I am a Senior Researcher at Microsoft Research, New York City, a relatively new collaborative and interdisciplinary basic research lab.
The goal of my research is to develop mathematically rigorous, empirically grounded frameworks to understand and design algorithms for eliciting and aggregating information, preferences, and beliefs. I am interested in developing general methods that allow us to reason formally about the performance of algorithms with human components in the same way that traditional computer science techniques allow us to formally reason about algorithms that run on machines alone. My research draws on ideas from economics, machine learning, probability theory, optimization, and beyond. For several years, my research has centered mostly on elicitation and aggregation using prediction markets, wagering mechanisms, and other crowdsourcing approaches.
At MSR I often have the opportunity to work with amazing summer interns. Ph.D. students with strong publication records in relevant areas are encouraged to apply in late fall. (List me as a contact to make sure I see your application.)
My standard bio (for talk announcements, etc.) is here.
I will be giving another version of my tutorial on Making Better Use of the Crowd at ACL 2017. The survey paper/position paper/best practice guide I wrote to accompany the NIPS tutorial is available here and video is available here.
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.
We've released a white paper on Mathematical Foundations of Social Computing drawing on discussions from our CCC visioning workshop last June.
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.