Jesse Windle

DYDX Capital→

Driving data-driven deal sourcing and investment prioritization by leveraging advanced data science and analytics to identify, evaluate, and prioritize high-potential startup opportunities.

• Built a data management system for tracking startups and founders
• Designed and deployed a multi-stage startup prioritization pipeline combining rules-based filters, agent-based reasoning, and NLP-driven prediction models

Early technical validation for DYDX Capital

• Validated feasibility of data-driven deal sourcing approach

Hi Fidelity Genetics / Technologies

Built out and led the entire team, including engineering, systems, and data science.

• Raised $2M+ in non-dilutive funding (ARPA-E, SBIR)
• Built the company's entire data science and analytical infrastructure from scratch
• Conceived and co-invented a device for in-field root growth measurement
• Developed data systems to manage large-scale plant phenotyping experiments
• Developed a novel 3D root growth modeling framework

Duke University

Taught introductory statistics and conducted research in Bayesian statistics

Duke University

University of Texas at Austin

University of Nebraska - Lincoln

Statistics & modeling (Expert)

Bayesian inference, Probabilistic modeling, Simulation, Optimization, State-space models, Hierarchical models

Machine learning (Expert)

scikit-learn, PyTorch, JAX/Flax, Lightning, Pyro, NLP pipelines

Data & systems (Expert)

Python, SQL, MongoDB, FastAPI, SQLAlchemy, Next.js, Prisma, R

Capturing in-field root system dynamics with RootTracker

Plant Physiology • November 2021

J.J. Aguilar, M. Moore, L. Johnson, R.F. Greenhut, E. Rogers, D. Walker, F. O'Neil, J.L. Edwards, J. Thystrup, S. Farrow, J. Windle, P.N. Benfey. Plant Physiology, 187(3):1117–1130

A tractable state-space model for symmetric positive-definite matrices

Bayesian Analysis • December 2014

J. Windle and C. Carvalho. Bayesian Analysis, 9(4):759-792

The Bayesian Bridge

Journal of the Royal Statistical Society Series B • September 2014

N. Polson, J.G. Scott, and J. Windle. Journal of the Royal Statistical Society Series B, 76(4):713–733

Bayesian Inference for Logistic Models Using Polya–Gamma Latent Variables

Journal of the American Statistical Association • December 2013

N. Polson, J.G. Scott, and J. Windle. Journal of the American Statistical Association, 108(504):1339-1349

rootmodel

The code (R and Stan) supporting the paper Inferring monocotyledon crown root trajectories from limited data

R • Stan • Root modeling

gmmfun

A Python package for fitting distributions via their moment generating function using the generalized method of moments

Python • Statistics

ctgauss

A Python package for sampling from a Gaussian random variable conditioned on a piecewise linear function

Python • Statistics

BayesLogit

An R package for sampling from the family of Polya-Gamma distributions

R • Bayesian statistics

Inferring monocotyledon crown root trajectories from limited data

Manuscript on root modeling methodology

Root modeling • Statistics

Sampling from a Gaussian distribution conditioned on the level set of a piecewise affine, continuous function

Manuscript on HMC sampling within a constrained space

Statistics • Sampling

Efficient Data Augmentation in Dynamic Models for Binary and Count Data

Manuscript on Bayesian analysis of time series data with binary and count observations

Statistics • Bayesian methods

Forecasting High-Dimensional, Time-Varying Variance-Covariance Matrices

Ph.D. Thesis, University of Texas at Austin, 2013

Statistics • Time series