I recently completed my PhD in Computer Science from UMass Amherst, where I worked in the DREAM Lab with Dr. Gerome Miklau and Dr. Daniel Sheldon. 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.



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Recent News

  • [April 2023] I will be presenting my work on differentially private synthetic data at the UCLA Synthetic Data Workshop
  • [February 2023] I am honored to recieve the UMass CICS Outstanding Dissertation Award!
  • [September 2022] I joined the Federated Learning team at Google Research!
  • [September 2022] I presented AIM, our newest mechanism for differentially private synthetic data at VLDB 2022! [YouTube]
  • [June 2022] I gave invited talks on differentially private synthetic data at UC Santa Barbara, the Alan Turing Institue, and the MIT-IBM Watson AI Lab!

Publications

  • R. McKenna, B. Mullins, D. Sheldon, G. Miklau, “AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data” in Proceedings of 48th International Conference on Very Large Data Bases (VLDB), 2022. [Code]
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  • R. McKenna, S. Pradhan, D. Sheldon, G. Miklau, “Relaxed Marginal Consistency for Differentially Private Query Answering” in Proceedings of 35th International Conference on Neural Information Processing Systems (NeurIPS), 2021. [Code]
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  • R. McKenna, G. Miklau, D. Sheldon, “Winning the NIST Contest: A scalable and general approach to differentially private synthetic data” in Proceedings of the Journal of Privacy and Confidentiality (JPC) special issue on data challenges, 2021. [Code]
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  • R. McKenna, D. Sheldon, “Permute-and-Flip: A new mechanism for differentially private selection” in Proceedings of 34th International Conference on Neural Information Processing Systems (NeurIPS), 2020. [Code]
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  • R. McKenna, R.K. Maity, A. Mazumdar, G. Miklau, “A workload-adaptive mechanism for linear queries under local differential privacy” in Proceedings of 46th International Conference on Very Large Data Bases (VLDB), 2020. [Code]
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  • D. Pujol, R. McKenna, S. Kuppam, M. Hay, A. Machanavajjhala, G. Miklau, “Fair Decision Making using Privacy-Protected Data” in Proceedings of 3rd International Conference on Fairness, Accountability, and Transparency (FAT*), 2020.
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  • R. McKenna, D. Sheldon, G. Miklau, “Graphical-model based estimation and inference for differential privacy” in Proceedings of 36th International Conference on Machine Learning (ICML), 2019. [Code]
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  • 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]
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  • 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]
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  • 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.
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  • 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.
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  • 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.
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  • 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.
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Selected Blog Posts

Blog Posts