In addition to my academic and clinical roles, I aim to facilitate the application of advanced computational methods to high-value tasks throughout biomedicine.

I provide guidance at the technical intersection of machine learning, molecular biology, and clinical medicine. Typically, this occurs during prototyping and pipeline build-out for development teams or during due diligence for investors.

Prior collaborations have included:

  • Providing technical due diligence on investment opportunities in long-read sequencing technology.

  • Modeling how the composition of a somatic polygenic diagnostic panel affects power and endpoint considerations in oncology clinical trials.

  • Product-market fit analysis for a portable electronic health record product.

  • Evaluating obstacles to implementing observational study workflows in claims data.

  • Prototype guidance for development of a protein mutation effect prediction pipeline.

Technologies and practices

    • Graph neural networks

    • Embedding space analysis

    • Tensor factorization

    • Knowledge graph construction

    • Statistical network analysis

    • Object detection

    • Fine-tuning methods

    • Deep mutational scanning

    • RNAseq

    • Single-cell RNA sequencing

    • Long-read sequencing

    • CRISPR knockout and activation screening

    • Mass spectrometry proteomics

    • Pathway analysis

    • Spatial transcriptomics

    • Electronic health records

    • ICD coding

    • Billing standards

    • Clinical decision-support systems

    • Patient recruitment

    • Medication nonadherence

    • Drug-drug interactions

    • Telemedicine practices

    • Surgical staffing

    • Polygenic risk scores

    • Clinical trial stratification

    • Propensity score matching