About
Tahmina Sultana Priya is a Ph.D. Candidate in Computer Science at Virginia Tech. Her research is driven by a simple question: how can we make machine learning reliable and understandable enough to actually help clinicians make better decisions?
Her work focuses on machine learning for healthcare, explainable AI, and precision medicine. She is particularly interested in developing models that can help with prognosis, identify meaningful patient subgroups, and better understand clinical outcomes in complex diseases. What she enjoys most about research is working with real-world clinical data and finding patterns that can lead to insights that are useful in practice.
During her research journey, she has worked as a bioinformatics research intern and collaborated with the Mayo Clinic, gaining experience working with electronic health records and multi-omics data. These experiences have strengthened her interest in using computational methods to address challenging problems in healthcare and precision medicine.
She earned her M.S. in Computer Science from Virginia Tech and her B.Sc. in Computer Science and Engineering from Khulna University of Engineering & Technology in Bangladesh.
She is currently preparing for her Ph.D. defense and looking for postdoctoral or research opportunities where she can continue exploring meaningful problems at the intersection of machine learning and healthcare. In the long term, she hopes to build a career in academia, where she can pursue independent research, mentor students, and contribute to research that has a meaningful impact on people’s lives.
Outside of research, Tahmina enjoys meeting new people, spending time with friends, traveling, and exploring new places. She is naturally curious, enjoys learning from others, and is always up for a good conversation or a challenging problem.
Research Interests
- Machine Learning for Health
- Explainable AI
- Precision Medicine
- Large Language Models
- EHR & Genomic Data Integration
- Subgroup Discovery
- Clinical Outcome Prediction
Education
Experience
- Analyze large-scale EHR, clinical, and genomic datasets to support precision medicine research.
- Develop reproducible pipelines for healthcare data integration, preprocessing, and validation.
- Build and evaluate predictive and statistical models for prognosis, risk stratification, and outcome prediction.
- Apply explainable AI and subgroup modeling to identify clinical factors associated with disease progression.
- Analyzed EHR, clinical, and genomic data to identify factors associated with disease progression and patient outcomes.
- Performed data preprocessing, integration, validation, and statistical analysis of large-scale biomedical datasets.
- Developed predictive models for patient risk assessment, outcome prediction, and healthcare analytics.
- Conducted survival and drug response analyses to support precision medicine research.
- Assisted instruction for Data Structures, Computer Organization, and Android Development courses.
- Evaluated assignments and provided technical guidance to students.
- Taught undergraduate courses including Web Engineering, Data Mining, Machine Learning, Software Engineering, Digital Electronics, and Data Communication.
- Instructed over 200 students and supervised software development and research projects.
- Led development of scalable web applications using Django and Nuxt.js.
- Developed Python-based analytics dashboards, visualization tools, and cloud-based solutions.
- Designed IoT-based data collection systems and supported software deployment and operations.
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