Researchers at the University of Ottawa (uOttawa) will play a central role in the development of a promising AI tool aimed at supporting personalized dietary strategies for people living with IBD, thanks to a $10 million in funding from the Weston Family Foundation over the next three years.

The Gordon and Betty Moore Foundation selected Boston College Assistant Professor of Physics Qiong Ma as one of five creative scientists in the tenth cohort of Moore Inventor Fellows. Professor Ma’s invention of “twistronic” artificial synapses is connecting discoveries in advanced materials directly with neuroscience-inspired computing. The fellowship was launched in 2016 to commemorate the 50th anniversary of Moore’s Law, the groundbreaking prediction by Gordon Moore of exponential growth in computing power. The 2025 awards mark the fulfillment of a ten-year $35 million commitment to support “50 inventors to shape the next 50 years.” In that time, the program has been supporting breakthrough tools and technologies that accelerate progress in scientific discovery, environmental conservation, and patient care.

The Rotating Detonation Engine offers the ability to deliver satellites to precise orbits in outer space with greater robustness and reduced fuel consumption and emissions than with current conventional engines. However, there are many fundamental scientific challenges that remain related to designing materials systems that can perform under these extreme engine conditions. A new multi-institutional collaborative $2 million grant, "Thriving While Detonating – Materials for Extreme Dynamic Thermomechanical Performance,” led by Natasha Vermaak, an associate professor of mechanical engineering and mechanics in Lehigh University’s P.C. Rossin College of Engineering and Applied Science, addresses some of these materials design challenges. 

In machine learning tasks for applications as diverse as facial recognition and movie recommendations, neural networks have been used extensively. To address the complexities of neural networks and issues such as overfitting, loss of generalization power, and excessive hardware cost, Qing et al. developed a general compression scheme based on automatically differentiable tensor networks that considerably reduces the number variational parameters needed while maintaining or improving the network’s performance. Their work was published on May 15, 2025, in an article titled “Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters” in Intelligent Computing, a Science Partner Journal.