Hi, I'm Javier Huang 👋

Team lead of team 33. Led the team to win 1st place with a $15,000 prize among 430 competitors across 90+ teams in the IMI Big Data & AI competition hosted by the Institute for Management & Innovation (IMI) UTM BIGDataAIHUB in partnership with Scotiabank. Built a robust and interpretable machine learning based Anti-Money Laundering (AML) detection system. Analyzed largely unlabelled financial datasets, built several machine learning models to detect bad actors, and built a knowledge library with LLM enhanced explanations to interpret money laundering cases. Developed a web application to integrate the components together in a way that is user-friendly and effective for AML investigators to use in real-world scenarios.

Built Clover, a portable AI-powered pre-flight diagnostic system for D. Vision Aerials FPV drones. Clover replaces manual motor spin checks with automated go/no-go assessments, improving consistency in pre-flight decisions and reducing operational risk in dense urban environments. The system evaluates drone health by combining acoustic motor analysis with onboard flight controller blackbox telemetry. It compares expected motor behavior against observed performance to detect and localize faults prior to flight. Clover runs on an edge platform (Raspberry Pi), producing real-time, time-stamped diagnostic outputs with full audit logs, and syncing results to a Supabase cloud backend. Data is accessible remotely through a Flutter mobile app and a Next.js web dashboard for monitoring and review. For acoustic fault detection, it uses a multitask 1D CNN-ResNet architecture based on sound classification research. The deep learning model identifies fault types (motor, propeller) and infers flight direction. Audio inputs are preprocessed and mapped to the format of the training dataset for compatibility. The model was trained for 100 epochs on 324k audio samples, reaching 97.4% accuracy and F1 score on the reference dataset. In field testing, Clover achieved 81% accuracy/F1 on acoustic data and 87% on blackbox telemetry-based diagnostics.

Submitted to NeurIPS 2026 under the name "A Unified Framework for Scene Reliability Estimation and Classification in Autonomous Vehicle Perception". Product of the work under the SceneClarity UTMIST project that I led. Autonomous driving perception degrades under adverse conditions such as fog, rain, snow, and glare, where failures often co-occur and are difficult to diagnose at the system level. We introduce a modular framework for estimating scene-level reliability that aggregates perception outputs and environmental signals into a global reliability score, along with attribution to likely degradation factors. Unlike per-prediction uncertainty methods, the proposed approach represents reliability as a decomposition over semantically interpretable components at the scene level. The architecture separates perception, environmental inference, and aggregation modules through a fixed interface, allowing components to be replaced without redesigning the aggregation logic. We implement the framework in a real-time system that produces structured outputs and visualizations to support failure analysis, safety monitoring, and debugging.