I am a 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. My research draws on ideas from economics, machine learning, probability theory, optimization, and beyond. For several years, my research has centered on elicitation and aggregation using prediction markets, wagering mechanisms, and other crowdsourcing approaches. For a more complete picture of what I do, take a look at some of my publications.
I am also an Adjunct Assistant Professor at UCLA where I advise doctoral student Chien-Ju Ho. I am not taking on new students of my own, but do get to work with awesome summer interns at MSR. Ph.D. students with strong publication records in relevant areas are encouraged to apply in late fall.
The Machine Learning Journal Special Issue on Computational Social Science and Social Computing has been published.
I am working on an exciting new project.
I recently co-organized the 6th annual New York Computer Science and Economics (NYCE) Day, which was held on November 1, 2013.
I also co-organized a workshop on Crowdsourcing: Theory, Algorithms, and Applications which was held at NIPS in December. Videos will be available online.
Jake Abernethy and I gave a tutorial on Prediction, Belief, and Markets at AAAI in Bellevue, Washington. Previous versions were presented at ICML, KDD, and the Machine Learning Summer School at UC Santa Cruz.
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, but will answer to either.
Get In Touch
The best way to reach me is by email. I am jenn at microsoft.com.