Home > Seminars > Yang Zhou - Innovative Mining, Processing, and Application of Big Graphs

Yang Zhou - Innovative Mining, Processing, and Application of Big Graphs


3/1/2016 at 3:30PM


3/1/2016 at 5:00PM


119 DeBartolo


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Nitesh Chawla

Nitesh Chawla

VIEW FULL PROFILE Email: nchawla@nd.edu
Phone: 574-631-1090
Website: http://www.nd.edu/~nchawla/
Office: 384 Nieuwland Science Hall


College of Engineering Frank M. Freimann Professor
Dr. Chawla's research interests are broadly in the areas of Big Data: data science, machine learning, network science and their applications social networks, healthcare informatics/analytics, and climate/environmental sciences. He directs the Notre Dame Interdisciplinary Center for Network ...
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With continued advances in computing and information technology, big graphs have grown at an astonishing rate in terms of volume, variety, and velocity. Mining and processing such big graphs have huge potential to reveal hidden insights and promote innovation in many business, science, and engineering domains. My current research efforts are dedicated to developing effective and scalable big graph analytics frameworks for exploratory analysis of different types of large-scale graph data, and theoretically sound solutions of complex real-world problems.

In this talk, I will focus on three of my research efforts to better explore, process and understand large-scale networked data. First, I will discuss my work on graph mining algorithms to analyze and mine real-world heterogeneous information networks. Second, I will introduce several graph processing techniques to speed up the execution of graph applications in data mining and machine learning. Third, I will highlight my work on domain specific graph applications to understand the hidden patterns in software executions, storage systems and web service libraries. I will conclude the talk by sketching interesting future directions for big graph mining and processing.

Seminar Speaker:

Yang Zhou

Yang Zhou

Georgia Institute of Technology

Yang Zhou is a Ph.D. candidate in theCollegeofComputingat the Georgia Institute of Technology. His primary research interests include big data analytics, data mining, databases, machine learning, and parallel and distributed computing. More specifically, his Ph.D. research has been focused on large-scale graph mining and graph processing techniques for exploring, understanding, and learning from graph data. He has also worked with researchers in storage systems, web services, software engineering, cloud computing, and trust management, to build and deploy novel data analytics solutions in real-world settings. He has published more than a dozen papers in top journals and conferences, and some of his works have been included in the reading list and taught in courses at many universities world wide.