Jenn Wortman Vaughan


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.

For a more complete picture of what I do, take a look at some of my publications or my reasonably up-to-date research statement.

I am also an Adjunct Assistant Professor at UCLA. 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. (List me as a contact to make sure I see your application.)

My standard bio (for talk announcements, etc.) is here.

What's New?

I am serving as Workshop Co-Chair of EC 2017.

I will be speaking at the upcoming Technology Policy Institute event on Artificial Intelligence: The Economic and Policy Implications.

I will be giving an invited tutorial at NIPS 2016 on Crowdsourcing: Beyond Label Generation. Check back here for more information closer to December.

I'm co-organizing a workshop at HCOMP on Mathematical Foundations of Human Computation.

This spring/summer I am serving on the Senior Program Committees of EC and HCOMP, as an Area Chair of NIPS, and on the (new) NIPS workshop and symposium selection committee.

We've released a white paper on Mathematical Foundations of Social Computing drawing on discussions from our CCC visioning workshop last June.

I'm really excited about this new WWW paper with Ming Yin, Mary Gray, and Sid Suri on mapping the network of workers on Mechanical Turk.

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.)

The TENTH Women in Machine Learning Workshop is taking place in Montreal this December. Amy Greenwald, Hanna Wallach, and I will give the opening remarks. So excited to celebrate a decade of WiML and the progress we've made!

MSR-NYC is now accepting applications for postdocs in areas including algorithmic economics and machine learning. Strong graduating Ph.D. candidates are encouraged to apply!

I recently became an Associate Editor of ACM Transactions on Economics and Computation, a journal focused on the intersection of computer science and economics.

I am giving a talk on our work on incentivizing high quality crowdwork at the Conference on Digital Experimentation at MIT in October.

I am giving an invited talk at Strata in NYC this September.

I have been elected Secretary-Treasurer of SIGecom starting July 1.

I am excited to be a mentor for MSR-NYC's second Data Science Summer School. DS3 aims to increase diversity in computer science by providing an eight-week introduction to data science for college students in New York City.

My student Chien-Ju Ho just defended his thesis and was awarded the Google Outstanding Graduate Research Award at UCLA. Congratulations, Chien-Ju!

I am co-organizing a CCC visioning workshop on Theoretical Foundations for Social Computing this June.

More activities...


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