Mahbuba Tasmin

Ph.D. Candidate · Computer Science · UMass Amherst

Machine learning for the biology of antibiotic resistance

Ph.D. candidate studying drug resistance in Mycobacterium tuberculosis. Advised by Prof. Anna Green, SAGE Lab.

Open to postdoctoral & research scientist roles · flexible start, 2027

Amherst, MA, USA Google Scholar ORCID GitHub LinkedIn Email
Sequence-only ML models in genomics look impressive on benchmarks and then fail on new lineages, new genes, new populations. I build models that stay accurate and explainable by injecting the biology we already know — protein structure, evolutionary constraint, multi-omic context — into the model itself.

Research highlights

BIG-TB Benchmark

Developing a unified dataset and evaluation framework spanning 17,000 isolates and 11 drugs, built to compare resistance-prediction models across genomic and structural modalities on equal footing.

Resistance Forecast Project

Integrating structural, evolutionary, and machine-learning features to predict the functional impact of individual resistance variants.

Evolutionary Augmentation

Leveraging multi-species protein homologs to enhance sparse training data for structure-aware, protein-level models.

Full details in publications and on the research page.

Recent milestones

  • NextPreparing to propose my dissertation, "Integrating Biological Signal to Improve Generalizability and Interpretability of Machine Learning in Genomics," in early Fall 2026.
  • 2026Submitted the FARM manuscript (biophysics-aware resistance forecasting) — under review at PNAS.
  • 2026Selected as a fully funded graduate participant for the Tapia Conference.
  • 2026Featured as a graduate researcher in UMass's AI at UMass public-engagement campaign.
  • 2025–26Serving as elected Ph.D. Graduate Representative, Faculty Senate / CICS, UMass Amherst.
  • 2026Reviewing for Bioinformatics Advances and MLCSB (ISMB).
  • 2025Published a research article in eLife on predicting antibiotic resistance using protein structural context.
  • 2025Presented work at the ICLR MLGenX workshop and delivered a spotlight talk at MLCB.
  • 2025Completed my Master's in Computer Science and advanced to Ph.D. candidacy.

Want to talk research, collaboration, or TB genomics?
Book 30 minutes, no agenda required.

CV (PDF)

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