Quant • CS • Research

Varun Budati

I like the idea of building things that blend computer science, math, and finance.

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Email LinkedIn GitHub Blacksburg, VA

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^GSPC5,200+0.42%

^IXIC17,200+0.58%

^VIX13.2-3.1%

^TNX4.12%-5.4bps

BTC$97,500+1.2%

ETH$3,400+0.7%

Technical & Quantitative Work

I enjoy using math and data to make better decisions. This interest led me to Quantitative Finance and Operations Research.

Quant Research

I've worked on projects involving Order Execution and Optimization. I'm also building a Python library for non-linearity testing.

HCI & Research

Research has been a big part of my college experience. I started as a mentee and now mentor other students.

Education Research

At the REACH Lab, I worked on an NSF-funded review of rural CS education. During my MAOP internship, I used LLMs to analyze CS curricula from schools like MIT and Berkeley.

Presentations

I researched how to make CS education more relevant to different cultures. I'll be presenting a poster on this work at SIGCSE 2026.

Future Horizons

I plan to pursue a Ph.D. combining quantitative modeling and human-machine interaction. I want to make complex technology more transparent and useful for everyone.

ShadeFinder (Urban Shadow Mapper)

Streamlit geospatial app that projects building shadows in real time, blending solar physics, OSM data, and live climate feeds to surface cooler walking corridors in Kuwait.

PythonStreamlitGeospatialMapbox

GitHub

Sports Betting Algorithm & Analytics

Analytics system to surface profitable betting opportunities with statistical edges and ML models.

PythonData AnalysisStreamlit

View project

Optimal Trading Execution (RL + Almgren-Chriss)

Implements Almgren-Chriss with a reinforcement learning agent to minimize market impact.

PythonReinforcement LearningQuant

View project

MACD Strategy Visualizer

Interactive MACD visualizer and backtester for exploring technical strategy performance.

PythonStreamlitTechnical Analysis

View project

Video Poker

Classic video poker with a clean interface, probability focus, and quick iteration loops.

PythonPygameGame

View project

Black-Scholes (BSM) Options Explorer

Black-Scholes option pricing explorer with interactive inputs and deltas

PythonQuantStreamlit

View project

Treasurer & Market Research Analyst — FinTech Club

Aug 2024 — Present

Promoted to Treasurer (Jun 2025)

Manage the club's budget and all financial operations to fund initiatives and workshops for a community of 100+ members.
Organize and host speaker events with industry professionals from leading finance and technology firms to create career development and networking opportunities.
Led a project under Dr. Daniel Rodriguez replicating Evans & Archer (1968) via Python simulation, analyzing portfolio diversification and risk reduction.
Utilized Pandas, NumPy, and SciPy on historical stock data to model risk (std dev log returns) vs. portfolio size, quantifying diversification benefits.
Demonstrated and presented findings confirming that most unsystematic risk is mitigated with 10-20 assets, aligning with the foundational study.

Quantitative Researcher

Dataism Laboratory for Quantitative Finance (DLQF) · Oct 2024 — Present

Researching optimal order execution by analyzing the market microstructure of Bitcoin trade data to develop and implement advanced trading strategies.
Constructed benchmark execution algorithms in Python, including VWAP and TWAP, to analyze the market impact and transaction costs of trading Bitcoin.
Engineered a reinforcement learning and neural network architecture (PPO, DDQN) to create an adaptive agent that optimizes trade execution strategies in real-time.
Modeling quantitative performance using statistical methods and Python (NumPy, Pandas, SciPy) to analyze trade execution efficiency and market dynamics.

Summer Researcher, Virginia Tech MAOP Program

May 2025 — Jul 2025

Conducted a mixed-methods research study on pedagogical patterns in introductory Computer Science (CS1) to identify opportunities for improving student retention and engagement.
Analyzed a corpus of examples from CS1 textbooks of top-tier universities (MIT, UC Berkeley, CalTech) to understand current teaching paradigms.
Developed a quantitative framework with eight distinct categories through an open coding process to classify the thematic focus of programming examples.
Utilized computational text analysis software (LIWC-22) and large language models to quantitatively assess the linguistic and contextual nature of course materials.
Synthesized findings to reveal a high prevalence (57%) of abstract mathematical examples, providing data-driven recommendations for incorporating more culturally relevant and real-world applications in curricula.
Co-authored and designed a formal research poster, effectively communicating the project's methodology, results, and implications to an academic audience.

Research Mentor — REACH Lab

Jan 2025 — Jun 2025

Guided undergraduate researchers within the Reimagining Equity and Accessibility in Computing for Humanity (REACH) Lab at Virginia Tech.
Mentored peers on literature review best practices, database search strategies, and synthesizing findings for equitable computing research.

Undergraduate Research Assistant — REACH Lab

Mar 2024 — Jun 2025 · Blacksburg, Virginia · Hybrid

Conducted an extensive literature review under the leadership of Dr. Ihudiya Finda Williams on Rural Computer Science Education.
Synthesized over 80 articles to support a $500,000 National Science Foundation grant proposal.
Trained six research assistants on database search methodologies, prompt design for scholarly queries, and rigorous literature review techniques.

IMC Trading Competition — Top 15 US (0.005%)

Apr 2025

Placed 15th in the US out of 12,600 teams in IMC's Prosperity 3 trading competition, designing strategies for multi-asset markets.
Optimized trade execution under latency constraints by engineering fair value estimators (VWAP, EMA, stochastic modeling) and implementing dynamic bid shading.
Designed signal-driven market-making strategies by analyzing mean-reversion patterns and synthetic mispricing via exploratory data analysis (rolling z-scores, spread compression, correlation clustering).