What: DS Seminar
Date: February 13, 2018
Time: 4:00 – 5:00 P.M.
Location: Computer Science Building, Room 151
The Power of Simple Algorithms: From Data Science to Biological Systems
Abstract: In recent years, very simple randomized methods, such as stochastic iteration, sampling, and hashing, have become dominant computational tools in large-scale machine learning and data science. In this talk, he will discuss his efforts to understand and harness the remarkable power of these methods.
In particular, he will describe his research on developing simple, but principled, sampling methods for learning, estimation, and optimization. He will present a new class of iterative sampling algorithms, which give state-of-the-art theoretical and empirical performance for regression problems, low-rank matrix approximation, and kernel methods. In many cases, the computational improvement offered by these algorithms is quite surprising. For example, our methods can be used to compute a near-optimal low-rank approximation to any positive semidefinite matrix in sublinear time.
In addition to their power in algorithm design, he will discuss his efforts to understand simple, randomized methods through a different lens: by studying how complex behavior emerges from low-level randomized interactions in biological systems. He will demonstrate how many of the same mathematical tools used to study algorithms in data science can be applied to these systems. As an example, he will highlight his research on noisy estimation and decision making in social insect colonies.
Bio: Cameron Musco is a fifth year Ph.D. student (graduating spring 2018) in the Theory of Computation Group at MIT.
He is advised by Nancy Lynch and supported by an NSF Graduate Fellowship. He studies algorithms, focusing on applications in data science and machine learning. He often works on randomized methods and algorithms that adapt to streaming and distributed computation. He is also interested in understanding randomized computation and algorithmic robustness by studying computational processes in biological systems.
Before MIT, he studied Computer Science and Applied Mathematics at Yale University and worked as a software developer on the Data Team at Redfin.