Hello! 👋
My name is Mahshid Ahmadian.
I am currently a Virginia Sea Grant Research Fellow and a Ph.D. candidate in Systems Modeling and Analysis (Statistics and Data Science) in the Department of Statistical Sciences and Operations Research at Virginia Commonwealth University. I have a Master's degree in Economical and Environmental Statistics.
I have a passion for solving complex, real-world problems through statistical modeling, machine learning, and high-performance computing (HPC). My expertise spans Bayesian statistics, spatiotemporal modeling, predictive analytics, and statistical computing.
Technical Expertise
Bayesian Modeling & Inference: Including Markov Chain Monte Carlo (MCMC) methods (Metropolis-Hastings, Gibbs Sampling), hierarchical models, Logistic Regression, Mixed Effects Models, Multivariate Regression, and analysis of longitudinal data.
Geospatial Data Modeling: Environmental/ecological data analysis, with a focus on spatiotemporal patterns and their applications.
Machine Learning: Supervised and Unsupervised Learning, probabilistic modeling, and time series forecasting for predictive analytics.
Healthcare & Biomedical Data Analysis: Modeling of medical data, survival analysis, and managing missingness in clinical datasets.
High-Performance Computing: Parallel computing and optimization techniques for handling large-scale datasets.
Programming & Tools: Proficient in R and R-C++, Python, SAS, SQL, Excel, and Power BI for data analysis, visualization, and reporting.
Communication: Strong communication and writing skills especially in scientific writing and grant proposal development.
Research
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Develop a predictive modeling framework for movement data (PhD Thesis)Developed stochastic and Bayesian predictive models to impute and predict locations in movement trajectories. Methodologies included predictive sampling, MLE and Bayesian inference, and hybrid MCMC algorithms of Metropolis-Hastings, Gibbs sampling implemented with HPC, and RC++ for efficient computation. A prototype web tool was also developed to make model outputs accessible for ecological research and fisheries management.This project is funded by Virginia Sea Grant through a two-year competitive fellowship awarded to Mahshid Ahmadian, and supervised by Dr. Edward Boone (Virginia Commonwealth University), and Dr. Grace Chiu (Virginia Institute of Marine Science).
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Modeling clinical longitudinal ordinal data (Master's Thesis)Designed and implemented advanced statistical models for analyzing longitudinal ordinal data for a clinical study on migraine patients. The methodology combined mixed-effects modeling with ordinal link functions and incorporated appropriate strategies to handle data gaps and excess variability. View thesis
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Modeling continuous response variable using SAS software (Undergrad Research)During my undergraduate research, I focused on a broad range of statistical modeling techniques using SAS software, including regression analysis and experimental design. The project emphasized learning and applying various modeling approaches within SAS, strengthening my skills in both statistical theory and software implementation. View project
About My Current Project
As part of my current project, I am working on several advanced initiatives. One key focus is developing a new predictive model for imputing and predicting geospatial data, which will help improve the accuracy of our spatial analyses. I am also creating a web-based tool to support data-driven decision-making in fisheries management, enabling stakeholders to make more informed choices based on real-time data. To handle the computational demands of these tasks, I am optimizing large-scale models using High-Performance Computing (HPC) and parallelization techniques to ensure efficiency and scalability. Additionally, I am leveraging Rcpp to integrate efficient C++ implementations within R, which significantly accelerates our computations and enhances overall performance.
Awards and Honors
Boyd Harshburger Travel Award
Southern Regional Council on Statistics, the 2025 summer Research Conference.
The Honor Society of Phi Kappa Phi, elected member, Jan 2025
National interdisciplinary honor society recognizing academic excellence across all fields of study. View recognition
NSF Participation Award
Awarded for participating in the NSF Workshop on Data-driven Modeling and Prediction of Rare and Extreme Events.
Best poster presentation award
ASA Virginia Chapter 2023 Annual Meeting, Richmond, VA, Oct. 2023. View poster
2023 VASG Graduate Research Fellowship
Virginia Sea Grant, Virginia, Sep 2023. Learn more
Boyd Harshburger Travel Award
Southern Regional Council on Statistics, the 2022 Research Conference.
Scholarship Award of the Honor of Phi Kappa Phi
College of Humanities and Sciences, Virginia Commonwealth University, Richmond, VA, Apr. 2022.
NSF Graduate Travel Award
Southern Regional Council on Statistics, the 2021 Research Conference.
Education
- Virginia Commonwealth University, Systems Modeling and Analysis (Concentration in Statistics and Data Science), Ph.D.
- The College of William & Mary, Virginia Institute of Marine Science, Affiliate student and researcher of The Environmental Statistics and Transdisciplinary Data Science Lab.
- Isfahan University of Technology, Economical and Environmental Statistics, M.Sc.
- Isfahan University of Technology, Statistics, B.Sc.
Publications
- Reid, J.; Ahmadian, M.; Jennings, D.; Pepperl, A. A.; Adkins, A.; Anthony, K. C.; ... Johnson, A. A. (2025). Saying it Aloud: Inclusive Teaching Statements Impact on Sense of Belonging and Engagement. Journal of College Science Teaching 1–14. https://doi.org/10.1080/0047231X.2025.2487437
- Reid, J.; Ahmadian, M.; Wheeler, L.; Durfee, R. R.; Jennings, D.; Golding, S. E.; Johnson, A. A., Moving Beyond Pedagogy: A Measurement of the Impact Faculty's Presence has on the Learning Environment (submitted)
- Ahmadian M.; Boone, E. L.; Chiu, G. S., A New Perspective to Fish Trajectory Imputation: A Methodology for Spatiotemporal Modeling of Acoustically Tagged Fish Data (submitted)
- Uddyback, H.; Ahmadian, M.; Tivenan, S.; Lu, Q., Forecasts of Motor Vehicle Traffic Fatalities in the United States using Time Series Regression Models (submitted)
Leadership and Community Engagement
Community Involvement:
Committee Member, Feb 2025 - Present
Secretary, 2024 - Present
VCU Statistical Sciences and Operations Research Data Science Team
President, 2022–2023
Mentoring Experiences:
Student Lightning Talk Mentor
Mentored a student (more info) who delivered a lightning talk at VA-WHPC and won First Place Prize, Nov 2024.
STEM Undergraduate Student Mentor
Mentored STEM undergraduate students in the 2nd annual SURE Conference, Oct 2021.
Volunteering Experiences:
VCU Global Education Office
RAMily Captain program, Richmond, VA, Fall 2022