Seminar by Dr. Morteza Zihayat (University of Toronto)

Speaker: Dr. Morteza Zihayat
                University of Toronto

Title: Big Data to Data Science: Actionable Knowledge Discovery from Big Data

Date: Wednesday, February 1st, 2017

Time: 10:30 a.m

Place: EV3.309

ABSTRACT

With rapid advances in computing power and dramatic expansion of data collection and storage capability, businesses and organizations today have collected vast amounts of data about their business processes. These data are modern-day treasure stores that can be mined to glean insights into a business’ products, services and customers. However, the sheer size of data and rapid change in data characteristics have presented significant challenges in turning large real-world datasets into actionable knowledge. Despite the great efforts that have been made in the field of data analytics, there is a large gap between academic deliverables and business expectations and many questions still remain to be answered. How can we discover useful and actionable knowledge from dynamically-changing data? How can we effectively combine human and machine intelligence to more effectively discover useful insights from big data streams?

Discovering domain-driven actionable knowledge can suggest concrete and profitable actions to decision makers. Such actions can bring direct benefits (e.g., increase in profits, reduction in cost, improvement in efficiency, etc.) to the organization that uses big data analytics. I will talk about the problems, challenges and opportunities for discovering such knowledge from Big Data.  More specifically, my work revolved around building systems to resolve these two problems: 1) utility-based pattern mining over big data streams and 2) recognizing depression acuity using data collected from social media and wearable devices. I will also talk about my research projects with the industry and how theory can be implemented in real world systems.

BIO

Morteza Zihayat is a Postdoctoral Fellow at Faculty of Information at University of Toronto and Dapasoft Inc. (Microsoft Gold Certified Partner). His research concerns Big Data analytics, machine learning, optimization, social network analysis and computational social science. He won the prestigious Mitacs Elevate postdoctoral fellowship in 2016. Since 2012, he has been involved in designing and implementing several big industrial projects as a solution architect and data scientist in IBM Canada, Dapasoft Inc. and The Globe and Mail Inc. He received his Ph.D. in Computer Science from York University (2011-2016). He is specifically interested in designing scalable frameworks/algorithms to discover actionable knowledge in Big Data streams, social media and social networks. His research is published in data mining and machine learning top-tier venues like Machine Learning, Information Sciences, SIAM SDM, ECML/PKDD, EDBT and IEEE Big Data. He holds a B. Sc. and a M. Sc. in Information Technology from University of Isfahan and University of Tehran, Iran.