Rong Zheng

Professor, McMaster University

Talk: Mobile computing and machine learning: A happy marriage?

Significant progress has been made in recent years in applying machine learning in various applications. Today, predominantly, training of machine learning models and execution of inference tasks are done in the cloud due to the limited computation and storage capacity of mobile devices. However, such approaches are inadequate for latency sensitive tasks such as augmented reality and self-driven vehicles, or are too onerous when a large amount of data needs to be transferred over cellular data networks. In this talk, Rong will discuss ongoing projects in the Wireless System Research Group at McMaster university that develop machine learning algorithms for mobile applications and our efforts to make machine learning more amiable for mobile computing platforms.

Bio: Rong Zheng earned her Ph.D. degree from Dept. of Computer Science, University of Illinois at Urbana-Champaign. She is now an Associate Professor in the Dept. of Computing and Software, an associate member of the Dept. of Electrical and Computer Engr., and a member of the School of Computational Science and Engineering in McMaster University, Canada. Rong Zheng is a Joseph Ip Distinguished Engineering Fellow. She is a steering committe member of the MacData institute, which promotes collaboration among centers and researchers whose work involves different facets of data. Rong Zheng is also a principle investigator of the Computing Infrastructure Research Centre (CIRC).

Carolyn Chong

VP Product, Obie.ai

Talk: Applications of Machine Learning in the Workplace

Bio: Carolyn Chong leads the product at Obie.ai, a software startup based in Hamilton. At Obie, she works with the engineering team to leverage machine learning to deliver relevant answers to common team questions. Carolyn is passionate about the technology behind conversational interfaces and delighting users through the power of words. Born and bred in Hamilton, Carolyn graduated from the Arts & Science Program at McMaster University with a Combined Honours in Computer Science.

Michael Moncada

Senior Fullstack Developer, Prodigy Game

Talk: A Curious Case for a Learning Machine

Bio: Michael is a Senior Fullstack Developer for Prodigy Game with a keen interest in discovering innovative ways of solving complex challenges. Apart from being an engineer of twelve years, developing many solutions across a wide variety of web and mobile platforms, his new passion for software personalization (inspired by his current Master's work) has introduced him to the exciting and complex world of data science and machine learning. More specifically, Michael’s interests lie in exploring how data science methodology can be used to solve some of the hard challenges that stem from big data analysis.

12:00 p.m — 1:00 p.m.

Lunch time!

1:00 p.m — 2:30 p.m.

Jared Smith

Cyber Security Research Scientist, Oak Ridge National Laboratory

Talk: Beyond the Hype: Real Applications of Machine Learning in Security

Bio: From the foothills of Great Smokey Mountains, Jared is a Cyber Security Research Scientist at Oak Ridge National Laboratory (ORNL), the U.S. Department of Energy’s largest R&D laboratory, where he leads and collaborates on several R&D projects focused on securing modern computer networks, energy delivery systems, and vehicles. Pursuing a PhD in Computer Science while at ORNL, Jared is a Chancellor’s Graduate Fellow at the University of Tennessee, Knoxville, where his research focuses on Internet infrastructure resiliency and privacy-preserving methods of communication. Jared teaches data science, DevOps, and security at Treehouse and consults for both Fortune 50 companies and emerging Knoxville startups. Previously, Jared interned at ORNL and on the product security team at Cisco Systems, leveraged supercomputers and machine learning to discover IP risks in the patent ecosystem, and analyzed subatomic particle collisions produced from the Large Hadron Collider in CERN.

Mike Branch

Vice President of Business Intelligence, Geotab

Talk: Open Data, Machine Learning, and the Connected Car

Bio: Mike Branch is the Vice President of Business Intelligence at Geotab and leads the charge for developing solutions that enable insight from the over 2 billion telematics records that Geotab processes on a daily basis. Mike joined the Geotab team in 2016, and prior to that was the CEO of Inovex Inc. -- a software development company he founded in 2003 with expertise in the healthcare and energy sectors. Branch also served as president of the University of Toronto Engineering Alumni Association, currently serves on the board of Hillfield Strathallan College, and is past chair of the board for Streetwise Actors. Branch has been honoured with the University of Toronto Arbor Award and Early Career Award, Professional Engineers of Ontario Engineering Medal, Engineers Canada Young Engineer Award, and has been inducted into Hillfield Strathallan College's Hall of Excellence.

2:30 p.m — 2:45 p.m.

Break!

2:45 p.m — 4:00 p.m.

Rory Woods

Lead Data Scientist, Preteckt

Talk: The Data Science Loop

Bio: Rory is the lead data scientist at Preteckt. Over the past two years he has helped to grow the Preteckt data science team into a collaborative and effective branch of the company. Rory and his team have been intensely analyzing automotive data from a variety of sources from clients across North America to offer accessible solutions for predictive maintenance. He received his PhD in computational astrophysics from McMaster University in 2015 and made the transition to data science soon after.

Dominique Brunet

Research Physical Scientist, Environment Canada

Talk: Machine Learning in Thunderstorm Nowcasting

Bio: Dominique currently works full-time as a Research Physical Scientist at Environment and Climate Canada and part-time as a data scientist contractor. His research interests include mathematical and statistical methods for image processing and pattern recognition as well as image quality assessment and forecast verification. Dr. Brunet has applied mathematics and published on the problems of perceptually optimal image restoration, diagnostically lossless image compression, para-spinal tumor monitoring and tracking, thunderstorm and severe weather nowcasting, weather forecast post-processing and spatial forecast verification. Dominique received B.Sc. (2005) and M.Sc. (2007) degrees in Mathematics from Laval University in Québec City and a Ph.D. (2012) degree in Applied Mathematics from University of Waterloo.

4:00 p.m — 4:30 p.m.