Talk Abstract: Large scale machine learning applications are constantly dealing with datasets at TB scale and the anticipation is that soon it will reach PB levels. At this scale conventional algorithms fail and simple data mining operations such as search, learning, clustering, etc. become challenging. In this talk, I will start with a basic introduction to probabilistic hashing (or fingerprinting) and the classical LSH algorithm. Then I will introduce some of my recent work of using “Randomized Algorithms” for large-scale AI optimizations, and some of my recent research in making large-scale machine learning practical.
Location: Zoom (password: CS2022) Virtual attendees must be signed into Zoom with their university account or chose "Sign in with SSO" to attend this meeting.
Note: CS2003 students must attend in person in Sennott Square room 5317, unless permision to attend remotely has been granted. They must also sign in and out of the event.
Biosketch: Sameh Gobriel is a senior research scientist at Intel Labs where he leads a team to drive research enabling future Intel products to be best-in-class in performance using software and hardware optimizations. His research interests include AI optimizations, randomized algorithms, platform architecture optimization, I/O architecture, software networking, network packet processing. He received his BE in Electronics and Electrical Communications Engineering from Cairo University in 1999, the MS and the PhD degree in computer science from the University of Pittsburgh in 2007 and 2008, respectively.
Dr. Gobriel is the author of more than 40 research papers, articles and book chapter in first tier conferences and journals; he has more than 50 filed technology patents. For his outstanding research work at Intel Labs he has received many career awards including the prestigious Intel Achievement Award, as well as Intel Labs Gordy award. He has served on the program committees of numerous conferences and workshops.
Host: Daniel Mosse