About Me
Mechanical Engineering and Applied Mathematics student integrating physical intuition with AI-driven experimentation and quantitative research.
Undergraduate researcher focused on scaling AI, data-driven materials discovery, and quantitative market modeling. I thrive in fast-paced, collaborative environments where I can pair rigorous analysis with practical engineering judgment.
My current work spans unsupervised deep learning for ferroelectric materials, algorithmic trading systems, and continuum modeling of market microstructure. I’m always looking for ways to apply computational tools to challenging physical and financial systems.
Professional Experience
Recent roles spanning AI-first research, quantitative trading, and computational mechanics.
Undergraduate Researcher
Carnegie Mellon University | Aug 2025 – Present
- Developing 4-layer deep embedded clustering autoencoders (DEC/IDEC) on vector pair correlation functions to uncover structural features in ferroelectric materials.
- Trained models on 1,000+ STEM-derived embeddings, benchmarking cluster quality across 5–20 clusters using unsupervised metrics.
Quantitative Trading Intern
Limex | Aug 2025 – Sep 2025
- Built statistical arbitrage, breadth scalping, and momentum breakout strategies across 40–50+ liquid equities using Python, pandas, NumPy, and asyncio.
- Engineered low-latency execution engines that scanned 300+ opportunities per minute, reducing execution latency ~60% and improving real-time PnL tracking accuracy by 35%.
REU Research Fellow
National Science Foundation | Jun 2025 – Aug 2025
- Applied backpropagation neural networks, k-nearest neighbors, and random forest regression to predict mechanical properties of CFRP/GFRP composites.
- Deployed a YOLOv5 CNN to classify failure modes from high-resolution fracture surface imagery.
Undergraduate Researcher
Swanson School of Engineering | Sep 2024 – Jul 2025
- Modeled nanoscale contact mechanics and adhesion using MATLAB and profilometry tools.
- Analyzed 100+ micro/nanoscale scans (10 nm – 100 μm) to extract quantitative adhesion measurements.
Projects
Blending computational methods with financial engineering and applied physics.
Market Microstructure Liquidity Dynamics
Independent Research | Dec 2025
- Modeled limit-order book liquidity as a conserved density using continuum transport formulations.
- Derived a 1-D advection–diffusion PDE with a Reynolds-like number for regime classification.
Quantitative Portfolio Optimization
Financial Engineering | Jun 2025
- Built a modern portfolio theory optimizer across 5 equities using historical price data and Monte Carlo simulations.
- Constructed an efficient frontier via constrained optimization on the covariance matrix, cutting volatility 44% and improving Sharpe ratio 46% over naive allocations.
Publication: Linear Regression Solvers
arXiv Preprint | May 2025
- Author of "Comparing the Moore-Penrose Pseudoinverse and Gradient Descent for Solving Linear Regression Problems: A Performance Analysis."
- Benchmarked computational efficiency and stability across synthetic and experimental datasets.
Education
BS in Mechanical Engineering & Applied Mathematics
University of Pittsburgh, Pittsburgh, PA | Expected Graduation: 2028
- Exploring the intersection of computational methods, AI, and applied physics.
- Active in engineering and research communities to bridge mechanical systems with advanced analytics.
Advanced Regents High School Diploma
Sleepy Hollow High School, Sleepy Hollow, NY
- SAT: 1470 (Math: 750, EBW: 720)
- 2x AP Scholar & National African American Recognition Award Recipient
Technical Skills
Core programming, numerical methods, and analytical tools for engineering and finance.
Programming Languages
- Python, C, MATLAB
- Java, JavaScript
- HTML/CSS
Tools & Methods
- NumPy, pandas, scikit-learn
- Numerical PDEs, optimization, clustering
- Applied linear algebra and data analysis
Contact
Open to innovative projects, research collaborations, and challenging opportunities at the intersection of engineering, AI, and finance.