I’m starting a series of blog posts based on the notes I take when I read papers. The first one is about a paper I’m presenting in my Neural Network Mathematics reading group on 4/5: Adversarial Examples from Computational Constraints by Bubeck, Price, and Razenshteyn.
This semester, I participated in Berkeley’s 2-day PhD workshop on Communications for Engineering Leaders! We were challenged to design 5-minute lightning research talks, accessible to a broad audience of engineers and scientists.
I’m turning my talk into a blog post: coming soon!
Last year, I was accepted to the 2020-21 AMS research community on the Inverse Eigenvalue Problem. Since then, we’ve had an intensive learning workshop, and now a special session at the 2021 Joint Mathematics Meetings, leading up to our (virtual) collaboration this summer.
Check out our session at the 2021 JMM site for more information!
I also attended ITCS for the first time, and I highly recommend the short talk format. This is definitely a great conference for Theory students to attend and absorb new ideas!
I started off the new year by participating in a thought-provoking new mini course from Harvard GSAS: Disrupting the New Jim Code: Applying Principles of Anti-Racism to Algorithmic Fairness, Accountability, and Ethics.
We talked about many different lenses on ethical development of algorithms and machine learning, and read from literature spanning social science to law to computer science.
I highly recommend this course to researchers and practicioners alike, and to anyone in a position to work towards anti-racist, ethical decisionmaking.