Clone of Machine Learning in Science and Engineering Conference Enjoys Successful Second Year
Hosted by Georgia Tech’s Institute for Data Science and Engineering, MLSE 2019 was held in conjunction with the Women in Data Science Workshop.
The organizers who founded and spearheaded the Machine Learning in Science and Engineering (MLSE) Conference last year recently held the second-annual event June 9–12 on the Georgia Tech campus. Chaired by Dana Randall, Co-Executive Director of the Institute for Data Engineering and Science (IDEaS) and the ADVANCE Professor of Computing at Georgia Tech, the event brought together some of the brightest minds in machine learning from a variety of academic disciplines and institutions.
Machine learning spans a multitude of topic areas, therefore the conference attracted more than 400 attendees, including students, professors, and members of industry.
“There is a need for a conference like MLSE that focuses on the science and engineering domains that are being transformed by machine learning and AI,” said Randall. “All of the standard ML conferences are dominated by computer scientists and statisticians, so the many other scientists and engineers using ML never get critical mass to share challenges, opportunities, and progress.”
Georgia Tech’s Executive Vice President for Research Chaouki T. Abdallah launched MLSE 2019 with welcoming remarks before the lunchtime plenary talk. The conference included peer-reviewed talks and poster presentations from nine disciplines in which machine learning is playing an increasingly central role in new advances of scientific research: biomedical engineering, chemical engineering, chemistry, electrical and computer engineering, industrial engineering and operations research, materials science and engineering, mechanical engineering, physics, and public policy.
Last year, when the first MLSE Conference was held at Carnegie Mellon University (CMU), Randall and 2018 MLSE Chair Newell Washburn discovered more than 60% of the attendees responding to the post-conference questionnaire said they attended multiple tracks and benefited from talks in other fields in addition to their own. These results confirmed for Newell and Randall that the format of bringing these vastly different groups of researchers together created a formula for a unique and beneficial ML conference.
MLSE 2019 enjoyed as much success as the inaugural year. “Several people came up to me to say they were blown away by how strong Georgia Tech is in machine learning across so many different fields,” Randall said. This conference showcased that with more than 42 talks, two of three short courses, and a hackathon, as well as several tracks all given or run by GT faculty and students, “Georgia Tech had large representation across every discipline represented in the nine parallel tracks.”
In addition to Georgia Tech and Carnegie Melon University, which had the most prominent showings, attendees hailed from more than 40 institutions of higher learning and delivered talks and presented posters over the three-day conference. Among the participants were faculty and students from Columbia University, Brown University, Duke University, Stanford University, Rutgers University, the University of California, Berkeley, and dozens of other preeminent institutions.
MLSE 2019 featured plenary talks by well-known experts in machine learning, including Jennifer Neville, the Miller Family Chair and associate professor of Computer Science and Statistics at Purdue University; Eliu Antonio Huerta Escudero, head of the Gravity Group at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign; John F. McDonald, professor in the School of Biology at Georgia Tech; Daniel B. Neill, associate professor of computer science, public service, and urban analytics at New York University; and Ross Thomson, solutions architect for Scientific Computing at Google Cloud Platform of Google. Their topics spanned relational artificial intelligence, astrophysics, cancer research, and policy.
In addition to the nine parallel tracks and two poster sessions, MLSE 2019 offered three short courses on Sunday, June 9, held in conjunction with a day-long Women in Data Science Workshop (WDSW). Two of these courses were led by Georgia Tech professors: Yao Xie, the Harold R. and Mary Anne Nash Early Career Professor and assistant professor in the Stewart School of Industrial and Systems Engineering and Zsolt Kira, associate director of the Machine Learning Center at Georgia Tech, assistant professor in the School of Interactive Computing, and research scientist at the Georgia Tech Research Institute.
The WDSW was attended by almost 200 women from across the southeast and beyond. This workshop—together with MLSE 2019—formed the Annual Data Science Forum. Many of the women who attended the workshop remained in Atlanta to attend the MLSE conference, helping to make MLSE a more inclusive and diverse conference. Travel grants were provided to more than 60 students to attend one or both events composing the forum.
The WDSW featured a keynote talk by Lo Li, chief technology officer at Equifax, and talks by other prominent women in data science, such as Ellen Zegura of Georgia Tech, Jennifer Priestley of Kennesaw State University, Lakshmi V. Kalluri of Anthem, and Brandeis Marshall of Spelman College. Discussions following some of the talks gave attendees a platform for considering the opportunities for women in the field. The workshop concluded with the three parallel short courses, also attended by MLSE participants.
This year’s Annual Data Science Forum received generous support from NSF through grant number 1839340 and from the Microsoft Corporation, along with additional funding from several other companies.
The third MLSE event will be held in New York City, hosted by Columbia University. Details about next year’s conference will be posted on the IDEaS website as they become available.
- Workflow Status: Published
- Created By: Kristen Perez
- Created: 07/11/2019
- Modified By: Tess Malone
- Modified: 07/11/2019