Rebecca Saul

rsaul at berkeley dot edu

2024-present
Ph.D. in Computer Science
University of California, Berkeley

2022-2024
Machine Learning Researcher
Booz Allen Hamilton
Annapolis Junction, MD

2018-2022
B.A. Mathematics
Harvard University
Cambridge, MA


I am currently a Ph.D. student in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley. I am working on problems at the intersection of AI and computer security, and am advised by Professor David Wagner. I am supported by the National Defense Science and Engineering Graduate Fellowship.

I was previously a Machine Learning Researcher at Booz Allen Hamilton, where I worked under Dr. Edward Raff on deep learning approaches to cybersecurity problems (e.g. malware detection and analysis).

I received my B.A. in Mathematics, alongside a minor in Computer Science, from Harvard in Spring 2022.

A complete CV is available upon request, and a partial CV is available here.

Publications

* denotes equal contribution

  • Saul, R., Liu, C., Fleischmann, N., Zak, R.J., Micinski, K., Raff, E., Holt, J. (2024). Is Function Similarity Over-Engineered? Building a Benchmark. NeurIPS 2024 Datasets and Benchmarks Track.
  • Liu, C.* , Saul, R.* , Sun, Y., Raff, E., Fuchs, M., Southard Pantano, T., Holt, J., Micinski, K. (2024). Assemblage: Automatic Binary Dataset Construction for Machine Learning. NeurIPS 2024 Datasets and Benchmarks Track. PDF
  • Saul, R., Alam, M.M., Hurwitz, J., Raff, E., Oates, T., Holt, J.. (2023). Lempel-Ziv Networks. "I Can't Believe It's Not Better! - Understanding Deep Learning Through Empirical Falsification" at NeurIPS 2022 Workshops, in Proceedings of Machine Learning Research 187:1-11 PDF
  • Saul, R. (2022). Efficient Factoring and the Number Field Sieve  [Senior thesis]. Harvard College. PDF
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