Daily Blast Logo
Return to Daily Blast Home Submit A Daily Blast Entry FAQ Email/Daily Blast Guidelines Provide Feedback
Academic Lectures / Courses
CS@Mines Faculty Candidate Seminar: Effective & Scalable Big Data Computing: Algorithms and Systems

Department / Organization: Computer Science

Learn about problems, challenges, and solutions for collecting, processing, understanding, and learning big graph data with billions of vertices and edges

Effective & Scalable Big Data Computing: Algorithms and Systems

Presented by CS Faculty Candidate Yang Zhou

This talk will introduce problems, challenges, and solutions for collecting, processing, understanding, and learning big graph data with billions of vertices and edges. I will also discuss recent work for how to leverage algorithmic and systemic techniques to alleviate challenging bottlenecks in the development of advanced big graph data analytics tools in terms of both quality and scalability. I will conclude the talk by sketching interesting future directions for big data computing.

Dr. Yang Zhou received his Ph.D. degree in computer science at the Georgia Institute of Technology in December 2016. His primary research bridges several areas of big data algorithms and systems, including data mining, parallel and distributed computing, machine learning, database systems, and cloud computing, with a focus on the development of effective and scalable algorithms, systems, and applications that address the challenges of big data.

Where:Brown Hall 316B
Start:Tuesday, March 14, 2017 9:30 AM
End:Tuesday, March 14, 2017 10:30 AM
Cost:
Click here to download this event to your calendar

For more information, send email to: sroebuck@mines.edu

Published in Digest Date: Monday, March 13, 2017