Supporting Graduate Student Academic and Professional Success
GradQuant Staff
Dr. Kevin Esterling, GradQuant Faculty Director
Dr. Esterling is a professor in the Department of Political Science and the School of Public Policy at the University of California, Riverside. His research focuses on deliberative democracy in American national politics. His current work identifies the conditions that lead citizens to engage constructively in public discourse. He is the author of The Political Economy of Expertise: Information and Efficiency in American National Politics (University of Michigan Press, 2004). He has published in a number of journals, including The American Political Science Review, Political Analysis, The Journal of Politics, Rationality and Society, Political Communication, and the Journal of Theoretical Politics. His work has been funded by the National Science Foundation and by the MacArthur Foundation. Esterling was previously a Robert Wood Johnson Scholar in Health Policy Research at the University of California, Berkeley and a postdoctoral research fellow at the A. Alfred Taubman Center for Public Policy and American Institutions at Brown University. He received his Ph.D. in political science from the University of Chicago in 1999.
Email: kevin.esterling@ucr.edu
Dr. Diogo Ferrari, GradQuant Associate Faculty Director
Dr. Ferrari is an assistant professor in the Department of Political Science at the University of California, Riverside. His research focuses on political methodology and comparative political behavior. He investigates the relationship between socioeconomic conditions, cognitive perceptions about the socioeconomic environment, opinion formation, and political behavior. His research on political methodology focuses on causal inference, machine learning, Bayesian statistics, semi-parametric Bayesian models, and computational methods for social science. Before joining the University of California, Riverside, he worked at the University of Chicago where he taught at the Computational Social Science department. He is a research associate at the Center for Metropolitan Studies (CEM/USP) in Brazil. Dr. Ferrari holds Ph. D. in Political Science and Scientific Computing from the Department of Political Science and the Michigan Institute for Computational Discovery and Engineering (MICDE) and a graduate degree in statistics at the University of Michigan.
Email: diogo.ferrari@ucr.edu
Jason Chou, GradQuant and GSRC Program Specialist
Jason has been supporting student success at UCR since 2004. He has an M.A. in TESOL (Biola U.) and taught English language at Mt. San Antonio College and at Chiang Mai University before coming to UCR. He also has a B.A. in Physiology and Cell Biology (UC Santa Barbara) and an M.A. in Philosophy (Talbot Theological Seminary), and his background includes studies in higher education (UC Riverside) and music production (Musicians Institute). He enjoys spending time with his family, and in his solo moments he likes to read, play guitar, surf, or observe clear night skies with binoculars and a telescope.
Email: jason.chou@ucr.edu
Office Hours: Mondays, 1pm - 2pm
Office: Life Sciences 1425
Phone: (951) 827-4291
Lusha Xu, GradQuant Lead Consultant
Lusha Xu is a Ph.D. candidate in Economics and an M.S. student in Statistics at the University of California, Riverside. She also holds a Bachelor’s degree in Electrical Engineering. Her research focuses on the application of quantitative methods to fintech, macroeconomics, and financial risk, with projects spanning loan default prediction and banking analytics. She also investigates the theoretical implications of central bank digital currencies (CBDCs) on financial intermediation. Lusha’s work centers on applying advanced statistical modeling, machine learning, and data analysis to complex economic questions. She applies machine learning–based forecasting models to uncover complex temporal dynamics in financial and macroeconomic time series. She is passionate about supporting interdisciplinary research and helping graduate students build strong, data-driven approaches in their work.
Specialties: Quantitative Analysis, Statistical Modeling, Machine Learning, Time Series Forecasting, High-dimensional Statistics, Factor Models, Non-Parametric Statistics, Causal Inference, Probability Theory, R, Python, Stata, Matlab, SQL, LaTeX
Email: gradquant@ucr.edu
Office: Life Sciences 1425
Phone: (951) 827-4291
Harry Muttram, GradQuant Consultant
Harry is a Ph.D. student in the Department of Political Science at the University of California, Riverside, where he previously earned his M.A. His research interests include political representation, bureaucracy, policing, and quantitative methodology broadly. He is currently a graduate student researcher for the Everday Respect research team, an interdisciplinary group of researchers seeking to develop a multimodal, multi-perspective approach to study communication between officers and drivers during traffic stops. His work focuses on the compliance of police as street-level bureaucrats with formal institutional changes aimed at constraining officer behavior and the consequences of police interactions on the attitudes of the mass public. He approaches his work from both an institutional and a behavioral perspective.
Specialties: Statistical inference, Linear Regression, Generalized Linear Models, Multi-Level Modeling, Causal Inference, Matching, Instrumental Variable Models, Difference-in-Differences Methods, Synthetic Control Methods, Regression Discontinuity Designs, Survey Experiments, R, LaTeX
Vishvaditya Luhach, GradQuant Consultant
Vishvaditya is a graduate student in the Computer Science department at the University of California, Riverside, with a strong background in Data Science and Machine Learning. He has two years of professional experience working as a data scientist and has been a part of multiple research projects. He has experience wrangling huge datasets, creating data processing pipelines and fine tuning and deploying deep learning models at organizational scale. His research interests include computer vision, Vision-Language-Action models and diffusion models.
Specialties: Python, R, SQL, Big Data, Machine Learning tools (AWS, Databricks, Spark, and MLFlow)
Jackson Caudle, GradQuant Consultant
Jackson is a fifth year PhD student in the Evolution, Ecology and Organismal Biology Department, at UCR. His research focuses specifically on gene inactivation using the visual system of cetaceans (whales, porpoises and dolphins). Currently Jackson’s work focuses on using computational methods to find inactivations, develop species and gene trees, and use statistical methods to detect evolutionary forces. Jackson is also a consultant for TADP (Teaching Assistant Development Program), and has extensive experience in educating both undergraduate and graduate students in learning techniques. Jackson is passionate about helping students to feel comfortable with their work, whether that be teaching or research. Please feel free to reach out and ask any questions.
Specialties: Quantitative Analysis, Statistical Inference, Maximum Likelihood, Bayesian Methods, All Phylogenetic Tree Building Methods, Most Genetics Software, R, Statistical Modeling
Walter Navarro, GradQuant Consultant
Walter Navarro holds an M.A. in Mathematics and is currently a Ph.D. student in Applied Statistics. His academic interests bridge theory and application, grounded in a strong background in theoretical mathematics and statistics alongside applied mathematical and statistical modeling. He is particularly interested in how rigorous mathematical structure and statistical reasoning can be used to develop interpretable and reliable models for complex real-world systems.
His work is especially motivated by applications in the biological and medical sciences, where mathematical and statistical models play a critical role in understanding dynamic and uncertain processes. He has a strong interest in mathematical oncology and mathematical medicine, applying methods from applied mathematics and applied statistics—including differential equations and numerical methods—to study disease dynamics and biological systems. More broadly, he is interested in interdisciplinary research that applies quantitative methods across domains, including emerging applications in the humanities, and approaches his work with an emphasis on theoretical soundness, computational rigor, and practical relevance.
Specialties: Python, R, LaTeX, Mathematica, Mathematical and Statistical Modeling, ODEs/PDEs, Probability Theory, Linear Regression, Mathematical Oncology, Mathematical Medicine, Numerical Methods