Building the future of artificial intelligence at Kolosal AI. Researcher at UBC, where I teach machines to decode biology — from circular RNA to tumor suppressor genes — and occasionally, the stock market.
Open-source tools, platforms, and applications
Open-source platform for running large language models locally — private, fast, and fully under your control. Custom training and production-ready inference at scale.
Production-ready inference with TensorRT acceleration. Handles batching, model versioning, and GPU memory management out of the box.
End-to-end AutoML with Gradio UI — from hyperparameter tuning with Optuna to experiment tracking with MLflow, all in one platform.
Industrial-grade Self-Organizing Map and clustering library for Rust — published to crates.io with CPU, CUDA, and Metal backends. Supports SOM classification, KMeans variants, and automatic model selection via silhouette scoring.
Real-time pose estimation with MediaPipe for form correction, rep counting, and personalized workout recommendations.
High-performance nuclear chain reaction simulator in Rust — GPU-accelerated particle physics with real-time 3D visualization, 150+ isotope library from ENDF/B-VIII.0 nuclear data.
Desktop app for evaluating stocks through technical and fundamental analysis, powered by on-device ML inference and a local Qwen2.5 LLM — fully offline, no cloud required.
Community-driven platform connecting learners and researchers through structured courses and peer mentorship programs.
Building mental health awareness across Indonesia through accessible psychology content and peer-to-peer support networks.
First-author research in ML, computational biology, and quantitative finance
Undergraduate Researcher
Building deep learning pipelines for circular RNA classification and genomic sequence analysis.
Co-Founder
Architecting open-source tools for local LLM deployment, inference optimization, and MLOps automation.
First Author
"Deep Learning Algorithm with Gaussian Blur Data Pre-processing in Circular RNA Classification"
I chase problems where data hides something meaningful — a motif buried in RNA sequences, a leading signal in financial time series, or a chance to make ML infrastructure less painful.
Currently an undergraduate researcher at UBC building deep learning systems for genomic data, and co-founding Kolosal AI to make running LLMs locally actually simple. My first-author publication on circular RNA classification is live in URNCST Journal.
When I'm not debugging loss curves, I'm reading about macroeconomics or exploring Vancouver's trails.