Projets novateurs réalisés

Randomization in Byzantine Agreement

The topic of our research is to study how randomization is used to solve Byzantine Agreement problem in different computational models. Byzantine Agreement is a classic distributed computing problem, where processors try to agree on a value, but some of the processors try to disrupt the agreement or make sure that the algorithm never terminates. Byzantine Agreement problem can be solved in different computational models, e.g., partially asynchronous, asynchronous. Practical applications of Byzantine Agreement are clock synchronization problem (multiple machines trying to agree on what time it is right now) and blockchains (consensus algorithms for synchronizing the state of the blockchain database). We want to study how randomization is used to solve these problems and, as a final result, make a survey for these methods to further facilitate the research on consensus problem. To do so, we will study different methods for solving consensus, and possibly improve existing algorithms as a result.

Development and characterization of catalyst electrodes for electrochemical reduction of CO2

Electrochemical reduction of carbon dioxide (ERC) is a process by which carbon dioxide (CO2) is converted into valuable chemical products via chemical reactions driven by electricity. The goal of this project is to fabricate and test catalytic metal electrodes to increase the efficiency of ERC reactors converting carbon dioxide from industrial exhaust gas streams into formic acid. The catalytic metals/metal alloys will be electrochemically deposited onto porous, high surface area carbon foam electrodes and tested in the company’s ERC reactors to study their efficacy and also any catalyst degradation. These tests will help understand the catalyst degradation mechanism and could lead to development of reactivation techniques. The results from this project will thus allow Mantra Energy to develop a library of metals/ metal alloys to choose catalytic materials from and also contribute to prolonging the life of the ERC reactor electrodes, making the ERC process more profitable.

Generalization of SR.ai NLP Algorithms to New Data Sources and Stability Improvements

The field of responsible investing is rapidly expanding, with even greater attention on the importance of responsible investment with each passing year, as seen most recently in the aftermath of the impactful 2021 COP26 summit, where responsible investment was key focal point. Directing our financial resources in a sustainable direction has the potential to have a massive impact on helping us meet the Sustainable Development Goals set forth by the UN. Though the importance of better alignment between finance and sustainability is clear, a notion that now has very strong consensus built around it, investors still lack the right tools to support their research process in terms of sustainability. They rely on high level data that is largely known not to be reliable, which leads to massive misallocations of capital. The mission of SR.ai is to bring more rigour to the world of responsible investment, using our technology backed by peer-reviewed research conducted within the University of Toronto.

Optimal Transport in Quantum Dynamics and Chemistry

In the last 30 years, the theory of optimal transportation has emerged as a fertile field of inquiry, and a diverse tool for exploring applications within and beyond mathematics, in such diverse fields as economics, meteorology, geometry and engineering. More recently, it has empowers today’s machine learning research and become one of the most emerging topics to learn.

This project explores a novel avenue, by developing mathematical and machine learning methods based on optimal transport to overcome computational bottlenecks in chemical dynamics. Specifically, we aim to design new computational algorithms that can incorporate key quantum effects and accurately predict spectroscopic properties of molecules at close to classical computational costs.

Our research lies at the boundary between mathematics, physics, chemistry and machine-learning. It combines Augusto Gerolin’s expertise in Optimal Transport and Machine Learning, David Manolopoulos’s expertize in chemical dynamics, with Annina Lieberherr’s expertise in quasicentroid molecular dynamics.

Synthesizing Past, Present and Future Climate Variability in the Cambrien Lake, Nachicapau Lake and Fort McKenzie Area (Naskapi Territory)

The Cambrien Lake, Nachicapau Lake and Fort McKenzie area (the “CNFM”) has been attributed high conservation potential by Nunavik’s regional stakeholders due to its exceptional biophysical characteristics and its significance to the Naskapis, as it includes a major Naskapi gathering place. The CNFM’s important hydroelectric and mining potentials prevent strict protection status at this time, but it is the subject of an agreement setting out specific commitments regarding mining activities and hydroelectric development.
Little climate-related information is available for the CNFM. As a result, the capacity and vulnerability of geoecosystems remain difficult to quantify, preventing their proper consideration in decision-making. A synthesis of past, current and future climate variability is therefore required for the area. A state-of-the-art picture will act as a common basis to describe and quantify such variabilities, which will feed decision-making for future uses of the CNFM.

Development of chemical approaches and technology for the design and preparation of glycoconjugate vaccines.

Glycoconjugate vaccines have shown efficiency to control infectious diseases and hold great promises in cancer immunotherapy. Nonetheless, there is an important need to identify novel immunostimulating carriers for carbohydrate antigens as well as to optimize the currently used protein carriers. This Mitacs project aims at developing versatile biochemical approaches and technology for the design and preparation of glycoconjugate vaccines. The interns will design vaccines based on protein nanoparticles and will engineer a known immunogenic protein carrier to enhance its efficacy to deliver glycoantigens. This project will lead to key advancements in the methodology of glycopeptide synthesis, glycoconjugation, and in the engineering of protein carriers for glycoantigens. Accordingly, this proposal is entirely aligned with the mission of Glycovax Pharma Inc, an emerging, fast-growing, clinical-stage Canadian biopharmaceutical company that aims at implementing molecular architectures to develop glycovaccines able to prevent cancers and infectious diseases.

Développement d’un vaccin protéique contre la somatostatine porcine.

L’Organisation des Nations Unis pour l’alimentation et l’agriculture estime qu’il se consomme environ 112 millions de tonnes de porc annuellement. Chaque kilogramme de porc a son empreinte environnementale est de 2,92 kg GES équivalent CO2 et requiert entre 4 et 6 mètres carrés de surface cultivable. Afin de maintenir la production de viande porcine tout en réduisant son impact environnemental, nous devons travailler à améliorer l’efficacité d’utilisation des ressources alimentaires. L’hormone de croissance est connue pour son rôle sur la croissance. En fait, l’hormone de croissance favorise une utilisation plus efficace des nutriments par une meilleure déposition. Pour hausser la sécrétion de l’hormone de croissance, il est possible de réduire la sécrétion de l’inhibiteur de l’hormone de croissance, la somatostatine. Le projet présent de recherche cible donc spécifiquement l’hormone de croissance chez le porc et la somatostatine. Nous proposons donc de développer un vaccin qui réduira la somatostatine, provoquant ainsi un plus grand relâchement d’hormones de croissance.

Development of a bio-based non-isocyanate polyutherane

This project aims to innovate a Made-in-Canada methodology for producing non-isocyanate
polyurethane (NIPU) to replace traditional polyurethane (PU) rigid foam which is produced through a
hazardous process from toxic materials such as isocyanates and phosgene.1 We aim to partner with
Polychem Consulting Solutions Ltd, a privately held Canadian company based in Vancouver, BC and a
developer of polyurethane foam from recycled sources, to develop non-toxic, CO2 and forestry waste
based polyurethane using new catalyst technology developed in our group. If successful, the impact of
this project will be three fold: 1) the commercialization of a sustainable, made in BC product to replace
currently toxic material; 2) valorization of wood waste products such as lignin, and 3) utilization of
global warming gas CO2 in the reaction, creating a market for CO2 capture plants based in BC.

Novel Inhibitors of Tissue Transglutaminase

Tissue transglutaminase (TG2) is a protein that is able to mediate the cross-linking of other proteins. The unregulated activity of TG2 is implicated in fibrosis, celiac disease as well as cancer cell survival. Blocking this activity has been shown to decrease tumour size and growth in mice. We have previously designed small molecules that are highly effective at binding and blocking the activity of purified TG2, as has the Löser group. In this project, we will combine our design strategies to conceive more potent potent inhibitors, synthesize these inhibitors and test them against TG2, using activity assays developed in the Löser group. We will also take the best of these inhibitors and test them in cancer cells, to determine if they are both and selective. The results from this project will be used to design future molecules that are more selective for TG2, and will work more effectively inside human bodies as a cancer therapeutic.

Effects of host plant quality on the transcriptional signatures of flight in migratory butterflies

Migratory insects surpass migratory terrestrial vertebrates in biomass and play essential roles in global ecosystems and agroecosystems serving as pollinators, biological control, or food and nutrient sources. They can also have detrimental effects as vectors of plant and animal diseases or as ecological pests. It is therefore essential for food, ecological, and human security to forecast insect migratory behavior, particularly as migratory patterns change worldwide with anthropogenic activities and climate change. Human societies in semi-arid regions heavily rely on insect migration services but increased drought has led to decreasing host plant quality. How this decrease in host plant quality is altering the migratory behavior of insects remains unknown but could have major consequences to ecosystems and agroecosystems. Here, we will investigate the effects of decreasing host plant quality on the larval performance, flight capacity, and gene expression profiles of active flight in the migratory butterfly, Vanessa cardui. The results of this study have broad reaching relevance to agricultural, human and ecological security.

Social Lead Identification Year Two

Millions of people post information on social media sites about their interests, preferences, opinions etc. on a daily basis. LeadSift mines this data stream in real-time to generate incredibly accurate and targeted sales leads. Given the short text and ambiguity around a social post, it gets very difficult to accurately identify intent. This project will explore different Natural Language Processing techniques to analyze the inherent semantic structure of social posts. Due the real-time nature of the social data, the algorithms need to be extremely efficient and scalable. The expected benefit to LeadSift will be dramatic improvement in the quality of the sales leads we can generate from socia media

Ordonnancement des retraits d’équipements électriques de transport

L’ordonnancement des retraits d’équipements électriques de transport est un défi auquel de nombreuses compagnies oeuvrant dans le domaine de l’énergie doivent faire face. En effet, chaque équipement de transport (p.e., une ligne de transmission) s’use avec le temps et doit, durant son cycle de vie, être maintenu pour s’assurer de son bon fonctionnement. Une fois en période de maintenance, un équipement ne peut pas être utilisé pour le transport d’énergie, ce qui risque d’engendrer des pannes de courant pour les clients. Cela peut être évité par des mécanismes de redondance des équipements. Ainsi, l’enjeu de ce projet de recherche est de concevoir et d’implémenter des approches innovantes pour obtenir une planification de la maintenance des retraits d’équipements électriques de transport, tout en s’assurant de la stabilité du réseau de transport.