Laeeq Aslam

I work on low-cost drone-based wind estimation and wind-field reconstruction. This research uses multirotor telemetry, reference wind measurements, and physics-informed learning to estimate ambient wind and recover local flow structure from sparse aerial observations.

The current focus is robust estimation under real flight conditions, Physics-Informed Neural Network reconstruction, cross-platform generalization, and deployable inference pipelines for field experiments and atmospheric sensing.

I design physics-informed sequence models for wind, load, and near-surface temperature forecasting, targeting grid reliability and urban thermal-stress early warning. This includes regime-adaptive focal losses, adaptive physics penalties, attention-equipped hybrid recurrent / convolutional temporal modules, and hardware-aware optimization for Jetson-class devices.

I led research end-to-end from problem formulation to publication, producing 6 first-author papers. The work aligns with a provincial Key R&D program on wind energy and grid optimization (Project 2020WK2007).

Built and scaled a footwear analytics pipeline handling 60+ concurrent video streams and ~50,000 daily inferences. Delivered production-grade market intelligence under real deployment and bandwidth constraints.

Led semantic segmentation and real-time detection pipelines. Tuned UNet variants with attention and focal loss to improve segmentation accuracy by ~3% over in-house baselines and optimized inference for deployment.

Taught embedded systems, programming, and discrete-time signal processing. Built an Instrumentation & Measurement Lab for sensor-driven data acquisition in MATLAB. Supervised applied student projects in energy management, safety telemetry, and secure voting.

Investigated GAN-based medical diagnostics for breast cancer, with emphasis on data augmentation and robustness.