Research
Our research focuses on uncertainty quantification in complex systems that arise in computational science and engineering, with an emphasis on deep generative modeling. By integrating deep learning and scientific computing, the LUQI group develops scalable sampling techniques for high-dimensional distributions. This work enables Bayesian inference in large-scale PDE-based inverse problems (e.g., seismic and medical imaging) and provides a principled framework for building reliable, uncertainty-aware AI models for real-world applications.
Team
- Ali Siahkoohi, Assistant Professor, CS department
- Banafsheh Adami, PhD student, CS department, co-advised with Alain Kassab (Fall 2026–present)
- Anirudh Thatipelli, PhD student, CS department (Summer 2025–present)
Former members
- Davide Sabeddu, PhD student, ECE department (Fall 2025–Summer 2026)
Publications
For a full list of publications, see here.