There's a recognized gap between the experts who develop new algorithms and computational methods, and those who apply and adapt those technologies in specific domains.
It's a vexing interdisciplinary gap, and through its TRIPODS + X program, it is one that the National Science Foundation (NSF) seeks to bridge.
Supported by this national effort, Lehigh University is hosting a series of specialized workshops on the topic of machine learning as it applies to a variety of emerging applications and academic pursuits.
The first of these workshops, "Foundational & Applied Data Science for Molecular and Material Science & Engineering," is scheduled for May 22-24 and will bring together computer scientists, applied mathematicians, material scientists, chemists, biologists, and chemical, industrial, and bioengineers. Through a series of presentations, poster sessions, and networking opportunities, attendees will focus on recent developments and application of data science algorithms and tools in this field.
Academic and industry researchers interested in these pursuits are encouraged to attend; the deadline for online registration is May 1.
The event is the first in a series of conferences and lectures funded by an NSF TRIPODS-X grant awarded to Lehigh's Institute for Data, Intelligent Systems, and Computation (I-DISC). Topics will include applications of machine learning and big-data science in chemical processes, autonomous robotics, supply chain optimization, and cognitive neuroscience.
Late in 2018, the NSF unveiled its support for I-DISC to organize these open workshops, part of a broader funding announcement around $8.5 million in grants for 19 collaborative projects involving 23 U.S. universities. Two of the grants were awarded to Lehigh.
"These workshops are a perfect way for I-DISC to positively influence a field that touches many aspects of research while expanding our network and visibility," says Katya Scheinberg, co-director of I-DISC. "Through these workshops, we will bring together top experts from across academic disciplines that leverage machine learning techniques to advance their efforts and create new knowledge. Our intention is to develop an academic knowledge network around sophisticated computational tools that analyze data and improve researchers' ability to understand and harness phenomena associated with a wide variety of complex domains."
The second workshop in the series, scheduled for early Fall 2019, will focus on machine learning topics related to robotics, automated control, and dynamical systems.
About the May workshop
Data science has become ubiquitous in science and engineering. There is a tremendous recent surge in the adoption of machine learning (ML) 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.
According to the workshop's organizing committee, the "practitioners" of ML in these fields will benefit from close interaction with "developers" (i.e., data scientists) of modern ML tools who, in turn, can gain a deeper understanding of the challenges and needs of specific domains.
"Foundational & Applied Data Science for Molecular and Material Science & Engineering" will take place in Iacocca Hall on Lehigh University's Mountaintop Campus in Bethlehem, PA, beginning at approximately 12 p.m. on May 22, and concluding in the afternoon of May 24.
For more information or to register, visit the workshop website.