Foundations of Artificial Intelligence

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:

  • 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

  • Formal methods in AI

  • Combination of learning and reasoning

  • Trustworthy 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.

 

Selected Recent Papers from FoAI Researchers (2021-2024)

Jacob Abernethy

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Jacob Abernethy
 Associate Professor
Personal Webpage

Joy Arulraj

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Joy Arulraj
Assistant Professor 
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Suguman Bansal

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Suguman Bansal
Assistant Professor 
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Xu Chu

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Xu Chu
Assistant Professor 
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Constantine Dovrolis

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Constantine Dovrolis
Professor 
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Vijay Ganesh

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Vijay Ganesh
Professor 
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Anand Iyer

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Anand Iyer
Assistant Professor 
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Yingyan (Celine) Lin

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Celine Lin
Associate Professor 
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Ling Liu

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Ling Liu
Professor 
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Stephen Mussmann

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Steve Mussman
Assistant Professor (starting in Fall ’24)
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Kexin Rong

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Kexin Rong
Assistant Professor
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Sahil Singla

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Sahil Singla
Assistant Professor 
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Alexey Tumanov

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Alexey Tumanov
Assistant Professor 
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Santosh Vempala

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Santosh Vempala
Professor 
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