Welcome to IDEaS
The Institute for Data Engineering and Science (IDEaS) provides a unified point to connect government, industry, and academia to advance foundational research, and accelerate the adoption of Big Data technology. IDEaS leverages expertise and resources from throughout Georgia Tech's colleges, research labs, and external partners, to define and pursue grand challenges in data science foundations and in data-driven discovery. We are also dedicated to educating students and those already in the workforce through innovative educational and training programs.
Spotlight
Foundations of Artificial Intelligence Seminar Series
Automated Formalization of OEIS using the Sequencelib Platform
Walter Moreira, University of Texas at Austin
February 6, 2026 | 4pm - 5pm | Classroom 183, J. Erskine Love Building & online via Zoom
Abstract: The On-Line Encyclopedia of Integer Sequences (OEIS) is a web-accessible database cataloging interesting integer sequences and associated theorems. With more than 390,000 sequences and 12,000 citations, the OEIS is one of the most robust and highly cited resources in all of theoretical mathematics. The Sequencelib project provides an open-source computational platform to formalize the mathematics contained within the OEIS using the Lean programming language. With contributions made through a combination of hand-written formalizations, AI and metaprogramming, Sequencelib currently contains formalizations for more than 25,000 sequences and over 1.6 million theorems about their values.
Centers
Center for High Performance Computing
The Center for High Performance Computing (CHiPC) advances the state of the art in massive data and high-performance computing technology, and solves high-impact real-world problems. HPC scientists devise computing solutions at the absolute limits of scale and speed. In this compelling field, technical knowledge and ingenuity combine to drive systems using the largest number of processors at the fastest speeds with the least amount of storage and energy. The center's focus is primarily on algorithms and applications.
The Center for Artificial Intelligence in Science and Engineering (ARTISAN)
The Center for Artificial Intelligence in Science and Engineering (ARTISAN) aims to accelerate advances in science and engineering by integrating cutting-edge artificial intelligence techniques. We are dedicated to fostering interdisciplinary research, cultivating the next generation of AI experts, and developing innovative solutions that address complex challenges in our world.
The South Big Data Innovation Hub
Georgia Tech, along with the University of North Carolina’s Renaissance Computing Institute (RENCI), co-directs the South Big Data Regional Innovation Hub that serves 16 Southern states and the District of Columbia. It is part of the National Science Foundation’s four Regional Innovation Hubs, created to build innovative public-private partnerships addressing regional challenges from data analysis and research to data science workforce development. The Georgia Tech location is operationally run as a center of the Institute for Data Science and Engineering.
Featured Research Areas
Machine Learning
Unstructured and dynamic data analysis, deep learning, data mining, and interactive ML underpin big data foundations and applications.
Health & Life Sciences
Driving predictive, preventive, & personalized care using big data sets from genomics, systems biology, proteomics, and health records.
High Performance Computing
High-performance systems, middleware, algorithms, applications, software, and frameworks for data-driven computing.
Materials & Manufacturing
Microscopic views of materials and scalable modeling and simulation technologies for accelerated development of new materials.
Energy Infrastructure
Sensors and Internet of Things enable infrastructure monitoring. Data analytics improves energy production, transmission, distribution, and utilization.
Algorithms & Optimization
Streaming and sublinear algorithms, sampling and sketching techniques, high-dimensional analysis for big data analytics.
