About

I am a machine learning engineer at Reducto, where I help train our vision-language models for document intelligence.

Previously, I was at Liquid AI, helping train the multimodal LFM2 and LFM2.5 family of models optimized for edge deployment. I've also collaborated with Reticular on improving sparse autoencoder (SAE) feature consistency.

I completed my M.Eng. at MIT CSAIL, where I was affiliated with the Distributed Robotics Lab and advised by Daniela Rus. I obtained my B.S. in Computer Science and Engineering from MIT.

Selected Papers

Google Scholar
  1. Enforcing Orderedness to Improve Feature Consistency

    Sophie L. Wang*, Alex Quach*, Nithin Parsan, John J. Yang

    NeurIPS Mech Interp Workshop · 2025

  2. Continuous Autoregressive Generation with Mixture of Gaussians

    Alex Quach, Tsun-Hsuan Wang, Ramin Hasani, Mathias Lechner, Alexander Amini

    ICML Efficient Systems for Foundation Models Workshop · 2025

  3. LFM2 Technical Report

    Liquid AI Team

    arXiv · 2025

  4. Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models

    Makram Chahine, Alex Quach, Alaa Maalouf, Tsun-Hsuan Wang, Daniela Rus

    IEEE Robotics and Automation Letters · 2026

  5. Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks

    Alex Quach*, Makram Chahine*, Alexander Amini, Ramin Hasani, Daniela Rus

    Conference on Robot Learning · 2024

  6. Out of Distribution Generalization via Interventional Style Transfer in Single-Cell Microscopy

    Wolfgang M. Pernice, Michael Doron, Alex Quach, Aditya Pratapa, Sultan Kenjeyev, Nicholas De Veaux, Michio Hirano, Juan C. Caicedo

    CVPR Workshops · 2023

* Equal contribution