New Quantum Science and Engineering PhD Program

[Please forward this information to any undergraduate students in your program, or any relevant mailing lists or organizations you are aware of; we also have a postdoctoral fellowship in quantum computing. --Boaz] This year Harvard started a new Ph.D program in Quantum Science and Engineering. We are now accepting application for the first cohort of … Continue reading New Quantum Science and Engineering PhD Program

Opportunities at Harvard!

Computer Science at Harvard, and in particular theoretical computer science and machine learning, is growing fast, see my 21-Tweet thread: https://twitter.com/boazbaraktcs/status/1450171218070495238?s=20 Please consider applying for graduate studies in computer science (or encourage others to apply if like me, your grad-school days are behind you). In recent years, I've taken a special interest in the theory … Continue reading Opportunities at Harvard!

Nominate papers to SIGACT Research highlights

TL;DR: Know of a great recent paper that should be highlighted to the theory community and beyond? Email a nomination to sigact.highlights.nominations@outlook.com by Friday, Oct 22, 2021. The goal of the SIGACT Research Highlights Committee is to help promotetop computer science theory research via identifying results that are ofhigh quality and broad appeal to the … Continue reading Nominate papers to SIGACT Research highlights

RANDOM/APPROX conference

[Guest post by Mary Wooters; the conference already started but there are still great activities tomorrow and Wednesday. On an unrelated note, please make sure to watch the new Theory Shorts episode by the Simons Institute of Computing on lower bounds in computational complexity, featuring Madhu Sudan, Paul Beame, Faith Ellen, Jelani Nelson, and Manuel … Continue reading RANDOM/APPROX conference

Replica Method for the Machine Learning Theorist: Part 2 of 2

Blake Bordelon, Haozhe Shan, Abdul Canatar, Boaz Barak, Cengiz Pehlevan See part 1 of this series, and pdf version of both parts. See also all seminar posts. In the previous post we described the outline of the replica method, and outlined the analysis per this figure: Specifically, we reduced the task of evaluating the expectation … Continue reading Replica Method for the Machine Learning Theorist: Part 2 of 2

Replica Method for the Machine Learning Theorist: Part 1 of 2

Blake Bordelon, Haozhe Shan, Abdul Canatar, Boaz Barak, Cengiz Pehlevan [Boaz's note: Blake and Haozhe were students in the ML theory seminar this spring; in that seminar we touched on the replica method in the lecture on inference and statistical physics but here Blake and Haozhe (with a little help from the rest of us) … Continue reading Replica Method for the Machine Learning Theorist: Part 1 of 2

ITC 2021: Call for participation (guest post by Benny Applebaum)

The second edition of the recently created conference on Information-Theoretic Cryptography (ITC 2021) will take place virtually on July 24-26, 2021. The final program is out and contains exciting new works and invited talks that highlight the recent advances in the area by Benny Applebaum, Elaine Shi, Irit Dinur, Salman Avestimehr, Matthieu Bloch, and Mark … Continue reading ITC 2021: Call for participation (guest post by Benny Applebaum)

STOC feedback and TCS Wikipedia (guest post by Clément Canonne )

The 53rd Annual ACM Symposium on Theory of Computing (STOC'21) concludes today, after 5 days of action-packed, Gather-power talks, workshops, plenary talks, and posters. A huge thank you to all volunteers, organizers, speakers, and attendees, who helped make this virtual conference a success! We would like to ask for your feedback on the conference. Whether … Continue reading STOC feedback and TCS Wikipedia (guest post by Clément Canonne )

Machine Learning for Algorithms – virtual workshop

[H/T Jelani Nelson] In recent years there has been increasing interest in using machinelearning to improve the performance of classical algorithms incomputer science, by fine-tuning their behavior to adapt to theproperties of the input distribution. This “data-driven” or“learning-based” approach to algorithm design has the potential tosignificantly improve the efficiency of some of the most widely … Continue reading Machine Learning for Algorithms – virtual workshop