This page is no longer being maintained, as I have moved from Cornell to become a program officer at the National Science Foundation. I will leave it here as a memory/trace of my being here for as long as Cornell is willing to host it.
I was an associate professor in information science at Cornell University, and from 2016-2019 a program officer in Cyber-Human Systems and Secure and Trustworthy Cyberspace at the National Science Foundation. I graduated from the University of Minnesota's computer science department under advisors John Riedl and Loren Terveen.
My main interest for a long time has been helping people make sense of and manage information, both individually and as groups. More recently this has grown to include leveraging people's current behaviors online, along with social science theory, to produce individual and social goods that otherwise would not have been created.
More details on what this means are available in my research statement. For folks who want the overview of publications and collaborators, you can download my CV or my publications, or visit my Google Scholar profile.
These interests lead me into a number of cool domains; below are a few of the major ongoing themes. More details, and other projects, are available at the Reimagination Lab website.
I've been working on understanding and designing for participation in online communities for a long time, including MovieLens and Wikipedia (where I wrote the original version of SuggestBot). I tend to think about designs that map well to psychological and sociological theories about motivations for reading, sharing, and contributing content, with the hope that developing these designs provides technical novelty, theoretical insight, and practical benefit.
These days, my main involvement in this topic is through a research stream led by Brian McInnis, who I co-advise along with Gilly Leshed. Brian's done a rich set of studies around how crowd workers might contribute to large-scale deliberative discussions, tightly marrying ideas from the crowdwork literature with the theory and practice of facilitating deliberative discourse. This has led to interesting contributions about modeling and supporting newcomers' ability to productively contribute to deliberations. It also helped him develop policy and design recommendations around the design of Amazon Mechanical Turk based on participants' discussion of how Turk's policies around rejecting work place workers at risk.
People regularly disclose information about themselves online that is regularly used by both companies and researchers to make inferences about people's preferences, attitudes, and future behaviors. Much of my career arc has looked to leverage these data to improve people's experience of recommender systems and social media (including some of the work described above to support effective participation). Individual people also use these disclosures, both for forming impressions about and making decisions whether to interact with others, and (as in my early Pensieve work and the commercial systems Timehop and Facebook's Memories feature) as a personal archive that can support reminiscing, reflection, and self-understanding. This power of disclosed personal data raises natural questions about what can and should be done with it, and the benefits and risks of self-disclosure.
These days I am collaborating with folks on two main lines of related work. The first, led by Shruti Sannon, focuses on how people proactively manage their privacy concerns when asked for personal information by both companies and by other individuals. Shruti's come up with a number of contributions about how privacy concerns translate into privacy protective behaviors in different contexts -- specifically, privacy lies told to protect private information -- and the value of thinking about privacy through the lens of deception and morality. Through conducting the study on Mechanical Turk, Shruti found a number of stories of how the power relationships between employers and workers on Turk influence privacy decisions, leading to a study that will be published at CHI 2019.
The second, led by Yoon Hyung Choi and Natalie Bazarova, looks at how the combination of people's mental state and their perceptions of a communication channel's properties affects their concerns about self-presentation, their decisions to disclose personal information, the style and content of those disclosures, the way people react to those disclosures, and the social and psychological benefits people experience through those disclosures and responses.
This project, now quiescent but still worth listing, started from a question Amit Sharma asked: What if recommender systems were designed, not for individuals and purchasing, but from the ground up for social networks? What kinds of questions, use cases, metrics, and algorithms would emerge, and how would they be different from traditional recommender systems research?
One set of questions we've focused on is at the micro-level of sharing between individuals: how do people choose to share particular items with particular audiences, and what makes people accept the things other people share? Social influence, homophily, trust, and personal preferences all likely affect these decisions, making it important to account for them in models of sharing behavior and systems that support it.
A second set is at the macro level of how items diffuse in social networks: what effect do those micro-level choices have on the patterns of preferences and diffusion we observe in real networks, and are there regularities between networks? We'd like to build nuanced diffusion models that account for these micro-level choices and explain the diffusion observed in networks better than current models.
Because I am no longer at Cornell, and mostly doing independent work at NSF, I am not and will not be taking on any students for the forseeable future. That said, I'm often willing to talk with folks who're trying to think through applying for PhD programs, if they are close enough to my areas of work that I have useful things to say about their ideas and/or about people in my intellectual communities they might connect to.
I care deeply about teaching both undergraduate and graduate students. Most students and observing professors regard me as a solid classroom teacher, and I try to be a supportive, flexible advisor. I try to give students space to take assignments and projects in directions of their own interest, plenty of hands-on work both as individuals and groups, and copious support.
Having left Cornell, I am no longer teaching courses.
- INFO 6940, Finding, Filtering, and Sharing Information, taught Fall 2015. This course was aimed at giving MPS students a broad overview over systems where people interact around information, from technical, design, evaluation, and social perspectives.
- INFO 6010, Computational Methods for Information Science Research, taught Spring 2015 and Spring 2014. This evolved from INFO 4307/6307, Learning from Web Data, taught Fall 2011 and Fall 2009.
- INFO 6940, Readings in Recommender Systems, taught Spring 2014. PhD student Amit Sharma took the lead in developing and teaching the class, and I had his back.
- INFO/CS 1300, Introduction to Web Design and Programming, taught Fall 2014 and Fall 2013.
- INFO/COMM 3450 + INFO 4940, Human Computer Interaction, taught Fall 2012 (syllabus and Piazza site), Fall 2010, Spring 2008, and Spring 2007.
- INFO/COMM 6400, Advanced Human Computer Interaction, taught Spring 2012 (syllabus and Piazza site). I also taught a combined 4400/6400 version in Fall 2010, Fall 2008, and Fall 2007.
- INFO 6940, IS PhD professionalization, taught Fall 2011 as an experiment for helping the first-year PhDs think about school.
- INFO/CS 2300, Intermedate Web Design and Programming, taught Spring 2011.
- Introduction to Operating Systems, taught 2005 at University of Minnesota.
- Software Design, taught 2000 and 1999 at James Madison University.
- Algorithm Development, taught 2000 and 1999 at JMU.
- Software Engineering, taught 1999 at JMU.
- Being Productive with Computers, taught 1999 and 1998 at JMU.
A typical teaching scene from the 2012 version of HCI (pic by Chelsea Howe).