Appreciation and memories of Jim Simons (1938–2024), from the Simons Institute community. Featuring contributions from Avi Wigderson, Dick Karp, Shafi...
Simons Institute Senior Scientist Venkatesan Guruswami, along with Bingkai Lin, Yican Sun, and Berkeley theory graduate students Xuandi Ren and Kewen...
We are heartbroken by the loss of Luca Trevisan, who served as senior scientist at the Institute from 2014 to 2019.
The Simons Institute is currently welcoming applications for Science Communicator in Residence slots for Fall 2022 and Spring 2023. The application deadline has been extended to March 31.
The Simons Institute’s Research Pod in Quantum Computing invites applications for postdoctoral fellows and visiting research scientists for the 2022–23 academic year.
Venkatesan Guruswami joined the Simons Institute as our newest senior scientist on January 1. We hope you enjoy our conversation with Venkat about his visions for the field and for the Simons Institute.
Collaborative research is proceeding apace in Calvin Lab, amid the various health and safety regulations. This semester we’re hosting 136 long-term visitors in our Causality and Learning and Games programs, and we look forward to a vibrant semester with them. Omicron willing, we are hoping for blue skies, and many workshops and results in the months to come.
Guruswami's research interests span many areas of theoretical computer science and related mathematics, including coding theory, approximate optimization, randomness in computing, and computational complexity.
This fall, we embarked on the 10th year since the founding of the Simons Institute on July 1, 2012. Throughout the year, we will be celebrating the vision of Jim and Marilyn Simons, our founding benefactors, as well as the lasting friendships, breakthrough research, and interdisciplinary synergies that have been created over the years at the Institute that bears their name.
This week we say goodbye to the participants in our programs on Computational Complexity of Statistical Inference, and on Geometric Methods in Optimization and Sampling. It’s been a gift to share Calvin Lab with them this fall for our first in-person programs since early 2020.
In August 2014, a significant advance in computing made the cover of the journal Science. It was IBM’s 5.4 billion-transistor chip that had a million hardware neurons and 256 million synapses. Algorithms running on this “neuromorphic” chip, when fed a video stream, could identify multiple objects, such as people, bicycles, trucks, and buses. Crucially, the hardware neural network consumed a mere 63 milliwatts, about 176,000 times less energy per synaptic event than the same network simulated on a general-purpose microprocessor.