Synopsis: RandNLA is of great interest from a theoretical perspective, but it has the potential to be a transformative new tool for machine learning, statistics, and data analysis.
Start Time: Monday, September 16, 2019 8:30 AM
End Time: Wednesday, September 18, 2019 5:00 PM
Location: Busch Student Center Bsc
Address: 604 Bartholomew Road
Campus: Busch
City, State, Country: Piscataway, NJ US
Fee: see registration
Speaker: Aleksander Mądry, Massachusetts Institute of Technology
Sponsor: DIMACS
Category: Workshop, Course
Web Site: http://dimacs.rutgers.edu/events/details?eID=316
Contact Name: Nicole Clark
Contact Email: nicolec@dimacs.rutgers.edu
Contact Phone: (848) 445-5928
Additional Information: Many tasks in machine learning, statistics, scientific computing, and optimization ultimately boil down to numerical linear algebra. Randomized numerical linear algebra (RandNLA) exploits randomness to improve matrix algorithms for fundamental problems like matrix multiplication and least-squares using techniques such as random sampling and random projection. RandNLA has received a great deal of interdisciplinary interest in recent years, with contributions coming from numerical linear algebra, theoretical computer science, scientific computing, statistics, optimization, data analysis, and machine learning, as well as application areas such as genetics, physics, astronomy, and internet modeling. RandNLA is of great interest from a theoretical perspective, but it has the potential to be a transformative new tool for machine learning, statistics, and data analysis.
Target Audience: Current Students,  Researchers,  Graduate Students