Faculty Earn Fellowships for Heart Modeling and Data Optimization Research
The Society for Industrial and Applied Mathematics (SIAM) selected Elizabeth Cherry and Katya Scheinberg as Class of 2025 fellows.
Fill-in-the-Blank Training Primes AI to Interpret Health Data From Smartwatches, Fitness Trackers
The human body constantly generates a variety of signals that can be measured from outside the body with wearable devices.
AAAS Honors Seven Georgia Tech Researchers as Lifetime Fellows
The preeminent distinction recognizes outstanding achievements and contributions in the scientific community.
Georgia Tech unveils Tech AI to drive real-world AI impact
New Georgia Tech Initiative Accelerates AI Solutions for Industry, Government, and Society
Georgia Tech Leads the Way in AI Literacy with OpenAI Academy Collaboration
New OpenAI Academy at Georgia Tech offers education and resources to advance public knowledge in one of the top fields globally.
Georgia Tech Launches Tech AI to Accelerate the Real-World Impact of Artificial Intelligence
The announcement marks the start of Tech AI Fest, the Southeast’s leading AI event, bringing together leading academics, industry experts, government figures, and students for three days of creative partnerships and transformative ideas.
Tech AI Fest 2025: Georgia Tech's Premier AI Event
Georgia Tech's leading artificial intelligence event will bring together experts, researchers, industry professionals, policymakers, and students to explore the latest advancements and applications of AI.
Heart Fellows: BME Grad Students Training to Become Next Generation Cardiovascular Leaders
Launched in 2023, CBT@EmTech trains future cardiovascular research leaders through interdisciplinary study, clinical exposure, and impactful research.
Bringing Miniaturization Science to the Classroom
David Myers' hands-on microfluidics course lets students build sticker-based devices, enhancing understanding of miniaturization science through active learning.
Machine Learning Encoder Improves Weather Forecasting and Tsunami Prediction
Ph.D. student Phillip Si and Assistant Professor Peng Chen developed Latent-EnSF, a technique that improves how ML models assimilate data to make predictions.