Olive Franzese-McLaughlin




About Me








Research

My work is about auditing AI/ML systems without leaking sensitive information, by using secure cryptographic protocols. This allows auditors to provably verify that AI/ML systems are reliable, unbiased, and privacy-preserving, even in the presence of misaligned incentives and/or adversarial behavior. It also allows service providers build trust by actively attesting that their models are trustworthy, without revealing any information about user data or proprietary model parameters. I believe techniques like these are integral building blocks for effective regulation of AI/ML.

My published works in this area include methods for cryptographic verification of fairness (Alan Turing Institute Research Highlight, + followup work), confidence calibration, differential privacy, and robustness using zero-knowledge proofs and secure multiparty computation. I’ve worked on efficient zero-knowledge proof building blocks as well.

I have also contributed work on data-driven computational biology algorithms for cancer diagnostics that are more accessible to patients, signaling pathway extraction from protein-protein interaction data, and systematic experiment planning. In ancient history, I was a wet lab biologist.

Publications

  1. Secure Noise Sampling for Differentially Private Collaborative Learning Olive Franzese, C Fang, R Garg, S Jha, N Papernot, X Wang, A Dziedzic. Accepted to ACM CCS ’25.
  2. Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention S Rabanser, AS Shamsabadi, Olive Franzese, X Wang, A Weller, N Papernot. Accepted to ICML ’25.
  3. OATH: Efficient and Flexible Zero-Knowledge Proofs of End-to-End ML Fairness Olive Franzese, AS Shamsabadi, H Haddadi. arXiv preprint.
  4. Robust and actively secure serverless collaborative learning Olive Franzese, A Dziedzic, C Choquette-Choo, M Thomas, M Kaleem, S Rabanser, C Fang, S Jha, N Papernot, X Wang. NeurIPS 2023.
  5. Confidential-PROFITT: Confidential PROof of FaIr Training of Trees AS Shamsabadi, S Wyllie, Olive Franzese, N Dullerud, S Gambs, N Papernot, X Wang, A Weller. ICLR 2023. Notable Top 5% Commendation. Alan Turing Institute Top 10 Research Highlight of 2022-23.
  6. Constant-overhead zero-knowledge for RAM programs Olive Franzese, J Katz, S Lu, R Ostrovsky, X Wang, C Weng. ACM CCS 2021.
  7. ScalpelSig Designs Targeted Genomic Panels from Data to Detect Activity of Mutational Signatures Olive Franzese, J Fan, R Sharan, MDM Leiserson. RECOMB 2021, Journal of Computational Biology 2022.
  8. Hypergraph-based connectivity measures for signaling pathway topologies Olive Franzese, A Groce, TM Murali, A Ritz. PLoS Comp Biol 2019.
  9. CrossPlan: systematic planning of genetic crosses to validate mathematical models A Pratapa, N Adames, P Kraikivski, Olive Franzese, J J Tyson, J Peccoud, TM Murali. Bioinformatics 2018.
  10. Stem Cell-Like Dog Placenta Cells Afford Neuroprotection against Ischemic Stroke Model via Heat Shock Protein Upregulation S Yu, N Tajiri, Olive Franzese, M Franzblau, E Bae, S Platt, Y Kaneko, C V Borlongan. PLoS One 2013.
  11. Human umbilical cord blood for transplantation therapy in myocardial infarction S A Acosta, Olive Franzese, M Staples, N L Weinbren, M Babilonia, J Patel, N Merchant, A J Simancas, A Slakter, M Caputo, M Patel, G Franyuti, M H Franzblau, L Suarez, C Gonzales-Portillo, T Diamandis, K Shinozuka, N Tajiri, P R Sanberg, Y Kaneko, L W Miller, C V Borlongan. Journal of stem cell research & therapy 2013.
  12. Stroke in the eye of the beholder H Ishikawa, M Caputo, Olive Franzese, N L Weinbren, A Slakter, M Patel, C E Stahl, M A Jacotte, S Acosta, G Franyuti, K Shinozuka, N Tajiri, H van Loveren, Y Kaneko, C V Borlongan. Med Hypotheses 2013.
  13. Nestin overexpression precedes caspase-3 upregulation in rats exposed to controlled cortical impact traumatic brain injury Y Kaneko, N Tajiri, S Yu, T Hayashi, C E Stahl, E Bae, H Mestre, Olive Franzese, A Rodrigues Jr, M C Rodrigues, H Ishikawa, K Shinozuka, W Hethorn, N Weinbren, L E Glover, J Tan, A H Achyuta, H Van Loveren, P R Sanberg, S Shivsankar, C V Borlongan. Cell medicine 2012.

Funding

I am a Vector Distinguished Postdoctoral Fellow, an NSF GRFP fellow, and a National Cancer Institute CRTA recipient.




Teaching & Mentorship

Teaching and mentoring students is very important to me. In my pedagogy, I aim to expose the deep and compelling questions that underlie computing, rather than just teaching about the specifics of computing systems themselves. I take a lot of inspiration from the writings of contemporary mathematics educator Paul Lockhart, and seek to apply his insights in computer science education. I am especially interested in the developing role of computing as part of a liberal arts education. I think we are in great need of computing practitioners who can reason about the social context surrounding computing, in addition to programs themselves. I think a liberal arts education is exactly the right place to cultivate students with mastery of both. I was on the ACM SIGSCE 2025 Committee on Computing Education in Liberal Arts Colleges. Below are some of my experiences in undergraduate teaching and mentorship.

Research Advisees

Visiting Professor, Reed College

Graduate TA, Northwestern & University of Maryland

Undergraduate Teaching Assistant, Reed College