News
[Dec 2023] Our paper
Privacy Auditing in One (1) Training Run received an
outstanding paper award at NeurIPS 2023!
[June - Sept 2023] I enjoyed hosting
Karan Chadha as a student researcher, together with Nicolas Papernot! Stay tuned for his work, and hire him - he's on the job market!
[Aug 2023] Our paper
Tight Auditing of Differentially Private Machine Learning won a best paper award at USENIX Security 2023!
[July 2023] Our paper
"Extracting Training Data from Large Language Models" won runner up for the
Caspar Bowden award at PETS 2023!
[June 2023]
Lishan Yang and I cochaired the
DSML 2023 workshop, colocated with DSN 2023 in Porto, Portugal! Thank you to everyone involved, especially our attendees, keynote speakers (Paolo Rech and Andrew Paverd) and our steering committee!
About Me
I am a research scientist at Google DeepMind, working on
Andreas Terzis's team. I work on security, privacy, and memorization in machine learning systems. This includes directions like
privacy auditing,
memorization in generative models,
data poisoning, and
model stealing.
I received my PhD from Northeastern University, where I was fortunate to be advised by
Alina Oprea and
Cristina Nita-Rotaru, as a member of the Network and Distributed Systems Security Lab (
NDS2).
In other news, I enjoy running, swimming, and biking. I'm also a retired
Super Smash Brothers tournament competitor.