Tommy Tracy
Research Scholar at SRC Research Scholars Program
Charlottesville, Virginia, United States
Summary
👤
Senior🎓
Top SchoolTommy Tracy is a research-focused engineer with 12 years of experience at the intersection of machine learning, computer architecture, and FPGA acceleration, currently a Research Scientist at the University of Virginia. His work centers on automata processing and hardware-accelerated pattern detection for network intrusion detection, blending new algorithms with FPGA and DRAM-based architectures. Tommy’s PhD and postdoctoral research spanned software defined radio, bioinformatics, and runtime verification, giving him a rare cross-disciplinary perspective on applied ML and systems design. He has industrial experience porting ML workloads to novel hardware at Micron and earlier work in HMI and cybersecurity at GE, which informs his pragmatic approach to secure, high-performance systems. Colleagues describe him as a researcher who frequently turns theoretical insights into tangible accelerators and prototypes that move toward deployable network defenses.
12 years of coding experience
4 years of employment as a software developer
Bachelor of Science (B.S.), Computer Engineering, Bachelor of Science (B.S.), Computer Engineering at University of Virginia
Github Skills (37)
simulate9
crossword9
deterministic9
cpu9
asic9
von-neumann9
dfa9
automata9
fpga9
finite-automata9
co-simulation8
transform8
risc8
risc-v8
parse8
Programming languages (6)
Github contributions (5)
tjt7a/GR-Router
Nov 2013 - Apr 2015
Contributions:51 commits, 1 push, 1 branch in 1 year 5 months
tjt7a/ANMLZoo
Aug 2017 - Nov 2023
High-performance automata-processing engines are traditionally evaluated using a limited set of regular expression rulesets. While regular expression rulesets are valid real-world examples of use cases for automata processing, they represent a small proportion of all use cases for automata-based computing. With the recent availability of architectures and software frameworks for automata processing, many new applications have been found to benefit from automata processing. These show a wide variety of characteristics that differ from prior, popular regular-expression benchmarks, and these should be considered when designing new systems for automata processing. This paper presents ANMLZoo, a benchmark repository for automata-based applications as well as automata engines for both von-Neumann and reconfigurable data flow architectures.
Contributions:11 pushes in 6 years 3 months
casesvonvon-neumanntlacharacteristics
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