Talks and presentations

Using AI to Predict Antibiotic Resistance

November 17, 2025 · Interview · UMass Amherst · Amherst, MA

Interview on applying machine learning to predict antibiotic resistance in disease treatment.

I spoke with UMass Amherst about my PhD research on using machine learning models to predict antibiotic resistance in disease treatment.
Video: https://www.youtube.com/watch?v=O0k0XMh18G8

BIG-TB: A Benchmark Dataset for Genomic Resistance Prediction and Interpretability in Mycobacterium tuberculosis

September 10, 2025 · Talk · Machine Learning for Computational Biology (MLCB) Workshop · New York Genome Center, New York City

This spotlight talk presented the BIG-TB dataset — a multimodal benchmark of ~17,000 M. tuberculosis isolates curated to advance antibiotic resistance prediction and model interpretability.
The presentation highlighted how integrating sequence, structural, and evolutionary features enables models to generalize across resistance mechanisms and better align with biological reality.

Key topics discussed:

Dataset design principles and integration of WHO 2023 resistance catalogues
Evaluation of sequence-based (ESM, CNN) vs structure-aware models
Insights from causal variant discovery and explainability metrics (SHAP, Recall@k)

This work underscores the importance of interpretable, biologically grounded ML systems for global health and precision diagnostics.

Key topics discussed:

Dataset design principles and integration of WHO 2023 resistance catalogues
Evaluation of sequence-based (ESM, CNN) vs structure-aware models
Insights from causal variant discovery and explainability metrics (SHAP, Recall@k)

This work underscores the importance of interpretable, biologically grounded ML systems for global health and precision diagnostics.