Steve Mussmann

Assistant Professor, School of Computer Science, Georgia Tech
mussmann@gatech.edu
KACB 3320

Leads the ADDAPT lab.
Google Scholar, CV

Prospective Student Researchers: Please read this before contacting me.

My research focuses on data-centric machine learning, emphasizing the often-overlooked aspects of data such as sourcing, annotation, and validation, which critically impact the reliability and usability of ML systems. I combine theoretical and experimental methods to develop conceptual insights with practical relevance.

Within data-centric machine learning, I am especially interested in:

Active Learning and Experimental Design (methods to select data to collect supervision)
Statistical aspects of data algorithms and data-centric ML (e.g., noise, domains, concept shift)
Task specifications with instructions (e.g., prompts, concepts) and demonstrations (e.g., labeled examples)

Teaching

(Fall 2026) CS 7545 Machine Learning Theory
(Spring 2026) CS 4641 Machine Learning (tentative syllabus)
(Fall 2025) CS 7545 Machine Learning Theory (Syllabus, Lecture Notes)
(Fall 2024) CS 8803-DML Data-centric Machine Learning (Syllabus)

Bio

Prior to starting at Georgia Tech in Fall 2024, Steve spent a year as a machine learning researcher at Coactive AI. He finished a postdoc at the Paul Allen School of Compute Science and Engineering at the University of Washington with Kevin Jamieson and Ludwig Schmidt in September 2023. Steve graduated with a PhD in computer science from Stanford University in 2021, advised by Percy Liang, and a BS in math, statistics, and computer science from Purdue University in 2015.

Steve Mussmann

Teaching

Bio

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