Shannon Lecture

Thursday, June 18, 08:30 — 09:30

Robert Calderbank, Duke University


Coding theory revolves around the question of what can be accomplished with only memory and redundancy. When we ask what enables the things that transmit and store information, we discover codes at work, connecting the world of geometry to the world of algorithms.

This talk will focus on those connections that link the real world of Euclidean geometry to the world of binary geometry that we associate with Hamming. The narrative will follow my own contributions, but I hope it will provide a sense of how much has been accomplished by so many others.

Plenary Talks

Monday, June 15, 08:30 — 09:30

Bin Yu, University of California at Berkeley, and Peking University


Genome wide data reveal an intricate landscape where gene actions and interactions in diverse spatial areas are common both during development and in normal and abnormal tissues. Understanding local gene networks is thus key to developing treatments for human diseases. Given the size and complexity of recently available systematic spatial data, defining the biologically relevant spatial areas and modeling the corresponding local biological networks present an exciting and on-going challenge. It requires the integration of biology, statistics and computer science; that is, it requires data science.

In this talk, I present results from a current project co-led by biologist Erwin Frise from Lawrence Berkeley National Lab (LBNL) to answer the fundamental systems biology question in the talk title. My group (Siqi Wu, Antony Joseph, Karl Kumbier) collaborates with Dr. Erwin and other biologists (Ann Hommands) of Celniker's Lab at LBNL that generate the Drosophila spatial expression embryonic image data. We leverage our group's prior research experience from computational neuroscience to use appropriate ideas of statistical machine learning in order to create a novel image representation decomposing spatial data into building blocks (or principal patterns). These principal patterns provide an innovative and biologically meaningful approach for the interpretation and analysis of large complex spatial data. They are the basis for constructing local gene networks, and we have been able to reproduced almost all the links in the Nobel-prize winning (local) gap-gene network. In fact, Celniker's lab is running knock-out experiments to validate our predictions on gene-gene interactions. Moreover, to understand the decomposition algorithm of images, we have derived sufficient and almost necessary conditions for local identifiability of the algorithm in the noiseless and complete case. Finally, we are collaborating with Dr. Wei Xue from Tsinghua Univ to devise a scalable open software package to manage the acquisition and computation of imaged data, designed in a manner that will be usable by biologists and expandable by developers.

Tuesday, June 16, 08:30 — 09:30

Madhu Sudan, Microsoft Research New England


An error-correcting code is said to be L-locally testable if given any word it can be determined whether it is close to a codeword or not by sampling only L coordinates of the word. A code (or more precisely an encoding scheme) is said to be L-locally decodable if given a received word that is close to some codeword, any single coordinate of the message being encoded can be computed by sampling only L-coordinates of the received word. When L is much smaller than N, the block length of the code, locally testable and correctible codes offer reliability associated with N-long codewords while producing decoding delays of only L-long codewords.

In this talk we will describe some of the motivations that led to the study of local codes in theoretical computer science and then describe some basic constructions and mention some of the state of the art results (due to Kopparty, Meir, Ron-Zewi and Saraf) that show codes of sub-polynomial locality (L = N^{o(1)}) close to the Singleton bound.

Wednesday, June 17, 08:30 — 09:30

Terrence J. Sejnowski, Salk Institute

(Video unavailable)

Brains need to make quick sense of massive amounts of ambiguous information with minimal energy costs and have evolved an intriguing mixture of analog and digital mechanisms to communicate and compute. Spike coincidences occur when neurons fire together at nearly the same time. In the visual system, rare spike coincidences can be used efficiently to represent important visual events in the early stages of visual processing. At the highest levels of processing the information is distributed over many brain areas, which raises questions about how global brain states are controlled. As the BRAIN Initiative develops new neurotechnolgies for recording and perturbing activities in neural circuits, we will learn much more about neural codes and how the brain represents the world and takes actions.

Friday, June 19, 08:30 — 09:30

Ruediger Urbanke, École Polytechnique Fédérale de Lausanne


Reed-Muller codes are among the oldest and best studied families of codes. They have many beautiful and useful properties. Recently they have drawn renewed interest due to their close relationship with Polar codes. This begs the question: Do they achieve capacity?

[Based on joint work with S. Kudekar, M. Mondelli, and E. Sasoglu.]