drew-rwx

San Marcos, Texas

Hello! My name is Andrew Rodriguez. I am a PhD student at Texas State University (TXST) studying computer science. I am a member of the Efficient Computing Laboratory (ECL), advised by Dr. Martin Burtscher. My research interests include nature-inspired algorithms, data compression, computational complexity and game theory.

Previously, I attended the University of Texas Rio Grande Valley (UTRGV) and received my master’s in computer science. I researched with the Algorithmic Self-Assembly Research Group (ASARG) at UTRGV under Dr. Tim Wylie and Dr. Robert Schweller. Before that, I attended the University of Texas at Austin and received my bachelor’s in computer science.

Outside of research, I love to read and write. My favorite book right now is ‘Negative Space’ by B. R. Yeager. I also enjoy watching movies, and making my own mixes of my favorite songs.

news

Dec 22, 2025	SLEEK: Compressing Memory Copies for Floating-Point Data on GPUs accepted at IPDPS’26
Dec 15, 2025	Sucessfully proposed my dissertation “High-Throughput Lossy and Lossless Compression of Scientific Data”
Sep 5, 2025	On the Compressibility of Floating-Point Data in Posit and IEEE-754 Representation accepted at DRBSD’25
Mar 1, 2024	Adaptive Per-File Lossless Compression of Floating-Point Data accepted at ESSA’24
Jun 20, 2023	Complexity of Reconfiguration in Surface Chemical Reaction Networks accepted at DNA ‘23

selected publications

SLEEK: Compressing Memory Copies for Floating-Point Data on GPUs

Anju Mongandampulath Akathoott, Andrew Rodriguez, and Martin Burtscher

In Proceedings of the 40th IEEE International Parallel and Distributed Processing Symposium, 2026
On the Compressibility of Floating-Point Data in Posit and IEEE-754 Representation

Andrew Rodriguez, and Martin Burtscher

In Proceedings of the SC ’25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2025

Abs

The IEEE 754 floating-point standard is the most used representation for real numbers in modern computer systems, despite issues in accuracy for certain applications. The posit format, which has several advantages, has been proposed as a direct drop-in replacement for IEEE floats. Many works compare the use of posits to floats in a wide range of scientific computing domains. However, there has not been any work looking into the compressibility of posit data. In this paper, we compare the compression ratios of different algorithms when the input is encoded in IEEE format and in posit format. We evaluate 5 lossless general-purpose compressors as well as several new compression algorithms synthesized by our LC framework on 14 single-precision inputs from the SDRBench suite, encoded in float and posit format. Our results show that that 4 of the 6 compressors yield an average of 2.59% reduction in compression ratio on posit data, whereas bzip2 provides a 1.74% increase in compression ratio, with XZ providing the highest ratios for both encodings.
Adaptive Per-File Lossless Compression of Floating-Point Data

Andrew Rodriguez, Noushin Azami, and Martin Burtscher

In 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2024