Previously, I was Director of Data Science at the pioneering startup Hi Fidelity Technologies (HFT), where I helped invent a novel root sensing device, called RootTracker, and build its data management and analysis platform. RootTracker could monitor the root systems’ of thousands of plants simultaneously and, for the first time, enabled the optimization of root systems for improved nutrient uptake, stress tolerance, and reduced greenhouse gas emissions.

I am currently engaged as a contractor into 2024. Beyond that, I am interested in an early stage project that is looking to operationalize a data thesis. I am also interested in joining a data science team working on a complex, big data problem.

Below you will find an overview of my skills and work experience. You can also find me on LinkedIn.

Prior work and training

  • 2015-2023: Director of Data Science, Hi Fidelity Genetics / Technologies

  • 2014-2015: Visiting Assistant Professor, Duke University

  • Postdoc: Duke University, Statistics, 2014
  • PhD: University of Texas at Austin, Comp. and Appl. Mathematics, 2013
  • BS: University of Nebraska – Lincoln, Mathematics, 2005

Selected skills

Methods: Bayesian analysis, neural networks, random forests, kernel machines, linear and mixed models, hypothesis testing, time series

Software: Python, R, C++, SQL, Iceberg, Bash / Linux, AWS, Git

(Common Python & R packages: numpy, pandas, scipy, sklearn, statsmodels, pytorch, jax, sqlalchemy, pymongo, flask, pytest; rstan, tidyverse [e.g. ggplot2, dplyr, readr], lme4, kernlab, spaMM, quantreg, glmnet, bayeslogit, randomForest, etc., etc., etc.)

Scientific Communication: Reducing complex data to key metrics; conveying results in high-level, graphical summaries.

Selected professional experiences

  • Invented a device, called RootTracker, to measure root systems at scale

    Measuring root growth in the field has historically been a challenge, which has limited the use of root characteristics as a target of crop improvement. To overcome this problem, I invented a device that uses capacitance touch sensors to measure root systems at scale. This novel device enabled the optimization of root systems for improved nutrient uptake, stress tolerance, and greenhouse gas reduction. (You can read the patent here.)

  • Inferred root growth using RootTracker data

    For a species like corn, one can think of a root growing as a random walk. A root starts from where the stem touches the soil and then meanders outwards and down. RootTracker could tell us one point along this random walk, which is very limited information as to the appearance of the entire root. Using statistical modeling I was able to overcome this limitation to recreate realistic recapitulations of root growth from these data that captured the temporal and spatial patterns of the underlying ground truth. (You can read more here.)

  • Learned root phenotypes using AI

    One of the challenges in developing our technology was identifying ground truth. I developed a novel approach using AI to extract root phenotypes. It relied on conducting experiments where the outcome was effectively “plant” or “no plant”. Using a neural network trained on that outcome, it is possible to extract an intermediate layer that corresponds to root detections. (You can read more here.)

  • Built the RootTracker data platform

    For a single device, RootTracker captured data every 5 minutes. Thousands of devices might stream data in a single season. I helped build a system to capture these sensor data and track our experiments. A Flask-based API provided access to the data, which was modeled using a mixture of SQL and Apache Iceberg. A Voila App enabled data exploration, visualization, and analysis. (You can read more here.)

  • Produced results for all internal and client trials

    HFT conducted experiments for itself and others, including ag majors BASF, Bayer, and Corteva. I was responsible for analyzing and communicating the subsequent results. For both internal and external projects, this involved establishing the scientific questions of interest, data exploration and analysis, and then communicating results as a presentation or report. In one of our most exciting studies, I was able to show that the amount of roots present in the root crown was inversely related to nitrous oxide emissions, a major greenhouse gas emitted by row crop agriculture. (You can read more here.)

  • Predicted performance of maize hybrids

    HFT had a proprietary breeding program. I used DNA data to predict the performance of untested varieties using measurements from related plants.

  • Built out the team and company

    As employee #1, I helped build the company from the ground up — inventing the technology, assembling the early team, and then overseeing our data engineering, device engineering, and data science teams as the company grew. Through the writing of grants, I helped raise over $2 million in non-dilutive funding.