About me

Research Scientist Snap Research

I am a Research Scientist on the User Modeling and Personalization team at Snap Research, where I work on LLM adaptation and efficient training for generative recommendation. I earned my Ph.D. in Computer Science and Engineering at the University of Michigan, Ann Arbor in April 2026, advised by Danai Koutra. My doctoral research was supported by an NSF Graduate Research Fellowship and a Rackham Merit Fellowship.

Donald Loveland

01 / Research

Some of my
recent work

I study how large language models learn and adapt for personalized recommendation, with a focus on training efficiency and model behavior. I also investigate how graph structure shapes what models learn and how they reason about connections.

Explore research

02 / Experience

Research and
industry experience

From scientific machine learning at national labs to graph learning and personalization in industry. A look at the places and people that have shaped my work.

Explore experience
  1. Sep. 2025–present

    Snap Research 

    Research Scientist

  2. Aug. 2021–Apr. 2026

    University of Michigan 

    Ph.D. · Computer Science & Engineering

  3. May–Sep. 2025

    Amazon 

    Applied Research Intern

03 / Mentorship

Supporting other researchers

As a first-generation college student, I am deeply grateful for the guidance I have received throughout my academic and professional career, and I aim to pay this forward by supporting students from similar backgrounds. I served as a peer mentor for first-generation undergraduate students throughout my graduate studies, and while at Lawrence Livermore National Laboratory, I helped organize and mentor a two-week machine learning challenge focused on teaching students how to conduct applied research.

At Snap, I advise research interns working on generative recommendation, conversational recommendation, and graph foundation models. Please feel free to reach out if you’d ever like to chat.

Let’s connect LinkedIn
From the paper

View figure in paper ↗