Events

May
8
Wed
4th Annual ANSYS Innovation Conference @ Brook Street Hotel
May 8 @ 09:00 – 15:00
4th Annual ANSYS Innovation Conference @ Brook Street Hotel | Ottawa | Ontario | Canada

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.

View Agenda

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

 

Jun
20
Thu
Fields-CQAM Public Lectures: What is missing from common practice in machine learning? @ Carleton University
Jun 20 @ 19:00 – 20:00

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.

Jun
21
Fri
FIELDS CENTRE OF QUANTITATIVE MODELLING AND ANALYSIS: WORKSHOP ON Machine Learning in the Presence of Class Imbalance @ Residence Commons, Carleton University
Jun 21 @ 08:30 – 16:30
FIELDS CENTRE OF QUANTITATIVE MODELLING AND ANALYSIS: WORKSHOP ON Machine Learning in the Presence of Class Imbalance @ Residence Commons, Carleton University | Ottawa | Ontario | Canada

 

8:30 am – 9:00 am Registration
9:00 am – 9:15 am Opening Remarks Rafik Goubran Carleton University
9:15 am – 10:00 am Keynote Presentation:

Data Mining and Machine Learning for Authorship and Malware Analyses
Abstract

Benjamin C. M. Fung
Biography
McGill University
10:00 am – 10:30 am Break
10:30 am – 11:45 am Cybersecurity: Top 5 class imbalance ML challenges and data sets
Abstract
Stephan Jou
Biography
Interset
Class Imbalance in Fraud Detection
Abstract
Robin Grosset
Biography
MindBridge Analytics Inc.
Handling class imbalance in natural language processing
Abstract
Isuru Gunasekara
Biography
IMRSV Data Labs
11:45 am – 12:45 pm Lunch
12:30 pm – 2:10 pm Adaptive learning with class imbalanced streams
Abstract
Herna L. Viktor
Biography
University of Ottawa
Radar-based fall monitoring using deep learning
Abstract
Hamidreza Sadreazami
Biography
McGill University
Privacy-preserving data augmentation in medical text analysis
Abstract
Isar Nejadgholi
Biography
National Research Council
Failure modelling of a propulsion subsystem: unsupervised and semi-supervised approaches to anomaly detection
Abstract
Julio J. Valdés
Biography
National Research Council
2:10 pm – 2:25 pm Break
2:25 pm – 3:40 pm TBD Reddy Nellipudi DB Schenker
AuditMap.ai: Hierarchical Sentence Classification in Unstructured Audit Reports
Abstract
Daniel Shapiro
Biography
Lemay.ai
Deep Learning techniques for unsupervised anomaly detection
Abstract
Dušan Sovilj
Biography
RANK Software Inc.
3:40 pm – 3:50 pm Closing Remarks

 

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