Jesse Windle

DYDX Capital→

Driving Data-Driven Deal Sourcing and Investment Prioritization --– leverage advanced data science and analytics to identify, evaluate, and prioritize high-potential startup opportunities --- from proof-of-concept to internal platform.

• Extensive research to validate proof-of-concept
• Built data management system for tracking startups and people
• Developed prediction pipeline for scoring startups and people

Hi Fidelity Genetics / Technologies

Employee #1 --- led systems and data science team.

• Conceived and co-invented a device to measure root growth
• Raised $2M+ in non-dilutive funding
• Developed data systems to manage large-scale plant phenotyping experiments
• Built root growth analysis pipeline
• Developed novel root modeling approach for recapitulating 3D growth

Duke University

Taught introductory statistics and conducted research in Bayesian statistics

Duke University

University of Texas at Austin

University of Nebraska - Lincoln

Applied mathematics and statistics (Expert)

Mathematical and statistical modeling, Inference, Simulation, Optimization

Data analysis (Expert)

R, Stan, Python, NumPy, Pandas, Statsmodels

Prediction (Expert)

Python, scikit-learn, PyTorch, Jax, Flax, Lightning, Pyro

Systems (Advanced)

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

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