Foundations of Machine Learning

Person made out of numbers and data

The Foundations of Machine Learning is a research area within Georgia Tech’s School of Computer Science (SCS) that focuses on the development of algorithms that leverage data and statistical tools to solve complex human tasks, to explore novel applications of such tools, and to better understand the apparent success of ML in practice. Instead of focusing on specific applications (e.g., computer vision, NLP or robotics), the Foundations of Machine Learning area focuses on general principles and novel approaches that can be applied across a wide spectrum of applications.  

We are particularly interested in topics such as machine learning theory,  scalable and distributed training, heterogeneity-aware inference, and robust dynamically adaptive algorithms that help navigate multi-dimensional tradeoff spaces spanned by ML accuracy, model size, latency, and spatio-temporal cost efficiency of both training and inference. 

The Foundations of Machine Learning area at SCS has made significant contributions:

  • Online learning

  • Reinforcement learning

  • Systems support for distributed ML frameworks

  • Resource management for distributed ML frameworks

  • Continual learning

  • Learning theory

  • Federated learning

  • AutoML

  • Explainable ML

  • Systems support for heterogeneity-aware ML inference

  • Neural Architecture Search (NAS)

  • Neuro-inspired AI

Our major sources of funding are the National Science Foundation (NSF) and the Defense Advanced Research Projects Agency (DARPA). Additionally, we participate in interdisciplinary research that brings together machine learning, neuroscience, biology, mathematics and statistics, and theoretical computer science. We welcome the involvement of graduate and undergraduate students in our research projects and the broader intellectual community.

Faculty

​Coordinator: Constantine Dovrolis (constantine@gatech.edu)

 

ML Foundations Qualifier Exam requirements

Please learn more at our qualifier exam information page.