Bio

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Education
Research interests
Previous positions

CV

Mon, 01 Jan 0001 00:00:00 +0000

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Current position

Caltech · Postdoctoral Researcher, Computing and Mathematical Sciences · Pasadena, CA · Sep 2026 – Present

PI: Katie Bouman

Education

Ph.D., Electrical and Computer Engineering · Rice University, Houston, TX · Jan 2021 – Sep 2026

Thesis: From Guided Sampling to Learned Solvers: Diffusion Models for Inverse Problems
Advisor: Santiago Segarra

M.S., Electrical and Computer Engineering · Rice University, Houston, TX · Jan 2021 – Nov 2023

Publications

Mon, 01 Jan 0001 00:00:00 +0000

Google Scholar

Machine learning conferences

Normalized Energy Models for Linear Inverse Problems — Nicolas Zilberstein, Santiago Segarra, Eero Simoncelli, Florentin Guth
Intl. Conf. on Machine Learning (ICML) 2026 [paper] [code]
Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems — Nicolas Zilberstein, Morteza Mardani, Santiago Segarra
Advances in Neural Inf. Process. Syst. (NeurIPS) 2026 [paper] [code]
Repulsive Latent Score Distillation for Solving Inverse Problems — Nicolas Zilberstein, Morteza Mardani, Santiago Segarra
Intl. Conf. Learn. Repr. (ICLR) 2025 [paper] [code]
Scalable Implicit Graphon Learning — Ali Azizpour, Nicolas Zilberstein, Santiago Segarra
Intl. Conf. on Artif. Intell. and Stat. (AISTATS) 2025

Journals

Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation — Victor M. Tenorio, Nicolas Zilberstein, Santiago Segarra, Antonio G. Marques
Under Review - IEEE Trans. Signal Inf. Process. Netw. 2026
Solving Linear Inverse Problems using Higher-Order Annealed Langevin Diffusion — Nicolas Zilberstein, Ashutosh Sabharwal, Santiago Segarra
IEEE Trans. Signal Process. 2024
Unsupervised learning of sampling distributions for particle filters — Fernando Gama, Nicolas Zilberstein, Martin Sevilla, Richard G Baraniuk, Santiago Segarra
IEEE Trans. Signal Process. 2023
Annealed Langevin dynamics for massive MIMO detection — Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
IEEE Trans. Wireless Commun. 2022
A BCS microwave imaging algorithm for object detection and shape reconstruction tested with experimental data — Nicolas Zilberstein, Juan Augusto Maya, Andres Altieri
Electr. Lett. 2021

Signal processing conferences

Prior-informed Flow Matching for Graph Reconstruction — Haoming Chen, Nicolas Zilberstein, Santiago Segarra
Under Review 2026 [paper] [code]
Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials — Nicolas Zilberstein, Santiago Segarra, Luiz Chamon
Asilomar Conf. Si`nals, Syst. and Comp. (ASILOMAR) 2025 [paper]
Joint channel estimation and data detection in massive MIMO systems based on diffusion models — Nicolas Zilberstein, Ananthram Swami, Santiago Segarra
IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP) 2024
End-to-end learning of Gaussian mixture proposals using differentiable particle filters and neural networks — Benjamin Cox, Sara Perez-Vieites, Nicolas Zilberstein, Martin Sevilla, Santiago Segarra, Victor Elvira
IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP) 2024
Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics — Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP) 2023
State and Dynamics Estimation with the Kalman–Langevin filter — Martin Sevilla*, Nicolas Zilberstein*, Benjamin Cox, Sara Perez-Vieites, Victor Elvira, Santiago Segarra
Asilomar Conf. Signals, Syst. and Comp. (ASILOMAR) 2023
Accelerated massive MIMO detector based on annealed underdamped Langevin dynamics — Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
Graph Signal Processing Workshop 2023
Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics — Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
European Signal Process. Conf. (EUSIPCO) 2022 [doi]
Robust MIMO detection using hypernetworks with learned regularizers — Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
European Signal Process. Conf. (EUSIPCO) 2022
Unrolling Particles: Unsupervised Learning of Sampling Distributions — Fernando Gama, Nicolas Zilberstein, Richard G Baraniuk, Santiago Segarra
IEEE Intl. Conf. Acoust., Speech and Signal Process. (ICASSP) 2022
Particle filter with unknown noise statistics and with prior knowledge — Nicolas Zilberstein, Bruno Cernuschi-Frias
2018 Argentine Conference on Automatic Control (AADECA) 2018

Workshops

Topology Preserving Regularization for Independent Training of Inter-operable Models — Nicolas Zilberstein, Akshay Malhotra, Yugeswar Deenoo, Shahab Hamidi-Rad
NeurIPS workshop UniReps 2024

Preprints

Model-Driven Graph Contrastive Learning — Ali Azizpour, Nicolas Zilberstein, Santiago Segarra
arXiv preprint arXiv:2506.06212 2025

Patents

Systems and methods for statistics based and topology based interoperable AI/ML model monitoring — Akshay Malhotra, Yugeswar Deenoo, Nicolas Zilberstein, Shahab Hamidi-Rad, Mohamed Salah Ibrahim. InterDigital, US Patent Ref. 18889096, 2024.

Teaching

Mon, 01 Jan 0001 00:00:00 +0000

Teaching Assistant · Department of Electrical and Computer Engineering, Rice University

ELEC 573, Network Science and Analytics (graduate) — Fall 2022, 2024
ELEC 301, Signals and Systems (undergraduate) — Spring 2022, 2023, 2024, 2025

Teaching Assistant · Graduate School of Computer Engineering, University of Buenos Aires

Deep Learning — Fall 2021

Teaching Assistant · Department of Physics, University of Buenos Aires

82.02, Physics II (Electromagnetism) — Spring 2020, Fall 2020

Teaching Assistant · Department of Electronic Engineering, University of Buenos Aires

Welcome on Nicolas Zilberstein

Bio

CV

Current position

Education

Publications

Machine learning conferences

Journals

Signal processing conferences

Workshops

Preprints

Patents

Teaching