Managing the development of AI and Machines that learn @ Room 4359, Mackenzie Building
May 15 @ 18:00 – 19:30
Managing the development of AI and Machines that learn @ Room 4359, Mackenzie Building | Ottawa | Ontario | Canada

A talk on AI (Artificial Intelligence) titled “Managing the development of AI and Machines that learn” presented by the CEO of, Niraj Bhargava.


It is widely acknowledged that AI technology will
transform organizations and society around the globe – and with recent
advancements in processing power, data accessibility, and algorithms we
suddenly find ourselves at the beginning of this transformation.
Introducing the Machine Trust Index (MTI), assisting us to boldly
proceed with a responsible, innovative spirit. The goal of the MTI is to
establish an open and versatile framework for measuring and managing
trust in the evolving sphere of AI. The MTI provides measurable
evidence, transparency and accountability of an AI solution, allowing
providers to effectively communicate the value of their solutions as
well as their measurable trustworthiness.


Niraj Bhargava is Founder and CEO of,
the developers of the Machine Trust Index™ (MTI) for managing AI
deployments. He is also Chair of the Innovation Committee of the Board
at the Royal Ottawa. Niraj was President and CEO of Enerstat
Limited, and led it through its turnaround and acquisition, Founding CEO
of QCED Inc., a faculty member in Entrepreneurship at Queen’s
University, a Director of the Queen’s Executive MBA, and then Dean of
the Business School at Royal Roads University. Niraj practiced
engineering at Bell-Northern Research and global marketing at General
Electric, and was the founding General Manager of GE Energy Management. Niraj was the founding CEO of in Montreal and Co-Founder, Chairman and Chief Executive Officer of Energate Inc.


Introduction to IBM Cognos Analytics 11.1! @ 4359 Mackenzie Building
May 21 @ 18:00 – 19:30
Introduction to IBM Cognos Analytics 11.1! @ 4359 Mackenzie Building | Ottawa | Ontario | Canada

Why should you attend?


·      Data analytics is a priority for many organizations

·      Many jobs now call for some level of analytic

·      Storytelling with data will soon become a “must
have” skill


What is Cognos Analytics?


“IBM® Cognos® Analytics integrates data preparation,
reporting, modeling, self-service analysis, dashboards, stories, event
management and now automated predictive analytics into one stack. Moreover, the
latest release makes extensive use of AI, including machine learning, natural
language processing (NLP) and natural language generation (NLG), in order to
automate as many features for the end user as possible, in an effort to make
BI, analytics and predictive analysis easy for business users.”


About the speaker:

Mohammed Omar Khan is an Offering Manager on the IBM Cognos
Analytics. He works with the development, design, sales, marketing, support and
more teams to make Cognos Analytics a leader in the BI market. He is a Carleton
University Alumni. Some of his achievements include 2nd place in Data Day 5.0
Poster Competition held at Carleton University, IBM VP Award, and IBM Managers
Choice Award


Technical Talk: Recent Results and Open Problems in Evolutionary Multiobjective Optimization @ Colonel By (CBY), Room A-707, University of Ottawa
May 30 @ 18:30 – 20:30
Technical Talk: Recent Results and Open Problems in Evolutionary Multiobjective Optimization @ Colonel By (CBY), Room A-707, University of Ottawa | Ottawa | Ontario | Canada

You are invited to the technical talk entitled

Recent Results and Open Problems in Evolutionary Multiobjective Optimization


Thursday May 30th, 2019


6:15 PM Arrival and networking (light snacks available)

6:45 PM Approximate start of talk (40-60 mins)

7:45 – 8:00 PM Q&A period

8:00 – 8:30 PM Post-talk networking and discussion


Colonel By (CBY) A-707

Faculty of Engineering
University of Ottawa
161 Louis Pasteur Private, Ottawa, K1N 6N5

admission is free but registration is required on EventBrite


Professor Carlos Coello, CINVESTAV-IPN, Mexico, IEEE CIS Distinguished Lecturer


Evolutionary algorithms (as well as a number of other metaheuristics) have become a popular choice for solving problems having two or more (often conflicting) objectives (the so-called multi-objective optimization problems). This area, known as EMOO (Evolutionary Multi-Objective Optimization) has had an important growth in the last 15 years, and several people (particularly newcomers) get the impression that it is now very difficult to make contributions of sufficient value to justify, for example, a PhD thesis. However, a lot of interesting research is still under way. In this talk, we will review some of the research topics on evolutionary multi-objective optimization that are currently attracting a lot of interest (e.g., handling many objectives, hybridization, indicator-based selection, use of surrogates, etc.) and which represent good opportunities for doing research. Some of the challenges currently faced by this discipline will also be delineated.

Speaker Biography

Carlos Artemio Coello Coello received a PhD in Computer Science from Tulane University (USA) in 1996. His research has mainly focused on the design of new multi-objective optimization algorithms based on bio-inspired metaheuristics, which is an area in which he has made pioneering contributions. He currently has over 470 publications which, according to Google Scholar, report over 43,900 citations (with an h-index of 83). He has received several awards, including the National Research Award (in 2007) from the Mexican Academy of Science (in the area of exact sciences), the 2009 Medal to the Scientific Merit from Mexico City’s congress, the Ciudad Capital: Heberto Castillo 2011 Award for scientists under the age of 45, in Basic Science, the 2012 Scopus Award (Mexico’s edition) for being the most highly cited scientist in engineering in the 5 years previous to the award and the 2012 National Medal of Science in Physics, Mathematics and Natural Sciences from Mexico’s presidency (this is the most important award that a scientist can receive in Mexico). He is also the recipient of the prestigious 2013 IEEE Kiyo Tomiyasu Award, “for pioneering contributions to single- and multiobjective optimization techniques using bioinspired metaheuristics” and of the 2016 The World Academy of Sciences (TWAS) Award in “Engineering Sciences”. Since January 2011, he is an IEEE Fellow. He is also Associate Editor of several journals including the two most prestigious in his area: IEEE Transactions on Evolutionary Computation and Evolutionary Computation. He is currently Vicepresident for Member Activities of the IEEE Computational Intelligence Society (CIS), an IEEE CIS Distinguished Lecturer and Full Professor with distinction at the Computer Science Department of CINVESTAV-IPN in Mexico City, Mexico.


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.




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.




6:00 PM – 7:00 PM.


7:00 PM – 8:00 PM.




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

Benjamin C. M. Fung
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
Stephan Jou
Class Imbalance in Fraud Detection
Robin Grosset
MindBridge Analytics Inc.
Handling class imbalance in natural language processing
Isuru Gunasekara
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
Hamidreza Sadreazami
McGill University
Privacy-preserving data augmentation in medical text analysis
Isar Nejadgholi
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 Hierarchical Sentence Classification in Unstructured Audit Reports
Daniel Shapiro
Deep Learning techniques for unsupervised anomaly detection
Dušan Sovilj
RANK Software Inc.
3:40 pm – 3:50 pm Closing Remarks




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