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.
FoAI Ph.D. Qualifying Exam
A student is deemed to have passed the FoAI qualifying exam if:
• the student has met the FoAI area depth requirement (see next section)
• the student presents a high-quality (e.g., publishable at a top conference)
research project to the committee, including answering questions, and at least
two of the three voting members vote “pass”
Course depth requirement
To satisfy the depth requirement, a PhD student must receive A’s in three courses: either one
foundational course and two related area courses, or two foundational courses and one related
area course.
Foundational courses:
• CS 6601: Artificial Intelligence
• CS 7641: Machine Learning
• CS 7643: Deep Learning
• CS 7545: ML Theory
Related area courses:
Databases
o CS 6220: Big Data Systems and Analytics
o CS 6400: Database Systems Concepts and Design
o CS 6422: Database System Implementation
Algorithms
o CS 6550: Advanced Algorithms
o CS 7530: Randomized Algorithms
Logic and formal languages
o CS 8803-LCS: Logic in Computer Science
o CS 8803-SAT: SAT/SMT Solvers
Computer Systems
o CS6210: Advanced Operating Systems
o CS7210: Distributed Systems
o CS 8803-SMR: Systems for Machine Learning
o CS 8803-SAL: Systems for AI
Empirical Machine Learning
o CS 6476: Computer Vision
o CS 7650: Natural Language Processing
o CS 8803-DML: Data-centric Machine Learning
o CS 8803-EML: Efficient Machine Learning
