Seminar by Dr. David Shaohua Wang (Queen’s University)

Speaker: Dr. David Shaohua Wang
                Queen’s University

Title: Building Smart and Reliable Software Systems Using Massive User and Software Data

Date: Wednesday, February 8th, 2017

Time: 10:30 a.m

Place: EV3.309

ABSTRACT

Today's software systems, such as web-based and various phenomenal systems, mobile, data analytics, Internet-of-Things, and cloud systems, become giant and complex eco-systems that create billions of dollars of value to our economy. They are transforming our lives at an unprecedented pace. People are empowered to do amazing things using software systems, such as shopping on-line and riding in self-driving cars. To grow these eco-systems, smart and reliable systems must be built for billions of users. Massive user and software datasets having valuable knowledge (e.g., human choices) are generated everyday on these eco-systems. Learning to analyze such big data is critical to add intelligence and reliability into everyday systems. Therefore, I dedicate my research life to the research goal: help build reliable and smart everyday software systems understanding user needs by analyzing massive data using machine learning, natural language processing, and big data techniques. In this presentation, I first introduce my research goal and philosophy. Second, I present four prior research projects that tackles wide-spread and pervasive problems for improving the smartness and reliability of software systems. Last, the presentation touches my future research plan for building smart and reliable software systems, and concludes the talk.

BIO

Dr. Shaohua Wang is currently a postdoc fellow at Queen’s University. He got his Ph.D from Queen’s University. His current research is interdisciplinary, and lies at the intersection of software engineering, machine learning, and systems. He was a research student at IBM from 2012 to 2016, and won IBM Fellowship. His thesis was generously funded by IBM. Moreover, his current "Smart Internet-of-Things" project is partially funded by Microsoft Azure Research Award. He held multiple software engineering positions in Toronto, and has extensive full-time working experience in web systems involving machine learning, NLP, and big data techniques. He was involved in multiple exciting projects being used by thousands of users, notably the recommendation and data analytics engine for Toronto International Film Festival website, and the application and fraud detection tools for President's choice Financial. Dr. Wang has co-authored 20+ publications on IEEE and ACM journal and conferences, and mentored 3 junior Ph.Ds, 3 Masters, and one intern.