I am a PhD student studying Computer Science at UMass Amherst.

I work in the Database Lab with Dr. Gerome Miklau . My research interests include data privacy, machine learning, numerical optimization, probabilistic modeling, and high-performance computing. In my free time, I enjoy functional programming, solving project euler problems, analyzing baseball data, and blogging about all the above and more.

Recent News

  • [January 2019] Gave a tutorial on linear query answering under differential privacy with Gerome Miklau and Sasho Nikolov at the Data Privacy Bootcamp (Simons Institute, UC Berkeley)
  • [January 2019] Published a pre-print on graphical-model based estimation and inference for differential privacy on arxiv!
  • [January 2019] Passed my portfolio with distinction!
  • [November 2018] Accepted an internship at Microsoft for summer 2019!
  • [October 2018] Published my code for the high-dimensional matrix mechanism on GitHub!
  • [October 2018] Presented my work on the high-dimensional matrix mechanism at TPDP in Toronto!
  • [August 2018] Presented my work on the high-dimensional matrix mechanism at VLDB in Rio de Janeiro!
  • [July 2018] Presented my work on efficient inference in differential privacy at the PiMLAI workshop at ICML in Sweden!
  • [May 2018] Presented my work on the high-dimensional matrix mechanism at the U.S. Census Bureau!
  • [April 2018] Attended the RSA Conference in San Francisco as a RSA Security Scholar!


  • R. McKenna, G. Miklau, M. Hay, A. Machanavajjhala “Optimizing error of high dimensional statistical queries under differential privacy,” in Proceedings of 44th International Conference on Very Large Data Bases (VLDB), 2018. [Code]
  • D. Zhang, R. McKenna, I. Kotsogiannis, M. Hay, A. Machanavajjhala, G. Miklau “Ektelo: A Framework for Defining Differentially-Private Computations,” in Proceedings of Special Interest Group on Management of Data (SIGMOD), 2018. [Code]
  • G. Bernstein, R. McKenna, T. Sun, M. Hay, G. Miklau, D. Sheldon “Differentially Private Learning of Undirected Graphical Models using CGMs,” in Proceedings of 34th International Conference on Machine Learning (ICML), 2017.
  • R. McKenna, S. Herbein, A. Moody, T. Gamblin, M. Taufer “Machine Learning Predictions of Runtime and IO Traffic on High-end Clusters,” in Proceedings of 2016 IEEE International Conference on Cluster Computing (CLUSTER), 2016.
  • R. McKenna, V.K. Pallipuram, R. Vargas, M. Taufer “From HPC Performance to Climate Modeling: Transforming Methods for HPC Prediction into Models of Extreme Climate Conditions,” in Proceedings of the 10th IEEE International Conference on e-Science and Grid Technologies (eScience), Munich, Germany, 2015.
  • C. Sahin, P. Tornquist, R. McKenna, Z. Pearson, J. Clause “How Do Code Obfuscations Impact Energy Usage?” in Proceedings of the 30th International Conference on Software Maintenence and Evolution (ICSME), 2014.

Selected Blog Posts