Deep learning methodologies like supervised learning have been very successful in training machines to make predictions about the world. But because they’re so dependent upon large amounts of human-annotated data, they’ve been difficult to scale. Dr. Phil Bachman, a researcher…
Interview with Megha Srivastava | Tell us about yourself: Although I recently graduated with a B.S. in Computer Science from Stanford, I have always viewed my minor in Creative Writing and general love for the creative arts—including musical and visual…
| Sebastian Tschiatschek and Katja Hofmann
Imagine moving to a new city. You want to get from your new home to your new job. Unfamiliar with the area, you ask your co-workers for the best route, and as far as you can tell ... they’re right!…
Awards | ACM SIGCOMM
SIGCOMM Test of Time Award, 2019: "VL2: A Scalable and Flexible Data Center Network" by Albert Greenberg, James R. Hamilton, Navendu Jain, Srikanth Kandula, Changhoon Kim, Parantap Lahiri, Dave A. Maltz, Parveen Patel, and Sudipta Sengupta. from SIGCOMM 2009
Awards | International Conference on Automated Deduction (CADE)
Nikolaj Bjorner and Leonardo de Moura received the Herbrand Award 2019 for their exceptional and numerous contributions with Z3 to SMT solving, including theory, implementation, and application to a wide range of academic and industrial needs.
There’s a lot of excitement around self-driving cars, delivery drones, and other intelligent, autonomous systems, but before they can be deployed at scale, they need to be both reliable and safe. That’s why Gurdeep Pall, CVP of Business AI at…