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:
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Online learning
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Reinforcement learning
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Systems support for distributed ML frameworks
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Resource management for distributed ML frameworks
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Continual learning
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Learning theory
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Federated learning
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AutoML
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Explainable ML
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Systems support for heterogeneity-aware ML inference
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Neural Architecture Search (NAS)
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Neuro-inspired AI
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Formal methods in AI
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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
![Jacob Abernethy](/sites/default/files/images/general/2023/jacob_abernethy_0.png)
Associate Professor
Joy Arulraj
![Joy Arulraj](/sites/default/files/images/general/2023/joy_arulraj_0.png)
Assistant Professor
Suguman Bansal
![Suguman Bansal](/sites/default/files/images/general/2023/bansalweb.jpg)
Assistant Professor
Xu Chu
![Xu Chu](/sites/default/files/images/general/2023/xu_chu_0.png)
Assistant Professor
Constantine Dovrolis
![Constantine Dovrolis](/sites/default/files/images/general/2023/constantine_dovrolis_0.png)
Professor
Vijay Ganesh
![Vijay Ganesh](/sites/default/files/images/general/2023/vijayganesh_web.jpg)
Professor
Anand Iyer
![Anand Iyer](/sites/default/files/images/general/2023/anand_pp52_copy.jpeg)
Assistant Professor
Yingyan (Celine) Lin
![Celine Lin](/sites/default/files/images/general/2023/celine_-headshot36_0.jpg)
Associate Professor
Ling Liu
![Ling Liu](/sites/default/files/images/general/2023/ling_liu_0.png)
Professor
Stephen Mussmann
![Steve Mussman](/sites/default/files/images/general/2023/cropped2.jpg)
Assistant Professor (starting in Fall ’24)
Kexin Rong
![Kexin Rong](/sites/default/files/images/general/2023/kexin3_0.jpeg)
Assistant Professor
Sahil Singla
![Sahil Singla](/sites/default/files/images/general/2023/sahil-singla-headshot.jpg)
Assistant Professor
Alexey Tumanov
![Alexey Tumanov](/sites/default/files/images/general/2023/alexey_tumanov_0.png)
Assistant Professor
Santosh Vempala
![Santosh Vempala](/sites/default/files/images/general/2023/santosh_vempala_0.png)