Juan Elenter

Research Scientist, Spotify

juane [at] spotify.com

New York, New York

Bio

> 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.

My work

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.

Projects

ATAC-seq analysis and motif discovery
@ Stanford, KundajeLab
Prediction of complex traits in agriculture.
@ UdelaR, FarielBerry Lab
I find this case very confusing, and have not thoroughly checked the result. R.A.Fisher
CMS Open Data Initiative
@ CERN, CMS

Vitæ

The blueprint for this website can be found in this GitHub repo. Feel free to use it.