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Research · MMXXVI

I'm a research scientist using machine learning to uncover how genetic variation drives complex disease phenotypes via causal changes to cellular systems.

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Index
§ 01 / EXP

Experience.

  1. 2025 — Present

    Machine Learning Research Scientist, Integrative Phenotyping

    Insitro

    Develops ML methods for extracting biologically salient phenotypes from high-content clinical data and runs statistical genetics studies for novel target discovery. Sequence-to-function modeling to dissect the regulatory role of disease-related genetic variants for target prioritization.

  2. 2020 — 2025

    Biomedical Informatics PhD Researcher

    UCSF · Kampmann & Yala labs

    Developed a fully human iPSC-derived excitatory neuron and astrocyte co-culture model with genetically encoded calcium indicators, plus a segmentation and analysis pipeline for single-cell activity time series. Built a network-aware masked autoencoder for neuronal activity dynamics, enabling high-content genetic screens for modifiers of network function.

  3. Summer 2024

    Machine Learning Intern

    Insitro

    Trained and benchmarked large-scale self-supervised models for extracting gene–phenotype mappings from genome-wide optical pooled gene knockout perturbation screens.

  4. 2023 — 2024

    Technical Consultant

    Tipping Point Biosciences

    Built QC and computer-vision pipelines for large-scale, high-content small-molecule screens and deployed a deep learning model for compound ranking within those campaigns.

  5. 2020 — 2022

    Teaching Assistant

    UCSF

    PhD Algorithms course — taught graduate students across several UCSF programs.

§ 02 / EDU

Education.

  1. 2020 — 2025

    Ph.D., Biomedical Informatics

    University of California, San Francisco

    Dissertation: Methods for Multiscale Biological Representation Learning. Advised by Dr. Martin Kampmann and Dr. Adam Yala. GPA 4.00 / 4.00.

  2. 2016 — 2020

    B.S., Bioengineering & Applied Mathematics

    University of Washington, Seattle

    Magna Cum Laude · Bioengineering Departmental Honors · Washington Research Foundation Undergraduate Research Fellow · Mary Gates Research Scholar. GPA 3.86 / 4.00.

§ 03 / PUB

Publications.

  1. 01

    Network-aware self-supervised learning enables high-content phenotypic screening for genetic modifiers of neuronal activity dynamics.

    Grosjean, P. et al.

    Nature Machine Intelligence · 2025

  2. 02

    Leveraging protein language model embeddings for catalytic turnover prediction of adenylate kinase orthologs in a low-data regime.

    Grosjean, P. et al.

    arXiv · 2025

  3. 03

    Global endometrial DNA methylation analysis reveals insights into mQTL regulation and associated endometriosis disease risk and endometrial function.

    et al., Grosjean, P., et al.

    Communications Biology · 2023

  4. 04

    Embryo-scale, single-cell spatial transcriptomics.

    et al., Grosjean, P., et al.

    Science · 2021

  5. 05

    Guided vascularization in the rat heart leads to transient vessel patterning.

    et al., Grosjean, P., et al.

    APL Bioengineering · 2020

§ 04 / AWD

Awards.

  1. 2020

    Magna Cum Laude

    University of Washington, Seattle

    Awarded at undergraduate commencement for academic distinction.

  2. 2020

    Departmental Honors in Bioengineering

    University of Washington, Seattle

    Conferred for sustained undergraduate research and an honors thesis.

  3. 2019 — 2020

    Washington Research Foundation Fellow

    Washington Research Foundation

    Undergraduate research fellowship supporting independent investigation in a faculty lab.

  4. 2018

    Mary Gates Research Scholarship

    Mary Gates Endowment, University of Washington

    Merit scholarship recognizing exceptional undergraduate research promise.