BLISS Seminar

About

The BLISS seminar (formerly NCD seminar) is co-sponsored by generous grants from Microsoft Research and Qualcomm, and is the area seminar of the Berkeley Laboratory for Information Systems and Sciences. Talks at the seminar cover topics including but not limited to information and coding theory, signal processing, optimization and statistics. The list of talks for the current semester can be found below, and past seminars from 2016 onwards are listed here. For an archive of all talks from 1996-2015, visit the old webpage.

Spring 2018
Location: 540 Cory Hall
Regular seminar time: Monday 3-4PM

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To give a talk at the seminar, contact Tom Courtade, Ashwin Pananjady, or Orhan Ocal.

Spring 2018 Talks

Dates marked in bold indicate that talks are at non-regular times/venues.

Jan 26 Sivan Toledo (Tel-Aviv) The ATLAS Reverse-GPS System: High-Throughput Wildlife Tracking details
Feb 7 Raymond Yeung (CUHK) Shannon's Information Measures and Markov Structures details
Feb 22 Yuantao Gu (Tsinghua) RIP of Random Projection for Low-Dimensional Subspaces details
Mar 9 John Wright (Columbia) Nonconvex Sparse Deconvolution: Geometry and Efficient Methods details
Mar 12 Reinhard Heckel (Rice) Robust Storage of Information in DNA Molecules details
Mar 19 R. Srikant (UIUC) Queues, Balls and Bins, and Association details
Mar 23 Angie Wang (Berkeley) An Agile Approach to FFTs and Hardware DSP Generation details
Apr 2 Balaji Prabhakar (Stanford) Self-Programming Networks: Architecture and Algorithms details
Apr 9 Bruce Hajek (UIUC) Gene regulatory network reconstruction from high throughput sequencing data details
Apr 12 Jun Chen (McMaster) From Gaussian Multiterminal Source Coding to DistributedKarhunen–Loève Transform details
Apr 16 Sid Banerjee (Cornell) Allocating Resources, in the Future details
Apr 23 Inderjit Dhillon (A9) Stabilizing Gradients for Deep Neural Networks details
Apr 30 Leo Miolane (INRIA) Phase transitions in generalized linear models details
May 4 Fanny Yang (Berkeley) Computational guarantees for statistical learning algorithms details
May 7 Eva Tardos (Cornell) Learning with Low Approximate Regret with Partial Feedback details