Postdoc at ASU/Harvard on ML and Privacy

A joint postdoc position at Arizona State University and Harvard University in the area of machine learning and privacy is available immediately. The successful candidate will be working with the research groups of Prof. Lalitha Sankar and Prof. Flavio du Pin Calmon.

Specific topics of focus are the interplay of machine learning and privacy with focus on both rigorous information-theoretical results as well as practical design aspects.

The appointment will be for a period of 12 months initially, with a possibility for renewal. We are looking for strong applicants with an excellent theoretical background and a proven capacity for high-quality research in form of a strong publication record. Knowledge of privacy literature is desirable.

Interested applicants should submit a current CV, a 1-page research statement and a list of three references. Candidates should contact us via email at lsankar@asu.edu and/or flavio@seas.harvard.edu.

DIMACS Workshop on Distributed Optimization, Information Processing, and Learning

My colleague Waheed Bajwa, Alejandro Ribeiro, and Alekh Agarwal are organizing a Workshop on Distributed Optimization, Information Processing, and Learning from August 21 to August 23, 2017 at Rutgers DIMACS. The purpose of this workshop is to bring together researchers from the fields of machine learning, signal processing, and optimization for cross-pollination of ideas related to the problems of distributed optimization, information processing, and learning. All in all, we are expecting to have 20 to 26 invited talks from leading researchers working in these areas as well as around 20 contributed posters in the workshop.

Registration is open from now until August 14 — hope to see some of you there!

Register soon for the 2017 North American School of Information Theory

The site for the 2017 North American School of Information Theory is now live and registration will begin next week. The IT Schools have been going strong for the last few years and are a great resource for students, especially new students, to get some exposure to information theory research beyond their own work and what they learned in class. Most schools do not have several people working on information theory. For students at such institutions the school provides a great way to meet other new researchers in the field.