Artificial Intelligence

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.

Dr. Zixiang Xiong received a National Science Foundation grant to gain a fundamental understanding of learned source coding — or data compression that uses machine learning — and to create parameters for the unprecedented tools now available through artificial intelligence.

Texas A&M University professor Dr. Shuiwang Ji recently received a National Science Foundation grant to develop an artificial intelligence (AI) method to make predictions from geometric graphs.

The Texas A&M RoboMasters Robotics team placed first overall in the 1-versus-1 competition and third overall in the 3-versus-3 competition at the annual RoboMaster North American Regional competition.