Experience

  1. Probabilistic Machine Learning Researcher / PhD Candidate

    University of Michigan
    • Developing innovative statistical methods for spatial transcriptomics data by combining variational inference and graph neural networks.
    • Engineering scalable probabilistic models to capture intricate patterns in high-dimensional spatial transcriptomics data, expanding our understanding of spatial gene expression and cell (group) interactions.
    • Developed hierarchical Bayesian models to incorporate customized prior spatial knowledge.

  2. Data Science Lead / AI Engineer

    Skillet Labs
    • Led the generation, fine-tuning, and statistical evaluation of large language models used by management service providers (MSPs).
    • Implemented an AI system that optimized MSP talent deployment, improving ticket resolution times.
    • Developed and deployed a multi-agentic workflow that assigned specific sub-tasks to specialized agents, decreasing hallucinations by nearly 100%.
    • Led a team of four senior software engineers and collaborated with two PhD researchers in economics and psychometrics to ship an autonomous, intelligent agent for IT ticket deployments.
    • Assistant author of a collection of structural equation models meant for tracking experiential learning of burgeoning IT talent.
    • Front end consultant of time series visualization for metrics including workload variation, burnout, churn, and expected profit.

  3. Data Science Intern

    Progressive Insurance
    • Performed interactive network visualization, analysis, and inference for employment transitions in Claims Control.
    • The analysis of these models helps Progressive’s Claims Control department properly allocate new labor, decisions that cost the department about $3-4 million annually.

Education

  1. PhD Candidate in Statistics

    University of Michigan
    Thesis on Advanced Statistical Inference in Spatial Transcriptomics. Lead author of deepST and BayXenSmooth models.

  2. MA in Statistics

    University of Michigan

    GPA: 3.81/4.0

    Courses included:

    • Stochastic Analysis
    • Probability Theory
    • Bayesian Statistics
    • Regression Theory
    • Multivariate Data Analysis
    • Advanced Programming/Algorithms: C++, Python, R
    • Statistical Inference
    • Artificial Intelligence
    • Machine Learning

  3. BS in Statistics, Economics, Actuarial Science Concentration

    Case Western Reserve University
    GPA: 3.92/4.0
Skills & Hobbies
Technical Skills
Python

numpy, pandas/polars, scipy, tensorflow, pytorch, pymc3, nltk, beautifulsoup

Bayesian Statistics

bayesian regression, variational inference, hierarchical modelling

Programming

Python, R, SQL, C#, Stata, Javascript, Matlab

Data Science

time series forecasting, decision trees, clustering, scalable data algorithms (Hadoop, Spark, Tableau), graphical modelling

Deep Learning

neural networks, LLMs, statistical model evaluation

Hobbies
Software Development
Charity Fundraising
Teaching
Poker
Awards
Exam P
Society of Actuaries ∙ July 2017
Score: 9
Best Methodology Poster
University of Michigan ∙ April 2022

Awarded Best Methodology Poster at MSSISS 2022 for the talk:

“deepST: A Graph Convolutional Autoencoder for Spatial Transcriptomics”

Rackham Merit Fellowship
Rackham Graduate School ∙ June 2019
Research award granting 50% funding during the PhD Program.
Michelson-Morley Scholarship
Case Western Reserve University ∙ August 2016
$25.5K annual scholarship named in honor of the historic experiment by Case School of Applied Science Professor Albert Michelson and Western Reserve College Professor Edward Morley and awarded to exceptional students who plan to major in science, technology, engineering or mathematics fields.
Languages
100%
English
95%
Russian
20%
Spanish