Celebrating Don Knuth’s 80th Birthday

~ shacharlovett

January 8-10, 2018
Piteå, Sweden
http://knuth80.elfbrink.se/

Donald Knuth’s 80th birthday on January 10, 2018 will be celebrated by two connected events, both in Piteå, Sweden.

January 8-10 (Monday-Wednesday morning): The scientific symposium ”Knuth80: Algorithms, Combinatorics, and Information” in honor of Don’s career-long efforts that have inspired us all. Don’s interests and accomplishments are very broad, and the symposium will therefore include contributions from distinguished scientists in several areas where Don’s influence has been important.

January 10, afternoon (Wednesday): The world premiere of Fantasia Apocalytica, a multimedia work for pipe organ and video that Don has written. It will be performed by the Canadian organist Jan Overduin on the magnificent new pipe organ in Studio Acusticum in Piteå.

Symposium on Simplicity in Algorithms 2018

~ shacharlovett

January 7-10, 2018
New Orleans
https://simplicityalgorithms.wixsite.com/sosa

Submission deadline: August 24, 2017

The Symposium on Simplicity in Algorithms is a new conference in theoretical computer science dedicated to advancing simplicity and elegance in the design and analysis of algorithms. The 1st SOSA will be co-located with SODA 2018 in New Orleans. Ideal submissions will present simpler algorithms for important algorithmic problems, or present simpler analyses of known algorithms, or offer insights that simplify our understanding of important computational problems.

Omni Buss Celebration

~ shacharlovett

July 14, 2017
UC San Diego
http://cseweb.ucsd.edu/~slovett/workshops/sam-buss-60/

In celebration of Samuel Buss’s 60th birthday, we are organizing an Omni Buss celebration. As Sam’s work has had major impact on many areas of mathematics and computer science, including logic, proof complexity computational complexity, algorithms and graphics, the celebration will feature an eclectic combination of speakers.

New Challenges in Machine Learning – Robustness and Nonconvexity

~ shacharlovett

June 23, 2017
STOC 2017, Montreal, Canada
https://users.cs.duke.edu/~rongge/stoc2017ml/stoc2017ml.html

Submission deadline: May 27, 2017

Machine learning has gone through a major transformation in the last decade. Traditional methods based on convex optimization have been replaced by highly non-convex approaches including deep learning. In the worst-case, the underlying optimization problems are NP-hard. Therefore to understand their success, we need new tools to characterize properties of natural inputs, and design algorithms that work provably in beyond-worst-case settings. In particular, robustness and nonconvexity are two of the major challenges.