I'm a retired senior military officer (Captain, U.S. Coast Guard) with an active security clearance and a passion for putting AI into the hands of warfighters. I lead teams deploying edge AI systems—computer vision and large language models on resource-constrained hardware—for defense applications. My work spans from the lab to the field, where my team and I test our products alongside Soldiers.
With a Master of Information and Data Science from UC Berkeley and a career built on achieving results under pressure, I bring a rare combination: hands-on AI/ML engineering skills paired with deep expertise in leadership, operations, and strategy. If you're looking for someone who bridges the gap between cutting-edge technology and real-world mission needs, let's connect.
Technical Leadership. Maturing products from inception to TRL-6 and beyond. Transitioning projects from government labs to PEOs. Architecting for ATO.
Edge AI Deployment. Deploying CV models and LLMs to phones and Jetsons using vLLM, Ollama, MediaPipe, LiteRT, and TensorRT.
Small Language Models. Using SLMs to structure raw operational data and extract actionable information at the edge.
ATAK Development. Architecting and leading projects built on the Android Team Awareness Kit for tactical applications.
Internal Investment. Directing R&D initiatives that shape organizational technical direction.
Bridging Tech and Mission. Translating cutting-edge AI capabilities into tools that work for warfighters in the field.
UC Berkeley School of Information
Master of Information and Data Science
May 2021
UC Berkeley Haas School of Business
Master of Business Administration
December 2008
Massachusetts Institute of Technology
S.M., Naval Architecture and Marine Engineering
S.M., Mechanical Engineering
May 2004
U.S. Coast Guard Academy
B.S., Naval Architecture and Marine Engineering
Center for a New American Security
MIT Seminar XXI
Databricks Certified Data Engineer Associate
December 2022
Certified TensorFlow Developer
January 2021
Professional Scrum Product Owner I
April 2022
KNIME L1
January 2022
Getting it Righter, Faster | CNAS | August 2020
In a report published by the Center for a New American Security, a leading Washington, DC-based security policy think tank, Dr. Kaythrn McNabb Cochran and I argue that effective prediction is the cornerstone of agile decision-making. We survey predictive methodologies available to policymakers and present the results of a consulting engagement conducted with Good Judgment, Inc. and the U.S. Department of State. We identify key political and structural impediments to acting on model outputs and provided targeted recommendations to overcome them. Our work was informed by interviews with current and former cabinet secretaries, leaders and program managers at IARPA and In-Q-Tel, and members of the academic community.
San Francisco Bar Pilots | 2024
A visualization of San Francisco Bay vessel traffic included in a commemorative book produced by the San Francisco Bar Pilots.
Rule5.ai - Probabilistic Prediction of Vessel Trajectories
My group and I trained a novel deep learning model on automatic identification system (AIS) data. The model provides tactically-meaningful probabilistic predictions of the trajectories of vessels in San Francisco Bay over the course of 30 minutes using six minutes of input data. Our solution is entirely novel and stands apart from recently published deterministic models.
TensorFlow | AWS Athena | H3 | D3 | Leaflet
Visualizing San Francisco Bay Vessel Traffic
My team and I built a visualization of vessel traffic in San Francisco Bay using a large set of transponder data collected by the Coast Guard and provided by NOAA. Our aim was to provide professional mariners with a tool to understand vessel traffic patterns. To that end, we conducted nearly a dozen user tests with professional mariners and prioritized features using the MoSCoW framework.
Python | Vega-Lite | Altair | GeoPandas | Mapshaper | HTML/CSS | Javascript
Toward Automated Celestial Navigation with Deep Learning
A colleague and I set out to demonstrate that automated celestial navigation could be carried out at the edge. We built an exotic deep regression model in TensorFlow using the functional API, trained it in the cloud with a custom loss function and synthetic images we created using open source astronomy software, deployed the model to an NVIDIA Jetson TX2, and showed promising results against synthetic test images.
Python | TensorFlow | Keras | ktrain | Bash | Docker | Edge
Detecting Evidence of Gender Discrimination in Fijian Court Documents
A colleague and I built models to detect evidence of gender discrimination from court documents hand-labeled by domain experts. We explored the challenges that arise from having large variation in document sizes and content. Our modeling effort spanned word2vec to CNNs to cutting edge BERT-based transformer models.
Python | NLP | TensorFlow | ktrain | Gensim | Huggingface | Explainable AI
A/B Testing for Email Fundraising
My team and I consulted with a non-profit to optimize their end-of-year email fundraising drive. We designed an experiment to test choices of from and subject lines, performed power calculations, provided random assignments to the client, built an analysis pipeline using the Mailchip API, and conducted statistical tests on the results. We even managed to find a statistically significant result.
R | data.table | knitr | Causal Inference | Regression Analysis | Robust Statistics
Detecting Pneumonia in Pediatric Chest X-Rays with Deep Learning
I applied a CNN to detecting pneumonia in pediatric chest x-rays, achieving 92% accuracy. I provided visualizations of intermediate activations to give a sense of what is happening in the convolutional layers.
Python | TensorFlow | Keras | NumPy
Crime in North Carolina
I conducted a classic econometric analysis of crime data from North Carolina. I developed and compared regression models, considering the Gauss-Markov assumptions in each case and settling on a parsimonious model.
R | knitr | Regression Analysis | ggplot2 | Robust Statistics
Modeling Probabilistic Forecasting
I built a model of probabilistic forecasting based on information learned from human interaction as a term paper for George Mason University's Introduction to Computational Social Science. I wrote the paper in Shiny to allow readers to interact with the model.
R | Shiny | Bokeh | Computational Social Science
Network Dynamics of Hip-Hop Collaboration
I built a temporal graph of Oakland hip-hop collaborations from crowd-sourced data and used it to explore brokerage dynamics, detect the signature of the hyphy movement in network metrics, and develop a novel method for community detection based on connection acquisition behavior.
R | Network Analysis | Temporal Graphs | Community Detection | Web Scraping
Iris Classifier Shiny App
The Hello World of data science, which I implemented in 2015. I built a Shiny application to demonstrate classification using random forrests and decision trees as part of Johns Hopkins University's Data Science Specialization on Coursera.
R | Shiny | highcharteR | Decision Trees | Random Forrests