Foundational & Applied Data Science for Molecular & Material Science & Engineering Conference
Iacocca Hall, Lehigh University. (Held May 22-24, 2019)
Data science has become ubiquitous in science and engineering. There is a tremendous recent surge in the adoption of machine learning tools in physics, chemistry, chemical engineering, materials science, and related disciplines to elucidate and design complex processes (chemical/biological, engineered/natural) or material systems with wide ranging applications addressing grand challenges in energy, health, environment, and water. At this critical juncture, the “practitioners” of ML in these fields, in both academia and industry, will benefit from close interaction with “developers” of modern ML tools (i.e. data scientists) who, in turn, can learn the problems and the needs of specific domains. To foster this relationship in the spirit of the NSF TRIPODS+X program, this interdisciplinary conference brings together foundational data scientists and domain practitioners to give talks to a common audience. The specific focal area of the conference is the development and application of data science algorithms and tools to address problems in molecular and materials science and engineering (i.e. any problem spanning the length scale of atoms to bulk materials).
Srinivas Rangarajan and Jeetain Mittal, Chemical & Biomolecular Engineering.
Payel Das, IBM Research AI, T J Watson Research Center.
Joshua Agar, Materials Science & Engineering, Lehigh University.
Paulette Clancy, Johns Hopkins University
“Merging Physical Science and Machine Learning to Tackle Complexity and Combinatorics in Materials Processing.”