// CS @ UC Santa Cruz · class of 2027

Giwin Vincent
Edwin Omesh

> embedding ideas into products

I build machine learning systems that ship: match-forecasting models, councils of AI agents, and spatial models that fix real map data. I care about honest evaluation as much as the model itself.

01 / selected work

Projects, as points in the space

forecasting Mar 2026 – present z = [0.82, -0.14, 0.37]

Prem Predict

A Premier League match-forecasting product, from raw data to a live site.

Ingests 33 seasons of match and player data, builds leakage-safe rolling pre-match features, and serves calibrated home, draw, and away probabilities. The production model is Dixon-Coles with ridge-shrunk team ratings and time decay, and an XGBoost blend is only promoted when a gameweek-block bootstrap confidence interval says it actually wins. Users can sign in and save lineup scenarios to re-run against the current model.

matches in final fit
12,809
walk-forward accuracy
46.6%
log loss
1.028
RPS
0.209

log loss falls across model iterations (illustrative)

  • Python
  • XGBoost
  • Dixon-Coles
  • FastAPI
  • Next.js
  • Supabase
  • Docker
multi-agent AI Oct 2026 · UCSC AI Hackathon z = [-0.61, 0.73, 0.12]

MAGI: Incident Council

Three AI agents with competing mandates negotiate a live production incident.

One agent restores service, one contains harm, one protects customers, and each sees only the evidence it's entitled to. They propose blind, rank options with a Schulze vote, critique and amend, and act alone only on reversible steps they agree on. When they genuinely disagree about something irreversible, MAGI phones a human with the crux. I built an evaluation harness on 13 decision points adapted from real postmortems (Cloudflare, GitLab, AWS S3, Knight Capital) and reported the results honestly, including where the council lost to simpler baselines.

agents
3
labeled decision points
13
harmful actions run
0%
false evidence rejected
3/3
  • Python
  • FastAPI
  • TypeScript
  • OpenAI / Anthropic APIs
  • n8n
  • Twilio
  • SQLite

02 / training log

Timeline at UC Santa Cruz

  1. epoch 06 · Oct 2026

    MAGI: Incident Council UCSC AI Hackathon

    Multi-agent incident response built over a hackathon weekend for the Cisco Outshift / AGNTCY cloud track.

  2. epoch 05 · 2026

    1st place, NVIDIA Cloud Track Hack-a-Claw, Baskin Engineering

    Co-built ClawForge, an AI agent factory for small businesses: describe a need in plain English and get an agent with tools, memory, guardrails, and a deployment plan. Built on NVIDIA Nemotron and Brev.

  3. epoch 04 · Jan – Mar 2026

    Research Overture Maps Foundation

    TruePin: an XGBoost classifier on Uber H3 spatial features to correct map POI entrance coordinates, beating the Overture baseline by 7%. Presented to engineers from Amazon, Meta, Microsoft, and TomTom.

  4. epoch 03 · May – Jun 2025

    Software engineer LAIQA

    Shipped a real-time biometric tracking module on Apple Watch and Fitbit APIs, containerized MongoDB services, and contributed to an HRV-based stress detection algorithm.

  5. epoch 02 · Apr – Jun 2024

    Developer intern Here For You

    Worked on a platform built to support people going through a tough time.

  6. epoch 01 · Sep 2023 – Apr 2024

    Software engineer Soshal

    Full-stack developer on a Flutter app for UCSC students to discover club events, from MVP to university pitch.

  7. init · Sep 2023

    B.S. Computer Science UC Santa Cruz

    Expected June 2027.

03 / feature space

What I work with

ml and data

Python, XGBoost, scikit-learn, CNNs, GeoPandas, H3, probabilistic models, calibration, cross-validation

agents and llms

Multi-agent protocols, OpenAI and Anthropic APIs, NVIDIA Nemotron, prompt engineering, eval harnesses

product engineering

FastAPI, Next.js, TypeScript, React Native, Flutter, PostgreSQL, MongoDB, Docker, CI/CD

04 / inference

Let's build something.

I'm open to ML engineering and software engineering internships. The quickest way to reach me is email.