HDSR includes both an video recording and written transcript of the interview below. The transcript that appears below has been edited for purposes of grammar and clarity.
Francesca Dominici (FD): Hi, my name is Francesca Dominici. I am a professor of biostatistics, data science, and population health at the Harvard T. H. Chan School of Public Health; a Co-Faculty Director of the Harvard Data Science Initiative; and now an interim co-Editor-in-Chief of the Harvard Data Science Review.
David C. Parkes (DCP): Hi, I'm David Parkes. I'm a professor in the computer science department over in the School of Engineering and Applied Sciences. I am also co-Faculty Director of the Harvard Data Science Initiative, and now also interim co-Editor-in-Chief of the Harvard Data Science Review.
FD: First of all, we wanted to thank Xiao-Li Meng, the Editor-in-Chief of the journal, who also launched the journal. We wanted to thank him for its success in getting the PROSE Award—“PROSE” stands for Professional and Scholarly Excellence—for this journal, and how honored we are, Xiao-Li, to take the role of co-Editors-in-Chief as you are going into sabbatical. I also want to thank the wonderful members of the editorial board.
So, what is a fun fact about me? I have to confess that one of the reasons I decided to move to Boston from Baltimore is because I’m a marathon runner, and I love running the Boston Marathon. I got super interested in data science when I was involved in a project where I was able to gather data from thousands and thousands of marathon runners in the past few years, and from that data, could identify the best split time trajectory to qualify for the Boston Marathon.
More seriously, how did I get into data science? I’ve always been interested in advancing the science of data, so as a statistician by training, I always wanted to develop new methodologies for the design and the analysis of data to address important questions. More recently, I've been really interested in causal inference and machine learning, because assessing causality, to me, is really important for making policy decisions.
So, what does data science mean to me? Well, I think these days, as we are battling both COVID-19 and climate change, it’s becoming more and more evident what is the critical, important role of data science, and again, especially in the context of making policy decisions. To me, data science is how to extract knowledge from real-world data that can be translated in policy action to benefit society.
What type of submissions would we like to see? What's wonderful about the Harvard Data Science Review is that it’s a journal that features foundational thinking, research milestones, educational innovation, and major applications, and so we have a primary emphasis on reproducibility, replicability, and readability. It’s really a global forum on everything data science and data science for everyone. David, back to you.
DCP: Thank you, Francesca. I was not going to mention my sporting endeavors, but seeing as you did, I used to race road bike when I was in graduate school, but unlike unlike you, I now am not nearly as sporty now. Another confession is that I was a young data scientist, and in the months leading up to going on holiday in Europe, I would chart the trends of the weather in the city that we were to visit. Also, when we would play Scrabble as kids, I would chart the scores that me, my sister, my mum, and my dad were getting. I also remember a time in secondary school when I was a member of a team competing in a simulated business competition— I spent far too long trying to reverse engineer, from the data, the way the simulator worked. So, always been into data, never knew I could be a data scientist.
My own research program relates to AI and systems with multiple AIs , as well as people, and applies to the design of and operation of economic and social systems, and to human decision-making. Much as you described, Francesca, for me, also the essence of data science is working with data—typically complex, often observational, multifaceted—to get some new understanding. This can mean lots of different things, and it requires a myriad of very careful attention, all the way from the data, through to the methodology, through the interpretation, and then how we communicate the results of our data science.
Like you, I've been very excited about our journal, because by design it reflects the breadth of the field of data science. Methodology, applications, ethical questions, industry application, education—just to name a few. I would like to encourage authors to continue to submit papers that can benefit from the broad readership of the journal. I also would like to encourage papers that educate about this new field and communicate breakthroughs, challenges, where care is required, and the shape and the contours of this very fluid space. Also, as a computer scientist, I'm eager to encourage authors in computer science to consider the journal as a home for their best work.
In terms of goals, we will continue to work as co-interim Editors-in-Chief to represent multiple different voices. I just wanted to note that in the past year we've had downloads from 81 countries all around the world. We will work to maintain the mission of the journal, which is to define and shape data science as a rigorous field of study with global impact. Looking forward to working with you!
This transcript is © 2021 by the author(s). The editorial is licensed under a Creative Commons Attribution (CC BY 4.0) International license (https://creativecommons.org/licenses/by/4.0/legalcode), except where otherwise indicated with respect to particular material included in the article. The article should be attributed to the authors identified above.
Photograph by Kris Snibbe/Harvard Public Affairs and Communications