Statistics and statistical learning

Department of Mathematics and Statistics, including associated and adjunct faculty. These faculty also work in computer science, neuroscience, public health, or Robinson College of Business. Links to their departmental profiles document these connections; their related research fields are described further below.

• Li-Hsiang Lin — statistical methodology, including independent component analysis; Brains & Behavior seed grant (2024) on transformations for ICA.
• Gengsheng (Jeff) Qin — survival analysis, diagnostic testing, empirical likelihood, and nonparametric inference.
• Chi-Kuang Yeh — functional data, statistical learning, high-dimensional inference, dependence, and experimental design.
• Yichuan Zhao — survival analysis, ROC methods, nonparametric statistics, Monte Carlo, and high-dimensional data.
• Vince Calhoun — associated faculty; Neuroscience / TReNDS. Multimodal data fusion, independent component analysis, machine learning, and neuroimaging.
• Jun Kong — adjunct faculty; Computer Science. Biomedical image analysis, machine learning, and integration of imaging and genomic data.
• Ruiyan Luo — associated faculty; School of Public Health. Functional data, Bayesian methods, genetic data, and biological networks.
• Liang Peng — associated faculty; Robinson College of Business. Extreme values, heavy-tailed time series, copulas, empirical likelihood, and actuarial statistics.

Biostatistics and population health

School of Public Health. This group includes both methodological biostatistics and quantitative epidemiology. The school’s applied biostatistics and epidemiologic methodology community provides another route into these collaborations.

• Brian Barger — epidemiological and psychometric methods for developmental disabilities and health services.
• Gerardo Chowell — infectious-disease models, epidemic forecasting, and uncertainty in outbreak dynamics.
• Zongshuan Duan — longitudinal tobacco-use data, health disparities, and tobacco regulatory science.
• Hua Hao — environmental epidemiology combining large health datasets with pollution, wildfire, and temperature exposures.
• Jidong Huang — quantitative public-health research and tobacco regulatory science.
• Alexander Kirpich — biostatistics, disease forecasting, molecular epidemiology, and viral genomic data.
• Ruiyan Luo — functional data, Bayesian methods, genetic data, and biological networks.
• Kevin Maloney — infectious-disease modeling and network approaches to HIV and sexually transmitted infections.
• Greta Massetti — violence epidemiology, population surveys, and prevention research.
• Karen Nielsen — multilevel and longitudinal models, wearable measurements, and multimodal data.
• Ike Okosun — biostatistics and epidemiology of metabolic disease and genetic/environmental risk factors.
• Lucy Popova — randomized trials and statistical modeling of tobacco-policy communication.
• Dennis Reidy — intervention evaluation, measurement, and quantitative research on violence prevention; Health Policy & Behavioral Sciences.
• Wenshan Yu — survey and data science; Population Health Sciences and the applied-biostatistics community.

Machine learning, data science, and bioinformatics

Department of Computer Science. The emphasis here is learning from data, computational biology, and simulation. See also the department’s research areas and current directory.

• Esra Akbas — graph mining, graph neural networks, and learning from relational data.
• Mohammed Alser — scalable bioinformatics, metagenomics, and computational analysis of genomic data.
• Rafal Angryk — large-scale data mining, machine learning, and astroinformatics; also Physics and Astronomy.
• Berkay Aydın — spatiotemporal and time-series mining, including solar datasets.
• Zhipeng Cai — machine learning, language models, privacy, and big-data methods.
• Xiaolin Hu — data-driven simulation, data assimilation, and agent-based models of complex systems.
• Dustin Kempton — machine learning, tracking, information retrieval, and surrogate modeling; research faculty.
• Kiril Kuzmin — machine learning, bioinformatics, graphs, and discrete optimization; teaching faculty.
• Yoon Jae Lee — machine learning for physiological signals, wearable sensors, and health monitoring.
• Wei (Lisa) Li — privacy-aware computing, big data, and game-theoretic algorithms.
• Yingshu Li — AI for connected devices, privacy-aware computing, and social networks.
• Armin Mikler — computational epidemiology, agent-based simulation, and public-health informatics.
• Murray Patterson — phylogenetics, cancer evolution, sequencing data, and computational genomics.
• Yue Wang — trustworthy machine learning and sparse signal processing.
• Yubao Wu — graph mining, network analysis, Monte Carlo methods, and bioinformatics.
• Bingyi Xie — machine learning, computer vision, and privacy; teaching faculty.
• Dong Hye Ye — deep learning and computational methods for medical imaging; Brains & Behavior seed grant (2023) on predicting recovery after mild traumatic brain injury.
• Alex Zelikovsky — computational biology, combinatorial optimization, and genomic algorithms.
• Yanqing Zhang — machine learning, computational intelligence, data mining, and health informatics; also Neuroscience.

Brain imaging and quantitative neuroscience

Across computer science, mathematics, and neuroscience. These researchers connect statistical learning and signal processing to brain measurements.

• Vince Calhoun — multimodal brain-data fusion, machine learning, and neuroimaging; TReNDS; Brains & Behavior seed grant (2022) on reproducibility of stimulation-related brain connectivity.
• Armin Iraji — brain signal processing, time-series analysis, and machine learning; Brains & Behavior seed grant (2025) on combining PET/fMRI signals in dementia.
• Jingyu Liu — imaging genetics, multimodal learning, and integration of brain and molecular data; Brains & Behavior seed grant (2025) on gene-expression/imaging associations in schizophrenia.
• Robyn Miller — dynamic brain connectivity, high-dimensional signals, and statistical/deep learning; Brains & Behavior seed grant (2022) on connectivity changes during creative flow.
• Sergey Plis — computational learning from multimodal brain data across spatial and temporal scales; Brains & Behavior seed grant (2026*) on AI tools for rodent brain imaging.

Mathematical modeling and inverse problems

Department of Mathematics and Statistics, with interdisciplinary appointments. These connections are useful for students interested in mechanistic models, data assimilation, and the mathematical foundations of learning. Research descriptions are collected on the department’s research page.

• Igor Belykh — data-driven dynamics, neural-network connectivity, and quantitative models of biological and social systems; Brains & Behavior seed grant (2024) on tools to identify seizure origins.
• Russell Jeter — machine learning for healthcare, reinforcement learning, and stochastic dynamics; Brains & Behavior seed grant (2026*) on motor outcomes following chemotherapy.
• Yi Jiang — multiscale biological models, image analysis, and statistical analysis of biomedical data.
• Yaroslav Molkov — data assimilation and mathematical models of neural and motor systems; Brains & Behavior seed grant (2026*) on axonal remodeling and impaired motor neurons after spinal injury.
• Alexandra Smirnova — regularization and inverse problems, including epidemiological applications.
• Xiaojing Ye — stochastic point processes, optimal transport, and mathematical methods for machine learning.
• Jun Kong — adjunct faculty; cancer-image analytics, machine learning, and multimodal biomedical data.

Actuarial science, risk, and financial statistics

Robinson College of Business, principally the Maurice R. Greenberg School of Risk Science. These connections link probability and statistical inference to insurance, finance, and decision-making.

• Alejandro Del Valle — empirical analysis of disaster and climate risks and policy responses.
• Glenn W. Harrison — structural econometrics, Bayesian inference, and experimental measurement of risk preferences.
• Liang Peng — extreme values, heavy-tailed time series, copulas, empirical likelihood, and actuarial statistics.
• Stephen H. Shore — income volatility, financial risk, and statistical models of income dynamics.
• Ajay Subramanian — applied probability, stochastic processes, mathematical finance, and dynamic risk models.
• Qiuqi Wang — actuarial methods, quantitative risk, financial engineering, and operations research.

Business analytics and AI

Institute for Insight and related Robinson departments. See the institute’s faculty directory.

• Yichen Cheng — Associate Professor of Business Analytics, with a Ph.D. in Statistics. Her work includes high-dimensional data analysis, statistical learning, text analytics, health analytics, and applications in marketing, finance, and information systems.
• Ugur Kursuncu — machine learning, knowledge graphs, and health/social computing.
• Péter Molnár — data analytics, machine learning, and modeling and simulation; clinical faculty.
• Saeid Motevali — machine learning, image analysis, and medical informatics; clinical faculty.
• Denish Shah — data-driven marketing, customer behavior, and social-media analytics; Marketing and Insight.
• Saber Soleymani — text analytics, knowledge graphs, and computational studies of social behavior; clinical faculty.
• Yanqing Wang — statistical learning, survival analysis, treatment decisions, and longitudinal/image data.
• Yusen Xia — generative AI, structured and unstructured data analysis, and operations management.
• Houping Xiao — machine learning, uncertainty in crowdsourced information, and healthcare analytics.

Econometrics and experimental methods

Andrew Young School of Policy Studies. This group connects statistical inference to policy evaluation and economic behavior. The Experimental Economics Center’s research overview describes several of these connections.

• Stefano Carattini — causal policy evaluation, applied microeconometrics, field experiments, and survey research.
• Susan Laury — experimental methods for decisions under risk and insurance behavior.
• Nguedia Pierre Nguimkeu — econometric theory, statistical methods, and applied development econometrics.
• Charles Noussair — experimental markets and measurement of beliefs and risk preferences.
• Jonathan Oxley — applied econometrics and experiments on nonprofits and donor behavior; Public Management and Policy.
• Vjollca Sadiraj — experimental analysis of risk and social preferences.
• J. Todd Swarthout — experimental design and analysis of auctions and strategic behavior.
• Rusty Tchernis — Bayesian and applied econometrics, program evaluation, and spatial methods.

Educational measurement and quantitative research

College of Education & Human Development. The Research, Measurement and Statistics program is a natural home for students interested in how to measure learning and evaluate interventions.

• Lily An — causal inference, educational measurement, and accountability-policy evaluation.
• Kevin Fortner — research design, program evaluation, and teacher-effectiveness studies.
• Michael Frisby — psychometrics, structural equation models, applied statistics, and statistical programming.
• Min Kyu Kim — AI-supported learning, assessment, and personalized learning environments; Learning Sciences.
• Hongli Li — item response theory, cognitive diagnostic models, and educational assessment.
• Terri Pigott — meta-analysis and systematic-review methodology; also Population Health Sciences.
• Yinying Wang — social-network analysis and text mining in educational leadership and policy.

Retired and emeritus faculty

Notes and sources

Updated September 27, 2026 using the linked GSU profiles, research pages, and directories. This is a broadly scoped research guide, not an official or exhaustive university census: inclusion reflects an identifiable connection to statistical methodology, quantitative modeling, or data science in public descriptions. It does not imply that every person is a statistician or is accepting students. Check the relevant graduate program for advising eligibility, funding, and current openings.

Seed-grant links point to the official Brains & Behavior award listings, checked September 27, 2026. *For Jeter, Plis, and Molkov, 2026 is inferred from their placement under “Current Seed Grants” alongside the 2026 award timetable (funding July 2026–June 2027), with 2025 listed in the archive; their individual project entries do not explicitly state an award year. Other years follow the dated award listings. Topics are abbreviated, and the links highlight selected funded projects rather than complete grant histories.

The main directories are Mathematics and Statistics, Public Health, Computer Science, Neuroscience, Robinson, and Education. Associated faculty are cross-listed where their departmental connection is useful to readers. Retired faculty are separated above; older GSU pages alone are not treated as evidence of a current appointment.