Sandeep Poudel

PhD Candidate · Civil & Environmental Engineering · Cornell University

My work sits at the intersection of AI, hydrology, and climate risk. I develop physical, machine-learning, and physics-informed ML models to study environmental systems, quantify risk, and improve predictions.

View Research
Sandeep Poudel

About Me

Hi! I'm Sandeep, a PhD student in Civil and Environmental Engineering at Cornell University and a Graduate Research Assistant with the Steinschneider Research Group.

I grew up in Nepal and came to the United States for graduate school. My work sits at the intersection of AI, statistics, and hydrology. I study water, environmental, and climate systems and aim to help improve their resilience in a changing climate.

When I'm not working with data and models, I'm usually somewhere out in nature, reading a sci-fi novel, watching a good show, or playing a few games of chess.

Education

2024 – Present

PhD, Civil and Environmental Engineering

Cornell University · Ithaca, NY

2022 – 2024

MS, Water Resources

University of Connecticut · Storrs, CT

2014 – 2019

BS, Civil Engineering

Institute of Engineering, Pulchowk · Kathmandu, Nepal

Experience

Jun 2024 – Present

Graduate Research Assistant

Steinschneider Research Group · Cornell University · Ithaca, NY

Aug 2022 – Jun 2024

Graduate Research Assistant

Knighton Ecohydrology Lab · University of Connecticut · Storrs, CT

Jan 2020 – Jul 2022

Civil Engineer — Hydraulic & Hydrologic

Hydro-Consult Engineering · Nepal

Apr 2019 – Aug 2019

Data Specialist

Cloud Factory · Nepal

Research Projects

Browse my research projects on GitHub.

Under Review

Physics-Based Machine Learning in Hydrology

Integrating neural networks with process-based hydrological models in a end-to-end gradient-based framework to improve out-of-sample prediction and fidelity under climate change.

In Preparation

Attention-Based LSTM Models for Streamflow Prediction

Evaluating attention-enhanced LSTM networks against standard architectures to improve accuracy in ungauged basin prediction.

Under Review Journal of Hydrologic Engineering

Bayesian Hierarchical Modeling for Flood-Change Projection Uncertainty

A Bayesian hierarchical framework for regionally pooling flood-change projections from deep learning and hybrid models, reducing uncertainty for water infrastructure planning.

Published Journal of Hydrology

Uncertainty in Design Flood Changes Across Hydrological Model Types

Investigating uncertainty in climate-driven design flood projections across process-based, deep learning, and hybrid hydrological models under varying levels of precipitation uncertainty.

Published Nature Comm. Earth & Environment

US Coastal Housing Market and the National Flood Insurance Program

Investigating the impact of future climate risks to US housing market and National Flood Insurance Program.

Published Environmental Research Letters

Flood Insurance, Risk Perception, and Coastal Housing Dynamics

Analyzing historical housing data from NY/NJ/CT, finding that NFIP participation, risk perception, and flood memory are key regional drivers of home values following Hurricane Sandy.

Recent Publications

Full list of publications on Google Scholar.

Loading publications…

Press & Media

Get in Touch

I'm always happy to connect about research, collaboration, or just a conversation.