: PhD in Electrical and Computer Engineering, University of Maryland, College Park, 2010 B.Tech in Electrical Engineering, Indian Institute of Technology (IIT) Kanpur, 2004 Research Interests: His research spans information theory and its applications to wireless networks, machine learning, security, and privacy. He focuses on trustworthy machine learning, privacy-preserving AI, federated learning, secure communications, and private information retrieval. His work aims to develop foundational theories and practical systems for secure, fair, and efficient AI and communication systems. Recent Research Trends: His most recent publications (2023–2025) emphasize trustworthy AI, including fairness-accuracy tradeoffs, robustness, uncertainty quantification, and privacy in generative models and causal inference. He also continues to advance information-theoretic foundations of secure communications and distributed learning. Scientific Awards: NSF CAREER Award (2017) Keysight Early Career Professor Award (2018) Best Paper Award, IEEE GLOBECOM 2011 Advising and Grants: Dr. Tandon actively supervises PhD and MS students, with former students placed at Apple, Google, Meta, NXP, and other leading tech firms. He has led significant research projects, including an NSF SaTC grant on differential privacy in graph mining. His editorial service includes roles at IEEE Transactions on Information Theory, IEEE Transactions on Wireless Communications, and IEEE Transactions on Communications. Labs and Teams: He leads a research group focused on information theory, machine learning, and privacy, collaborating with researchers in computer science, applied mathematics, and industry. His work bridges theoretical foundations with real-world AI and communication system design.

Keying Yeمشاهده پروفایل

استاد

• Bayesian inference
• Objective Bayesian analysis
• Normalized power Bayesian analysis
• +۷ مورد دیگر

Keying Ye is a Professor of Statistics in the Department of Management Science and Statistics at The University of Texas at San Antonio (UTSA), part of the Alvarez College of Business. Previously, he held positions at Virginia Tech (1990–2005) and Shanghai Jiaotong University (1985). He earned his Ph.D. in Statistics from Purdue University, an M.S. in Mathematics from the Institute of Applied Mathematics at Academia Sinica (China), and a B.S. in Mathematics from Fudan University (China). Research Interests : Bayesian inference and methods, objective Bayesian analysis, normalized power Bayesian analysis, clinical trial design (e.g., Continual Reassessment Method), variable selection, Bayesian model averaging, and applications in biostatistics, cyber security, environmental science, and experimental design. He currently serves as an Associate Editor for Bayesian Analysis . His teaching spans courses such as Bayesian statistics, regression analysis, statistical learning, and advanced inference methods. His publications focus on methodological advancements and applied statistical solutions in diverse fields.

Robert S Wallaceمشاهده پروفایل

دانشیار

• Plant Systematics
• Phylogenetics
• Cactaceae Family Relationships
• +۳ مورد دیگر

Dr. Robert S. Wallace is an Associate Professor at Iowa State University, affiliated with the Faculty Senate. His research focuses on plant systematics and phylogenetics, particularly within the Cactaceae family. Key areas include intergeneric relationships in Cacteae, evolution of Opuntioideae subfamily, and phylogenetic analysis of the Portulacaceous Alliance (Caryophyllales order). He has contributed to molecular studies using chloroplast DNA, rbcL sequences, and cytogenetic analysis to clarify taxonomic classifications. Education: B.S. Biology (Wilkes College, 1981), M.S. Botany/Plant Physiology (Rutgers University, 1984), Ph.D. Botany/Plant Physiology (Rutgers University, 1988). Research interests span cactus evolution, photosynthetic pathway variation, and systematic revisions of plant families. His work integrates molecular data with traditional taxonomic methods to resolve evolutionary relationships. Recent articles explore topics like Didiereaceae phylogeny, Ferocactus relationships, and corporate governance in France—a notable interdisciplinary venture. Publications span 1974–2007, highlighting contributions to plant systematics, genetics, and unexpected fields like ergonomic instrument design. No awards explicitly listed, though his sustained academic output indicates recognition in botanical circles.

Sahara Aliمشاهده پروفایل

استادیار

• Artificial Intelligence
• Spatiotemporal Data Mining
• Causal Inference
• +۳ مورد دیگر

Sahara Ali is an Assistant Professor at the University of North Texas (UNT), focusing on interdisciplinary research at the intersection of artificial intelligence and Earth sciences. Her work emphasizes causal inference, spatiotemporal data mining, and big data analytics applied to environmental challenges like Arctic sea ice prediction and climate modeling. Education: Ph.D., University of Maryland M.S., University of Maryland B.Sc., University of Engineering & Technology Lahore Research Interests: Her research bridges machine learning and environmental science, with a focus on developing causal inference frameworks for spatiotemporal data. Key areas include: Climate modeling and Arctic amplification Deep learning for sea ice forecasting Spatiotemporal causal analysis in Earth systems Publications Trends: Her recent articles (2022–2024) reflect a strong emphasis on causal inference applications in climate science. Notable themes include: Developing causality-driven models for Arctic sea ice dynamics Integrating Fourier transforms and residual learning for environmental prediction Comparative analysis of climate prediction models Grants & Advising: No specific grants or advisees are listed in the provided text. Her work likely involves collaborative projects with climate research groups and machine learning labs. Labs/Teams: Details about specific affiliated research groups or labs are not explicitly mentioned.

• Causal Inference
• Data Management
• Machine Learning
• +۳ مورد دیگر

Babak Salimi is an Assistant Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego. Previously, he was a postdoctoral research associate at the University of Washington's Department of Computer Science and Engineering, working with Prof. Dan Suciu. He holds a Ph.D. in Computer Science from Carleton University, advised by Prof. Leopoldo Bertossi. His research focuses on integrating causal inference, theoretical data management, and machine learning to build decision-support systems that enhance interpretability for diverse users. Key areas include causal relational learning, fairness in data-driven decisions, and robust offline evaluation frameworks. He has pioneered work on causal DAG summarization, adversarial data corruption testing, and model-agnostic data valuation systems like KAIROS. Salimi's contributions have been recognized with prestigious awards including the ACM SIGMOD Research Highlight Award and Best Paper Award, and the VLDB Best Demonstration Paper Award. His work bridges foundational data science principles with societal impact, emphasizing ethical AI practices and algorithmic accountability. Education: Ph.D., School of Computer Science, Carleton University Research Lab: HDSI's Causal Data Science Group Key Projects: Development of CausaLens, Zorro, and DEMA

Saptarshi Chakrabortyمشاهده پروفایل

استادیار

• Statistical Computing
• Bayesian Modeling
• Machine Learning
• +۸ مورد دیگر

Saptarshi Chakraborty is an Assistant Professor in the Department of Biostatistics at the School of Public Health and Health Professions, State University of New York at Buffalo. He serves as Director of the Statistical Consulting Lab at the Biostatistics, Epidemiology and Research Design (BERD) Core of CTSI. His research focuses on statistical computing, Bayesian modeling, cancer genomics, and high-dimensional data analysis. Chakraborty holds a PhD from the University of Florida (2018), an MS from the Indian Statistical Institute (2013), and a BSc from Presidency College, Kolkata (2011). He completed a postdoctoral fellowship in statistical genomics at Memorial Sloan Kettering Cancer Center (2018–2020). His work bridges theoretical statistics and applied biomedical research, with contributions to machine learning, computational biology, and drug safety assessment. Notable areas include developing Bayesian frameworks for envelope models, analyzing somatic mutations in cancer, and optimizing nanoparticle drug delivery systems. He is also involved in mentoring through his roles and serves as IBS Biometric Bulletin Correspondent for ENAR. Publications highlight interdisciplinary collaboration, with recent work on photoacoustic imaging, nuclear morphology in cancer, and prenatal exposure effects. Chakraborty is affiliated with the American Statistical Association and International Indian Statistical Association, emphasizing his commitment to advancing statistical methodologies in health sciences.

• Ecological Modeling
• Hierarchical Bayesian Models
• Soil Respiration
• +۷ مورد دیگر

Kiona Ogle is a Professor in the School of Informatics, Computing, and Cyber Systems at Northern Arizona University. Her interdisciplinary research bridges ecology, statistics, and computational modeling, focusing on ecosystem responses to environmental change. Her research interests center on ecological modeling, particularly using hierarchical Bayesian methods to understand soil respiration, tree growth, soil moisture dynamics, and climate change impacts. She investigates how ecosystems respond to precipitation pulses, drought, and temperature shifts, integrating field data with advanced statistical frameworks. Her work emphasizes uncertainty quantification and data-model integration in ecohydrology and biogeochemistry. The recent articles highlight a consistent trend in applying Bayesian and meta-analytic approaches to ecological questions across diverse systems—from soil water content imputation to global analyses of plant responses to rainfall pulses. Her research spans forest ecology, riparian systems, and carbon cycling, often involving large-scale collaborations and open science practices. Kiona Ogle actively contributes to scientific data sharing, with multiple datasets published on platforms like Zenodo and Dryad, supporting reproducibility and collaborative research in environmental science.