Research Portfolio

About

Working at the intersection of research and implementation.

Anything that deals with Machine Learning and algorithms have always piqued my interest. Recently I've been analyzing polymarkets and developing trading strategies for cryptocurrency markets. I have a strong passion for research and am always looking for opportunities to learn and grow in the field of financial engineering and machine learning.

My research centers on reinforcement learning for sequential decision-making, optimal trade execution, and market microstructure, applying rigorous statistical and quantitative methods to real trading problems.

Research Interests

Reinforcement Learning for Sequential Decision-Making Optimal Execution & Market Microstructure Statistical & Quantitative Methods in Finance

About me

FocusML + Financial Engineering
SchoolVirginia Tech
DegreeB.S. Computer Science
MinorMathematics & Finance

Education

Academic background

Aug 2023 — May 2027

Virginia Tech

Bachelor of Science in Computer Science with a Minor in Mathematics and Finance

GPA: 3.67 / 4.0

Jan 2010 — May 2023

IES Bhavans, Kuwait

Senior School Certificate Examination in Computer Science

Secondary School Examination in Information Technology · GPA: 94.3/100

Experience

Research, leadership, and competition results

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.
Optimized trade execution under latency constraints by engineering fair value estimators and implementing dynamic bid shading.
Designed signal-driven market-making strategies by analyzing mean-reversion patterns and synthetic mispricing.

Quantitative Researcher - Dataism Lab for Quantitative Finance

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.

Market Research Analyst — FinTech Club

Aug 2024 — Present

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 vs. portfolio size, quantifying diversification benefits.
Demonstrated and presented findings confirming that most unsystematic risk is mitigated with 10-20 assets.

Summer Researcher - Virginia Tech MAOP Program

May 2025 — Jul 2025

Conducted a mixed-methods research study on pedagogical patterns in introductory Computer Science to identify opportunities for improving student retention and engagement.
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 of abstract mathematical examples, providing data-driven recommendations for incorporating more culturally relevant and real-world applications in curricula.

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

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.

Leadership

Leadership roles

Co-President — FinTech Club

Jun 2025 — Present

Organize Hokies on Wall Street, a trip to New York City for students to network with finance professionals and Alumni from firms like Elequin Capital, Evercore, Citi, and more.
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.

Student Summer Ambassador — MAOP Program

Summer 2025

Represented the MAOP program and helped support student engagement throughout the program.
Gave multiple talks and speaker events to prospective students about the program and spoke about multiple research opportunities at Virginia Tech.

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.

Publications

Research publications

SIGCSE TS 2026 · 2026

Bridging the Code Gap: Understanding Pedagogical Patterns in CS1 Programming Courses

Naomi Chioma Udenze, Varun Budati, Ihudiya Finda Ogbonnaya-Ogburu

An exploratory pilot study analyzing examples and exercises used in CS1 programming courses at top universities, investigating pedagogical patterns and diversity in computer science education.

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Working Paper · 2026

Order Execution for Cryptocurrency using Mixture of Experts

Ali Habibnia, Alexander Ardaiz, Varun Budati

This project addresses optimal order execution, how to liquidate a large asset position while minimizing transaction costs, in cryptocurrency markets, which are more illiquid and volatile than traditional equities. We train a mixture-of-experts policy over specialist execution strategies using reinforcement learning (PPO, DDQN), benchmarking performance against VWAP and TWAP baselines on Bitcoin market microstructure data.

Projects

My work

Optimal Trading Execution System with Reinforcement Learning

Implementation of the Almgren-Chriss model for optimal trade execution, enhanced with a reinforcement learning agent to minimize market impact.

Python Reinforcement Learning Quantitative Finance

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Options Pricing Calculator & Graph Using the Black-Scholes Model

A tool for pricing European options using the Black-Scholes model, with visualizations for the Greeks.

Python Quantitative Finance Streamlit

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MACD Trading Strategy Visualizer

An interactive web application to visualize the MACD indicator and backtest trading strategies on historical stock data.

Python Streamlit Technical Analysis

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Sports Betting Algorithm & Analytics System

A sophisticated analytics system to identify profitable betting opportunities using statistical models and machine learning.

Python Data Analysis Streamlit

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ShadeFinder

An urban shadow mapping project that helps identify shaded walking paths and cooler routes using geospatial analysis.

Python Geospatial Streamlit

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SafeNight

SafeNight is a proactive safety ecosystem designed to prevent emergencies from escalating. By empowering users with real-time tools to monitor alcohol intake, stay tethered to a trusted community network, and access instant intervention, we are moving the needle from notification to prevention.

Hackathon Full Stack Collaboration

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