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, optimization, and probabilistic modelling. In my free time, I enjoy functional programming, solving project euler problems, working on sabermetrics problems, and blogging about all the above and more.


  • [PAPER] 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.
  • [PAPER] 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.
  • [PAPER] 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.
  • [SHORT PAPER] 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.
  • [POSTER] R. McKenna, A. Moody, T. Gamblin, M. Taufer “Forecasting Storms in Parallel Filesystems,” ACM Student Research Competition (SRC) Poster at the 27th IEEE International Conference on Supercomputing (SC15), Austin TX, 2015.
  • [PAPER] 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.
  • [PAPER] 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.


Probabilistic Modeling

How do you model a real world probabilistic process? And how do you efficiently do learning and inference under such a model?

Differential Privacy

How do you release useful statistical information from a data source while providing formal privacy gurantees for the individuals in the data?


How do you model baseball interactions? And how do you use that model to make better game-time decisions?

Personal Projects

I have an awesome Github page.


Laser Game

Laser game is a simple but addicting computer game that I wrote using Dr. Racket as a side project in my freshman year.


I wrote Battleship! in my intro to computer science class using Dr. Racket, and included support for two-player games over a network or single-player games against an AI.

Nim's Game

Nim's game is a mathematical strategy game often used as a toy example in combinatoric game theory. I created an android app for two variations of the game.

Minesweeper Bot

As a fun exercise in AI, I created a minesweeper bot - a program that takes control of your mouse and solves minesweeper boards for you!

RopeIt Pro!

RopeIt Pro! is a java application I created for my software engineering class that allows golfers to visualize their shots and track their progress.

Optimized LU Factorization

In my Computer Architecture class, I implemented a highly optimized LU Factorization algorithm in C to target multi-core machines.

Poker Bot

I used to have an ambitious goal of creating a bot that can play poker in the same way my other bot played minesweeper. I created a bot that can automatically play blackjack without human intervention, but never generalized it to more profitable games like texas holdem.

Project Euler

I have solved over 400 project euler problems since I started in January 2013, and I plan to continue solving more in the future. To keep in line with the Project Euler spirit, I am not publishing my solutions to GitHub.


University of Massachusetts Amherst

Ph.D. Computer Science — 2021 (est)

M.S. Computer Science — 2019 (est)

  • Machine Learning
  • Interactive Machine Learning
  • Probabilistic Graphical Models
  • Applied Statistics and Data Analysis
  • Algorithms
  • Empirical Research Methods
  • Randomized Algorithms
  • Databases

University of Delaware

B.S. Computer Science — 2016

B.S. Mathematics — 2016

Advanced Courses
  • Algorithms
  • Artificial Intelligence
  • Data Mining
  • Parallel Programming
  • Combinatorics
  • Abstract Algebra
  • Advanced Statistics
  • Computational Math I and II



I welcome you to contact me through one of the methods below.