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Cody Rivera

Incoming Ph.D. Student
University of Wisconsin-Madison
codyjr3@illinois.edu


About Me

Hi! I’m an incoming 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 and verifying systems software.

I am currently at the University of Illinois Urbana-Champaign. I work as a visiting graduate student with Tianyin Xu on compositional verification of liveness properties for Kubernetes controllers. We are implementing support for this on top of Anvil, a Kubernetes verification framework for the Rust verifier Verus. I previously earned an M.S. degree supervised by Madhusudan Parthasarathy. Together, we developed a predictable automatic verification paradigm based on intrinsic, or local, definitions rather than recursive definitions.

Prior to that, I was an undergraduate student at the University of Alabama, where I did research in high-performance computing with Dingwen Tao. My research focused on using GPU performance engineering to improve scientific computing applications.

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.

Miscellaneous


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