Ananth Kalyanaraman — accepted AI-conference papers

2026

ForeSWE: Forecasting Snow-Water Equivalent with an Uncertainty-Aware Attention Model

Krishu K Thapa, Supriya Savalkar, Bhupinderjeet Singh, Trong Nghia Hoang, Kirti Rajagopalan, Ananth Kalyanaraman

AAAI 2026technical

Various complex water management decisions are made in snow-dominant watersheds with the knowledge of Snow-Water Equivalent (SWE)---a key measure widely used to estimate the water content of a snowpack. However, forecasting SWE is challenging because SWE is influenced by various factors including to

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2024

Attention-Based Models for Snow-Water Equivalent Prediction

Krishu K Thapa, Bhupinderjeet Singh, Supriya Savalkar, Alan Fern, Kirti Rajagopalan, Ananth Kalyanaraman

AAAI 2024technical

Snow Water-Equivalent (SWE)—the amount of water available if snowpack is melted—is a key decision variable used by water management agencies to make irrigation, flood control, power generation, and drought management decisions. SWE values vary spatiotemporally—affected by weather, topography, and ot…

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2024

Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach

Mohammed Amine Gharsallaoui, Bhupinderjeet Singh, Supriya Savalkar, Aryan Deshwal, Ananth Kalyanaraman, Kirti Rajagopalan +1

IJCAI 2024poster

Predicting the spatiotemporal variation in streamflow along with uncertainty quantification enables decision-making for sustainable management of scarce water resources. Process-based hydrological models (aka physics-based models) are based on physical laws, but use simplifying assumptions which can…

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2022

Scalable and Memory-Efficient Algorithms for Controlling Networked Epidemic Processes Using Multiplicative Weights Update Method

Prathyush Sambaturu, Marco Minutoli, Mahantesh Halappanavar, Ananth Kalyanaraman, Anil Vullikanti

IJCAI 2022poster

We study the problem of designing scalable algorithms to find effective intervention strategies for controlling stochastic epidemic processes on networks. This is a common problem arising in agent based models for epidemic spread. Previous approaches to this problem focus on either heuristics with…

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