Events

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

 

Mar
19
Thu
[CANCELLED] Seminar: Drone-assisted Mobile Edge Computing
Mar 19 @ 18:00 – 19:30

NOTE: This event as been cancelled due to COVID-19 precautions

Seminar presented by the IEEE Ottawa Section, Communications Society, Consumer Electronics Society, and
Broadcast Technology Society Joint Chapter (ComSoc/CESoc/BTS), Instrumentation & Measurement
Society Chapter (IMS), Reliability Society and Power Electronics Society Joint Chapter (RS/PELS), IEEE
Ottawa Educational Activities (EA) and Algonquin College IEEE Student Branch:

 

IEEE Ottawa Section is inviting all interested IEEE members and nonmembers to a distinguished Lecture:
Drone-assisted Mobile Edge Computing

By

Nirwan Ansari, Distinguished Professor of Electrical and Computer Engineering at

the New Jersey Institute of Technology (NJIT)

 

DATE:

Thursday, March 19, 2020.

 

TIME:

Refreshments, Registration and Networking: 6:00 p.m.; Seminar: 6:30 p.m. – 7:30 p.m.

PLACE:

Ciena Optophotonics Lab, Room T129, T-Building, School of Advanced Technology, Algonquin College,

1385 Woodroffe Ave., Ottawa, ON Canada K2G 1V8.

 

PARKING:

Parking at Lots 8 and 9 after 5 p.m. is $5 flat rate, pay at a machine and display the ticket on your dashboard. Please respect restricted areas.

 

ADMISSION:

Free. Registration required. To ensure a seat, please register by e-mail contacting: Wahab Almuhtadi.

 

MORE INFO:

Ottawa ComSoc/CESoc/BTS Chapter website.

 

Abstract:

In mobile access networks, different types of Internet of Things (IoT) devices (e.g., sensor nodes and smartphones) will generate vast traffic demands, thus dramatically increasing the traffic loads of their connected access nodes, especially in the 5G era. Mobile edge computing enables data collected by IoT devices to be stored in and processed by local fog nodes as well as allows IoT users to access IoT applications via these nodes at the same time. In this case, the communications latency critically affects the response time of IoT user requests. Owing to the dynamic distribution of IoT users, drone base station (DBS), which can be flexibly deployed over hotspot areas, can potentially improve the wireless latency of IoT users by mitigating the heavy traffic loads of macro BSs. Drone-based communications poses two major challenges: 1) DBS should be deployed in suitable areas with heavy traffic demands to serve more users; 2) traffic loads in the network should be allocated among macro BSs and DBSs to avoid instigating traffic congestions. Therefore, we propose a TrAffic Load balancing (TALL) scheme in such drone-assisted fog network to minimize the wireless latency of IoT users. In the scheme, we divide the problem into two sub-problems and design two algorithms to optimize the DBS placement and user association, respectively. Extensive simulations have been set up to validate the performance of TALL.

 

Speaker Bio:

Dr. Nirwan Ansari, Distinguished Professor of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT), received his Ph.D. from Purdue University, MSEE from the University of Michigan, and BSEE (summa cum laude with a perfect GPA) from NJIT. He is a Fellow of IEEE and a Fellow of National Academy of Inventors.

He authored Green Mobile Networks: A Networking Perspective (Wiley-IEEE, 2017) with T. Han, and coauthored two other books. He has also (co-)authored more than 600 technical publications. He has guest-edited a number of special issues covering various emerging topics in communications and networking. He has served on the editorial/advisory board of over ten journals including as Associate Editor-in-Chief of IEEE Wireless Communications Magazine. His current research focuses on green communications and networking, cloud computing, droneassisted networking, and various aspects of broadband networks. He was elected to serve in the IEEE Communications Society (ComSoc) Board of Governors as a member-at-large, has chaired some ComSoc technical and steering committees, is current Director of ComSoc Educational Services Board, has been serving in many committees such as the IEEE Fellow Committee, and has been actively organizing numerous IEEE International Conferences/Symposia/Workshops. He is frequently invited to deliver keynote addresses, distinguished lectures, tutorials, and invited talks. Some of his recognitions include several excellence in teaching awards, a few best paper awards, the NCE Excellence in Research Award, several ComSoc TC technical recognition awards, the NJ Inventors Hall of Fame Inventor of the Year Award, the Thomas Alva Edison Patent Award, Purdue University Outstanding Electrical and Computer Engineering Award, the NCE 100 Medal, and designation as a COMSOC Distinguished Lecturer. He has also been granted more than 40 U.S. patents.

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