Puja Das

Research Scientist (Weather and Climate Risks)

PhD in Interdisciplinary Engineering specializing in extreme weather modeling and AI-driven solutions for precipitation forecasting and flood risk assessment.

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About Me

Puja Das

I am a data science expert in the field of climate and weather risks with a PhD in Interdisciplinary Engineering from Northeastern University. My research focuses on extreme weather modeling and developing AI-driven solutions for precipitation forecasting and flood risk assessment.

Currently serving as a Postdoctoral Research Fellow at the Institute of Experiential AI, I have led multi-institutional projects involving NASA, national laboratories, and federal agencies.

With expertise spanning machine learning, remote sensing, earth system model data analysis, and hydrological engineering, I am passionate about leveraging cutting-edge technology to address climate adaptation challenges and build resilient communities.

5+
Years Experience
10+
Journal and Conference Publications
5+
Invited Presentations

Professional Experience

Feb 2025 - Present

Postdoctoral Research Fellow

Institute of Experiential AI, Northeastern University

Leading advanced research in AI applications for weather, climate and hydrologic forecasting.

Leading the WEAVE project science team and developing operational flood forecasting systems with TVA. Coordinating multi-institutional collaborations and translating research into practical applications.

Sep 2020 - Feb 2025

Project Lead & Research Assistant

Northeastern University

Led the NASA-funded RAIN project, coordinating with multiple institutions including NASA, ORNL, and TVA. Developed hybrid AI-physics models for precipitation nowcasting.

Jun 2022 - Aug 2022

Data Science & Remote Sensing Intern

Capella Space Corp.

Led flood depth estimation project using SAR imagery and high-resolution topography data for disaster response applications.

May 2021 - Aug 2021

Machine Learning Intern

NASA Ames Research Center

Developed uncertainty-aware ML algorithms for quantitative precipitation estimation from geostationary satellites.

Technical Skills

Programming

  • Python
  • MATLAB
  • R
  • SQL
  • VBA

Machine Learning & AI

  • Supervised Learning
  • Time Series Forecasting
  • Computer Vision

Geospatial Analysis

  • ArcGIS Pro
  • QGIS
  • Google Earth Engine

Climate Modeling

  • Earth System Model Data Analysis
  • Statistical and Dynamical Downscaling

Watershed Modeling

  • HEC-RAS
  • HEC-HMS
  • SWMM

Data & Computing

  • NetCDF/HDF/GRIB Data Handling
  • High-Performance Cloud Computing

Research Projects

RAIN Project

Remote-sensing data driven Artificial Intelligence for precipitation-Nowcasting. Developing hybrid physics-ML methodologies for intense orographic precipitation prediction in Appalachia.

Machine Learning Remote Sensing Climate Science

WEAVE Project

Weather Ensemble Analytics and Visualization Environment - Leading science team developing advanced ensemble weather analytics and visualization tools for enhanced decision-making in weather-sensitive operations.

Ensemble Analytics Visualization Decision Support

Urbanization Impact Study

Analyzing precipitation extreme statistics and design curves for hydraulic infrastructures, focusing on urban vs non-urban regions across CONUS.

Urban Planning Statistics Infrastructure

Next-Generation Climate Modeling

Systematic evaluation of CMIP6 vs CMIP5 models revealing that finer resolutions and comprehensive physical processes improve runoff projections across 30 major global watersheds.

Climate Modeling Hydrology Data Analysis

SAR Flood Mapping

Synthetic Aperture Radar based flood depth estimation and damage assessment using satellite imagery and digital elevation models.

SAR Imaging Flood Mapping Disaster Response

Selected Publications

Hybrid Physics-AI Outperforms Numerical Weather Prediction for Extreme Precipitation Nowcasting
npj Climate and Atmospheric Science 7, no. 1 (2024): 282
Das, P., Posch, A., Barber, N., Hicks, M., Vandal, T., et al.
Finer Resolutions and Targeted Process Representations in Earth Systems Models Improve Hydrologic Projections
npj Climate and Atmospheric Science, 8, 247 (2025)
Das, P., Ganguly, A. R.
Floods, Facts, and Fictions: Numbers and Narratives Behind Bangladesh's 2024 Regional Floods
Preprint at Earth arXiv, 2025 (Under Review)
Das, P., Ganguly, A., Rabb, N., Smith, K., Islam, S.
Non-linear dynamical approaches for multi-sector climate resilience under irreducible uncertainty
npj Climate and Atmospheric Science 8, no. 1 (2025): 329
Mawalagedara, R., Ray, A., Das, P., Watson, J., Pal, A., Duffy, K., Bhatia, U., Aldrich, D., Ganguly, A.
Google Scholar ORCID

Teaching & Mentorship

University-Level Instruction

Northeastern University | 2020-2024

Served as a teaching assistant and delivered guest lectures as well as contributed to curriculum development. Courses include Climate Science Engineering Adaptation and Policy, Time Series and Geospatial Data Sciences, Probability and Engineering Economy for Civil Engineering and Critical Infrastructure Resilience.

Guest Lectures Curriculum Development Tutorial Design

Outstanding PhD Student Award for Teaching

Northeastern University | 2023

Recognized for exceptional contributions to teaching and academic support, including conducting interactive tutorials, providing one-on-one student guidance, and fostering collaborative learning environments.

Award Winner Student Mentoring Academic Excellence

Study Abroad Program Coordinator

Dialogue of Civilizations | 2023-2024

Coordinated international study programs in India and Nepal, designing orientation sessions, managing logistics, and facilitating cultural immersion activities for undergraduate students.

International Programs Cultural Immersion Program Management

Student Mentorship & Outreach

Multi-level Mentoring | 2021-2024

Mentored PhD students, undergraduates, and high school students in machine learning, climate modeling, and research methodology. Conducted specialized tutorials for students at Tufts University.

PhD Mentoring K-12 Outreach Research Training

Professional Training & Workshops

Various Institutions | 2023-2024

Assisted in developing AI for Science course materials at Northwestern University and conducted Earth System Model data analysis tutorials, enhancing understanding of complex climate science concepts.

Workshop Leader Professional Development Technical Training

YouTube Tutorials

Earth System Model Data Visualizationa and Analysis Tutorial

Learn how to analyze climate data using Python and various data science techniques.

Watch on YouTube

Machine Learning for Precipitation Prediction

Explore machine learning applications in weather forecasting and climate modeling.

Watch on YouTube

GIS Story Maps & Dashboards

Rising Sea Level, Boston Underwater

Understanding different sea level rise scenarios in Boston's neighborhoods.

ArcGIS StoryMap 2020
View Story Map

Hydropower Plants in US

Information about power generation along with temperature and precipitation in 2003-2020.

Interactive Dashboard 2021
View Dashboard

In the News

Media coverage of my research and achievements

NEW

Drying rivers could cause water shortages for 850 million people

Research led by Northeastern University scientists using advanced climate models predicts that 40% of major rivers will experience reduced runoff by 2100, affecting nearly a billion people worldwide - significantly more than previously estimated.

Earth.com 2025
Read Article
NEW

High-Level Meeting on the Midterm Review of the Sendai Framework for Disaster Risk Reduction

Comprehensive coverage of the UN's midterm review of the Sendai Framework, assessing global progress in disaster risk reduction and establishing new initiatives for enhanced resilience and risk-informed sustainable development.

UNDRR May 17, 2023
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With the help of Northeastern, Tennessee Valley Authority experiments with a new forecast model to better predict extreme rainfalls

In collaboration with a Northeastern researcher, the Tennessee Valley Authority this summer plans to test an AI-generated weather forecasting model to see if it will do a better job of predicting extreme rainfalls than traditional models.

Northeastern University News July 23, 2024
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The short-term rain forecast system is broken. Can AI do a better job of predicting deadly floods?

Northeastern University researchers develop AI-powered flood prediction systems to help communities prepare for extreme weather events.

Northeastern University News December 4, 2023
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A Climate Dialogue To Remember: South Asia and the 2023 Monsoons

A Dialogue of Civilizations course led by CEE Professor Auroop Ganguly traveled to India during the summer of 2023 to study climate science and engineering, adaptation and policy, data science and artificial intelligence, and cultural immersion.

Northeastern University News October 20, 2023
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Can AI help reduce the risk of climate change disasters?

Research on using advanced modeling and AI techniques to reduce the impact of climate-related disasters and improve community resilience.

Northeastern University News May 25, 2023
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Get In Touch

Let's collaborate on risk assessment, forecasting, and AI applications.

Email pujadas251@gmail.com
Phone +1 (857) 340-8425
LinkedIn Connect with me
Location Malden, MA