> I'm a math and music nerd currently at Spotify as a Research Scientist, working on large-scale recommendation systems and generative sequential modeling. I recently earned an MA in Statistics from The Wharton School and an MEng in Systems Engineering from SEAS at the University of Pennsylvania.
Before graduate school, I worked on genomic motif discovery through ATAC-seq analysis at Stanford's KundajeLab. I also interned at CERN
, contributing to the CMS Open Data initiative alongside Dr. Lassila-Perini.
Originally from the beautiful city of Montevideo, Uruguay , I studied at Universidad de la República. During this time, I played for Uruguay's national Waterpolo
team and worked as a software developer at IBM. If any of this sounds interesting, or if you want to share some mate
, feel free to reach out.
Broadly, I work on developing reliable learning systems that scale effectively with data and compute. At Spotify, I apply these methods to large-scale recommendation systems, with a particular focus on sequential generative modeling.
Throughout my graduate studies, I adopted a data-centric perspective of ML, focusing on continual model fine-tuning as new, diverse data emerges. This has driven my work in active and continual learning using large pre-trained models. On the theoretical side, my work on duality-based constrained optimization has shown that dual subgradient methods can yield near-optimal and near-feasible solutions, without randomization, despite non-convexity. This allows models to not only excel at their main task, but also meet critical requirements such as robustness and fairness.
I also have a particular interest in problems involving biological signals such as genome sequences, medical images and the gut microbiome.
Near-Optimal Solutions of Constrained Learning Problems
Juan Elenter, Luiz Chamon, Alejandro Ribeiro
International Conference on Learning Representations (ICLR), 2024
LoRanPAC: Low-rank Random Features and Pre-trained Models for Continual Learning
Liangzu Peng, Juan Elenter, Joshua Agterberg, Alejandro Ribeiro, René Vidal
International Conference on Learning Representations (ICLR), 2025
Feasible Learning
J. Ramirez * , I. Hounie * , J. Elenter *, J. Gallego *, A. Ribeiro, S. L. Julien
AISTATS, 2025
A Lagrangian Duality Approach to Active Learning
Juan Elenter, Navid NaderiAlizadeh, Alejandro Ribeiro
Neural Information Processing Systems (NeurIPS), 2022.