Selected work

Long before it was fashionable, I was preoccupied with one question: what happens when systems become intelligent enough to correct themselves, and what that frees a person to build. It started with a Google hackathon project in C++ called Smart Code Writer, an early attempt at program synthesis, a program that writes and ideally repairs other programs. That thread has run through my research and into my PhD ever since.

These days the work usually begins with reasoning mathematically about the problem, and I have built systems end to end, from conception to production. With agentic development layered on that foundation, the distance between an idea and something running in production is close to limitless. What follows is a mix of ventures, research, and industry experience.

How I got here

At that Google hackathon I ran straight into how hard Smart Code Writer actually was. The version I wanted would not just synthesize programs, it would correct its own mistakes while doing so. I took the problem to one of my professors, and he told me that what I was describing, at a high level, was the field of artificial intelligence.

So I went and studied it. I found that his own thesis work was in computer vision, applying state-of-the-art machine learning and AI, and I wanted to learn it properly. He said he would teach a course on it if I gathered enough signatures on a petition. I got the signatures, and he taught the course.

Ever since 2015, when I learned to code and finished my internship at Google, I have kept turning over the same question: what sufficiently intelligent, self-correcting systems mean for a single person, and how they change what one individual can take on when the work lives in a computational space. That question became one of the foundations of my PhD application, and it is the same one agentic development is now answering in practice.

Ventures

Industry & Research

  • ArcScore AI logo

    ArcScore AI

    Founding Engineer (Contract). Architected and built the production platform end to end: a Next.js/React/TypeScript front end, a FastAPI back end, and a provider-agnostic Python data pipeline. Designed and implemented the core scoring and NIL-valuation methodology with two other modeling leads, and own significant portions of the application, data, infrastructure, and engineering roadmap.

  • Cornell University logo

    Cornell University

    Graduate researcher, advised by David Bindel. Dissertation: Practical Algorithms for Multi-Step Lookahead Bayesian Optimization. Developed trajectory-based rollout acquisition functions with adjoint-mode stochastic gradients whose cost is independent of input dimension, an adaptive horizon-scheduling framework, and cost-aware rollout policies.

  • Cornell University logo

    Cornell University

    Engineering Summer Mathematics Institute Associate with the Office of Inclusive Excellence. Guided 15 students through project-based learning in applied mathematics and designed 10 professional development sessions focused on academic growth, career readiness, and self-advocacy.

  • LinkedIn logo

    LinkedIn

    Applied Research Intern. Built a predictive model for a confidential nine-figure hardware-allocation forecasting problem, improving on the existing methodology by 13.39%. The implementation remains confidential.

  • AMD logo

    AMD

    Scientific ML Research Intern. Developed Bayesian and ensemble uncertainty-quantification methods for ML surrogate models that accelerate physics-based simulations, enabling on-the-fly detection of unreliable predictions. Also built neural-network-based approaches to reduce inter-node communication in distributed PDE solvers, and the SHAPE project, a C++ system with Python integration for training and deploying ML models in HPC environments.

  • IBM logo

    IBM

    Developed local control algorithms for 2D-mesh routing in analog AI hardware, working on next-generation chip architectures to accelerate AI applications.

  • Adobe logo

    Adobe

    Integrated NLP-based voice and gesture commands for image manipulation, bridging natural language understanding with computer vision in an industry research lab.

  • Stanford University logo

    Stanford University

    Research Intern studying Human-Computer Interaction.

  • Google logo

    Google

    Engineering Practicum Intern. First exposure to industry software engineering standards.

Publications & Patent Filings

Full list on Google Scholar.

Education

  • Cornell University

    PhD in Computer Science

    Advisor: David Bindel. Dissertation: Practical Algorithms for Multi-Step Lookahead Bayesian Optimization. Defended July 2026; degree conferred August 2026.

  • Cornell University

    MSc in Computer Science

    Advisor: David Bindel.

  • Morehouse College

    BS in Computer Science

    Graduated top of class. Phi Beta Kappa Honor Society. Advisor: Shelby Wilson.

Resume & CV

Three versions, current as of July 2026. I take consulting and fractional technical leadership work through this site, and I'm open to the right full-time role.