Summary: On May 10, 2017 an internal symposium titled Machine Learning in Science and Engineering was held at Carnegie Mellon University to identify ways in which these computational tools are advancing a diversity of fields. In a very exciting day that featured 23 research talks by CMU faculty and 17 posters were presented by graduate students and post-‐doctoral associates on topics ranging from the search for dark matter in the universe to development of tools for product design. Based on the strong response at CMU with over 120 registered across computer science, engineering, and science, the organizers are planning a larger, open conference on June 6-‐8, 2018 at the CMU campus in Pittsburgh.
This conference will be co-‐organized with Georgia Tech. While both CMU and GT will be strongly represented, the conference is expected to have 350 attendees from academia, government (NIST, Department of Energy, Department of Defense), and a diversity of companies that are implementing data sciences and artificial intelligence in their operations.
On the morning of June 6, short courses in a variety of topics will be offered for attendees on topics ranging from basics of machine learning to advanced topics in computer vision. The afternoon of June 6 will feature plenary talks from leading researchers across disciplines as well as a panel discussion on industry and government interests in these areas. June 7-‐8 will have two days of focused programming, allowing attendees to learn about the state-‐of-‐the-‐art in machine learning applied across science and engineering fields.
Intellectual Merit: While machine learning has revolutionized many areas of biological and biomedical research, its impact across the physical sciences and engineering is at an early stage. This conference will bring together researchers in a diversity of STEM areas focused on applying machine learning to problems of
fundamental or applied nature. Presentations will focus on adapting existing
machine learning methods to current research areas, developing new machine learning algorithms specific to science and engineering, and identifying new frontiers of research that may only be pursued using a data-‐centric approach. The conference will reflect the broad technical scope of the 2017 symposium while also offering attendees focused short courses taught by experts in machine learning on a variety of cutting-‐edge tools that are critical in advancing these fields.
Broader Impacts: Disseminating machine learning methods across science and engineering could have broad implications for US research. A conference grant from NSF is sought to support general conference costs, attendance of students from regions outside Pittsburgh, as well as involving participation of students and faculty from underrepresented groups. CMU has strong relationships with a number of minority-‐serving institutions, such as Morgan State University and University of Texas – El Paso.
The organizers, themselves represent a very diverse group. Beyond intellectual diversity, the main organizers include 3 women. The tracks are each co-‐led by a CMU and a GT faculty member, and among these there are 6 women, 2 of whom are also URMs. The organizers are committed to promoting participation among underrepresented groups, junior researchers and students, and including tutorials to widen accessibility to as large a group of attendees, as possible. We will make sure the symposium is advertised broadly, including HBCs, especially in the
Pittsburgh and Atlanta regions.