Sterling Baird obtained his B.Sc. in Applied Physics and M.Sc. in Mechanical Engineering from Brigham Young University in 2018 and 2021, respectively. Sterling obtained his Ph.D. in Materials Science and Engineering from the University of Utah in 2023 in Dr. Taylor Sparks' materials informatics group, where he was awarded the Gregory B. McKenna graduate fellowship.
During his Ph.D., he used machine learning to discover new materials for energy and structural applications and has made notable contributions to the field of materials informatics. From May 2021 to May 2023, he published ten first-author peer-reviewed manuscripts, co-authored five manuscripts, and delivered nine oral presentations and two tutorials. Sterling's contributions extend beyond publications and presentations. He made thousands of contributions across hundreds of code repositories and contributed over one hundred thousand lines of open-source code to materials informatics projects. Sterling is passionate about data-driven materials discovery enabled by Bayesian optimization, self-driving laboratories, and educational platforms that reduce the barrier to entry for state-of-the-art algorithms and equipment.
In addition to his research interests, Sterling enjoys spending time with his family, eating delicious food, breakdancing, and studying Japanese.