Artificial Intelligence

Dr. Joseph Sang-Il Kwon combines machine learning with chemical engineering to streamline the drug manufacturing process, leading to cost-effective, higher-quality medicine.

Dr. Le Xie was invited to advise the White House Office of Science and Technology Policy on the responsible development of artificial intelligence in clean energy transition.

Dr. Mladen Kezunovic and his team are combining big data and machine learning to predict outages and help consumers prepare for loss of power.

Petroleum engineering researchers achieve fast, low-error subsurface reservoir production forecasts by combining machine learning with geomodel compression and a neural network.

Graduate student Jim Tronolone’s new system for standardizing the production of organs-on-a-chip allows for reproduction in research across different labs.

Funded by the National Science Foundation, Dr. Tianbao Yang is developing deep learning technologies for medical image classification, making deep models interpretable and the training more efficient by sampling multiple CT scans of a patient for computation.

Drs. Srinivas Shakkottai and Dileep Kalathil recently received a National Science Foundation award to study real-time intelligent control of highly programmable cellular networks through machine and reinforcement learning called EdgeRIC.

The center will serve as an epicenter for discovering and exploring novel materials for applications in extreme conditions, leading to new frontiers of materials research.

Liu’s research interests include connecting artificial intelligence and nuclear technology to fight climate change. He is excited to pursue impactful research, collaborate with others and teach talented engineering students.

Dr. Joshua Peeples is collaborating with faculty from the College of Agriculture and Life Sciences to develop a centralized framework to analyze data collected from Texas A&M AgriLife Research's new state-of-the-art Plant Growth and Phenotyping Facility.