Sankalp Pandey

Hello, I'm Sankalp Pandey

I am a final-year honors undergraduate at the University of Arkansas pursuing B.S. degrees in Computer Science and Computer Engineering. I work as a research assistant in the Quantum AI Lab and the Computer Vision and Image Understanding Lab advised by Dr. Khoa Luu.

My research focuses on multimodal reasoning for scientific discovery, with applications in vision-brain modeling and quantum materials.

Publications

TIMBRE

TIMBRE: Time-aware Multimodal Vision-Brain Encoding

Xuan-Bac Nguyen, Sankalp Pandey, Suhyun Kim, Hojin Jang, Arabinda Kumar Choudhary, Khoa Luu

A time-aware multimodal framework that models temporal neural dynamics to improve vision-brain encoding.

Under review

phi-Adapt

φ-Adapt: A Physics-Informed Adaptation Learning Approach to 2D Quantum Material Discovery

Hoang-Quan Nguyen, Xuan-Bac Nguyen, Sankalp Pandey, Tim Faltermeier, Nicholas Borys, Hugh Churchill, Khoa Luu

A physics-informed adaptation method for robust 2D quantum material characterization across imaging domains.

Under review

QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery

Xuan Bac Nguyen, Hoang-Quan Nguyen, Sankalp Pandey, Tim Faltermeier, Nicholas Borys, Hugh Churchill, Khoa Luu

A physics-aware instruction-tuning pipeline that improves multimodal reasoning for quantum material discovery.

Under review

CLIFF

CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification

Sankalp Pandey, Xuan-Bac Nguyen, Nicholas Borys, Hugh Churchill, Khoa Luu

A continual learning strategy for incremental flake-feature identification with reduced catastrophic forgetting.

NeurIPS AI for Accelerated Materials Discovery Workshop, 2025

QMoE

QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks

Hoang-Quan Nguyen, Xuan-Bac Nguyen, Sankalp Pandey, Samee U. Khan, Ilya Safro, Khoa Luu

A quantum mixture-of-experts architecture that routes inputs to specialized experts for scalable quantum neural networks.

IEEE QCE QCRL Workshop, 2025