The third annual MLSE conference will highlight the latest research in artificial intelligence and machine learning that are advancing science and engineering fields at large. This conference will demonstrate how data-driven approaches can help solve emerging challenges, and will showcase innovative thinking from a diverse range of technological disciplines. Throughout this two day virtual conference, representatives from academia, government, and industry will gather together to explore the future of science and engineering across ten dedicated tracks.
In 2017, an internal symposium on machine learning in science and engineering was held at Carnegie Mellon University (CMU) to identify ways in which these computational tools advance diversity in several fields. Based on the strong response, the first open MLSE conference was held in 2018 at the CMU campus in Pittsburgh in partnership with Georgia Tech. The second MLSE was held at Georgia Tech in 2019 in conjunction with CMU and Columbia University. The second and third MLSE conferences are partially supported by an NSF TRIPODS+X award.
Columbia University will expand the scope of the previous conferences by adding:
- New tracks in science and engineering fields, such as biology, civil engineering, earth sciences, neuroscience, and transportation.
- A track on the foundations of machine learning, driven by science and engineering data.
- A track on the design of computational systems, from the hardware layer (CPUs, GPUs, TPUS, FPGAs) through the software stack, that focus on data-driven science and engineering.
William Dally, Chief Scientist & Senior VP of Research, Nvidia; Professor-Research, Computer Science & Electrical Engineering, Stanford University
- Barbara Engelhardt, Associate Professor, Department of Computer Science, Princeton University
David W. Hogg, Group Leader, Flatiron Institute; and Professor of Physics & Data Science, Department of Physics, New York University
- Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science, Duke University