Name: Massimiliano Lupo Pasini
Pronouns: he/him/his
Biography:
Massimiliano (Max) Lupo Pasini obtained his Bachelor of Science and Master of Science in Mathematical Engineering at the Politecnico di Milano in Milan, Italy. The focus of his undergraduate and master studies was statistics and discretization techniques and reduction order models for partial differential equations. He obtained his PhD in Applied Mathematics at Emory University in Atlanta (GA) in May 2018. The main topic of his doctorate work was the development of efficient and resilient linear solvers for upcoming computing architectures moving towards exascale. Upon graduation, Max joined the Oak Ridge National Laboratory (ORNL) as a Postdoctoral Researcher Associate in the Scientific Computing Group at the National Center for Computational Sciences (NCCS). Since 2020 Max has been a Data Scientist in the Scalable Algorithms and Coupled Physics Group in the Advanced Computing Methods for Engineered Systems Section of the Computational Sciences and Engineering Division at ORNL. Max’s research focuses on the development of surrogate models for material sciences, scalable hyper parameter optimization techniques for deep learning (DL) models, and acceleration of computational methods for physics applications. He is currently the technical lead of the Artificial Intelligence for Science and Discovery thrust of the ORNL Artificial Intelligence Initiative.
Institution/Lab: Oak Ridge National Laboratory
Website: https://www.ornl.gov/staff-profile/massimiliano-lupo-pasini
SRP Collaboration Topic/Title: Surrogate models for materials science
Field or research area: Artificial Intelligence for Materials Science
Please select all the topical areas that apply to your project:
Computational Science Applications (i.e., bioscience, cosmology, chemistry, environmental science, nanotechnology, climate, etc.); Computer Science (i.e., architectures, compilers/languages, networks, workflow/edge, experiment automation, containers, neuromorphic computing, programming models, operating systems, sustainable software); Data Science (i.e., data analytics, data management & storage systems, visualization); High-Performance Computing; Machine Learning and AI
Brief Abstract:
Material design and discovery are crucial for the US Department of Energy (DOE) in order to advance energy technologies, improving efficiency, and addressing sustainability and environmental challenges. Exploring the space characterized by different materials is extremely complex due to the myriad of possibilities to mix different natural elements at different percentages. Such exploration is impractical given traditional technologies. In fact, laboratory experiments are labor-intensive, and physics computational models require massive computational resources. Both experimental and computational approaches preclude an effective (fast and thorough) exploration of several materials. Artificial Intelligence (AI) and Machine Learning (ML) enable an effective exploration of a several materials within a fraction of the time required by traditional experimental and computational methodologies. This internship offers the opportunity to become acquainted with the scientific challenges that the US-DOE is facing to achieve materials design and discovery, and appreciate the advantages that AI and ML can provide to this field.
Desired relevant skills, background, or interests:
The applicant should desirably be familiar with basic AI and ML concepts. Familiarity with Python packages for ML (e.g., Scikit-Learn and PyTorch) is desired, but not required.
Other comments:
Do any special requirements apply? Minimum GPA (specify what GPA in comments below); In-Person Only; U.S. Citizen Only; Permanent Resident OK; International OK
Other, specify:
Keywords:
Artificial Intelligence Machine Learning Deep Learning Materials Science Scientific Computing High Performance Computing
Lightning Talk Title: Artificial Intelligence for Materials Science