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Meghna Iyer (Biology 2026) came to Georgia Tech trying to understand her mother’s illness. What she discovered is changing how scientists connect three neurodegenerative diseases.
Parker H. Petit’s commitment will launch a research hub focused on using artificial intelligence to model diseases, identify treatment paths, and move discoveries closer to patient care.
Meghna Iyer came to Georgia Tech determined to understand the disease that was slowly taking her mother's life. Working with researchers in the Georgia Tech Laboratory for Pathology Dynamics, she helped use AI to analyze 36 million biomedical research papers, uncovering biological connections among ALS, Alzheimer's disease, and frontotemporal dementia. After losing her mother, Meghna returned to the lab with an even stronger resolve to continue the work, earning first-author publication on the study and helping create a tool that could accelerate future research for families facing these devastating diseases.
Three College of Sciences alumni have been selected as members of the Georgia Tech Alumni Association's 2026 class of 40 Under 40.
Mathematics Assistant Professor Wei Zhu has been awarded a five-year, $500,000 CAREER Award from the National Science Foundation. The award will support Zhu's research and education initiatives focused on artificial intelligence.
By combining physics-based AI with climate and engineering insights, Ali Sarhadi is redefining how we predict compound flooding, safeguard power grids, and build hurricane-resilient infrastructure.
Experts In The News
In an article published by The Conversation, Professor Joseph Montoya, Postdoctoral Fellow Xing Zhou, and their collaborator discuss sargassum blooms and their impact on the Caribbean and Gulf coasts and the coasts of the tropical Atlantic. According to their research, the vast mats of smelly, brown seaweed affecting these areas may have entered a "self-sustaining cycle, effectively feeding their own expansion."
The Conversation July 28, 2026Researchers in the School of Physics, working with King Fahd University of Petroleum and Minerals, have found that standard methods for identifying chirality in topological phases fail when applied to mixed quantum states. The study reveals that established diagnostics, including bulk-boundary correspondence and the modular commutator, are unreliable in these complex systems. Consequently, the research presents two new measures, based on relative entropy, to accurately diagnose chirality and determine the chiral central charge in decohered topological phases, indicating a need for a new set of tools when investigating mixed-state topology.
Quantum Zeitgeist July 16, 2026
