In 2020, the I School has had the great pleasure of seeing several of its young faculty receive prestigious awards and fellowships for their research.
In early February, assistant professor Aditya Parameswaran was awarded the Sloan Research Fellowship, an honor given yearly to the brightest up-and-coming scientists in North America. Shortly after, in March, assistant professors Josh Blumenstock and David Bamman were awarded the National Science Foundation (NSF) CAREER award, which supports early-career faculty who have the potential to serve as academic role models in research and education, as well as lead advances in the mission of their organization.
In honor of their recent achievements, we’ve interviewed these three assistant professors to get a closer look at their current research, their experience teaching at the I School, and more.
Assistant Professor (I School and EECS department)
Joined the I School faculty in 2019
Joined the I School faculty in 2016
Join the I School faculty in 2015
Where were you before the I School?
Aditya: I was at the University of Illinois in computer science for four years. And prior to that I was at MIT as a postdoc, and a Ph.D. student at Stanford.
Joshua: I did my Ph.D. at the I School, finishing in 2012. I then spent 4 years in Seattle as an assistant professor at the Information School at the University of Washington.
David: I did my Ph.D. at Carnegie Mellon University (Language Technology Institute); before then, I was a senior researcher for the Perseus Project, a flagship of the digital humanities focusing on NLP (natural language processing) for Greek and Latin. My undergrad major was classics.
What are some courses you’ve recently taught? What’s teaching at the I School like?
Aditya: I’m currently teaching human-in-the-loop data management — a class that spans the spectrum from classical databases to modern data lifecycle management in the era of data science and AI. This class also takes a human-centered perspective on data management. The I School attracts a vibrant mix of students fresh out of their undergrad, and those that have spent some time in industry. The students from industry often bring unique real-world experience, while those fresh out of undergrad are more up-to-date on their fundamentals. This makes for more balanced discussions in class, and team projects end up being both creative and practical.
Joshua: Recently, I’ve taught Applied Machine Learning — a large lecture class, extremely high demand — and Big Data and Development — a smaller research-oriented class.
David: Some courses I’ve taught include Natural Language Processing (INFO 159/259) and Applied Natural Language Processing (INFO 256). Courses at the I School tend to fall in an interdisciplinary space — including mine, which focus on the core technical methods in natural language processing, but with a special view toward how they are applied to answer social and empirical questions about the world. Teaching is rewarding not only because of the heterogeneity of the students (where backgrounds in computer science and public policy give rise to great discussions in class), but also because of the appetite that students in the I School have to take what they’ve learned and immediately apply it to make the world a better place.
What kind of research are you currently doing?
Aditya: The work conducted by my group is centered on democratizing data science by building accessible and efficient tools for data at scale. Our work is inherently interdisciplinary, and so the I School’s vantage point as an interdisciplinary unit with an emphasis on the synergy between people, information, and technology is a great fit. I’ve already started collaborating with multiple faculty here.
Joshua: At the moment, I’ve put many of my ongoing research projects on hold to focus on COVID-19 response. This work has fallen into two types: 1) Helping policymakers and humanitarian organizations working in developing countries better respond to COVID-19, and 2) Using new sources of mobility data (e.g., from phones and social media) to better understand the spread of COVID-19.
David: At a broad level, my current research is focused on large-scale cultural analytics — mining cultural data in the form of books, newspapers, social media and music to help support empirical work in the humanities and social sciences. Most of my work either addresses a substantive research question in cultural analysis (such as measuring the varying attention given to male and female characters over 200 years of literary history) or aims to improve the low-level methodologies that would enable other researchers to conduct this work themselves (such as improving event detection or coreference resolution in literature).
What awards or fellowships have you received in the past year?
Aditya: Earlier this year, I was awarded the Sloan Research Fellowship in Computer Science. Late last year, I was awarded the VLDB (Very Large Data Bases) Early Career Research Contributions Award for “for developing tools for large-scale data exploration, targeting non-programmers.” I celebrate the accomplishments of my students as much as my own so I’d like to share a couple of them: a former student Silu Huang, now a senior researcher at Microsoft Research, won the SIGMOD Jim Gray Best Doctoral Dissertation award honorable mention a few days ago. Another student, Doris Lee, also at the I School, won a Facebook Research Fellowship earlier this year (one of about 36 out of 1800 applicants).
Joshua: The most recent/noteworthy is the NSF CAREER award.
David: The NSF CAREER award, focused on designing computational methods to reason about literature, and, in turn, learning from fiction to inform the design of systems in the real world.
In your own words, describe the I School community.
Aditya: The I School community — both the students and the faculty — care deeply about society, specifically, the impact of technology and data on society. I find that perspective to be increasingly important in this day and age.The I School community is small and fairly tightly knit. We seem to get along well and agree on most things, despite coming from very different backgrounds!
David: The I School is a very diverse, heterogeneous place — both the students and faculty have an amazing breadth in their backgrounds (from computer science, design, law, sociology, and the humanities), and the work that’s done here typically sits at the intersection of multiple fields. This heterogeneity makes for an intellectually vibrant community.
What do you hope to accomplish while here at the I School?
David: The work that I carry out at the I School, and plan for the future, is building out an emerging community of practice in cultural analytics — understanding the creative artifacts of culture in part through the use of empirical methods (such as NLP, ML and data science). While my work has always involved collaborations with researchers in the social sciences and humanities, part of the long-term plan for sustaining this vibrant community includes training the next generation of humanists and social scientists in empirical methods — students who are already have theoretical depth in a discipline and are looking for a computational perspective on their work. My goal is to send this next generation out into the world and have them look back at the UC Berkeley Information School as their home.