University of Tennessee Assistant Professor Mahshid Ahmadi and Assistant Professor Jonghee Yang contributed to two significant research projects on perovskites, published in Nature journals. These breakthroughs, involving international collaboration, advance the potential of perovskites for technologies like solar panels and LEDs. Ahmadi’s lab integrates traditional methods with machine learning for high-throughput material creation and characterization.

This spring, 75 students from Lone Star College, San Jacinto Community College and Houston Community College met with Rice University faculty, staff and graduate students five times over three months to explore how data science can be used to solve real-world sustainability challenges. Students worked on problems related to the environment, human health and the energy transition in a program funded by Google and led by the Department of Computer Science, the Rice Sustainability Institute and the Glasscock School of Continuing Studies.

As water becomes more scarce and demand rises, researchers are pioneering a new management approach that can help avert disastrous drought impacts. By collaborating with experts in Chile, the team aims to provide policymakers with the tools needed to integrate long-term environmental and social changes into water governance and ensure resilience in a warming world.

The Heartbeat Detector, which utilizes an algorithm developed by researchers at the Department of Energy’s Oak Ridge National Laboratory, detects individuals attempting to hide in vehicles at security checkpoints. The technology has become the industry standard since it was first licensed by Geovox Security Inc. from ORNL nearly 30 years ago and it continues to evolve.

The information age is built on mathematics. From finding the best route between two points, over predicting the future load on a national power grid or tomorrow's weather, to identifying ideal treatment options for diseases, algorithms share a common structure: they take input data, process it through a series of calculations, and deliver an output. Powering the ongoing AI revolution are increasingly sophisticated algorithms, often composed of millions of lines of code. And the more steps a model goes through before presenting a solution, the costlier it is in the number of physical computing units, time, and energy required.

Optimizing these mathematical models is at the heart of the work of the Machine Learning and Data Science Unit (MLDS) at the Okinawa Institute of Science and Technology (OIST). Led by Professor Makoto Yamada, the unit strives to unlock the full potential of machine learning (ML) and improve efficiency, optimizing not just data science but also education and the scholarly output within the unit through a distributed hierarchy.