Immediately following my PhD in 2021, I worked at Aitia (previously known as GNS Healthcare) on a causal discovery platform to build causal machine learning models that illuminate the hidden biological mechanisms of disease. These models leverage huge multi-omic datasets to reverse engineer causal graphs that can both answer counterfactual questions and provide mechanistic explanations for the answers to those questions.

In April 2025, I joined Novartis as a Principal Data Scientist on the AICS (AI and Computational Sciences) team. The goal of our team is to build and apply AI tools and methods broadly across the entire drug discovery process.

During my PhD, I conducted microscopy experiments to study soft matter systems. Using various image analysis techniques such as particle tracking and particle image velocimetry (PIV), these systems were interrogated to understand the underlying physics.

After graduating I began work in AI-driven drug discovery. This work requires a deep understanding of machine learning and bayesian parametric modeling coupled with disease domain knowledge in the context of genetics. Additionally, expertise in NGS, WGS, and clinical data processing and analysis is required.

Programming Languages and Tools

• Python: TensorFlow, Pytorch, Hugging Face, Pandas, NumPy, Scikit-learn
• R: BioConductor, Seurat, Glmnet, Tidyverse, iGraph, Shiny
• SQL
• Git
• Linux and HPC: StarCluster, ParallelCluster, AWS, BASH
• Matlab
• ImageJ
• Microscopy and Microfluidics

Hard Skills

• Machine Learning: GLMs, Causal, Deep Learning
• Modeling: Statistical, Probabilistic, Bayesian
• Multi-omic data pipelines and modeling: RNAseq, WGS, single-cell, proteomics, metabolomics
• Clinical Data Processing and Analysis

Soft Skills

• Critical thinking
• Project Management
• Troubleshooting
• Detail Oriented
• Communication
• Collaboration