About Me
Hi, I’m Torin. I’m a computer science graduate student specializing in machine learning and intelligent systems. I build high-performance desktop utilities, software tools, libraries, and ML models. My current focus is on video processing and optimization pipeline projects. When I’m not programming, you can usually find me training Brazilian Jiu Jitsu or gaming.
Professional Projects
HDR to SDR
Visit Site →Desktop GUI application with over 2,500 downloads for converting HDR video to SDR using FFmpeg. Features a live side-by-side frame preview, multiple tonemappers (Reinhard, Mobius, Hable), and static or dynamic conversion modes. The Pro tier adds GPU-accelerated tonemapping via libplacebo (Vulkan), hardware encoding (NVENC / AMF / QSV), batch queue conversion, and a node-locked licensing system backed by hardware fingerprinting and HMAC-signed tokens.
AI Code Bot
GitHub →Open-source AI coding assistant. Contributed as a software engineer by integrating Ollama as a model provider, enabling offline coding assistance with any locally hosted LLM (Llama 3, Mistral, etc.). Abstracted the model interface for dynamic configuration and expanded the test suite to validate Ollama API responses.
Sankaku Analytics Closed Source
ML-powered retail analytics platform for CPG companies. Ingests point-of-sale data from Nielsen, SPINS, IRI, and Whole Foods, then clusters SKU portfolios via K-Means, visualizes commercial DNA through radar and PCA charts, and surfaces prescriptive trade-spend and distribution recommendations through a rules-based strategy engine.
NASA L'SPACE PDR
Download PDF →250-page Preliminary Design Review delivered to NASA as part of the L'SPACE Mission Concept Academy. Covers mission architecture, CDH subsystem design, trade studies, and a revised scope under a simulated 22% budget cut. Co-led as Deputy Project Manager and CDH Systems Lead.
Resume
Skills
Python, C/C++, PyTorch, scikit-learn, NumPy, pandas, Machine Learning, Statistics, Quantitative Analysis, NLP, Scientific Computing, Git, Linux, REST APIs, FFmpeg, Test-Driven Development, Technical Documentation
Education
Projects
- Engineered a high-performance Python/FFmpeg utility for GPU-accelerated video processing and mathematical tone mapping.
- Designed a thread-safe, asynchronous rendering pipeline with real-time tracking, machine fingerprinting, and Azure code signing.
- Used Test-Driven Development with unittest.mock to maintain over 90% test coverage across core processing paths.
- Developed a GPU-accelerated processing architecture, increasing encoding throughput by 400% via hardware-specific optimizations for Intel, AMD, and NVIDIA GPUs.
- Built an automated real-time statistical analysis system in Python for ingesting, scoring, and filtering high-frequency probability signals.
- Implemented a historical variance filtering model to analyze probability distributions and filter out non-systematic noise signals.
- Developed a multithreaded polling architecture with variance estimation, slippage estimation, and consensus weighting for signal confidence scoring.
- Developed an Attention UNet model using satellite remote sensing data to map underserved regions in Sub-Saharan Africa.
- Achieved 75% segmentation accuracy and used explainable AI techniques (XAI) to interpret spatial feature representations.
- Received the Future Prospects and Recognition Award for adapting a medical imaging model to satellite data.
Experience
- Co-led a 12-person team through a NASA mission concept, delivering a 240-page PDR and revising the mission scope to fit a simulated 22% budget cut.
- Defined CDH subsystem requirements, including variable data transmission rates (30s Science / 1s Telemetry) for critical mission phases.
- Conducted trade studies for avionics and mechanical components (IMU, Motors), selecting hardware based on thermal operating ranges (−193°C to 111°C) and power budgets.
- Developed the MVP for a high-performance data analytics platform, focusing on scalable backend architecture and efficient data processing.
- Implemented and optimized K-Means clustering algorithms for automated data segmentation and pattern discovery.
- Architected the core system from the ground up, making critical decisions on tech stack and data integrity to support rapid prototyping and iteration.
- Translated complex business requirements into a technical roadmap, balancing feature velocity with system stability.
Personal Projects
Open Source
OpenTurbo
GitHub →Experimental CUDA prototype for 3-bit KV-cache compression in LLM inference. Implements a TurboQuant-style FWHT encoder with QJL residual sign correction, GQA-aware scoring, and a downstream bridge targeting llama.cpp integration.
ShrinkWrap
GitHub →CLI tool that compresses AI agent instruction files (CLAUDE.md, .cursorrules, system prompts) into a token-optimized format. Keeps security rules and architecture constraints byte-identical via SHA-256 checksums while aggressively deduplicating volatile status and todo sections.
Quantitative Analysis Betting Bot
GitHub →24/7 automated trading bot for the Polymarket prediction platform. Implements a Smart Copy system that analyzes historical trader performance and filters luck-based variance to identify consistently profitable whales. Built on a multithreaded polling architecture with slippage calculation and consensus weighting. Simulated trades only; real execution is not implemented. Project is deprecated.
Heart Disease Prediction
GitHub →Binary classification project predicting heart disease presence from clinical features. Compares Logistic Regression and Random Forest on the Kaggle heart disease dataset, with full EDA, feature importance analysis, and ROC curve evaluation. Random Forest achieved 100% test accuracy on this corpus.
Closed Source
Discord YouTube Bot
Self-hosted Discord music bot with YouTube playback, queue management, and playlist support. Uses yt-dlp with browser cookie fallback for age-restricted content and backs off automatically when YouTube rate-limits a session.
Electricity Access Mapping
Attention UNet trained on IEEE GRSS 2021 satellite imagery to identify underserved settlements in Sub-Saharan Africa lacking electricity access. Achieved 75% accuracy with GradCAM-based explainability. Received the Future Prospects and Recognition Award for adapting a medical imaging architecture to satellite data.