Find Academic Supervisors

Drew CrealView profile

Professor

• Econometrics
• Macroeconomics
• Finance
• +3 more

Drew Creal is a Professor of Economics at the University of Illinois Urbana-Champaign, affiliated with the Department of Economics within the College of Liberal Arts & Sciences. His research focuses on econometrics, macroeconomics, and finance, with an emphasis on time series analysis, stochastic volatility models, and asset pricing. He has published extensively in top journals such as the Journal of Econometrics, Journal of Financial Econometrics, and Journal of Business and Economic Statistics. His work often involves methodological advancements in econometric modeling, including Bayesian estimation, dynamic factor models, and generalized autoregressive score (GAS) models. His academic contributions span theoretical econometrics and applied finance, addressing topics like monetary policy uncertainty, international yield curves, and credit risk. He serves as an Associate Editor for several leading journals, including the Journal of Econometrics and the Journal of Applied Econometrics. His research has been supported by grants and collaborations with institutions such as the National Science Foundation and the Tinbergen Institute. Recent publications highlight innovations in cluster covariance estimation, observation-driven filtering techniques, and Bayesian regression trees for asset pricing. His work often bridges theory and practice, offering tools for analyzing financial markets and macroeconomic phenomena. Creal’s academic profile is further enriched by his role in organizing conferences and workshops, such as the Midwest Econometrics Group Conference, fostering interdisciplinary dialogue in econometrics and finance.

• Econometric Theory
• Time Series Analysis
• Econometrics
• +3 more

Dr. Majid Al Sadoon is an Associate Professor and Director of Research at the Business School, Durham University. He holds a PhD from Cambridge University (2011) and has held roles including Assistant Professor at Universitat Pompeu Fabra and Robert Solow Postdoctoral Fellow at Cambridge. His research focuses on Econometric Theory and Time Series Analysis , with contributions to linear rational expectations models, Granger causality, and panel data methodologies. His work bridges theoretical econometrics with practical applications in dynamic modeling and statistical inference. Key research contributions include advancements in spectral approaches to rational expectations models, subspace Granger causality testing, and consistent estimation techniques for panel data. He has published in top journals like Econometric Theory , Journal of Econometrics , and Econometric Reviews . Current research trends emphasize regularization methods for econometric models and identification problems in linear systems. Dr. Al Sadoon advises students on topics such as dynamic panel data and econometric modeling. His academic leadership includes directing research initiatives at the Business School, fostering interdisciplinary collaboration in quantitative economics.

Yeying ZhuView profile

Associate Professor

• Statistics
• Causal Inference
• Machine Learning
• +3 more

Yeying Zhu is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Her research focuses on causal inference, machine learning applications in networks, and statistical methodologies for handling missing data and confounding variables. She holds a Ph.D. in Statistics from The Pennsylvania State University (2013), an M.S. in Statistics & Applied Probability from the National University of Singapore (2008), and a B.S. in Statistics with a minor in International Economics & Trade from East China Normal University (2006). Her work bridges theoretical statistics with practical challenges in causal analysis, including mediation analysis, propensity score modeling, and network treatment effects. Recent research explores UAV-enabled integrated networks using reinforcement learning and anomaly detection in satellite systems through ensemble learning techniques. She has contributed to advancements in high-dimensional causal mediation analysis and semi-parametric estimation techniques. Notable areas of contribution include: Causal inference frameworks for non-ignorable missing data Machine learning-driven solutions for UAV trajectory optimization and satellite anomaly detection Development of robust statistical methods in longitudinal data and instrumental variable models Her articles reflect interdisciplinary collaborations spanning statistics, computer science, and engineering, emphasizing methodological innovation and real-world applicability.

Justin SilvermanView profile

Assistant Professor

• Bayesian Statistics
• Machine Learning
• Genomics
• +7 more

Dr. Justin Silverman is an Assistant Professor of Information Science & Technology, Statistics, and Medicine at Pennsylvania State University, with affiliations in the Department of Statistics and the Institute for Computational and Data Science. He holds an MD from Duke University School of Medicine (2020), a PhD in Computational Biology and Bioinformatics from Duke (2019), and a BS in Physics and Biophysics from Johns Hopkins University (2011). His research integrates Bayesian statistics, machine learning, and genomics to address impactful biomedical questions. Key focuses include microbiome analysis, gene expression studies, and scale-reliant inference methodologies. His work emphasizes methodological rigor and clinical relevance, with applications in personalized medicine, agriculture, and epidemiology. The Silverman Lab develops cutting-edge tools like the fido and philr packages for compositional data analysis. Collaborations span academia and industry, addressing challenges in livestock health, viral surveillance, and precision medicine. He also leads Anarres Analytics LLC, translating statistical methods into practical solutions for clients. His advising includes PhD and MS students working on topics like scalable Bayesian models, microbial co-occurrence networks, and topological data analysis in oncology. Notable projects include studies on ancient microbiomes, dairy cow treatments, and pandemic modeling. Labs and teams: Silverman Lab (PSU), Anarres Analytics. Current positions: PI, startup founder, and collaborator in multi-institutional projects.

• Social Psychology
• Group Processes
• Intergroup Relations
• +5 more

Jared B. Kenworthy is a Professor of Psychology at The University of Texas at Arlington, where he has held academic positions since 2005, including roles as Lewis Hollweg Endowed Professor of Industrial-Organizational Psychology (2023–present). His primary affiliations include the Department of Psychology and contributions to the College of Science. Kenworthy’s research focuses on social psychology, particularly intergroup relations, group processes, collaborative creativity, and meta-analysis. He has extensive expertise in studying how intergroup contact reduces prejudice, the dynamics of creative group processes, and the role of technology in enhancing collaborative innovation. Kenworthy’s education includes a PhD in Social Psychology from the University of Southern California (2002), preceded by an MA (1998) and BS in Psychology (1996) from the same institution. His work has been supported by grants from the National Science Foundation (NSF), Army Research Office, and other federal agencies, totaling over $10 million in funding. Key projects include studies on collaborative creativity in dynamic networks, AI integration in team processes, and reconciliation in post-conflict societies. His research has led to over 100 peer-reviewed articles, book chapters, and edited volumes, including seminal works on group brainstorming strategies and intergroup trust. Notable publications include Group and Team Processes: Science and Practice (2021) and Creativity and Innovation: Cognitive, Social, and Computational Approaches (2022). Kenworthy has advised over 30 doctoral and master’s students, guiding them in topics ranging from social identity theory to computational creativity assessment. He has served on numerous university committees, including the Graduate Affairs Committee and the College of Science Research Space Committee, and has been a frequent presenter at conferences such as the Interdisciplinary Network for Group Research (INGRoup). His teaching spans advanced social psychology, group processes, and multivariate data analysis, reflecting his commitment to both research and education.

Brian KellerView profile

Assistant Professor

• Missing Data Analysis
• Bayesian Statistics
• Statistical Computing
• +3 more

Brian Keller is an Assistant Professor in the Department of Educational, School & Counseling Psychology at the University of Missouri's College of Education & Human Development. His research focuses on statistical methodologies, particularly missing data analysis, Bayesian statistics, structural equation modeling, and multilevel modeling. He is the developer of Blimp statistical software, which supports advanced statistical computing methods. Key areas of expertise include missing data imputation techniques, Bayesian computational methods, and the application of statistical models to educational and psychological data. His work emphasizes methodological innovation, software development, and the rigorous analysis of complex datasets. Recent research trends in his articles highlight advancements in latent variable modeling, nonlinear effects, and the integration of computational tools like Blimp. His contributions address challenges in multilevel models, missing data mechanisms, and model comparison criteria such as DIC and WAIC. No specific scientific awards are listed for Brian Keller in the provided information. His professional affiliations include the College of Education & Human Development and the Blimp Statistical Software initiative. He advises students in the department but no specific advisee names are provided. Research grants and funding details are not explicitly mentioned in the text. Blimp Statistical Software is a key collaborative project, reflecting his commitment to advancing statistical methods and software solutions for researchers.

Yinghui WeiView profile

Associate Professor

• Statistics
• Biostatistics
• Public Health
• +4 more

Yinghui Wei is the Associate Head of School (Resources) and Associate Professor of Statistics at the University of Plymouth's School of Engineering, Computing and Mathematics. Her research focuses on statistical methodology applied to medicine, clinical trials, observational studies, and evidence synthesis, with expertise in big data analytics using NHS records and national databases. She leads impactful health data science initiatives, including the MSc Health Data Science and Statistics program, and has secured significant research funding (e.g., £3M NIHR INSIGHT grant). Her awards include the Vice-Chancellor's Award for Equality, Diversity & Inclusion (2024) and leadership in REF2021 impact case studies. She chairs equality committees and has held roles at institutions like the MRC Clinical Trials Unit and MRC Biostatistics Unit. Education: PhD and MSc in Statistics from The University of Manchester (2008, 2004). Research interests span Bayesian inference, clinical prediction models, infectious disease epidemiology, and survival analysis. Notable projects include MRC-funded work on long-term effects of COVID-19 and methodological contributions to meta-analysis of time-to-event outcomes via Stata's ipdfc module. Grants and collaborations include £970K ESRC funding for coastal community research, Royal Academy of Engineering diversity programs, and international partnerships like the Yunnan-Plymouth Joint Doctoral Training Programme. Supervised over 10 PhD students across health data science, biosensors, and kidney transplantation outcomes. Active in professional societies, including the Royal Statistical Society and Royal Society International Exchanges Committee.

Steven J. RehseView profile

Professor

• Biomedical Physics
• Analytical Chemistry
• Medical Diagnostics
• +3 more

Dr. Steven J. Rehse is the Department Head and Professor in the Department of Physics at the University of Windsor, specializing in Experimental Biomedical Physics and AMO (Atomic, Molecular, Optical) Physics. His research focuses on laser-induced breakdown spectroscopy (LIBS) for rapid bacterial pathogen detection in clinical specimens such as blood, urine, and cerebrospinal fluid. He has led a prolific research group, mentoring over 20 graduate and undergraduate students, and has presented at international conferences including LIBS XIII, SciX, and the Canadian Association of Physicists (CAP) Congress. His work bridges physics, biomedicine, and chemistry, aiming to develop faster diagnostic tools for infectious diseases. Dr. Rehse holds a Ph.D. from Colorado State University and has held academic positions at the University of Western Ontario and Wayne State University. His awards include the Roger Thibert Teaching Excellence Award (2013) and multiple NSERC grants. His lab collaborates with medical professionals and industry, advancing LIBS applications in clinical settings. Recent projects include emission enhancement techniques using silver thin films and interdisciplinary outreach with pre-optometry students. Key achievements include developing LIBS protocols for urinary tract infections and bacterial meningitis diagnosis, with publications in Spectrochimica Acta, Applied Spectroscopy, and other journals. He advocates for undergraduate research through programs like the Outstanding Scholars initiative and has engaged the public via media appearances and science outreach events.

Alwell Julius OyetView profile

Professor

• Longitudinal Data Analysis
• Spatial Data Analysis
• Time Series Analysis
• +3 more

Alwell Julius Oyet is a Professor of Statistics in the Department of Mathematics and Statistics at Memorial University of Newfoundland. He earned his B.Sc. (1986) and M.Sc. (1992) from the Federal University of Technology, Owerri, Nigeria, and his Ph.D. (1997) from the University of Alberta, Canada. His research focuses on longitudinal data analysis, spatial statistics, time series, statistical computing, multinomial data, and robust methods. He maintains active research collaborations in these areas. Oyet teaches undergraduate and graduate courses including Design of Experiments, Time Series Analysis, Multivariate Analysis, and Computational Statistics. He has extensive experience developing course materials and statistical computing resources.

• Health Promotion
• Wellness Assessment
• Program Evaluation
• +7 more

Kimberly A. Smith, PhD, is an Assistant Professor in the Department of Family and Community Medicine at the University of Alabama at Birmingham (UAB) School of Medicine. She also serves as Director of Evaluation at the UAB Center for Clinical and Translational Science (CCTS). Her roles include developing program evaluation models focused on outcome measurement, implementation, and study design. She holds a PhD in Education from the University of Southern Mississippi, with concentrations in Research, Evaluation, Statistics, and Assessment, along with master’s degrees in Educational Research Studies and Human Performance (Exercise Physiology). Dr. Smith’s research focuses on advancing translational research and outcomes in healthcare delivery, particularly through collaborative programs addressing cardiovascular health disparities and public health initiatives. She leads teams in designing research agendas, capacity assessments, and evaluation plans to drive evidence-based practices. Her expertise includes advanced statistical methodologies, program evaluation, and quality improvement projects across disciplines. Her professional activities include membership in the NSCA’s Long-Term Athletic Development Special Interest Group Executive Council and roles in journal reviewing (e.g., Journal of Clinical and Translational Science ). She has secured grants from the Alabama Department of Public Health and NIH-funded CTSA programs, emphasizing rural health engagement and cardiovascular disease reduction. As an educator, Smith teaches courses like Research Methods and Applied Multivariate Statistics, mentoring graduate students in doctoral committees. Her teaching philosophy emphasizes active, goal-oriented environments to foster lifelong learning and community impact.

Neal Martin KingstonView profile

Professor

• Large-Scale Assessment
• Formative Assessment
• Learning Maps
• +6 more

Neal Martin Kingston is a University Distinguished Professor in the Department of Educational Psychology at the University of Kansas. He serves as Director of the Achievement and Assessment Institute (AAI) and Vice Provost for Jayhawk Global and Competency-Based Education. His work focuses on large-scale assessment, learning maps, and psychometric methods. He leads AAI, overseeing 400+ staff and 150+ temporary employees, managing projects like the Dynamic Learning Maps Alternate Assessment. Education includes a B.A. from SUNY Stony Brook (1974) and multiple degrees from Teachers College, Columbia University: M.A. (1977), M.Ed. (1978), M.Phil. (1983), and Ph.D. (1983) in Educational Measurement. Research emphasizes integrating assessment with learning, including formative assessment, games-based testing, and support for students with disabilities. Over 250 grants funded his work, notably the Dynamic Learning Maps project (then KU’s largest grant). Notable awards include the University Distinguished Professor title. His advising spans 17 listed students, many now in academia or assessment roles. Key labs include the AAI, involved in projects like I-SMART and Kansas Assessment Program.

Christophe RoyonView profile

Professor

• Particle Physics
• High-Energy Physics
• Collider Physics
• +3 more

Christophe Royon is a Foundation Distinguished Professor at the University of Kansas, leading research in high-energy particle physics with a focus on collider experiments at the Large Hadron Collider (LHC). His work primarily involves analyzing data from the CMS experiment to explore fundamental questions in particle physics, such as dark matter detection, Higgs boson properties, and quantum chromodynamics (QCD) dynamics. Research interests include probing new physics scenarios through precision measurements of top quark interactions, vector boson production, and jet substructure. He actively contributes to studies of heavy ion collisions to understand quark-gluon plasma behavior and gluon saturation effects. His experimental expertise spans detector calibration, systematic uncertainty quantification, and machine learning applications in data analysis. Notable achievements include the APS Award for collaboration with Brazil (2017-2018), recognizing his international research contributions. His recent publications (2024-2025) highlight advancements in measuring W boson properties, constraining Higgs Yukawa couplings, and discovering anomalous top quark pair production signals. Dr. Royon collaborates extensively with global physics communities, advancing experimental techniques for future collider projects like the Electron Ion Collider (ECCE). His work bridges theoretical predictions with experimental validation, contributing to both precision Standard Model tests and searches for physics beyond the Standard Model.

Mudassir M. RashidView profile

Assistant Professor

• Process Control
• Biomedical Engineering
• Pharmaceutical Engineering
• +2 more

Mudassir M. Rashid serves as Assistant Professor of Chemical Engineering and Director of the Pharmaceutical Engineering Program at Illinois Institute of Technology's Armour College of Engineering. His work bridges chemical engineering fundamentals with cutting-edge applications in biomedical systems and pharmaceutical manufacturing. His academic credentials include: Ph.D. in Chemical Engineering from McMaster University, Hamilton, Canada (2016) B.Eng. in Chemical Engineering from McMaster University, Hamilton, Canada (2011) Dr. Rashid's research program centers on three interconnected domains: Data-driven modeling and control for biological/chemical systems, specializing in recursive identification and adaptive control of nonlinear time-varying processes with variable delays Diabetes technology development through metabolic modeling, simulation of physiological pathways, and automated closed-loop insulin delivery systems Pharmaceutical manufacturing innovation via process analytical technology for real-time monitoring, control, and optimization of drug production quality attributes His methodology integrates advanced control theory with practical healthcare and industrial applications, demonstrating particular expertise in artificial pancreas systems and pharmaceutical process optimization. Analysis of his 15 most recent publications (2016-2019) reveals a dominant research trajectory in adaptive control systems for diabetes management, constituting over 80% of his output. Key themes include glucose prediction algorithms, plasma insulin estimation techniques, and disturbance-handling strategies for meals/exercise within artificial pancreas frameworks. The remaining publications address economic model predictive control applications in chemical batch processes and metallurgical systems, showcasing methodological versatility across biomedical and industrial contexts. No scientific awards are documented in the provided materials. While specific advising activities and grant funding details are absent from the source text, his leadership of the Pharmaceutical Engineering Program indicates significant academic administrative responsibilities. Dr. Rashid directs the Pharmaceutical Engineering Program at Illinois Tech, though the source material does not specify dedicated laboratory facilities or research team structures beyond his program oversight role.

Tzahi Y. CathView profile

Professor

• Water Treatment
• Wastewater Treatment
• Membrane Processes
• +12 more

Dr. Tzahi Y. Cath is a Professor in the Department of Civil and Environmental Engineering at the Colorado School of Mines , where he leads innovative research in water and wastewater treatment technologies. He serves as Director of the Advanced Water Technology Center (AQWATEC), Co-Director of the Colorado Center for a Sustainable WE2ST, and holds a Joint Appointment at the National Renewable Energy Laboratory (NREL) Water and the Environment division. His work focuses on enabling water reuse through membrane processes, energy-water systems, and data-driven solutions. Education PhD in Environmental Engineering, University of Nevada, Reno (2003) MS in Environmental Engineering, University of Nevada, Reno (2001) BSc in Mechanical Engineering, Tel Aviv University (1992) Research Interests Dr. Cath's research spans membrane filtration (forward osmosis, pressure-retarded osmosis), desalination (membrane distillation, nanofiltration), and direct potable reuse technologies. He explores integrated water-energy systems , data science applications in water treatment, and mineral harvesting from brines. His projects address oil and gas produced water treatment, scalability of membrane systems, and sustainability in industrial water use. Article Trends His recent publications emphasize reverse osmosis, membrane distillation, and data science tools for process optimization. Key subfields include PFAS removal, lithium recovery, ammonia resource recovery, and computational fluid dynamics modeling. Emerging themes are buoyancy-driven heat transfer, economic assessments of thermal desalination, and governance frameworks for net-zero urban water systems in arid regions.

Yongli SangView profile

Associate Professor

• Statistics
• High-dimensional Inference
• Correlated Data Analysis
• +3 more

Yongli Sang is an Associate Professor of Statistics in the Department of Mathematics at the University of Louisiana at Lafayette. She earned her Ph.D. in Statistics (2017) and M.S. in Statistics (2014) from the University of Mississippi, and holds a M.S. (2012) and B.S. (2009) in Mathematics from institutions in China. Ph.D. in Statistics, University of Mississippi (2017) M.S. in Statistics, University of Mississippi (2014) M.S. in Mathematics, Central China Normal University (2012) B.S. in Mathematics, Shandong Normal University (2009) Her research focuses on advanced statistical methodologies for high-dimensional data, time series analysis, nonparametric statistics, and robust statistical techniques for correlated data. She has developed innovative jackknife empirical likelihood methods for testing diagonal symmetry, homogeneity of variances, and K-sample problems with applications in diverse fields. Recent publications highlight her work on Gini correlations in high-dimensional settings, computational efficiency, and confidence interval estimation. She also explores transformations of linear processes and their memory properties, bridging theoretical statistics with applied data analysis challenges. At the University of Louisiana at Lafayette, she supervises Ph.D. student Sameera Hewage and teaches graduate courses including Mathematical Statistics, Regression Analysis, and Biometry.