Events

Jun
2
Sun
IEEE Ottawa Robotics Competition 2019 @ Earl of March Secondary School
Jun 2 @ 08:00 – 17:00

Arduinos, 3D printing, Lego Mindstorms and displays, submarine
robots, and AI, where can you find all this? All of this and MUCH MORE will be at the IEEE Ottawa Robotics
Competition (ORC), Ottawa’s largest robotics competition for grade 5 to 12
students. The ORC is taking place on Sunday,
June 2nd
at Earl of March
Secondary School
. Best times to show up are between 10:30 am to 12:30 pm and 1:30 pm to 4 pm. The ORC is completely
open to the public, so invite your friends and family too!

Check out previous competitions at https://youtube.com/user/ieeeorc/videos.

If you have any questions, please feel free to email us at orcinfo@ieeeottawa.ca.

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

 

Oct
11
Fri
Advanced optical sources for spectrally efficient photonic systems – Liam Barry, Dublin City University @ Advanced Research Complex (ARC), uOttawa
Oct 11 @ 09:00 – 10:30

Advanced Optical Sources for Spectrally Efficient Photonic Systems
Liam Barry,
Dublin City University

 

Abstract

The continuing growth in demand for bandwidth (from residential and business users), necessitates significant research into new advanced technologies that will be employed in future broadband communication systems. Two specific technologies which are becoming increasingly important for future photonic
systems are wavelength tunable lasers and optical frequency combs. Although these topics have been studied for over two decades their significance for the development of future ultra-high capacity photonic systems has only recently been fully understood. Wavelength tunable lasers are currently becoming the
norm in optical communication systems because of their flexibility and ability to work on any wavelength. However, as their operating principles are different to standard single mode lasers they can effect how future systems will operate.

For example as optical transmission systems move towards more coherent transmission (where the data is carried using both the intensity and phase of the optical carrier), the phase noise in these tunable lasers will become increasingly important. Optical frequency combs also have many applications for
future photonics systems, and for telecommunications they can be used to obtain the highest spectral efficiency in optical transmission systems by employing the technology of optical frequency division multiplexing (OFDM) that has been widely employed to increase spectral efficiency in wireless systems. Wavelength tunable lasers and optical frequency combs are thus topics at the leading edge of current photonics systems research, and their detailed understanding promises new applications in all-optical signal processing, optical sensing and metrology, and specifically telecommunications. This talk will focus on the development and characterization of various wavelength tunable lasers and optical frequency combs, and then outline how these sources can be employed for developing optical transmission systems and networks which make the best use of available optical spectrum.

Bio

Liam Barry received his BE (Electronic Engineering) and MEngSc (Optical Communications) from University College Dublin and his PhD from the University of Rennes. His main research interests are: all-optical signal processing, optical pulse generation and characterization, hybrid radio/fibre communication
systems, wavelength tuneable lasers for reconfigurable optical networks, and optical performance monitoring. He has worked as a Research Engineer in the Optical Systems Department of France Telecom’s Research Laboratories (now known as Orange Labs), and a Research Fellow at the Applied Optics Centre in Auckland University. He is currently a Full Professor in the School of Electronic Engineering at Dublin City University, establishing the Radio and Optical Communications Laboratory, and is a Principal Investigator for Science Foundation Ireland. He has published over 500 articles in internationally peer reviewed journals and conferences, holds 9 patents in the area of optoelectronics, and has co-founded two companies in the photonics sector.

 

Oct
19
Sat
IEEE Ottawa Seminar Series on AI and Machine Learning – Sponsored by IEEE Ottawa CS Chapter, ComSoc Chapter, and SP Chapter, jointly with Vitesse- Reskilling
Oct 19 @ 00:07 – 01:07

Date Wednesday, Oct 30, 2019

Location 359 Terry Fox Drive, Kanata, Ontario

Agenda

       11:30 AM – 12:00 PM: Light Lunch and Networking

       12:00 PM – 1:00 PM  : Presentation and Q&A

1:00 PM – 1:30 PM    : Post Presentation Networking

Title of the Talk AI-Powered 5G Networks
& Beyond

Speaker  Hatem Abou-zeid 

Summary

5G Networks are anticipated
to transform modern societies by providing an ultra-reliable, high-speed
communications infrastructure that will connect billions of devices including
vehicles, machines, and sensors. Both the complexity of such networks and the
diversity of application requirements will be unprecedented. This mandates
novel, autonomous network configuration and operation that can anticipate and
react to changes in traffic, topology, and interference conditions to ensure
seamless quality of experience and reliability. In this talk I will discuss
AI-driven networking use-cases elaborating on the practical challenges of
industrial deployments. I will then highlight directions where research is
needed to further expedite and facilitate the development of AI-powered
networks.

Biography

Hatem Abou-zeid is a
Senior 5G Systems Designer at Ericsson Canada where he drives research and
system development for 5G radio access networks. Prior to that he held
industrial positions at CISCO Systems and Bell Labs in addition to postdoctoral
and research assistant affiliations at Queen’s University, Canada. His research
focuses on the application of machine learning in 5G networks with particular
emphasis on anticipatory and adaptive algorithms drawing on methods from
reinforcement learning, spatio-temporal forecasting, deep learning and
stochastic optimization. Dr. Abou-zeid is very passionate about developing
strong industry-university collaborations that foster applied, innovative
research, and he leads multiple academic partnerships on intelligence and
analytics in future networks.

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