Ryan Steed - Homepage

Ryan Steed (he/him)

I am a Ph.D. candidate at Carnegie Mellon's Heinz College of Information Systems & Public Policy and Machine Learning Department, advised by Alessandro Acquisti. I evaluate ethics and privacy in algorithmic systems to help improve tech governance. Previously, I studied Computational Economics at the George Washington University.


Working Papers


I'll be presenting our work-in-progress on privacy-preserving analytics adoption Sep. 11-12 at PEPR.


Our work-in-progress on privacy-preserving analytics adoption will be discussed at PLSC '23.


I'll be presenting our work-in-progress case study on privacy risks of public statistics to residents of subsidized housing at the NBER Conference on Data Privacy Protection and the Conduct of Applied Research May 4-5.


I'll be speaking about our Science paper at Hot Topics in the Science of Security.


I'm giving a talk on our Science paper at the Boston Data Privacy Group this Friday, 10/14/2022.


We wrote a blog post for Brookings about our study on bias in unsupervised computer vision and its policy implications.


Our paper on statistical uncertainty, differential privacy and census-guided funding was published in Science. We'll be presenting it at EAAMO 2022 October 6-9 - tune in!


I'm presenting some work-in-progress at the Theory and Practice of Differential Privacy Workshop @ ICML 2022 - come stop by our poster!


I'm (virtually) attending ACL 2022 to present our paper on bias transfer in pre-trained language models.


Received the Suresh Konda Ph.D. First Research Paper Award from Heinz for our paper on the disparate impacts of uncertainty (and privacy) on census-guided grants.


Awarded a Meta PhD Research Fellowship.


Awarded a TCS Presidential Fellowship.


Excited to be joining Oracle to work on fairness & bias as a research intern this summer.


I'm (virtually) attending FAccT 2021 and presenting our paper on unsupervised bias in computer vision - DM me on Twitter or in the conference platform if you want to chat!


Contributed to an awesome transparency tool for the Cook County Assessor's Office. Check out our presentation at the SolveForGood DataFest!


Our paper was accepted to the Participatory Approaches to Machine Learning workshop @ ICML! We propose and test an alternative way to elicit & incorporate stakeholder preferences in ML systems.


Excited to join Carnegie Mellon's Heinz College of Information Systems & Public Policy to start my PhD this fall. Thank you to all the wonderful people who helped me along the way!


Very proud to have (virtually) defended my undergraduate thesis in front of a wonderful committee of GWU faculty.


I'll be hanging out at AIES-2020! Say hi if you're around - I'm excited to learn about new work in the field.