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I am fourth year Ph.D. student at the ECE department at Rice University, advised by Santiago Segarra. My research develops algorithms to solve inverse problems, mainly focused on 5G wireless networks and image restoration. In particular, I have worked on designing fast and scalable algorithms that incorporate any pre-trained generative models as prior distributions. Also, I have been researching graphon estimation using implicit neural representations, zero-shot stitching, algorithm unfolding, and its application to Particle filters.

Sep 2024 New preprints coming:
- Scalable Implicit Graphon Learning
- Topology Preserving Regularization for Independent Training of Inter-operable Models
May 2024 Started internship in InterDigital, Los Altos, CA
May 2024 New preprint Repulsive Latent Score Distillation for Solving Inverse Problems
Jan 2024 Paper Solving linear inverse problems with Higher-order Annealed Langevin dynamics accepted at IEEE transactions on Signal Processing
Nov 2023 Successful Master defense, entitled Annealed Langevin dynamics for MIMO communications
Nov 2023 Preprint of Joint channel estimation and data detection in massive MIMO systems based on diffusion models
Oct 2023 Paper Unsupervised Learning of sampling distributions for Particle filters accepted at IEEE transactions on Signal Processing
May 2023 Preprint of Solving linear inverse problems with Higher-order Annealed Langevin dynamics
Feb 2023 Paper accepted at ICASSP 2023 Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics
Oct 2022 Paper Annealed Langevin Dynamics for Massive MIMO Detection accepted at IEEE transactions on Wireless Communications
May 2022 Two papers accepted at EUSIPCO 2022.
January 2022 One paper accepted to ICASSP 2022.