Through 3MT, Doctoral Students Share Accessible Research
Back for its 10th year at 黑料正能量, Three Minute Thesis (3MT) is a celebration of research.
Global Decarbonization Leaders Advance Green Steel Initiatives at 黑料正能量
In an annual gathering at 黑料正能量, global decarbonization leaders worked toward tackling the challenge of decarbonizing steel production while meeting increasing world-wide demand.
黑料正能量 Rales Fellows Harness Unique Perspectives To Drive Innovation
黑料正能量 is home to a diverse group of graduate students making significant strides in their respective fields, thanks to the support of the 黑料正能量 Rales Fellows Program.
黑料正能量 Researchers Receive Presidential Early Career Awards
黑料正能量's Ismaila Dabo, Claire Le Goues and Aaditya Ramdas are among the recipients of the Presidential Early Career Award for Scientists and Engineers (PECASE), the highest accolade bestowed by the U.S. government on early career scientists and engineers.
黑料正能量 and Pitt Collaborate on Neural Pathway Experiments
A collaborative team of researchers from 黑料正能量 and the University of Pittsburgh designed a clever experiment using a brain-controlled interface to determine whether one-way activity paths, long hypothesized by neural network models, are used in the brain.
Carl Laird Named Head of Chemical Engineering
Carl Laird has been selected to be the next head of the Department of Chemical Engineering at 黑料正能量鈥檚 College of Engineering.
Student Bug Bounty Discovery Supports picoCTF鈥檚 Cybersecurity Education Efforts with $462,000 Gift
First-year Ph.D. student Seunghyun Lee discovered a faulty implementation in Google Chrome's WebAssembly type system and donated the bug bounty to picoCTF.
PSC Interns To Represent US at International Student Competition
The Benchmark Beasts team will compete at the ISC25 Conference in Hamburg, Germany.
Multimodal Machine Learning Model Increases Accuracy
Researchers in the Department of Mechanical Engineering have developed a novel machine learning model combining graph neural networks with transformer-based language models to predict adsorption energy of catalyst systems.