Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Cathode Design for All-Solid-State Lithium-Tellurium Batteries

Battery technologies are urgently needed for emerging high-tech applications, such as medical implants, wireless sensors, wireless devices. These new devices have very limited space and require high reliability, and therefore demand the batteries could provide high energy per volume and high safety. Current Li-ion batteries cannot meet this demand due to its relatively low energy per volume and safety risks (leakage, fire, and explosion). To address these challenges, Prof. Jian Liu’s group at The University of British Columbia and Fenix Advanced Materials, a clean technology company specializing in the manufacturing of ultra-high purity metals, team up to develop all-solid-state lithium-tellurium (Li-Te) batteries. This new-generation Li-Te battery is expected to possess volumetric energy density about 2-3 times folds of current Li-ion batteries, and intrinsic high safety, and will have a positive economic, environmental, and social impact in BC and Canada.

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Faculty Supervisor:

Jian Liu

Student:

Mohammad Hossein Aboonasr Shiraz;Hongzheng Zhu

Partner:

Fenix Advanced Materials Inc

Discipline:

Engineering - other

Sector:

Manufacturing

University:

Program:

Accelerate

Applied next generation AI accelerator algorithm hardware co-optimization: using quantization, sparsity and hardware constraints during neural net training

This work aims to explore software and hardware co-optimization for deep neural network (DNN) inference applications. Once a model is trained to sufficient accuracy, the model is used to make inference or predictions based on this trained model. With increasing performance, more people are using these models for tasks such as translation, self-driving cars and speech recognition. This has greatly increased the demand for high performance inference hardware. The goal for this project is to investigate novel techniques to reduce latency and power consumption during inference while maintaining the same model accuracy.

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Faculty Supervisor:

Gennady Pekhimenko

Student:

Yingying Fu

Partner:

Untether AI Corp.

Discipline:

Computer science

Sector:

University:

Program:

Accelerate

Understanding Real-time Particle Systems for Health, Entertainment and VR

The proposed research is a collaboration between Persistant Studios’ PopcornFX and SFU’s iVizLab to collaboratively work on ways to understand the processes involved in content creation using a real-time particle system. The iVizLab’s research focuses on using real-time visuals with the biodata from the users as one of the main interfaces to create affective systems that can intelligently interact with the users. In creating the visuals for the iVizLab, it is important to be able to create content that can be modified in real-time with the incoming data. The intern will be working closely with the partner organization to work on understanding complex design processes and to break them down into simpler components to better understand the involved processes. Further, the intern will be working on ways to document these processes and share this with the community of PopcornFX users in the industry and world-wide.

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Faculty Supervisor:

Steve DiPaola

Student:

Ioana Sandor

Partner:

PopcornFX

Discipline:

Journalism / Media studies and communication

Sector:

University:

Program:

Accelerate

Assessing and Addressing Health Disparities Related to Utilization of Preventive Care Services in Ontario

Health disparities arise as a result of long-standing societal disadvantage and discrimination. As machine learning models become more popular in the healthcare sector, understanding of current health disparities becomes even more critical. Without careful management of existing biases, the models can inherit and amplify health disparities, leading to highly undesirable clinical outcomes. This project focuses on health disparities in access to preventive care services. Preventive care services such as screening and preventive medicine allows for early diagnosis and timely interventions. This project aims to provide an understanding of if and how patterns of preventive care utilization aggravates health disparities in Ontario, by employing advanced data exploration and visualization techniques. After establishing such a relationship, this project also provides an individual risk profiling tool to assess the efficacy of preventive services, using advanced feature representation and deep learning techniques.

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Faculty Supervisor:

Marzyeh Ghassemi

Student:

Xuling (Shirly) Wang

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

University:

Program:

Accelerate

Audience Allocation to Retail Geo-clusters

Based on the user’s geo-location, timestamp and other attributes (eg. time of day, past visit history and app behavior categories, etc.), a machine learning algorithm can be developed to find which cluster the users belong to. Overall, the data of geo-location and timestamp are used to roughly locate the potential clusters. This project will involve some techniques and algorithms like cloud computing i.e Google Cloud Dataproc, sliding windows, histogram and machine learning algorithms. The challenge of first phase would be coming up with a good way of estimating the number of clusters. Then by applying all the above techniques, the decisive attributes can be decided and combined to determine which cluster the users belong to.

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Faculty Supervisor:

Scott Sanner

Student:

Congwen (Emily) Yang

Partner:

Pelmorex Media Inc

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Program:

Accelerate

Question-to-question semantic similarity for Question Answering System

Question Answering (QA) system automatically answer questions raised by users in natural languages, and it is a crucial component of a human-machine conversation system. A typical QA system collects human written question-answer groups and structures them in a database system. However, in order to answer questions that are semantically similar to the questions stored in the database but are worded differently, the QA system needs to be able to calculate the semantic similarity between different questions. In this research project, the intern will explore different techniques used in question-to-question semantic similarity measurement and try to improve upon the state- of-the-art performance. From participating in this project, RSVP Technology Inc. could seed for more opportunities to collaborate with Canadian community to improve the quality of QA systems used in many other fields and products, such as customer service chatbots and smart home device. Further, this project could serve as the foundation for next step research and development.

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Faculty Supervisor:

Graeme Hirst

Student:

Zihang Fu

Partner:

RSVP Technologies Inc

Discipline:

Computer science

Sector:

University:

Program:

Accelerate

Understanding cell-cell interactions with deep learning-based profiling

The aim is to understand how fibroblasts, the most common connective tissue in animals, and cancer cells interact with each other through image analysis. These co-culture imaging screens, containing fibroblasts and cancer cells, will help identify novel signaling mechanism involved in cancer. The objective is to apply deep learning techniques to these image-based assays to study interactions between and identify novel therapeutics that can make cancer therapies more effective.

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Faculty Supervisor:

Jimmy Ba

Student:

Sumeet Ranka

Partner:

Phenomic AI Inc

Discipline:

Computer science

Sector:

University:

Program:

Accelerate

Ground-Based Remotely Piloted Aerial Vehicle (RPAV) Tracking System

Drone Delivery Canada (DDC) designs and operates high performance Remotely Piloted Aerial Systems (RPAS) to deliver payloads between depots and warehouses. The DDC engineering department is looking to design and deploy a ground-based system to track and point at the Remotely Piloted Aerial Vehicle (RPAV) during flight in real-time. However, DDC’s RPAS must be able to operate in remote areas making the use of communication technology infrastructure difficult due to the need to be able to have communication between the RPAS and ground control station (GCS) over long distances without the use of communication relay nodes or heavier, more powerful communication modules on the RPAV. To solve this challenge, advanced communication equipment such as high-gain antenna(s) will be needed in addition to novel antenna tracking algorithms for the system to be interfaced with the GCS to receive telemetry data from the RPAV. The end product is a robotic tracking system which applies positional feedback data from the RPAV (such as altitude and GPS location) to a control system to dynamically point an antenna at the RPAV throughout flight. Review of existing literature on tracking systems and required infrastructure/resources would be performed to guide the design process. TO BE CON’T

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Faculty Supervisor:

Kamran Behdinan

Student:

Siu Hong (Thomas) Leung

Partner:

Drone Delivery Canada

Discipline:

Aerospace studies

Sector:

Transportation and warehousing

University:

Program:

Accelerate

Improving the Performance and Convergence Rate of Transformer-Based Language Models

The pre-trained Bi-directional Encoder Representation from Transformers (BERT) model had proven to be a milestone in the field of Neural Machine Translation, achieving new state-of-the-art performances on many tasks in the field of Natural Language Processing. Despite its success, it has been noticed that there are still a lot of room for improvement, both in terms of training efficiency and structural design. The proposed research project would explore the detailed design decision of BERT on many levels, and optimize them wherever possible. The expected result would be an improved language model that achieves higher performance on NLP tasks while using less computational resources.

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Faculty Supervisor:

Jimmy Ba

Student:

Xiaoshi Huang

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

University:

Program:

Accelerate

Altering Plant Microbiomes for Flavour and Nutrition

The goal of this project is to use naturally occurring bacterial partners to improve the flavour and nutritional properties of plants grown in hydroponic and aquaponics systems. This study will investigate ability of plant associated bacteria to alter the metabolic profile of select vegetables and leafy greens. Vertical farming is an increasingly popular solution for the production of plant produce year-round at a local level. However, it involves the growth of plants in engineered systems without natural soils. Soils are inhabited by tens of thousands of species, some of which move into plant tissues and contribute to their natural nutritional and flavour profiles. Our aim is to match produce plant metabolisms to those of naturally occuring bacterial associates and to test the ability of these partners to enhance the quality of food produced in vertical farms.

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Faculty Supervisor:

Roberta Fulthorpe;Apollinaire Tsopmo

Student:

Jessica Castillo;Gabriela Yunuén Campos Espinosa

Partner:

Discipline:

Chemistry

Sector:

Manufacturing

University:

Program:

Accelerate

Tizen OS Support for SOTI MobiControl Interoperability

In this project, we propose to expand support of SOTI’s MobiControl (MC) to Tizen Operating System. SOTI MobiControl has assisted numerous enterprises to overcome the management issues due to lack of security and improve business performance by monitoring the health and safety of employees, and increasing productivity, with the introduction of wearables and other IoT (Internet of Things) devices. On the other hand, Tizen is a Linux-based mobile operating system developed by Samsung Electronics and offers support to Samsung smart devices, including Samsung Gear Watches, Samsung Smart TVs, Samsung Cameras and many other devices. This project aims to deploy the MobiControl management service to the Tizen devices in order to provide solutions to smart devices management to our customers.

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Faculty Supervisor:

Eyal de Lara

Student:

Yin-Hung Chen;Qi Zhao

Partner:

SOTI Inc

Discipline:

Computer science

Sector:

Information and cultural industries

University:

Program:

Accelerate

Sentiment Analysis in Dialogue Systems

More and more companies are choosing to automate various aspects of their customer service using chatbots. While these chatbots are still in their technological infancy, they currently provide useful customer service to many people around the world. They will continue to become more desired by companies as a single chatbot system can engage millions of customers with minimal scaling costs. In these many interactions, there is a substantial amount of potential information to extract. This project focuses on extracting user sentiment information from a large set of chatbot-customer interactions. The ultimate goal of this project is to develop a module that collects and summarizes customer’s sentiment about the company’s products and services as well as their overall sentiment when engaging with the chatbot. Ideally, if successful, the information generated by our sentiment analysis module can be used by companies to more efficiently identify and handle customer issues.

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Faculty Supervisor:

Graeme Hirst

Student:

Paul Adrien Briggs

Partner:

Ada Support

Discipline:

Computer science

Sector:

University:

Program:

Accelerate

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