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Emanuele Viola is a professor in the Khoury College of Computer Sciences at Northeastern University, which he joined in 2008. Professor Viola earned his PhD from Harvard University in 2006, and was subsequently a member of the Institute for Advanced Study...
Xin Li is currently an associate professor at Johns Hopkins University. Xin's research interests are in theoretical computer science, especially, the use of randomness in computation, complexity theory, coding theory, algorithms, and cryptography.
Ronen Shaltiel is currently at the University of Haifa and their research interests include Complexity Theory, Pseudorandomness, Coding Theory and Cryptography.
Season’s greetings from Berkeley, where we have donned our light jackets and the campus squirrels are newly plump for winter. We just concluded a fantastic semester with two highly energetic programs on Complexity and Linear Algebra, and on Algorithmic Foundations for Emerging Computing Technologies. It was energizing to witness Calvin Lab bustling with collaborations filling up its open spaces.
The Simons Institute has received $250K in support from the Google DeepMind x Google.org AI for Math Initiative, which was launched in late October. The Simons Institute will be a member of the newly created consortium, along with Imperial College London, the Institute for Advanced Study, Institut des Hautes Études Scientifiques (IHES), and the Tata Institute of Fundamental Research (TIFR).
Fast matrix multiplication is a central goal in algorithms research. The goal is to find the smallest real value omega such that n by n matrices can be multiplied in n{omega + o(1)} time in the worst case. The current best bound is omega < 2.37134. In this Richard M. Karp Distinguished Lecture in the Complexity and Linear Algebra program, Virginia Vassilevska Williams examines progress on matrix multiplication algorithms over the decades and offers some intuition about where the research area may be headed.
In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Nevertheless, the path toward a full-stack scalable technology is a work in progress. In this talk from our fourth annual Quantum Industry Day, newly minted Nobel laureate John Martinis shows how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits and employing system engineering approaches.
Some types of virtualization, such as virtual memory, are implemented by providing a layer of indirection between what the program sees and what the system implements. This layer of indirection is typically ignored in theoretical analysis but has a real (and, in some cases, increasing) impact on system performance. In this Richard M. Karp Distinguished Lecture in the Algorithmic Foundations for Emerging Computing Technologies program, Martín Farach-Colton covers a variety of cases where the cost of indirection becomes significant, including new architectures, such as for hardware accelerators and shared memory.