About
I'm a data scientist and applied AI researcher working at the intersection of information retrieval, machine learning, and legal practice.
My work centers on technology-assisted review and high-recall retrieval — methods for finding nearly every relevant document in a large collection with provable statistical guarantees. I publish primarily in ACM venues (ICAIL, SIGIR) and translate that research into practice as Associate Director of Data Science at Elevate Law.
In parallel, I build curricula and teach: explaining precision, recall, and F1 to legal operations teams; running tutorials on TAR for the IR community; and mentoring junior analysts through the reality of evaluating ML systems under partial labels. I'm currently exploring instructor and lecturer roles where industry experience can sit inside the classroom rather than alongside it.
Based in Washington, DC. Available for invited talks, panels, and teaching engagements.
- 2024
M.S., Data Science
Johns Hopkins University
- 2021
B.S., Computer Science
Florida Atlantic University
- Nov 2022 — present
Associate Director, Data Science
Elevate Law · Washington, DC
Lead client-facing analytics engagements. Design exploratory analysis and predictive modeling workflows in Python. Build visualizations and documentation that translate technical evaluation into actionable guidance for legal teams and non-technical stakeholders.
- Oct 2021 — Nov 2022
Data Scientist
Redgrave Data
Conducted exploratory analysis and statistical modeling on structured and unstructured datasets. Designed predictive models supporting technology-assisted review workflows in legal analytics.
- Oct 2021 — Nov 2022
Senior Advisor
Redgrave LLP
Led technical aspects of consulting engagements involving large-scale data analysis and legal technology workflows.
- 2024
Relativity AI Visionary Award
For contributions to applied AI in legal practice
- 2023
Best Paper
International Conference on Artificial Intelligence and Law (ICAIL)
- 2021
Master's Fellowship
National GEM Consortium