Elyse Cornwall - Demo Class
Abstract
Bloom Filters are probabilistic data structures that leverage hash functions to represent a set of elements using very little memory. This lecture will build on students’ prior knowledge of hash functions and hash tables, and consider the tradeoffs made by Bloom Filters that allow them to be so space efficient. We will explore various real-world applications of Bloom Filters, which range from biology to databases.
Bio:
Elyse Cornwall is an M.S. student in Human-Computer Interaction at Stanford, where she received her B.S. in Computational Theory. The highlight of Elyse’s collegiate career has been serving thousands of students in Stanford’s introductory programming sequence as an instructor of record and teaching assistant. She has taught Stanford’s introduction to data structures and algorithms course, and currently serves as the Head Teaching Assistant for Stanford’s intro to programming course.