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.
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.
THURSDAY, JUNE 20TH, 2019.
6:00 PM – 7:00 PM.
7:00 PM – 8:00 PM.
HEALTH SCIENCE BUILDING, RM. 1301 (LOCATED ON THE GROUND FLOOR), CARLETON UNIVERSITY.
FREE ADMISSION FOR THIS PUBLIC LECTURE.
PLEASE REGISTER HERE.
|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
|Benjamin C. M. Fung
|10:00 am – 10:30 am||Break|
|10:30 am – 11:45 am||Cybersecurity: Top 5 class imbalance ML challenges and data sets
|Class Imbalance in Fraud Detection
|MindBridge Analytics Inc.|
|Handling class imbalance in natural language processing
|IMRSV Data Labs|
|11:45 am – 12:45 pm||Lunch|
|12:30 pm – 2:10 pm||Adaptive learning with class imbalanced streams
|Herna L. Viktor
|University of Ottawa|
|Radar-based fall monitoring using deep learning
|Privacy-preserving data augmentation in medical text analysis
|National Research Council|
|Failure modelling of a propulsion subsystem: unsupervised and semi-supervised approaches to anomaly detection
|Julio J. Valdés
|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
|Deep Learning techniques for unsupervised anomaly detection
|RANK Software Inc.|
|3:40 pm – 3:50 pm||Closing Remarks|
IEEE Ottawa Seminar Series on AI and Machine Learning
Hosted by IEEE Ottawa PHO Chapter, EMBS Chapter, CS Chapter, and SP Chapter Jointly with Vitesse Reskilling
Deep Learning for Medical Image Analysis
Wednesday, June 26, 2019
359 Terry Fox Drive, Suite 200, Kanata, Ontario
11:30 – 13:30
Medical imaging, (e.g., computed tomography (CT), magnetic resonance
imaging (MRI), positron emission tomography (PET), mammography, ultrasound,
X-ray) has advanced at a rapid speed over last decades. Currently, the medical
image interpretation is mostly performed by human experts, which is a tedious
task and subject to high inter-operator variability. Deep learning is providing
exciting solutions for medical image analysis problems. Recent advances in deep
learning have helped to identify, classify, and quantify patterns in medical
images. In this seminar, we introduce the principles and methods of deep
learning concepts, particularly convolutional neural network (CNN). We show how
CNN operates. I will describe several interesting applications of deep learning
for medical image analysis, including my recent works on segmenting myocardial
scar (injured) tissue in the heart, prostate tumor detection, and kidney lesion
localization in 3D MRI and CT images.
Fatemeh Zabihollahy is currently
a Ph.D. candidate at Carleton University. She obtained her MASc (2016) and BASc
(2001) both in Biomedical Engineering from Carleton University, Canada and
Shahid Beheshti University, Iran, respectively. She worked in the medical
devices industry as an R&D engineer for ten years. Her research interest is
in the field of application of deep learning techniques for medical image
is free, but space is limited. All
participants must register in advance.
follow the link to register
For more information, please contact: Kexing Liu firstname.lastname@example.org
Speaker 1: Hisham Abed, P.Eng., Ericsson
Topic: Power Integrity – Best design practices
Speaker 2: Dr. Ihsan Erdin, Celestica
Topic: Power Integrity Optimization amidst MLCC shortage
Registration: Free, and is on a first to reply basis. Preference given to IEEE EMC and CPMT society members. Seating is limited. E-mail reservation is required.
Pizza and soft drinks will be served.
Organizer: Dr. Syed Bokhari, Chairman, IEEE Ottawa
Office :(613) 595 – 0507 Ext. 377, Cell: (613) 355 – 6632
Antennas for Autonomous Vehicles:
What You Need to Know!
and reliable positioning recently became a critical property of autonomous
vehicles like drones, driverless cars and more. Tallysman Wireless will explain
why the GNSS antenna is the most important component for accurate positioning
and will present the challenges of selecting the appropriate GNSS antenna for
diverse types of autonomous vehicles. Multiple properties of a GNSS antenna
like its phase center variation, ability to reject interferences or multipath
and sensibility to its environment will be analysed and guide lines will be
Refreshments will be served!
Location: 4359 Mackenzie Building, Carleton University.
Time: 6:00 – 7:00 PM
Date: July 17th , 2019
Hautcoeur received the M.Sc. degree in radio communication systems and
electronics from the Ecole Polytechnique of the University of Nantes, Nantes,
France, in 2007 and the Ph.D. degree in signal processing and
telecommunications from the Institute of Electronics and Telecommunications of
Rennes 1, Rennes, France, in 2011. In 2011, he was involved in postdoctoral
training at the University of Quebec in Outaouais (UQO), Gatineau, QC, Canada.
His research field was optically transparent antenna systems for telecommunications.
Since 2014 he works at Tallysman Wireless in Ottawa, Canada and specialized in
the design of high performance GNSS antennas and associated electronics.