cv

Basics

Name Kendall A. Reid
Label Ph.D. Student in Computer Science, Johns Hopkins University
Email kreid20@jh.edu
Url https://kreid415.github.io
Summary I am interested in creating AI systems for scientific research in human genetics and genomics. Current models excel on well-defined problems but struggle when evidence is uncertain or contradictory. I aim to build systems that overcome these limitations, and to develop rigorous evaluations to ensure that their reasoning is transparent and conclusions can be trusted. By harnessing these models for human genetics, we can discover the mechanisms of disease and identify therapeutic targets.

Education

  • 2026.09 - Present

    Baltimore, MD

    Ph.D.
    Johns Hopkins University
    Computer Science. Advisors: Alexis Battle and Brian Caffo
  • 2024.09 - Present

    Baltimore, MD

    M.S.
    Johns Hopkins University
    Bioinformatics
  • 2021 - 2024

    College Park, MD

    Post-baccalaureate
    University of Maryland
    Biology and Chemistry
    • Gen Chem I & II
    • Orgo I & II
    • Physics I
    • Cell Bio
    • Microbio
    • Biochem
  • 2018.09 - 2019.05

    New Brunswick, NJ

    Post-baccalaureate
    Rutgers University
    Computer Science
    • Intro to CS
    • Data Structures
    • Intro to AI
    • Algorithms
  • 2013 - 2017

    New Brunswick, NJ

    B.A.
    Rutgers University
    Mathematics and Economics, summa cum laude
    • Linear Algebra
    • Linear Optimization
    • Cryptography
    • Intro to CS
    • Differential Equations
    • Mathematical Methods for Data Mining
    • Combinatorics
    • Number Theory
    • Probability
    • Real Analysis
    • Cross Sectional Econometrics
    • Time Series Econometrics
    • Mathematical Reasoning
    • Calc I, II, and III
    • Micro and Macro Economics

Publications

Work

  • 2025.09 - 2026.09

    Baltimore, MD

    Senior Programmer Analyst
    Johns Hopkins University, Biostatistics, and Neuroscience
    • Designed and led a study of whether latent variable models isolate known gene programs in single-cell data. First author; manuscript in preparation.
    • Led the computational biology for a companion study applying latent variable models to amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) to identify similarities and differences in disease pathology. Second author; manuscript in preparation.
  • 2024.09 - Present

    Baltimore, MD

    M.S. Student, Bioinformatics
    Johns Hopkins University
    • Recruited a team of researchers from across the globe to investigate the discordance in metrics used to evaluate perturbation prediction models, resulting in a preprint. Second author; submission in progress.
    • Designed a controlled comparison of Wasserstein critics and discriminators to study the tradeoff between removing batch effects and preserving biology in adversarial batch correction for single-cell data. First author; manuscript in preparation.
    • Identified that computing energies along a protein diffusion model's reverse diffusion trajectory with trapezoidal integration is a discrete realization of the Stratonovich integral, contributing to a publication on protein energies in diffusion models.
    • Built a blended genome-exome polygenic risk score for ketamine response in treatment-resistant depression, in collaboration with the Broad Institute. First author; under review.
  • 2024.06 - 2025.09

    Baltimore, MD

    Programmer Analyst
    Johns Hopkins University, Department of Epidemiology
    • Contributed to an agent-based simulation library for HIV population dynamics using Dask.
    • Co-authored a study that used this library to forecast the effect of a BMI maintenance intervention on diabetes risk.
    • Led the interpretability analysis for a study of virologic failure in people with HIV, investigating whether a linear Cox model and a nonlinear deep survival network identified the same influential risk factors. Third author; manuscript in preparation.
  • 2022.05 - 2024.06

    Washington, DC

    Research Assistant, Orthopedic Surgery
    Children's National Hospital
    • Directed longitudinal data collection for an industry-sponsored clinical trial in a rare skeletal dysplasia.
  • 2019.01 - 2021.01

    San Francisco, CA

    Co-Founder; Data Scientist, Computer Vision and Natural Language Processing
    Productivity Industries
    • Built the company's document-parsing system: multimodal models combining computer vision and natural language processing to predict the reading order of unstructured text in PDF documents.
    • Built the continuous-integration infrastructure for training, evaluating, and deploying the models.
  • 2017.06 - 2018.07

    Seattle, WA

    Data Scientist, Computer Vision and Artificial Intelligence
    Iunu
    First Data Scientist at a small company. Built the team up to 6 data scientists and engineers.
    • Built AI-based control systems for greenhouse temperature, water and light.
    • Developed a deep learning pipeline that measured plant material in multispectral images and predicted yield from image time series.
    • Trained convolutional networks to identify plant disease and alert growers prior to plant loss.

Awards

Skills

Languages
Python
SQL
Machine learning
PyTorch
PyTorch Lightning
TensorFlow
Hugging Face
scikit-learn
OpenCV
Scientific computing
NumPy
pandas
Dask
scverse
matplotlib
seaborn
VCFtools
Infrastructure
Linux
Git
Docker
SLURM
Snakemake
Nextflow
GitHub Actions
pytest

Languages

English
Fluent