Events

Innovation enables organizations to open new avenues of product differentiation by customizing products. In today’s rapidly changing business environment, engineers must innovate quickly to incorporate new features while reducing development costs and delivering new products to the market before the competition. Simulation plays a key role in helping engineers drive innovation, enabling complete virtual prototypes of complex systems to be validated across all physics and engineering disciplines.

Join us as we return to Ottawa for our 4th Annual ANSYS Innovation Conference on May 8, 2019! This one-day conference will provide detailed insight into how leading companies are utilizing simulation to advance their product development. We will bring together ANSYS users, partners, developers, and industry experts for networking, learning, and sharing of new ideas.

What You Will Learn

• Experience new simulation capabilities that provide unprecedented design insight as they speed your time to market
• Incorporate various productivity enhancement tools and techniques into your engineering department’s workflow
• Gain insights into 5G system development with physics-based simulation and cover critical design issues, such as antenna performance, semiconductor reliability, and thermal integrity
• Identify signal integrity issues early in the design cycle for electronics IC packages, PCBs, connectors and other complex interconnects
• Modify antenna design, predict antenna efficiency and the overall thermal and EM performance of the product based on electromagnetic and thermal coupling solutions

Fields-CQAM Public Lectures: Ali Ghodsi, University of Waterloo

What is missing from common practice in machine learning?

AI, and machine learning in particular, is enjoying its golden age. Machine learning has changed the face of the world over the past two decades but we are still a long way from achieving a general artificial intelligence. In this talk, I will discuss a couple of elements that I believe are missing from common practice in machine learning, including incorporating causality and creating a new framework for unsupervised learning.

Biography

Ali Ghodsi is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research involves statistical machine-learning methods. Ghodsi’s research spans a variety of areas in computational statistics. He studies theoretical frameworks and develops new machine learning algorithms for analyzing large-scale data sets, with applications to bioinformatics, data mining, pattern recognition, robotics, computer vision, and sequential decision making.

DATE:

THURSDAY, JUNE 20TH, 2019.

PRESENTATION

6:00 PM – 7:00 PM.

NETWORKING

7:00 PM – 8:00 PM.

LOCATION

HEALTH SCIENCE BUILDING, RM. 1301 (LOCATED ON THE GROUND FLOOR), CARLETON UNIVERSITY.

FREE ADMISSION FOR THIS PUBLIC LECTURE.
PLEASE REGISTER HERE.