Research
Self-supervised representation learning for sensor networks, time-series forecasting, and robust systems.
Research Statement
My research focuses on self-supervised representation learning for real-world time-series and sensing data, particularly in domains where precursor detection is critical: environmental flood warning, glacial lake stability, and anomaly detection in distributed financial networks.
I approach modeling through a systems-oriented lens: designing ML pipelines that remain computationally efficient under dynamic workloads without assuming massive cluster availability.
A central pillar of my methodology is research reproducibility and falsifiability. I structure experimental workflows around pre-registered protocols and report both positive performance and negative failure bounds.
Core Areas & Interests
Active Research Threads
1. flood-sentinel: Temporal Encoders for Hydrological Sensing
Self-supervised representation learning on high-frequency USGS streamgage observations to isolate precursor signatures of flash floods.
[Project Details]2. BPFeat: Backpressure-Driven Elastic Feature Windows
Adaptive feature extraction mechanisms designed to regulate latency under variable data streaming rates in edge and server setups.
[Project Details]3. sentinel-gl: Remote Sensing for Glacial Lake Hazards
Masked autoencoding architectures applied to multispectral satellite time series for retrospective GLOF risk evaluation.
[Project Details]4. Topological Financial Network Anomaly Detection
Applying persistent homology computations over dynamic transaction graphs to uncover coordinated collusion structures.
[Project Details]