Hussein Hazimeh

Hussein Hazimeh

Massachusetts Institute of Technology

Google Research

About Me

Welcome to my page! I’m a research scientist at Google in New York. My research is broadly on machine learning and optimization. Recently, I’ve been focusing on important challenges that arise when deploying models in practice, such as model efficiency and robustness. I’m also directly involved in productionizing research and have served as a technical lead for multiple product launches. As part of my role, I’ve also been managing Google-sponsored research collaborations with universities.

My research has been recognized with best paper awards and honorable mentions from INFORMS ICS (2023), KDD (2022), INFORMS IOS (2020), INFORMS ICS (2020), MIT (2020), MIP Workshop (2019).

I completed my PhD at MIT where I was advised by Rahul Mazumder and worked on scalable algorithms for sparse learning. Before that, I did my masters at UIUC where I worked with ChengXiang Zhai on improving information recall in search engines.

Current Research

  • Model compression and efficiency
    • Pruning transformer-based foundation models (focus on reducing latency)
    • Improving routing in mixture of experts
    • Feature selection (best subset selection) in standard statistical learning
  • Model robustness
    • Generalized notions of adversarial robustness that commonly appear in practice
    • Principled methods for robustness under distribution shifts
  • Applications of the above to trust and safety and autonomous systems