Machine Learning and Artificial Intelligence

Clearpath Robotics robot with front-mounted camera

Artificial intelligence and machine learning enable the solution of complex problems by extracting information from patterns present in data. Problems range from image classification and system optimization and control to biological molecules and materials synthesis. Some areas have matured quickly and are impacting applications in biology and medicine, while others are at the level of fundamental research and development. Faculty in the Department of Electrical and Computer Engineering work with students and industry, both on teaching and research, to push the frontiers of artificial intelligence and machine learning to realize their potential for long-term societal impacts.

Building trustworthy, sustainable artificial intelligence

Dr. Suin Yi is working to develop a new computing system known as a memristor computer, which will bridge the gap between digital computers and quantum computers to provide a sustainable solution for emerging artificial intelligence and machine-learning applications.

Dr. Dileep Kalathil sitting at desk with tiny autonomous vehicles

Kalathil addresses reinforcement learning challenges

Dr. Dileep Kalathil’s research is focused on a class of machine learning known as reinforcement learning. He is investigating the robustness, safety and adaptivity of these algorithms so that they can be successful in real-world settings.

Concept image of an AI wearing a digitized human face

Studying the theoretical limit of learned source coding

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.

(From left to right): Dr. Seth Murray, Dr. Joshua Peeples, Yash Zambre and Akshatha Mohan

Texas A&M researchers integrating new AI tools for plant analysis

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.

Navy helicopter landing on a ship deck out at sea.

Automating aircraft ship landings at rough seas

The U.S. Navy has turned to Texas A&M University to develop an automated solution for landing helicopters on ship decks during rough seas. Drs. Moble Benedict and Dileep Kalathil are merging disciplines to design the next generation of fully autonomous vertical takeoff and landing aircraft.