A statistical method for competing risk survival analysis with clustered big data

Over the last few years, the data revolution occurred with the emergence of “Big data”. In medical field, the term big data refers to large databases in terms of patients and/or information from varied sources. Nevertheless, heterogeneity is encountered in this kind of data. Indeed, data arise from different medical centers. Furthermore, we can’t perform traditional statistical methods on these large databases: major problem are multicollinearity and overfitting. Lots of regularization methods have been proposed in order to adapt classical methods. Mittal et al.

Stressless Gamification to Improve Workplace Health

Stress is a top workforce risk and according to a Towers-Watson 2013 survey, half of all employers identify improving the emotional and mental health of employees as their top priority for building health and productivity programs. The objective of this research is to improve StressWelliQ’s systems and product portfolio by creating gamified technology solutions and thereby improving the lives of Canadians. The creation and evaluation of this technology will allow StressWelliQ to create a more effective augmented stress management platform.

Mucoadhesive nanoparticle eye drop drug delivery

Eye diseases, such as dry eye syndrome, affect about 15% of the population. Eye drop formulations are the most common way to treat eye diseases, but patients struggle with the multiple daily applications required and the resulting side effects. Our goal is to develop an eye drop formulation that requires less frequent application using nanoparticle drug carriers. Drugs are quickly cleared from the eye due to blinking and tear turnover, but these nanoparticles can attach to the corneal surface to prevent rapid clearance.

Linking microbiome to eco-industrial function: the in silico and metagenomic exploration of microbial dark matter and taxonomic blind spots

"A large majority of microbes cannot be cultured. Recently, microbiome sequencing has begun to identify the genetic potential of these lineages, often referred to as microbial “dark matter”. By integrating my taxonomic profiling methods with function profiling developed by the academic supervisor, I aim to develop and apply a comprehensive pipeline for microbial dark matter characterization, placing unclassified taxa into both a taxonomic and functional context.

Energy Efficient Subthreshold design for NVM circuits

In this research we are aiming to build smart wearable and standalone units which can be used in our everyday lives for things such as: i) tracking vitals, and storing information; ii) monitoring sporting activities; iii) collecting ambient (in house and in air) statistics for smart home monitoring systems; iv) location tracking; v) emergency response .... .These wearable’s and stand alone devices have the advantage of not being connected to battery and can harvest its required energy from motion and sun light.

Human Motion Inverse Optimal Control Constraint Learning and Inertial Measurement Unit Sensor Design for Rehabilitation

During physiotherapy a continuous assessment and progress tracking of a patient’s performance is of clinical interest. In this project, based on the promising results from the initial prototype, we will redesign the wearable sensors to improve tracking accuracy, communication speed and robustness, incorporate onboard data storage and computation, and minimize cost and size. Furthermore, we will develop automated algorithms for the analysis of the measured data to help physiotherapists identify the causes of changes to the patients' movement profile.

Developing Tools to Evaluate the Effectiveness of NGO WaSH Interventions in Low to Middle Income Countries

H2O4All is one of many small, non-governmental organizations (NGOs) working to address the needs of the 663 million who lack access to safe drinking water and the 2.4 billion who are without access to sanitation facilities in low- and middle-income countries. Unfortunately, evaluation of water, sanitation, and hygiene (WaSH) interventions are rarely a priority for NGOs like H2O4All whose projects are time and budget sensitive; this is concerning because without evaluation we cannot determine H2O4All’s impact on the communities in which it works.

Clinical development of avocado-derived lipids as modulators of fatty acid oxidation for the treatment and management of obesity and diabetes

Obesity and diabetes are a significant global burden and there is an immediate need for novel treatments and management strategies. Our lab has shown that an avocadoderived lipid is a potent inhibitor of fatty acid oxidation (i.e., the cellular breakdown of fat for energy) which reduces mouse weight gain without toxicity. Inhibition of fatty acid oxidation for the management and treatment of obesity and diabetes is an established therapeutic strategy. The objectives of this project are two-fold.

Chemical Synthesis of Graphene-Based Nanomaterials from Value-Added Carbon Obtained by Catalytic Conversion of CO2

CO2 is a greenhouse gas that impacts climate change. The Carbon Upcycling Technologies has developed a technique to attach gaseous CO2 molecules to graphite. In this project, this graphite will be converted to graphene using chemical oxidation methods. The one-atom thin monolayer graphene oxide will have potential applications in variety of industries, such as waste water treatment, gas separation and reinforcement of plastics.

Nonlinear adaptive neural controllers - Year two

Contemporary machine learning has been very successfully applied to processing static images and words in consumer applications, resulting in billions of dollars in recent acquisitions of machine learning companies by Microsoft, Amazon, Facebook, and Google. However, applications to dynamic information (e.g. movies, controlling robotics) has been less well-developed. In this project, will develop and apply a novel machine learning method to neural control system for a sophisticated robotic arm.