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M.S. in Hydrology

• Cumulative GPA: 4.0
• Thesis: A Hybrid Differentiable Land Surface Model for Improved Land Surface Flux Predictions
• Advisor: Andrew Bennett
• Coursework: HWRS 642 Analysis of Hydrologic Systems, APPL 527 Physics-informed machine learning, HWRS 640 Computational Methods for Data Driven Earth Sciences

B.S. in Civil Engineering

• Average mark: 75.9% (top 5% of 1000+ at university level)
• Best in department: Senior year (86.0% mark)
• Senior thesis: Assessing Climate Change Impacts on Crop Yield: A Case of Tulsipur, Dang

Graduate Research Assistant

• Supervisor: Prof. Andrew Bennett
• Developed a physics-AI hybrid model for land surface fluxes
• Embedded feedforward neural networks for latent and sensible heat flux process equations
• Implemented in an end-to-end differentiable framework in JAX Python

Post-Baccalaureate Research Assistant

• Supervisor: Prof. Dr. Vishnu Prasad Pandey
• Developed HimSim.jl, a process-based hydrological model for Himalayan catchments (snow and glacier focus)
• Set-up model ODEs using ModelingToolkit.jl in Julia

Cereal yield and water requirements in response to irrigation and soil fertility management in a changing climate: a case of Tulsipur, Western Nepal

• Authors: Md Zuber*, Nabin Kalauni*, Dr. Nirman Shrestha, Prof. Vishnu P Pandey (*Equal contributors)
• DOI: 10.2166/wcc.2025.543
• Studied climate change impacts on crop yield and irrigation water requirements of rice and wheat
• Statistically downscaled CMIP6 climate projections in R using quantile mapping

Xarray for Deep Learning Cookbook

• Authors: Keenan Ganz, Nabin Kalauni, Project Pythia Community
• Authored an open-source interactive tutorial on integrating Xarray with PyTorch for Earth System Science data pipelines

A Guide to Nepal Physics Olympiad

• Authors: Laxman Bista, Nabin Kalauni, Sandesh Parajuli

Hybrid Land-Atmosphere Dynamics Model

• Physics-AI Hybrid Modeling
• Developed a physics-AI hybrid model for land surface fluxes
• Embedded feedforward neural networks to learn process equations for latent and sensible heat fluxes
• Implemented in an end-to-end differentiable framework in JAX Python

Graph Neural Networks for Meteorological Data

• Deep Learning for Spatial Data
• Developed a novel graph neural network based method for interpolating station-based point meteorological data

Himalayan Hydrology Simulation Model (HimSim.jl)

• Modeling and Package Development
• Developed a process-based hydrological model with special focus on snow and glacier processes
• Set-up model ODEs using ModelingToolkit.jl in the high-performance Julia language

AGU Annual Meeting 2025

• A Hybrid Differentiable Land Surface Model for Improved Land-Atmosphere Flux Predictions (Oral Talk)

El Dia del Agua y la Atmosfera

• Co-Chair

International Physics Olympiad (IPhO)

• Student Team Leader

• Travel Grant & Selected Participant, Project Pythia Cook-off, National Center for Atmospheric Research (NCAR), Boulder, CO
• Travel Grant & Selected Participant, US-RSE INTERSECT Research Software Engineering Bootcamp, Princeton University

• Best in Department: Senior Year, Highest grade in senior year / 1000+ students, Dept. of Civil Engg, IOE

• IOE Four-Year Merit-Based Engineering Study Scholarship, Rank 100/15000+, Pulchowk Campus, IOE
• Nepal Physics Olympiad (NePhO) Winner, Rank 1 / 10000+, Nepal Physical Society