Development of femtosecond/picosecond laser and sample delivery for PIRL-DIVE-MS to enable spatial imaging of tissue and earliest possible detection of disease

Picosecond InfraRed Laser (PIRL) technology has finally realized the long-held promise of the laser to achieve the fundamental (single cell) limit to minimally invasive surgery – with the unexpected benefit of scar free healing. The very process of laser cutting involves the selective excitation of natural occurring water inside tissue to drive molecules into the gas phase in the form of an ablation plume.

Solidarity not Charity: Mutual-aid volunteer engagement, experiences, and retention strategies with the Bike Brigade in the context of COVID-19

The COVID-19 pandemic has produced a heightened interest in, and need for, mutual aid: a collective effort to meet community needs and redistribute resources. Mutual aid has been a long-standing practice of Black communities, and existed before terms like ‘caremongering’, ‘crowdfunding’ and ‘the sharing economy’ became popularized. Mutual aid is also more than just a crisis response; it builds solidarity and sustained relationships across community members.

Ubiquity: Intelligent Supply Chain Management

The intern will first determine which one of the two modules in the Ubiquity product is more likely to be improved using the machine learning and deep learning methods, and then implement the proposed method or research on other methods. To improve the sensitivity of the pricing and promotion module towards the small price changes, the intern will use deep neural network to improve the existing forecasting model.

Classical and Quantum Metaheuristic Optimization Tools to Improve the Constrained Vehicle Routing Problem Solutions

The constrained vehicle routing problem is a typical optimization problem with many real-life applications, such as last-mile route planning for delivery services. The goal is to find the optimal routes for a set of vehicles to deliver all the packages, such that the time and cost of delivery is minimized, sometimes with additional constraints such as a loading capacity for each vehicle. In recent years, quantum computing has started to show great potential in providing a speedup to optimization solutions.

Modeling Application Performance under Multi-Instance (Multi-Stream) Execution Scenarios

Multi-stream execution is a technique in GPUs that allows multiple operations/kernels from the same program to effectively use GPUs without explicitly stating the affinity of threads to the cores. Several recent optimizations in Machine Learning (ML) algorithms leverage multi-stream execution. While performance modeling of ML applications is well studied under single-stream execution, performance models of novel ML applications under multi-stream execution is lacking.

Clarifying Questions for Conversational Agents

Many conversational agents blend the functionalities of question-answering and chit-chat into a single system. This lays the foundation for a more interactive type of question-answering models which is the primary focus of the research project. Interaction between a user and the agent allows the user to ask an underdefined or ambiguous question, and, instead of the system returning its best response, the system is able to ask clarifying questions to help it hone in on an answer it can be more confident in.

Improving statistical literacy and accessibility of big data using dimension reduction

Information and data are more accessible and abundant than ever before, but this presents challenges in the face of rising misinformation and mistrust in science. Large-scale datasets or “big data” is particularly suited to a set of statistical analyses called dimension reduction techniques that can reduce the complexity of a dataset while preserving its information, but these analyses are currently difficult and costly for the average citizen to learn and implement.

Community Based Participatory Research Strategies for Combining Creativity with Sustainability in the Arts and Beyond

The proposed Creativity and Sustainability post-doctoral fellowship will be situated at Mass Culture (MC),and executed in cooperation with University of Toronto Scarborough’s (UTSC) Urban Just Transitions(UJT). Over the years, MC and the scholars involved in UJT have experimented with various forms ofcommunity-engaged methods in order to generate impactful research that will inform policy-making andadvocacy work to address inequities in their respective fields of interest.

Real-time Bidding Using Contextual Targeting

Ads keep the internet free. But, to keep them from becoming spam and degrading the user’s online experience, they need to be relevant to them. The traditional way the industry does this is by collecting a lot of information about every user and creating profiles that can be used to target users based on their online journey. However, users have different tolerances for how much information they want collected about them by any website/app. In general, the problem the industry needs to solve is to make sure the ads shown to users are relevant even in the absence of any user profile.

Development of chemical property and process models for a new fermentation approach

Bioethanol is well-established as an alternative to petroleum-based fuels. A current roadblock in ethanol fermentation is end-product inhibition: where the increasing concentration of ethanol slows the growth of the yeast cells and their ability to ferment biomass. There is work being done to use existing separation technologies to remove ethanol throughout the reaction to avoid end-product inhibition, however these methods are often energy-intensive and therefore not feasible on a commercial scale.