Steve Mussmann

Assistant Professor, School of Computer Science, Georgia Tech

Research interests include data-centric ML, active labeling/learning, and data selection. Affiliations include Foundations of AI (FoAI) at Georgia Tech.

Google Scholar, CV

Contact: mussmann@gatech.edu, KACB 3320

Fall 2024 drop-in hours

The location is my office, KACB 3320

Ends on November 21 (week before Thanksgiving)

About me

Bio

Prior to starting at Georgia Tech in Fall 2024, Steve was a full-time 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.

Research

Machine learning is a tool that is incorporated in a quickly increasing variety and number of systems and processes in society. My research is driven by making ML easier-to-use, more effective, and more likely to be used in beneficial ways. This often takes the form of abstracting machine learning issues (data efficiency, interpretability, robustness, etc.) from specific application areas (computer vision, NLP, computational biology, etc.) to discover insights that lead to more useful algorithms and more reliable best practices.​ By using a mix of theoretical and experimental techniques, my research takes a broad perspective while ensuring practical relevance

Research on learning algorithms has seen remarkable progress over the past decade, especially with regards to text and images, which has ignited interest in machine learning. While the learning algorithm is critical to an ML system, there are many other aspects that are under-studied, including data sourcing, pre-processing, annotation, cleaning, validation, and monitoring which all significantly affect the reliability and usability of the system. My work often falls under the umbrella of data-centric machine learning, where the focus is on improving the quality of the data while the model architecture and optimization algorithm are held fixed.

Much of my previous work falls into one of two categories: