Innovative Projects Realized

Reservoir Analytical Model Pattern Recognition

The proposed production optimizer uses production (rate, water/oil ratio, pressure) data, in either isolation or with geological data, and artificial intelligence to determine limiting factors in wells and fields. More specifically, the proposed production optimizer determines Original Oil in Place (OOIP), average permeability, permeability distribution, and relative permeability for wells and, by extension, reservoirs. This reservoir characterization information then is used to optimize the field.

Knowledge Intensive Processes Representation and Analysis: Process-Aware Work-Graphs and Predictive Approaches

Processes are important concepts in modern society since they control and standardize the interactions between businesses, consumers, governments and other organizations. However, the rise of knowledge-based industries such as financial services, healthcare, advanced manufacturing and software development have produced unstructured and knowledge-dependent processes. These Knowledge Intensive Processes (KIPs) KIPs range from partially structured to unstructured processes and require some control and standardization while guiding but not completely constraining knowledge workers’ actions. This interplay between providing agility and control promotes the emergence of complex work-graphs, which gather cross-cutting processes, tasks, people, information, rules and supporting software systems. Current challenges include representing process abstractions and defining methods for predicting process analysis. TO BE CONT’D

Understanding the mediators of employee wellness in an activity-based workspace

Adoption of an activity-based workspace is rapidly increasing. Employees in activity-based workspaces do not have a set workstation. Instead, employees can choose from a variety of workstations (e.g. desks, lounge space, quiet offices) according to their needs. Despite increasing adoption, we know little about the impact of activity-based workplaces on employee wellness, satisfaction, and productivity. The goal of this project is to understand person-related, work-related, and organization-related factors that determine who thrives in this workspace and who perceives the activity-based workspace as a source of stress. The CIBC CRE group operates under an activity-based workspace (branded ‘CIBC@work’) in which employees can book and work in a number of different workspace. It is expected that the current work will inform wellness interventions to be evaluated within the CIBC CRE workplace.

Étude sur certains déterminants et indicateurs de santé organisationnelle

En dépit de l’intérêt grandissant de la santé organisationnelle au sein des organisations et du milieu académique, des questions restent en suspens pour comprendre comment favoriser la santé psychologique, la santé physique et la performance des travailleurs. Devant ce besoin, la présente recherche vise à apporter un éclairage sur certains des déterminants et des indicateurs de santé organisationnelle. Pour ce faire, un minimum de 300 employés remplira des instruments de mesure validés à trois reprises. Des analyses seront réalisées pour répondre aux objectifs de la recherche et un portrait de la santé organisationnelle sera fourni au partenaire industriel. Les parties prenantes mettront en commun leurs efforts afin de fournir des preuves scientifiques susceptibles de guider les pratiques en ressources humaines et en psychologie du travail et des organisations.

UV Mapping Assistance through Deep Learning

The goal is to create a conversation loop between 3D designers and artificial intelligence programs. This will help the AI provide suggestions to the designer, while the designer provides the AI with feedback. This can help make it easier for designing complicated objects as well as complicated textures that belong to the surface of 3D objects. Through this interaction, the hope that AI can extend the utility of design software.

Teaching robotics programming in conjunction with virtual simulation software: An evaluation of learning behaviors in secondary students

Studies have indicated a positive correlation between teaching robotic programming using a virtual platform with speed of learning and deeper level understanding. We will study how students learn when provided with just physical robots, just virtual robotics software, and both in conjunction with each other. This study will also attempt to assess indicators of learning that are more in line with the direction of the Next Generation Science Standards (NGSS), under the assumption that they will be more relevant to modern students, educators, and the corporations in relevant fields. Students who utilize a more interactive or constructive mode of learning have been shown to achieve a deeper level of understanding compared to passive or even active learning. Therefore, this study will focus more on the learning behaviors of the students as a reflection of true learning, as opposed to testing for the content itself.

Smart Grid Diagnostics using Power Line Communications

Power Line Communication uses the existing infrastructure of electrical power lines for high frequency data transmission. The availability of such power line communication modems across the grid, which are typically used for enabling smart-grid connectivity, also gives us the opportunity to extract insight into the nature of the grid. This includes investigating the health of the power cables and also detecting and locating possible line faults. In this research project, we adapt theoretical formulations of this concept to incorporate practical types of cable degradations and faults typically encountered in the distribution network. We use machine learning techniques to enable the power line modems with distributed intelligent grid sensing abilities to automatically diagnose the grid by identifying fault locations, assessing the severity of cable degradations, and estimating the remaining service age of power cables throughout the grid.

Upper Fraser River bull trout management evaluation

Bull trout in the upper Fraser watershed (UFW) of British Columbia are important top predators and serve as the basis of a recreational fishery. Anglers in the region have asked government to consider changing current fishing regulations for bull trout from catch-and-release to regulations that allow them to take a portion of their catch home. Allowing for this regulatory change would increase the types of fishing opportunities in the area but could harm bull trout populations. This is further complicated as there is little information on the ability to sustainably harvest bull trout, and as the species has conservation listing. TO BE CONT’D

Structural investigation of a N-his6 variant of cytochrome P450cam (CYP 101A1) by protein NMR studies

Cytochromes P450 are enzymes that add oxygen to organic molecules, to convert them to water-soluble products that can be further metabolized. These oxidative enzymes are ubiquitous (found in animals, plants and microorganisms). We are working on cytochrome P450cam, isolated from a wild soil bacterium. This enzyme catalyzes the oxidation of camphor (an organic molecule that can be used by these bacteria as a sole carbon and energy source) to 5-exo-hydroxycamphor. The enzyme needs oxygen (O2) for this. We discovered an unexpected reduction reaction catalyzed by this enzyme, to give a camphor-reduced product, borneol, that occurs when O2 levels are insufficient for the bacteria to fully digest camphor. This reaction is used by the bacteria to signal that O2 levels are too low to metabolize camphor, which becomes toxic to them if they cannot digest it. I discovered that a variant of P450cam with additional residues, his6-P450cam, does not catalyze the reduction of camphor to borneol. Based on previous work, we hypothesize that, to reduce camphor, the P450cam needs to be in a closed state, as opposed to open states. “TO BE CONT’D”

AI solutions to patient-physician engagement

The goal of Engagement Intellect system (at Deloitte) is to use conversations between patients and physicians and convert them into useful information. This is done using recent advancements in artificial intelligence technologies that can automatically extract symptoms, medical history, and other relevant information from the voice recordings. Besides extraction, the project will focus on associating this with the relevant context such as people and their relations, or time and location information. Such technological advancement in the medical arena holds immense potential not only to improve the treatment, but also to make medical advice more accessible.

Prediction of Optimal Business Structure for Tax Efficiency

With the growing relevance of data, PwC has embraced Data & Analytics as part of its culture. The Tax practice, specifically, strives to bring value to market by empowering domain experts with automation and data-driven services. Among their initiatives, the Tax Technology team is investigating methods to evaluate the quality of corporate tax returns (T2). Given the complex nature of these documents, this problem requires exploring distinct issues:

1. A T2 can contain hundreds of sub-forms supported by large professional teams within distinct specializations. Scalable methods are needed to identify finalized tax returns among drafts and the large volume of work product produced by these teams.

2. A mapping between each field in a tax return and its corresponding sub-form does not exist.

3. Tax forms contain slips—copies of a tax question which must be answered for any number of relevant cases—yielding forms of vastly different structures.

With the University of Toronto’s support, PwC hopes to assess the integrity of its data and develop proof-of-concepts to optimize service delivery, along with identifying practices and procedures that will differentiate them in the marketplace.

Machine learning modelling of temporal enterprise data

In a few words, this is a fintech data analysis project. The idea is, given temporal data of financial nature, to build algorithms that predict its evolution over time. For instance, the data could be certain assets prices, or customer buying history. The objective would be to respectively predict this asset price in a close future, or to anticipate what the customer wants to buy next and make relevant recommendations. Such algorithms belong to the family of machine learning algorithms. In the last years, machine learning has been of keen interest to the worldwide scientific community, as a sub-branch of machine learning named deep learning has seen considerable algorithmic progress. This project would leverage recent and efficient deep learning methods.