Interdisciplinary collaboration

I am frequently engaged in research that crosses disciplinary boundaries, combining several research disciplines together. One example is my work at the interface between machine learning and science, combining both algorithmic development and practical applications In addition to my scientific domain (particle physics), I have collaborated with others in the following scientific and non-scientific domains: planetary science, medicine and art.

In planetary science, I have collaborated with researchers from the Johns Hopkins University Applied Physics Laboratory and NASA on a machine learning project analyzing geochemical terrains from MESSENGER mission data. As a proof of concept, we have learned to extract features from lunar elemental composition data and make accurate predictions of features of the Moon, including its albedo map. We are applying similar techniques to data collected by the MESSENGER mission from Mercury, where the relationships among geochemical terrains are much more complex and can shed light on the origin of Mercury. We are applying a similar approach to data from other planetary science missions, including future missions. I will attend the next Fall Meeting of the American Geophysical Union (AGU2018) where some of the results will be presented in a special session on machine learning in planetary science. Our latest planetary science results will be summarized in an upcoming publication in Spring 2019.

I am also collaborating with theoretical and experimental physicists in the area of strong gravitational lensing data and using machine learning techniques to study possible dark matter substructure.

In November 2018, I participated in a panel on machine learning for accelerated vaccine and immunotherapy development, focused on machine learning applications and predictive modeling to make more effective vaccines, part of the 2nd World Vaccine and Immunotherapy Congress.

In 2016, I have consulted and collaborated with Sanofi Pasteur, a global pharmaceutical company in order to develop better vaccines. We have applied modern machine learning techniques to challenges facing large-scale vaccine production, including making higher-quality antigens and spotting problems before they negatively affect vaccine production.

I always welcome interesting projects. Please contact me if you are facing a challenging scientific problem where an interdisciplinary approach may lead to a unique solution.