Professional Overview
BiographyI am a Machine Learning Engineer with a strong foundation in environmental engineering and a focus on climate-resilient AI solutions. My recent research tackled a critical challenge in flood modeling: accelerating hydrodynamic simulations using deep learning while maintaining accuracy across unseen terrains and flood conditions. I designed and implemented multiple architectures (UNet, attention-based models, GANs, custom designed architectures) and built a closure model to scale patch-based predictions to large domains.My strength lies in my ability to combine environmental domain knowledge with advanced ML techniques, particularly in handling complex spatiotemporal data. I’ve worked with high-resolution terrain and hydrograph datasets from USGS and NOAA, built simulation databases using HEC-RAS, and optimized deep networks in PyTorch for generalization and speed.What sets me apart is my interdisciplinary expertise, hands-on experience with environmental data and simulations, and ability to take a project from data generation through model deployment. I thrive in research-intensive roles and am deeply motivated to apply ML to pressing global challenges such as climate adaptation, water resource management, and remote sensing analytics.
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
