Published On: Tue, Aug 14th, 2018

Harvard Data Science Initiative rolls out new research areas

After a successful first year, the Harvard Data Science Initiative (HDSI) will focus its second on five research themes designed to foster opportunities for collaboration both among Harvard’s Schools and beyond its walls.

Come fall, research activity for the initiative will coalesce around personalized health, evidence-based policy, networks and markets, data-driven scientific discovery, and methodology. HDSI will fundraise to these themes, aligning them to program offerings, research grants, and student and postdoctoral support.

“The accomplishments of our first year are built on Harvard’s commitment to advancing data science, and we look forward to expanding our impact in the years to come,” said HDSI Co-Director David Parkes, the George F. Colony Professor of Computer Science. “These new research themes reflect conversations with key players across the University, and we feel confident that they convey the essence of what it means to do data science at Harvard. They recognize that successful data science must, first and foremost, respect rigorous data science methodologies, and then take things one step further, through innovative application to problems at the forefront of all fields.”

Establishing a strong foundation for future growth, since its launch in March 2017 HDSI has introduced three new master’s degree programs: biomedical informatics at Harvard Medical School (which also offers a Ph.D. program), health data science at the Harvard T.H. Chan School of Public Health, and data science through the Faculty of Arts and Sciences. HDSI-affiliated faculty at the Business School and the John A. Paulson School of Engineering and Applied Sciences have also created a certificate program in business analytics, a corporate membership program — the first of its kind at the University — to facilitate relationships between members of academia and industry looking to address challenges in data science.

To foster collaborations with researchers outside of Harvard, earlier in the year HDSI convened three roundtable discussions between Elsevier scientists and Harvard faculty to explore research partnerships in three broad areas: key factors for scientific impact on policy, interpretative models for precision medicine, and social and behavioral determinants of health care. In addition, a master sponsored research agreement has been created.

“Faculty are eager to use real-world data sets to answer complex questions of profound societal importance,” said Elizabeth Langdon-Gray, HDSI’s executive director. “Through these kinds of relationships, the HDSI is able to facilitate access to a wealth of data, and also foster the relationships between academic and industry researchers that are critical to meaningful engagement with that data.”

HDSI funds both faculty research grants and postdoctoral fellowships. At an open house event in May, three faculty awardees presented work that showcased the core of the initiative’s mission to support research.

Presenters included Professor of Physics Matthew Schwartz, who gave a lightning talk on how machine learning, which uses statistical models to automate computer science, is reshaping the frontiers of particle physics by significantly speeding developments; Gordon McKay Professor of Computer Science Yiling Chen, who spoke on the challenges of eliciting and studying data that isn’t based in directly observable, empirical information (such as subjective data culled from surveys); and T.H. Chan School Assistant Professor of Biostatistics Jeffrey Miller, who is working close the loop between statistics and science in the field of aging.

New grants from HDSI’s competitive research fund, which disbursed on June 1, will address opportunities in fields ranging from smartphone-based digital phenotyping to statistical modeling in climate science, scalable algorithms, and functional neuroscience.

HDSI postdoctoral fellows are also working on novel data science projects. Under the mentorship of cognitive psychologist Steven Pinker, Institute for Quantitative Social Science Director Gary King, and Ingrid Monson, Quincy Jones Professor of African American Music, fellow Samuel Mehr is directing Harvard’s Music Lab, which “asks what music is, how music works, and where music comes from” through large-scale online experiments analyzing ethnographies and field recordings from The Natural History of Song database. One of Mehr’s projects examines the underlying structure of lullabies. This research, conducted with Associate Professor of Psychology Max Krasnow, employs a data-driven, cultural evolutionary approach to learning more about the music itself, theorizing that parents developed the songs to let infants know that they were being taken care of, so the parents could leave them to sleep and accomplish other tasks.

In the fall, four new postdoctoral fellows, chosen from more than 260 applications, will join HDSI: Karianne Bergen, a computational and mathematical engineer from Stanford; Jenny Chen, who studied health sciences and technology at MIT; Max Kleiman-Weiner, a brain and cognitive scientist, also from MIT; and Gonzalo Mena, a statistician from Columbia. These fellows will have the opportunity to be mentored by and collaborate with the growing community of more than 100 Harvard scholars who share ideas and expertise in the data science.

“Today’s complex social problems require large-scale, partnered interventions,” said HDSI Co-Director Francesca Dominici, professor of biostatistics at the Harvard Chan School. “Through Harvard’s Data Science Initiative, computer scientists are working with oncologists, who are working with neuroscientists, who are working with data scientists to tackle vast, data-driven projects with shared topics of interest.

“We are imagining bold visions of how we’d like to change the world, and then actually going about doing so.”

The Data Science Initiative will hold its annual conference on Oct. 17 and 18, with details available here.

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Science & Technology – Harvard Gazette

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Harvard Data Science Initiative rolls out new research areas