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%

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^VIX13.2-3.1%

^TNX4.12%-5.4bps

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ETH$3,400+0.7%

The Technical & Quantitative Side

My foundation is built on mathematical rigor and a love for "thinking in algorithms". This led me to the world of Quantitative Finance and Operations Research, where I apply computer science to high-stakes decision-making:

Quant Research

I have spearheaded projects in Order Execution and Optimization and am currently developing a Python library for non-linearity testing.

Extra Curricular Related Stuff

I placed 15th in the U.S. in the IMC Prosperity 3 trading competition and recently took the CFA Level 1.

Human-Computer Interaction (HCI) & Research Leadership

While I enjoy technical abstraction, I am equally committed to the human element. I have been active in research since my freshman year, evolving from a mentee to a mentor who has trained others in research methodologies.

Pedagogical Impact

At the REACH Lab, I conducted an NSF-funded systematic review of rural CS education. Later, as a MAOP Summer Research Intern, I utilized LLMs to analyze 24,000 words of CS curricula from institutions like MIT and Berkeley.

Scholarly Contribution

My research into making CS education more culturally relevant and less abstract resulted in a poster presentation that will occur at SIGCSE 2026 soon.

Future Horizons

My ultimate goal is to pursue a Ph.D. focusing on the synthesis of quantitative modeling and human-machine collaboration. Whether I am optimizing a financial portfolio or designing an educational LLM, I aim to ensure that the "black boxes" of modern technology are redesigned with a deep, empathetic understanding of the global users they serve.

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).