B.S. Computational Engineering · B.S. Mathematics · UT Austin

Vignesh Winner

Quantitative research & systematic trading — fair-value modeling, signal research, and production ML pipelines across commodities and equities.

3.88GPA / 4.0

~1.1Backtest Sharpe · bp

57.8%OOS Directional Acc.

13%Backtest CAGR

2027Graduation

#Education

The University of Texas at Austin

Austin, TX · Graduating May 2027

B.S. Computational Engineering (Honors) · B.S. Mathematics (Statistics & Probability Pathway) · Minor in Business

GPA: 3.88/4.0
SAT: 1570/1600
ACT: 35/36

Relevant Coursework

Software Engineering/Design
Probability/Statistics
Computational Programming
Engineering Computation
Scientific Computation
Linear Algebra/Matrix Theory
Scientific Engineering
Real Analysis
Computational Fluid Dynamics
Computational Methods for Structural Analysis

#Experience

Trading Analyst Intern

bp — Supply, Trading & Shipping

May 2026 – Present

Created and deployed a Power BI dashboard displaying model decompositions/coefficients across Low Carbon products, used daily by the trading desk.
Developed 5 OLS fair value models across Biogas and Low Carbon markets (RINs, LCFS, and spread products) on the Fundamentals Modeling & Innovation team, engineering predictive features and building end-to-end Dataiku pipelines from data ingestion through production deployment.
Built and backtested systematic trading strategies on pricing signals over 10-year horizons; achieved a Sharpe of ~1.1 and Sortino of ~1.3.
Gained cross-desk exposure to crude oil and refined products through weekly analytics discussions, strategy reviews, trading simulations, and mock cases.

Dataiku
Power BI
OLS
Backtesting

Quantitative Trading Lead

Texas Energy Trading

January 2025 – Present

Built an N-BEATS forecasting model using NeuralForecast, pandas, and EIA API pipelines to predict WTI crude oil futures 3 months ahead.
Designed a multi-leg arbitrage strategy for a case competition using WTI/WTS spreads, contango futures, and pipeline congestion to optimize margins.
Coordinated case competitions, trading firm networking events, and guest speaker series while developing derivatives pricing and ML curriculum.

N-BEATS
NeuralForecast
EIA API
Arbitrage

Quantitative Finance Director

Texas Marketrics

August 2025 – Present

Designed quant curriculum spanning Python, probability/statistics, derivatives, risk modeling, backtesting, attribution, and regime stress-testing.
Built a 6-phase pipeline combining ARIMA forecasting, Markowitz optimization via CVXPY, and 10,000-path Monte Carlo simulations; stress-tested under −5%/−20%/−40% shocks with VaR/CVaR and backtested across 4 equities reporting Sharpe, Sortino, Calmar, and max drawdown.

ARIMA
CVXPY
Monte Carlo
VaR/CVaR

Software Engineering Intern

Gradesoar

May 2025 – August 2025

Engineered a modular question bank and course creation system in Next.js and React, shipping 6 major production features for dynamic content generation.
Designed RESTful APIs with MongoDB for data storage across assignments, question metadata, and user inputs, reducing data retrieval latency by ~24%.
Built full-stack JAMstack architecture across backend routing, layout logic, and unit testing frameworks to ensure seamless platform flow.

Next.js
React
MongoDB
REST

Flight Dynamics Lead

Texas Spacecraft Laboratories

January 2024 – Present

Led CubeSat mission analysis in ANSYS STK/Python across imaging, communications, sensor placement, and constraints within orbital configurations.
Conducted orbital trade studies comparing 500 km 28.5° and Sun-Synchronous (97°) profiles, finding 82% greater cumulative annual downlink time for the 28.5° orbit and identifying a −105° YPR star tracker mounting configuration achieving 0% obscuration.
Owned GNSS and ADCS subsystem testing, including OEM7600 receiver validation, pointing budgets, deorbit simulations, and interference analysis.

ANSYS STK
Python
GNSS
ADCS

Aerodynamics Engineer — Wheel Cover Lead

Formula SAE · Longhorn Racing Solar

August 2023 – May 2025

Led design and simulation of aerodynamic wheel covers using SolidWorks and ANSYS CFD, resulting in a 12% drag reduction in TACC simulations.
Fabricated structural components, including carbon-fiber aeroshell panels, bulkheads, and dual-sided canopy latch to improve safety and egress speed.

SolidWorks
ANSYS CFD
Composites

#Projects

Weather-Signal Commodity Forecasting Model

Fall 2025

Engineered 100+ features from NASA POWER weather anomalies, EIA inventory deltas, and price data to forecast weekly commodity futures returns.
Led a team of 4; trained XGBoost (Optuna-tuned) on 498-fold walk-forward CV across 5 futures (Brent, Henry Hub, ERCOT, Copper, Corn), achieving 57.8% out-of-sample directional accuracy; generated confidence-scaled LONG/SHORT/FLAT signals with a magnitude-threshold policy.
Built vectorized backtesting with transaction costs, slippage, and volatility regimes, yielding 13% CAGR and positive rolling Sharpe over 3+ years.

XGBoost
Optuna
Walk-Forward CV
NASA POWER

ML-Driven ETF Signal Research System

Spring 2026

Engineered cross-sectional and temporal features across 25 ETFs, improving signal stability under walk-forward validation and purged CV evaluation.
Identified and corrected lookahead bias inflating Sharpe to 16; validated signals across 15 ETFs with a median Sharpe of +1.04 on holdout folds.
Applied Half-Kelly sizing and Benjamini-Hochberg correction across 250 model/ticker combinations with IC decay and SHAP feature diagnostics.

Alpha Research
Purged CV
Half-Kelly
SHAP

Tax Deductibility ML Classifier

Summer 2025

Built and deployed a Flask-based ML pipeline using pandas and scikit-learn to classify tax-deductible purchase transactions with 89% accuracy.
Engineered a TF-IDF feature extraction process and trained a Logistic Regression model with confidence scoring and reasoning logic for classifications.

Flask
scikit-learn
TF-IDF

Rehearse.AI

Summer 2025

Developed an LLM-powered web app that analyzes uploaded scripts, infers character intent, and generates adaptive scene prompts for actor feedback.
Built a real-time speech analysis pipeline to evaluate tone, pitch, pacing, and emotional delivery using voice-processing libraries.
Engineered backend infrastructure using Flask, Redis, and Docker/Docker Compose for low-latency inference and production-ready deployment.

LLM
Flask
Redis
Docker

Space Launch Analytics API

Spring 2025

Built a containerized Flask API with Redis to analyze 4,300+ Kaggle-based space launches via REST endpoints for timelines, maps, and sector trends.
Developed a modular Kubernetes-orchestrated job queue and caching system with Redis workers for async plot generation and scalable streaming.
Automated ingestion and analysis of launch data via Python requests, revealing a 1995 crossover to private missions using crossover and frequency plots.

Kubernetes
Redis
REST
Flask

#Certifications

Dataiku Advanced Designer

Dataiku

Dataiku Core Designer

Dataiku

IBM Data Science

IBM

Google Data Analytics

Google

#Languages, Tools & Skills

Languages

Python
C/C++
Java
JavaScript
MATLAB
SQL
R
Ruby
HTML/CSS

Web & Backend

React
Next.js
Node.js
Flask
MongoDB
Redis
REST APIs

Infrastructure

Docker
Docker Compose
Kubernetes
Jenkins
Linux
Git
TACC

Quant & Data

Dataiku
Bloomberg
Kpler
Enverus

Simulation & CAD

ANSYS
OpenFOAM
Abaqus
SOLIDWORKS
Simulink
Ansys STK

#Awards & Interests

Awards & Honors

Eagle Scout
Survivor Texas (S7) Winner
2nd Degree Black Belt
National Qualifier — Duet Scene & Solo Monologue Acting

Interests

Theatre/Acting
Speedcubing
Ultimate Frisbee
Derivatives Trading
Poker
Charlotte/Kansas City Sports
Theta Tau
Delta Epsilon Psi