Research Fellow @ Massachusetts Institute of Technology, Resident Physician @ Mount Sinai Hospital

Publication

An Unsupervised Machine Learning Approach to Assess the ZIP Code Level Impact of COVID-19 in NYC

New York City has been recognized as the world’s epicenter of the novel Coronavirus pandemic. To identify the key inherent factors that are highly correlated to the Increase Rate of COVID-19 new cases in NYC, we propose an unsupervised machine learning framework. Based on the assumption that ZIP code areas with similar demographic, socioeconomic, and mobility patterns are likely to experience similar outbreaks, we select the most relevant features to perform a clustering that can best reflect the spread, and map them down to 9 interpretable categories. We believe that our findings can guide policy makers to promptly anticipate and prevent the spread of the virus by taking the right measures.

MIT COVID-19 Datathon: data without boundaries
Publication

MIT COVID-19 Datathon: data without boundaries

The COVID-19 virus is a formidable global threat, impacting all aspects of society and exacerbating the existing inequities of our current social systems. As we battle the virus across multiple fronts, data are critical for understanding this disease and for coordinating an effective global response. Given the current digitisation of so many aspects of life, we are amassing data that can be extrapolated and analysed for the effective forecasting, prevention and treatment of COVID-19. With responsible stewardship, the tools and data-driven solutions currently in development for the COVID-19 pandemic will serve in the present while providing a much-needed foundation for a data-based response to future outbreaks and disasters.

In response to COVID-19, and using data generated thus far, groups at the Massachusetts Institute of Technology (MIT) in partnership with the American Civil Liberties Union (ACLU) of Massachusetts, Google Cloud, Beth Israel Deaconess Medical Center (BIDMC) Innovations Group and Harvard Medical Faculty Physicians at BIDMC came together to host the MIT Challenge COVID-19 Datathon (COVID-19 Datathon) from 10–16 May 2020. A ‘datathon’ adopts the ‘hackathon’ model, with a focus on data and data science methodologies, which promotes collaboration, design thinking and problem solving. In a typical hackathon, participants with disparate but complementary backgrounds work together in small groups for a prescribed and intensive ‘sprint’, typically over the course of one weekend, to develop a new concept, product or business idea. Subject matter expert ‘mentors’’ oversee and advise the teams. At the conclusion of the event, the teams present to a panel of judges. Winners are selected and are typically awarded seed funding. Datathons differ from hackathons in that the output is data analysis. MIT Critical Data, one of the organising groups of the COVID-19 Datathon, has hosted 36 international healthcare datathons.

Research Profiles

Contact

Physician-scientist with extensive experience developing and translating nanotechnologies and biomedical optical technologies from the bench to clinic in areas of genetics, oncology, and cardiovascular diseases. Extensive experience in community building in healthcare innovation, research, medical, and physician-scientist communities through various leadership roles.

Email: freddytn@mit.edu

Arnold O. Beckman Postdoctoral Fellow
Institute for Medical Engineering and Science

Research Fellow, MIT Innovation Initiative
Former Co-Director, MIT Hacking Medicine
Regional Director – Europe, MIT Hacking Medicine
Co-Director, MIT COVID-19 Challenge
Co-Director, MIT Hacking Racism Challenge

Massachusetts Institute of Technology
77 Massachusetts Avenue
Cambridge, MA 02139

Email: freddy.nguyen@mountsinai.org

Resident Physician, PGY-3,
Department of Pathology, Molecular and Cell-Based Medicine

Icahn School of Medicine at Mount Sinai
Mount Sinai Hospital
One Gustave L. Levy Place, Box 1194
New York, NY 10029