Visual Analytics for Software Engineering and Data Intensive Domains / Big Data Differential Privacy Preservation

Wednesday, September 18, 2019 - 17:30
TH 331
Dr. Shah Rukh Humayoun & Dr. Jingyi Wang

Visual Analytics for Software Engineering and Data Intensive Domains (Shah Rukh Humayoun) Visual Analytics brings the visual perception and intelligence means to the data exploration and analysis process. It helps users in navigating through data to find hidden patterns and make better and well-informed decisions. Maintaining complex software systems is an intensive task that requires careful analysis of the software system’s structures and their evolution. This poses interesting research questions and challenges from the visual analytics perspective, e.g., how to best handle visual exploration of big software data in a meaningful and efficient manner, how to provide a clear representation of the underlying software architecture and the relations between different software elements, how to visually map the software evolution over time, etc. In the first portion of the talk, I will present how I tackle these challenges through designing intuitive visual representations and developing interactive visual analytics tools. These visualizations aim to enhance the software analysts’ ability to comprehend analysis results and form knowledge about the software system. While in the later part of the talk, I will present some of my on-going work in other data intensive domains such as social media and data science, to show how visual analytics could be useful to the experts in these domains to achieve their analysis goals more effectively and efficiently.

Big Data Differential Privacy Preservation (Jingyi Wang) Nowadays, many domains of intelligent systems such as smart metering, intelligent transportation, health care, sensor/data aggregation, crowd sensing etc., typically collect huge amounts of data for decision making, where the data may include individual or sensitive information. Since a vast amount of information is analyzed, released and calculated by the system to make smart decisions, big data plays a key role as an advanced analysis technique providing more efficient and complete solutions. However, data privacy breaches during any stage of these large scale systems, either during collection or big data analysis can be an undesirable loss of privacy for the participants and for the entire system. In this work, a series of privacy preserving data analytic and processing methodologies through data driven optimization are developed. The integration of the data analysis and data privacy preservation techniques provides the most desirable solutions for different big data scenarios with various application-specific requirements.


Shah Rukh Humayoun

An Assistant Professor in the Department of Computer Science at the San Francisco State University. Previously, he has worked as Postdoctoral Research Scholar in the Visual Analytics Lab at Tufts University and HCI and Computer Graphics Lab at University of Kaiserslautern. He received his Bachelor degree in Computer Science from the University of the Punjab, MSc degree in Software Engineering from the University of York, and PhD degree in Computer Engineering from the Sapienza University of Rome. His research interests include visual analytics, data visualization, human-computer interaction, computer-supported cooperative work, and software engineering.


Jingyi Wang

Jingyi Wang received her Ph.D. degree in Electrical and Computer Engineering from University of Houston in 2019 and M.S. degree in Electrical and Computer Engineering from Auburn University, Auburn, AL, in 2015.  Her research interests include the privacy preservation of cybersecurity and big data analytics. She was a recipient of Best Paper Award in Globecom 2017 and UH ECE Best Dissertation Award in 2019.