Shiv Mehta

Data Science @ UCSD | Optimization, Geometry, Inference, and Interpretability.

Pre-undergrad work and writing lives here: archive.shivmehta.com

Recent History

Graviton Research Capital
Quantitative Research InternJun 2026 – Aug 2026
UCSD Halıcıoğlu Data Science Institute
Undergraduate Researcher2026 – Present
Studying feature representations and latent geometry across diffusion timesteps with Dr. Yusu Wang, and steering methods for more controllable, efficient diffusion models.
Astera Holdings
Quantitative Research InternJun 2025 – Aug 2025
Built BART/XGBoost ensembles for MLB outcomes and player props with time-split validation and online Bayesian fair-value updates; a Kelly-criterion strategy reached <0.2 Brier.
SEELab, UCSD CSE Department
Undergraduate Researcher2025 – 2026
Improved numerical reasoning in quantized LLMs via chain-of-thought over sensor time-series, and built a DPO-tuned multi-agent pipeline with a Bayesian-signal grader under Dr Tajana Rosing and Dr Xiaofan Yu.
Multi Commodity Exchange (MCX)
Machine Learning InternJun 2024 – Aug 2024
Kernel-PCA factor models on commodity futures (60% dimensionality reduction), served through a PyTorch inference pipeline on a low-latency C++ (Treelite) backend.
bundl.ai
Machine Learning InternJun 2023 – Aug 2023
Built a RAG pipeline for a Llama model with hybrid vector/graph retrieval (PyTorch, Neo4j) and cut inference time 40% via distributed fine-tuning and TensorRT.
University of California, San Diego
B.S. in Data Science (Machine Learning & AI)2023 – Jun 2027
Coursework spanning linear algebra, vector calculus, probability & statistics, signal processing, spectral methods, stochastic processes, data structures & algorithms, and graph theory.

Highlighted Work

Partition Geometry

May 2026 – Present

A framework comparing trees, ReLU networks, and polyhedra on shared labeled grids via alignments; proved a general profile lower bound that gives exact tree complexities.

GitHub ↗write-up →

Prediction Market Liquidity Provision (Market Making)

Dec 2025 – Present

Deployed an end-to-end market-making system for BTC prediction markets with $10k+ volume across 100+ markets. Modeled fill probabilities with survival analysis (random survival forest, Cox PH) using time-split CV. Estimated execution quality (markouts) with random forest (quantile regression), using Treelite for low-latency inference. Optimized quoting via EV maximization over spread/size/timing using a thin-plate spline surface.

write-up →

IMC Prosperity

Apr 2025 – Apr 2025

Top 6% (peak rank 22 globally) in IMC Prosperity 3: ETF arbitrage, delta hedging, and volatility modeling.

GitHub ↗write-up →

Spectra

Jan 2025 – Present

A command-line tool that profiles tensor artifacts (.npy, .npz) for sparsity, entropy, and spectral structure, then routes each to the best compression strategy: quantization, sparse COO storage, or truncated SVD.

GitHub ↗PyPI ↗