Convergent Research Themes
2024 Proof-of-Concept | Development and integration of Intelligent Personal Assistant platform (IPA) in biomedical high-risk environment (HRE).
Overview:ÌýThis theme aims at developing and integrating an intelligent personal assistant (IPA) for supporting and navigating Containment Level 3 (CL3) facility users in their every-day work. Our IPA will help to centralize and monitor inventory, equipment, and rooms biosafety conditions. AI recommendation system of IPA will analyze the exploitation of contained zone and provide recommendations to facility manager for optimizing the lifespan of CL3 equipment.
Core Team:
Jérôme Waldispühl
Computer Science, Faculty of Science
Silvia Vidal
Human Genetics, Faculty of Science
Elena Nazarova
Computer Science, Faculty of Science
2024 Proof-of-Concept | Updating and retrieving genetic information for plant pangenome assemblies.
Overview: : Creating a simple, computational system for updating and retrieving genetic information for plant pangenome assemblies with the aim to identify climate-resilient genetic traits.
Core Team:
Martina Stromvik
Department of Plant Science
Stromvik Bioinformatics lab at º«¹úÂãÎèÌý
PhD students Juan Camargo Tavares, George Tarabain
Benoît Bizimungu
Research Scientist and Curator, Canadian Potato Gene Resources, AAFC, Fredericton, NB
Helen Tai
Research Scientist, AAFC, Fredericton, NB
Martin Lague
Computer scientist at AAFC, Fredericton, NB
Kyra Dougherty
Bioinformatician at AAFC, Fredericton, NBÌý
Hannele Lindqvist-Kreuze
Leader of the Genetics, Genomics and Crop Improvement at International Potato Center - a non-profit CGIAR center, Lima PeruÌý
Noelle Anglin
Research Leader, USDA-ARS, Idaho, USA
Dave Ellis Emeritus
member and former Head of Genebank at International Potato Center,Lima Peru
2024 Exploratory | Towards a causal inference framework for understanding microbiome etiology and informing interventions.
Overview: The main goal of this project is to develop, test, and implement a causal inference framework for analyzing complex microbiome data, to inform the development of effective population-level interventions.ÌýOur research design employes a group model building approach to develop a causal inference framework applicable to microbiome studies. The framework will help advance our understanding of relevant etiologies and related modifiable pathways of the gut microbiota, particularly during pregnancy. The application of causal inference methods in microbiota research is a strategic approach to mitigate bias when investigating the causal impact of microbiota and microbiota-mediated exposures or health interventions.
Core Team:
Tibor Schuster
Psychiatry, Faculty of Medicine
Cristina Longo
Pediatric epidemiologist, UdeM
Celia Greenwood
Oncology, Faculty of Medicine and Health Science
Stan Kubow
School of Human Nutrition, Faculty of Agricultural Environmental Sciences
Roxana Behruzi
Université du Québec à Trois-Rivières (UQTR)
Albina Tskhay
PhD student, Faculty of Medicine
2024 Exploratory | Developing Analysis Pipelines for Multimodal Digital Data Acquired from Patients At Risk for Psychosis
Overview: The purpose of this Exploratory project will be to identify methods which can be used to analyze multi-modal (digital and physiological) data in patients with early psychosis or psychosis risk, and to begin putting together a methods toolbox to be used in future studies and to be shared with other researchers. In order to better understand why some people develop serious mental illnesses, such as psychosis, we must understand how to combine different kinds of data collected from behavior (performance on tasks) and biology (for example, eye tracking or imaging). This will allow us to learn more about how to prevent or delay the onset of these conditions and to develop new treatments.
Core Team:
David Benrimoh
Psychiatry, Faculty of Medicine
Deven Parekh
Chief Data Scientist, McPsyt
Sara Jalali
Lab Manager, McPsyt
Lena Palaniyappan
Head of Center for Excellence in Youth Mental Health
Ìý
2023 Exploratory | Predicting the local impact of regional extreme weather events in smart cities
Overview: This theme explores the feasibility of coupling Numerical Weather Prediction models with Computational Fluid Dynamic models in order to quantify local influences of severe weather on smart cities. It will also explore the best strategies to communicate the results to decision-makers.
It represents areas in atmospheric sciences, structural and wind engineering, geographic information systems, and urban sustainability and resilience.
Core Team:
Djordje Romanic
Atmospheric and Oceanic Sciences, Faculty of Science
Laxmi Sushama
Civil Engineering, Faculty of Engineering
Raja Sengupta
Geography, Faculty of Science
Ìý
2023 Exploratory | Applications of natural language processing in clinical care at º«¹úÂãÎè
Overview: This theme aims at identifying clinical needs that can be best addressed with NLP-based tools, in order to improve patient outcomes. Research questions include structuring clinical text (from typed medical reports or interviews), the use of health chatbots, and mining medical literature to discover latent associations.
It represents areas of NLP, clinical outcomes, evaluative research and health services delivery.
Core Team:
Dan Poenaru
Pediatric Surgery, Faculty of Medicine
Jackie Cheung
Computer Science, Faculty of Science
Esli Osmanlliu
Pediatrics, Faculty of Medicine
Samira Rahimi
Family Medicine, Faculty of Medicine
Ìý
Ìý
2023 Exploratory | Using machine learning and natural language processing to predict real-world consumer decision-making and evaluation
Overview: This theme will explore applying machine learning and NLP tools to a very large data set of consumer choices and reviews, in order to predict decision-making and textual content of reviews. On a broader scale, this theme will develop computational methods for generating psychological insights from text.
It represents areas of cognitive neuroscience, decision-making, big data methodologies, machine learning and natural language processing.
Core Team:
Ross Otto
Psychology, Faculty of Science
Bruce Doré
Marketing, Desautels Faculty of Management
Brendan Johns
Psychology, Faculty of Science
Ìý
Ìý
2023 Exploratory | Challenges and rewards of developing an intelligent technology for high-risk biomedical environments
Overview: This theme will explore the development of an Intelligent Personal Assistant that will aid in planning, safety and day-to-day operations in high risk environments such as Containment Level 3 (CL3) laboratories. The first stage of the project will involve identifying needs and limitations of CL3 environments and creating software testing protocols to be evaluated first in lower risk laboratories.
It represents areas of software engineering, computer-human interactions, machine learning and natural language processing, and biomedical methods and protocols.
Core Team:
Jérôme Waldispühl
Computer Science, Faculty of Science
Silvia Vidal
Human Genetics, Faculty of Medicine
Elena Nazarova
Computer Science, Faculty of Science
Ìý
Ìý
2023 Proof-of-Concept | Developing a deep learning algorithm to improve cancer treatments
Overview: This theme aims at developing a deep learning algorithm for auto-segmentation of extremity soft tissue sarcomas (STS), and evaluating radiation doses to the areas that will be irradiated. Emphasis will be placed on the evaluation of the different volumes to be irradiated, which will give insights into the clinical significance of auto-segmentation.
It represents areas of STS imaging, DL auto-segmentation, and radiation therapy planning.
Core Team:
James Tsui
Radiation Oncology, º«¹úÂãÎè Health Centre
Carolyn Freeman
Radiation Oncology, º«¹úÂãÎè Health Centre
Shirin Enger
Medical Physics Unit, Gerald Bronfman Department of Oncology, º«¹úÂãÎè.
Ahmed Aoude
Orthopaedic Surgery, Faculty of Medicine
Anthony Bozzo
Orthopedic Oncology, Memorial Sloan Kettering Cancer Center
Orthopaedic Surgery, Faculty of Medicine
Sungmi Jung
Pathology, Faculty of Medicine
