Algorithms and uncertainty, multitasking and beyond.

Daniel Reichman - Postdoctoral fellow, UC Berkeley, affiliated with EECS and BAIR

Jan. 24, 2018, 10 a.m. - Jan. 24, 2018, 11 a.m.

McConnell 103

I will begin by presenting my work within algorithms and uncertainty: the study of algorithms with uncertainty in their inputs. This study arises in different contexts such as average case analysis and understanding properties of complex networks. I will present results on the computational complexity of solving NP-hard problems over instances that are subjected to random deletions (Percolation).

I will then discuss some of my work on sublinear algorithms and pattern recognition. Finally, I will present recent work concerned with mathematical modeling inspired by questions from artificial intelligence, cognitive science and neuroscience: Using graph theory to study multitasking in parallel architectures.


I will conclude with some future directions.


No familiarity with the topics in the lecture will be assumed.


Daniel Reichman is a Postdoctoral fellow at UC Berkeley affiliated with EECS and BAIR. He received his PhD in computer science at the Weizmann Institute and his research interests are algorithm and uncertainty: design and analysis of algorithms with uncertainty/randomness in their data, sublinear-time algorithms for pattern recognition and data analysis, cognitive science and artificial intelligence, cascading behavior in networks (in particular: Bootstrap Percolation).