Casey Meehan Email & Phone Number

San Francisco, California, United States

San Francisco Bay Area

Greater San Diego Area

San Francisco, California, United States

I joined with the broad goal of assessing whether self-supervised vision models (e.g. VICReg, SimCLR, Dino) memorize their training images. This has serious privacy implications since it opens the risk that images could be extracted from a trained model. However, how to define memorization for SSL models, how to quantify it, and how to demonstrate that.

Tumult Labs is making differential privacy (DP) accessible with their own platform offering DP data analysis and privacy accounting (quantification of privacy risk). Their clients in many cases have to release huge numbers of queries from massive datasets, which can lead to a high risk of exposing data if not careful. To address this problem, I worked with.

Palo Alto, CA

The Tesla sensing team wanted to design an in-house ultrasonic sensing system for running lab tests and for potential integration into vehicles. I joined to build this system from the ground up. That meant interfacing with chip vendors to evaluate the potential of their ultrasonic sensing products, electronic testbench construction, and algorithm design to.

Norwood, MA

I was a part of an analog design team that worked in the automotive division and then the audio division. The team focused on sigma-delta ADC design --- a broadly useful architecture for many application spaces. I designed, simulated, verified, and oversaw the layout of several blocks in an (at the time) SOTA 40nm process. Our designs are now in production.

B.S., Electrical Engineering & Signal Processing

M.S., Computational Science And Engineering

Doctor Of Philosophy - Phd, Computer Science

My work focused on privacy in various ML settings, both from an empirical perspective (model memorization and inversion attacks) and a.