Cody Rivera

Cody Rivera

Computer Science Ph.D. Student

University of Illinois Urbana-Champaign


Hi! I’m a second-year Ph.D. student at the University of Illinois Urbana-Champaign. I am broadly interested in programming languages and formal methods, with more specific interests in logic and automated reasoning tools, as well as applying language and verification techniques to the concurrent setting. Some projects I’m working on are as follows: one project, with Dr. Madhusudan Parthasarathy, focuses on developing a predictable automatic verification paradigm that centers around local, or intrinsic definitions of data structures rather than recursive definitions. Another project, with Dr. Mahesh Viswanathan, focuses on differential privacy verification, where I am extending approximate solvers for nonlinear real arithmetic to improve differential privacy verification tools.

Prior to joining Illinois, I did research in high-performance computing, where I worked primarily under Dr. Dingwen Tao. I did this work as part of the Randall Research Scholars Program, an honors interdisciplinary undergraduate research program at the University of Alabama. My research focus was parallel algorithm design and GPU performance engineering for scientific computing. Specific work I contributed to includes the lossy compressor cuSZ (part of the SZ compressor project) and high-performance linear algebra operations.

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

  • Formal Methods
  • Logic and Automated Reasoning
  • Concurrency and Parallelism
  • Ph.D. in Computer Science, 2022-Present

    University of Illinois Urbana-Champaign

  • B.S. in Computer Science and Mathematics, 2018-2022

    University of Alabama (Tuscaloosa)



(2024). Checking δ-Satisfiability of Reals with Integrals. Under Submission.


(2024). Predictable Verification using Intrinsic Definitions. To appear in PLDI ‘24.

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(2022). Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs. In IPDPS ‘22.

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(2021). Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs. In CLUSTER ‘21.

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(2021). Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures. In IPDPS ‘21.

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(2021). TSM2X: High-performance tall-and-skinny matrix–matrix multiplication on GPUs. JPDC, Vol. 151.

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(2020). cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data. In PACT ‘20.

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  • FIRST-NAMEjr3 AT illinois DOT edu (where FIRST-NAME = "cody")
  • 201 N Goodwin Ave, Urbana, IL 61801
  • Siebel Center for Computer Science, Room 2111