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Cody Rivera

Ph.D. Student
University of Wisconsin-Madison
cjrivera@wisc.edu


About Me

Hi! I’m a Ph.D. student at the University of Wisconsin-Madison. I am broadly interested in programming languages and formal methods, with more specific interests in concurrency, automated reasoning, and verifying systems software.

Most recently, I worked as a visiting student at the University of Illinois Urbana-Champaign on verifying liveness properties of Kubernetes controllers, where I was supervised by Tianyin Xu. I extended the Kubernetes verification framework Anvil with verified implementations of built-in controllers and support for compositional verification.

Previously, I earned an M.S. in Computer Science at the University of Illinois Urbana-Champaign developing a predictable automatic verification paradigm based on intrinsic (or local) definitions rather than recursive definitions, supervised by Madhusudan Parthasarathy. Prior to that, I earned a B.S. in Computer Science and Mathematics at the University of Alabama, where I did research in high-performance computing supervised by Dingwen Tao.

See my CV here, and please don’t hesitate to reach out to me.

Education

Research Interests

News

Publications

  1. Checking δ-Satisfiability of Reals with Integrals
    Cody Rivera, Bishnu Bhusal, Rohit Chadha, A. Prasad Sistla, Mahesh Viswanathan
    Object-Oriented Programming, Systems, Languages & Applications (OOPSLA), 2025.
    PDF Code DOI

  2. Anvil: Building Kubernetes Controllers That Do Not Break
    Xudong Sun, Jiawei Tyler Gu, Cody Rivera, Tej Chajed, Jon Howell, Andrea Lattuada, Oded Padon, Lalith Suresh, Adriana Szekeres, Tianyin Xu
    USENIX ;login:, 2024.
    Link

  3. Predictable Verification using Intrinsic Definitions
    Adithya Murali, Cody Rivera, P. Madhusudan
    ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2024.
    PDF Code DOI   ACM Europe Best Paper Award

  4. Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs
    Cody Rivera, Sheng Di, Jiannan Tian, Xiaodong Yu, Dingwen Tao, Franck Cappello
    IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2022.

  5. Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs
    Jiannan Tian, Sheng Di, Xiaodong Yu, Cody Rivera, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello
    IEEE International Conference on Cluster Computing (CLUSTER), 2021.

  6. Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures
    Jiannan Tian, Cody Rivera, Sheng Di, Jieyang Chen, Xin Liang, Dingwen Tao, Franck Cappello
    IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2021.

  7. TSM2X: High-Performance Tall-and-Skinny Matrix–Matrix Multiplication on GPUs
    Cody Rivera*, Jieyang Chen*, Nan Xiong, Jing Zhang, Shuaiwen Leon Song, Dingwen Tao (* equal contribution)
    Journal of Parallel and Distributed Computing (JPDC), Volume 151, 2021.

  8. cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data
    Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman Fulp, Robert Underwood, Sian Jin, Xin Liang, Jon Calhoun, Dingwen Tao, Franck Cappello
    ACM International Conference on Parallel Architectures and Compilation Techniques (PACT), 2020.

Mentoring

I am grateful to mentor these excellent students:

Miscellaneous


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