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The Foundations of Artificial Intelligence 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 AI in practice. Instead of focusing on specific applications (e.g., computer vision, NLP or robotics), the Foundations of Artificial Intelligence 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 Artificial Intelligence area at SCS has made significant contributions in:
Systems support for distributed ML frameworks
Resource management for distributed ML frameworks
Systems support for heterogeneity-aware ML inference
Neural Architecture Search (NAS)
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.
Coordinator: Constantine Dovrolis (email@example.com)
ML Foundations Qualifier Exam requirements
Please learn more at our qualifier exam information page.