Parker Seegmiller

Parker Seegmiller

Innovation Fellow, PhD Candidate @ Dartmouth CS | Applied Science Intern @ Amazon

Dartmouth CS


Hi! I’m Parker. I’m a third year PhD student in the Persist Lab at the Dartmouth College Department of Computer Science and an Innovation Program Fellow in the Dartmouth PhD Innovation Program. I’m also an incoming Applied Science Intern at Amazon.

My research so far has been at the intersection of deep learning, natural language processing, and statistics. I am particularly interested in in-context learning, structured information extraction, and statistical tools for evaluating and guiding large language models.

In addition to my research interests, I have a strong history in data science, sports (basketball/tennis/football) analytics, and teaching. I also love cats, I believe video games are good for the soul, and I eat one full kiwi (with skin!) every day.

  • NLP
  • Statistics
  • ML
  • Sports Analytics
  • Business Management
  • Skyrim
  • PhD in Computer Science, 2021-

    Dartmouth College

  • BSc in Statistics, 2017-2021

    Brigham Young University



Applied Science Intern
Aug 2024 – Present Sunnyvale, CA
  • Incoming
Dartmouth CS Dept
PhD Candidate
Sep 2021 – Present Hanover, New Hampshire
  • Research: language model evaluation, text embedding distributions, semantic information processing
  • Teaching: machine learning, discrete math, android programming
Aetna, a CVS Health Company
Data Science Intern
Jun 2020 – Aug 2020 New York City, NY
  • Personally developed machine learning model for predicting healthcare provider abusive upcoding on inpatient DRG claims, projected to save up to $1,000,000 each month via audit recommendations
  • Presented original research for VP of Aetna, preparing web application for live model prediction
  • Engineered 100+ features for abusive upcoding model
BYU Statistics Dept
Undergraduate Student
Jan 2017 – Apr 2021 Provo, Utah
  • Research Assistant: tennis analytics, marketing, url embeddings
  • Teaching Assistant: probability, data science, hypothesis testing, linear regression