UNIVERSITY PARK, Pa. — Recognizing the growing opportunities to use artificial intelligence (AI) and machine learning (ML) to power scientific investigations, Penn State’s Institute for Computational and Data Sciences (ICDS) has added two more AI specialists to its team.
ICDS announced that Anudha Mittal and Simon Delattre will join the institute’s Research Innovations with Scientists and Engineers (RISE) team, comprised of experts with extensive supercomputing experience and a deep understanding of academic research who are available for project-specific consulting.
Like other RISE team members, Mittal and Delattre will be available by hire to collaborate with research groups across campus.
Leveraging machine learning and artificial intelligence for scientific investigations and discovery is a key strategic goal of ICDS. AI and ML are tools that researchers are increasingly using to make their scientific investigations more efficient and less costly, as well as wider in scope. But, these tools, themselves, are also being studied, so that they are used in ethical and inclusive ways.
ICDS is promoting the ethical use of AI and ML in research through key hires of experts and has created several learning and research centers built around improving the understanding of AI and ML in industry and academia.
Mittal received her doctoral degree in chemical engineering from the University of Minnesota. Prior to joining Penn State, she worked as a software engineer at ZoNexus in California, developing web-based applications for automation in electron microscopes. Before that, Mittal served as a senior materials engineer at the Naval Nuclear Laboratory and a data analyst contractor at GE.
Mittal is proficient in a range of AI-ML programs, languages and tools, including tensor flow, neural networks and deep learning.
Delattre served as an assistant research professor at the Materials Research Institute, where he gained interdisciplinary experience working with researchers across the University. He received his doctorate from the Institut de Mineralogie et de Physique des Milieux Condenses, Sorbonne University.
He is proficient at Pytorch and GPflow libraries for machine learning and has successfully implemented deep neural networks and gaussian processes for scientific projects.