I am a Senior Researcher at Microsoft Research, New York City, a relatively new collaborative and interdisciplinary basic research lab.
My research interests are in machine learning and algorithmic economics. I am especially interested in the interaction between people and AI, and have often studied this interaction in the context of prediction markets and other crowdsourcing systems. My passion is for AI that augments, rather than replaces, human abilities. A big fraction of my work has been theoretical—I like a nice clean model that captures the essence of a problem and provable guarantees—but, thanks to the amazing interdisciplinary environment where I work, I've begun incorporating more experiments into my research in order to better understand and model human behavior in technological systems. I occasionally speak about societal issues around AI, and lately I've been spending a big chunk of my time on fair and interpretable machine learning.
For a more complete picture of what I do, take a look at some of my publications.
At MSR I have had the opportunity to work with an amazing set of summer interns including Chien-Ju Ho (who was also my PhD student at UCLA!), Alice Gao, Hoda Heidari, Ming Yin, Rachel Cummings, Ryan Rogers, Nika Haghtalab, Rupert Freeman, Rediet Abebe, Manish Raghavan, and Forough Poursabzi-Sangdeh. This summer I'm excited to welcome David Alvarez Melis, Ken Holstein, and Lily Hu. We start looking at internship applications for the summer in late fall. (List me as a contact to make sure I see your application.) Typically our interns are Ph.D. students with strong publication records in areas relevant to our lab.
My standard bio (for talk announcements, etc.) is here.
A fully revamped and expanded version of my crowdsourcing survey for machine learning researchers is out in JMLR.
I recently had the chance to speak about ethical issues in AI at the 2018 Economist Innovation Summit.
Sami Kaski and I will return as Tutorial Co-Chairs for NIPS 2018.
I posted a transcript of my opening remarks at WiML 2017: Nine things I wish I had known the first time I came to NIPS.
Video and slides from my talk at the NIPS Interpretability Symposium are now available here.
I am super excited to be giving an invited talk at the NIPS 2017 Symposium on Interpretable Machine Learning.
I am co-organizing the NIPS 2017 Workshop on Learning in the Presence of Strategic Behavior. Submissions are due October 23, but we encourage anyone interesting in attending to register now because NIPS is selling out fast!.
I am the track co-chair for the WWW 2018 Web Economics track. Submissions are due October 31 (abstracts by October 26).
I am excited to be giving the opening keynote at Broadening Participation in Data Mining 2017 at KDD.
I will be giving an invited talk about wagering mechanisms at the EC 2017 Workshop on Forecasting.
I am serving as Tutorials Co-Chair of NIPS 2017.
Hanna Wallach and I wrote a general-audience essay on The Inescapability of Uncertainty: AI, Uncertainty, and Why You Should Vote No Matter What Predictions Say.
I will be giving an invited talk at the NIPS 2016 Workshop on Crowdsourcing and Machine Learning.
I am serving a two-year term as Workshops Co-Chair of EC 2017 and EC 2018.
I will be speaking at the upcoming Technology Policy Institute event on Artificial Intelligence: The Economic and Policy Implications.
I was interviewed on Talking Machines, a general audience podcast on machine learning. Listen to me talk about the history of WiML, my career path, my favorite papers, and my research vision. (My segment runs from around 28:25 to the end.)
I came to MSR-NYC from UCLA where I was an Assistant Professor in the Computer Science Department. Prior to that I spent a year as a Computing Innovation Fellow at Harvard University where I was a member of the EconCS group and the Theory group, and an affiliate of the Center for Research on Computation and Society.
I received my Ph.D. in Computer and Information Science from the University of Pennsylvania in 2009. I was extremely lucky to be advised by Michael Kearns. My doctoral dissertation, Learning from Collective Preferences, Behavior, and Beliefs, introduced a series of new learning models and algorithms designed to address the problems commonly faced when aggregating local information across large population, and was awarded Penn's Rubinoff award for innovative applications of computer technology. During my time at Penn, I spent two fun summers interning in New York, first with the Machine Learning and Microeconomics groups at Yahoo! Research and then in the research group at Google.
Before coming to Penn, I completed a Masters in Computer Science at Stanford where I got my first taste of research working with the Multiagent Group. Further back in the day, I was a carefree undergrad at BU.
You might remember me as Jenn Wortman. When I got married, I took Vaughan (pronounced "von") as my "official" last name and moved Wortman to my middle name. This never fails to confuse people. I use both names together professionally and prefer that you do too.
Get In Touch
The best way to reach me is by email. I am jenn at microsoft.com.