cv

Indepedent Graduate Researcher

Interplay Lab (ROC-HCI Lab), University of Rochester

Design agentic LLM framework to engage K-12 children in collaborative reasoning.

Research Assistant

URseismo, University of Rochester

Develop deep learning models to solve seismic problems & computational imaging problems.

Master's

University of Rochester

Computer Science

• Large Language Models
• End-to-End Deep Learning
• Machine Vision
• AR/VR Interactive Design
• Parallel & Distributed Systems
• Machine Learning
• Data Mining
• Deep Learning

Bachelor of Science

Hong Kong Polytechnic University

Computing

Deep Algorithm Unrolling for Seismic Migration

2025 IEEE Conference on Computational Imaging Using Synthetic Apertures

We propose the inverse Radon transform Network (iRADNet), a deep learning-based framework that unrolls the Iterative Shrinkage Thresholding Algorithm (ISTA) into a trainable neural network, enabling automatic learning of parameters for solving the inverse Radon transform. Empirical results demonstrate the effectiveness of iRADNet in reconstructing noisy seismic data, showing enhanced accuracy and computational efficiency. Our code and data are publicly available at https://github.com/URseismology/iRADNet.