collaborators

9 papers

cs.LG2026

Multilevel Graph Wavelet Compressed Sensing with Scale-Aware Neural Recovery

Amirhossein Nouranizadeh, Sarang Rajendra Patil, Alan John Varghese +3

Scientific machine learning methods such as neural operators and physics-informed neural networks have advanced engineering applications and inverse problems, but their training ty…

cs.AI2026

HyperGuide: Hyperbolic Guidance for Efficient Multi-Step Reasoning in Large Language Models

Yuyu Liu, Haotian Xu, Yanan He +3

Multi-step reasoning remains a central challenge for large language models: single-pass generation is efficient but lacks accuracy; tree-search methods explore multiple paths but a…

cs.AI2026

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering

Yuyu Liu, Sarang Rajendra Patil, Mengjia Xu +1

Electronic health record (EHR) question answering is often handled by LLM-based pipelines that are costly to deploy and do not explicitly leverage the hierarchical structure of cli…

astro-ph.SR2026

Prediction of Magnetic Flux Evolution During Solar Active Region Emergence using Long Short-Term Memory Networks

Eren Dogan, Spiridon Kasapis, Sarang Patil +5

Solar active regions (ARs) are the primary drivers of space weather events, making their early prediction crucial for operational forecasting systems. We develop machine learning m…

astro-ph.SR2026

SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions

Spiridon Kasapis, Eren Dogan, Irina N. Kitiashvili +6

The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore…

astro-ph.SR2026

Forecasting Continuum Intensity for Solar Active Region Emergence Prediction using Transformers

Jonas Tirona, Sarang Patil, Spiridon Kasapis +5

Early and accurate prediction of solar active region (AR) emergence is crucial for space weather forecasting. Building on established Long Short-Term Memory (LSTM) based approaches…