Foundational & Applied Data Science for Molecular and Material Science & Engineering

May 22-24, 2019


  • This conference brings together foundational and applied data science experts to present the latest machine learning methods and their applications in molecular and materials science and engineering.
  • The conference is relevant to any developer or practitioner of data science in this broad domain from academia, industry, and the national lab.

Overview of Conference

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).

Organizing Committee:

  • Srinivas Rangarajan, Chemical & Biomolecular Engineering, Lehigh University
  • Jeetain Mittal, Chemical & Biomolecular Engineering, Lehigh University
  • Payel Das, IBM Research AI, T J Watson Research Center
  • Joshua Agar, Materials Science & Engineering, Lehigh University

Facilitators | I-DISC

  • Katya Scheinberg, Industrial & Systems Engineering, Lehigh University
  • Hector Munoz-Avila, Computer Science & Engineering, Lehigh University

Confirmed Speakers

Plenary Speaker

  • Paulette Clancy, Johns Hopkins University

Invited Speakers

  • Joshua C. Agar, Lehigh University
  • Chi Chen, University of California, San Diego
  • Lihua Chen, Ramprasad Group, Georgia Tech
  • Payel Das, IBM Research AI
  • Alex Dunn, Lawrence Berkeley National Lab
  • Amir Barati Farimani, Carnegie Mellon University
  • Habib Hajm, Sandia National Laboratories
  • Oles Isayev, University of North Carolina
  • Heather Kulik’s Group, MIT | Speaker: Jon Paul Janet
  • Yingyu Liang, University of Wisconsin-Madison
  • Tim Mueller, Johns Hopkins University
  • Paris Perdikaris, University of Pennsylvania
  • Jim Pfaendtner, University of Washington
  • Srinivas Rangarajan, Lehigh University
  • Subramanian Sankaranarayanan, Argonne National Lab
  • Katya Scheinberg, Lehigh University
  • Tess Smidt, Lawrence Berkeley National Lab
  • Martin Takac, Lehigh University
  • Pratyush Tiwary, University of Maryland
  • Zack Ulissi, Carnegie Mellon University
  • Venkat Viswanathan, Carnegie Mellon University
  • Edmund Webb III, Lehigh University
  • Wenwei Zheng, Arizona State University