Interdisciplinary collaboration

I frequently engage in research that is considered interdisciplinary, combining several research disciplines at once. One such example is my work at the interface between machine learning and science, combining both algorithmic development and practical applications. This includes both purely software and data analysis applicationsĀ  and applications in hardware.

In addition to applications in particle physics (my main scientific domain), I have collaborated with other scientists in the following scientific domains: planetary science and medicine.

In planetary science, I have collaborated with scientists 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 currently 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 planning to apply the same techniques to data from other planetary science missions, including future missions.

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.

In November 2018, I will participate 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, as part of the 2nd World Vaccine and Immunotherapy Congress.

I always welcome interesting projects, and if your goal is to push the boundary of problems currently considered very difficult or impossible, these are the kinds of challenges that interest me the most. Please feel free to contact me if you are facing such a problem, and we can explore together ideas and possible solutions.