Projets novateurs réalisés

CIO to CEO – Barriers and Opportunities – Phase II

This is a continuation of a MITACS sponsored study for the Chief Information Officers

Association of Canada, Ontario Chapter, to assist its members in long term career planning

and development. It will refine and further validate a model for advancement from CIO to

CEO, by interviewing CEOs who have successfully made the transition. In total, some 50

CEOs will participate, who are currently leading large organisations in Canada and the United

States. This is new work, addressing a topic about which there has been much speculation,

but little field research. Support of the CIO Association will facilitate recruitment of the study

participants. The project findings will be disseminated in a White Paper to be published by the

Association, in at least 1 refereed journal paper and will also be presented at a national CIO

Conference in 2012. The Intern will have a unique opportunity to meet with and interview top

executives from a wide range of organisations.

Grandir les hassidim de demain : l’expérience de New York

Comment les enfants grandissent-ils au sein des communautés juives hassidiques de New York ? Quelles représentations guident les pratiques des mères dans ce processus ? De quelle façon le genre, la langue et l’école sont pensés et interviennent dans la socialisation des enfants ? Et enfin, comment se déroulent les interactions entres mères et enfants hassidiques au quotidien ? Axé sur deux séjours de recherche de six semaines, ce projet explore la manière dont les enfants hassidiques grandissent à New York aujourd’hui. Deux niveaux d’analyse seront retenus : d’une part on retracera les représentations qui façonnent les pratiques des mères hassidiques; d’autre part, par le biais d’une enquête ethnographique, on examinera leur articulation avec le déroulement quotidien du processus de socialisation, observé dans les interactions entre mères et enfants. Cette recherche permettra d’aborder empiriquement et en dehors des controverses les enjeux de l’éducation au sein d’une communauté considérée comme fermée, tel que les juifs hassidiques. Dans une perspective comparative avec notre travail de thése, elle nous permettra de faire émerger la spécificité de la socialisation hassidique new-yorkaise, face à l’expérience montréalaise.

PROJET ANÉMONE. : Algorithmes d’aNticipation des dÉfauts, de leurs MOdéliation et de leurs Neutralisation anticipée.

Afin d’optimiser son processus et de tirer profit du cumul des connaissances accumulées dans les compagnies, le projet porte sur la mise en oeuvre et l’extension d’un dispositif d’interprétation des KPI générés dans le but de réduire l’implication humaine et de fournir aux experts un outil pour les guider dans l’analyse des causes dans le but de prévenir l’émergence de dysfonctionnements d’opération des équipements sous surveillance.
De plus, ce projet a pour principal livrable la réalisation d’un outil pour détecter des dérives multidimensionnelles complexes, de les anticiper et de communiquer avec la clientèle de manière persuasive à la manière d’un expert métier.

A data visualization framework to leverage text and knowledge graphs

The goal of this work will be to explore different ways to visualize and interact with knowledge extracted automatically from very large heterogeneous document collections. This extracted knowledge will be in the form of a multi-attribute graph of extracted entities and relationships between them. These relationships will be associated with both temporal and spatial information. The work conducted will focus in identifying the best ways to visually represent and interact with content from two application domains — medicine and journalism — both including thousands of entities and relationships. By leveraging these very distinct domains, we aim to provide a unified framework and initial prototype to navigate large multivariate knowledge graphs that is potentially applicable across multiple domains.

Analysis of bacterial motility on varied surface chemistry

Bacterial biofilm formation on medical devices remains an unsolved medical challenge. Biofilms are communities of bacteria within which bacteria are able to become highly resistant to antibiotics and the host’s immune system. Developing materials that prevent biofilm formation is limited by a lack of understanding about how bacteria attach to and form biofilm on surfaces. In this project single cells will be observed in three dimensions using digital holographic microscopy to see how they interact with surfaces. The project will establish computer algorithms that can rapidly analyze the images and extract key parameters such as speed, acceleration and curvature and categorize different swimming patterns. This will be done using a range of different materials that bacteria respond to differently to establish the variations in bacteria behavior that relate to the prevention in biofilm formation. These studies will, thus, help underpin the development of materials resistant to biofilm formation.

Development of Functional Nanoparticles for Targeted Biomaterial Cell Delivery

Genetic engineering has proven to be a useful approach for gene therapy and transformation of value-added agricultural plants. However, the current available technologies suffer from several limitations. These include relative low delivery efficiency with difficult-to-deliver cells or complex constructs and limit freedom-to-operate. Considering advances have been made in the applications of nanotechnology to life science and plant biology, particularly in the realm of gene editing technology, improving delivery efficiency is urgently needed. We propose to develop functional nanomaterials, which will provide an innovative, efficient, and low-cost alternative for conventional methods of biomolecule delivery. It will also accelerate the development of targeted therapy and value-added plants.

Operating Room Traffic Assessment: A Video Analysis Approach

Surgical Safety Technology aims to improve operating room safety by capturing and analyzing operation videos. Usually, operating room traffic (like people displacement) has a huge impact on surgery. Unnecessary movements can cause distraction of surgeons and pollution of the sterile environment. This project applies computer vision models to detect and track people movements in the operating room and assesses the relationship between adverse events and errors. Popular machine learning models such as Deep Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have the capability to analyze time sequential data. Trained on the well-labeled data directly from specific hospitals, these models could work out precise operating room traffic trace and its correlation with surgical events.

Early detection of Alzheimer’s disease symptoms using speech longitudinally

An early symptom of Alzheimer’s Disease is difficulty in remembering recent events. These trends are reflected in problems in language and patterns of speech. Speech patterns of an individual can hence be used to determine the trajectories of preclinical cognitive decline. The difference in the cognitive trends over subject groups, analyzed using speech data collected over a long period of time, can be used to detect Alzheimer’s even before it can be confirmed clinically.
With the help of machine learning models, this process can be automated completely by using automatic speech recognition systems to transcribe the speech followed by analysis of these transcripts. This project will explore machine learning- based strategies to automate the early AD diagnosis pipeline.

Recommendation system for retail shopping

People rely on recommendations from other people, friends’ word, news reports, and travel guide and so forth. Recommendation systems assist people to sift through available books, web pages, restaurants, and grocery products. [16]. We want to build a recommendation flow in the retail industry to serve Canadian citizens better. The system will understand the customers and help them to make better selections and improve their shopping experience. A retail recommendation is different from e-commerce as the basket is substantially larger and customer tends to buy same product over and over again. In this project to build models to understand the existing customer base and products for shopping suggestions, robust substitutions, and search ranking. The system will make recommendations base on the users that are similar. For example, the system will learn your shopping behaviours and make product recommendation based on purchased history of other users that share the similar shopping behaviours.

Intra-operative Error Detection on Surgical Video based on Computer Vision Analysis

The intra-operative errors that occurs in adverse events have been a major concern in healthcare and surgical industry. Conventionally, error-event assessment is done by peer surgeon review, which is time consuming and costly. With the advances in machine learning and computer vision techniques, it is possible to keep track of the operation surgical procedures based on recorded surgical videos to evaluate and classify the errors occurred. With the proposed computer vision-based algorithm, it is expected to predict the error event during surgery in a scalable process to ensure a better and safer patient and surgical environment.
Since the intra-operative error detection algorithm is mainly trained on recorded surgical video data, it is expected to have an impact on improving decision-making and performance in future operations for complex patients and surgical circumstances.

Segmentation of 3D microscopy images

In-vivo imaging provides a unique opportunity to examine complex cellular activity in live tissue. Images produced by these experiments are difficult to analyze manually, typically applied to mono-layer cell culture assays (i.e. cells in a dish). Recent advances in deep learning enable the opportunity to analyze these in-vivo tissue images with greater efficiency and accuracy. This project will apply deep learning based segmentation and classification technology to a dataset provided by a collaborating pharmaceutical company. Deep learning algorithms will be developed to segment different cell types and vascular structures in the dataset and quantify features (i.e. length, volume, protrusion number, marker intensity) of these objects. These features will be used to evaluate the effectiveness of therapeutic treatments.

FINITE MODELING OF BRIDGE ELASTOMERIC POT BEARINGS

Elastomeric Pot Bearings (EPBs) are High Load Multi-Rotational bearings developed in

Europe in the early 19605 to support a bridge superstructure while transmitting large

force demands to the supporting piers and abutments, and accommodating rotation

about any horizontal axis as a function of the applied loads. EPBs have usually been

designed according to a mix of empirical and theoretical procedures. Very often, the

rationale behind some of these design rules is unclear and should be evidenced to

assure that the design still meets current engineering practice. The main objective of this

research project is to investigate and understand the structural behaviour of EPBs using

advanced numerical modeling techniques. A numerical procedure will be developed for

the design of optimized dimensions of EPBs, while satisfying the requirements of current

state of practice in Canada and Quebec. The results obtained will be thoroughly

examined to identify the influence of important design parameters and to