2025
2025 Proof of Concept | BON in a Box
Overview: , developed by the GEO BON secretariat hosted in the Department of Biology, 秀色直播, is a web-based analytical tool for analyzing and summarizing biodiversity data in a standardized, scientifically robust, and user-friendly way to better understand the status and trends of the earth鈥檚 biodiversity. This collaborative, community-contributed, fully open and operationally transparent tool is intended to help countries and organizations track progress towards the goals and targets of the Kunming-Montreal Global Biodiversity Framework of the UN Convention on Biological Diversity. To foster collaboration and increase the use of the tool by countries reporting to the UN agreement, we will use the Convergent Research Themes award to make the platform more user friendly and connect it with high performance computing clusters that will allow for larger and more complex analyses.
Core Team
Jory Griffith
GEO BON
Andrew Gonzalez
Department of Biology, Faculty of Science
Jean-Michel Lord
GEO BON
Guillaume Larocque
Research Professional QCBS
Isaac Eckert
Department of Biology, Faculty of Science
Laura Pollock
Department of Biology, Faculty of Science
Dr. Kevin Winner
Yale Center of Biodiversity and Global Change
Aboubacar Samoura
General Director of the Guinean Ministry of Environment and Sustainable Development
2025 Exploratory | Did biodiversity change? Automating data processing pipelines into a user-friendly software tool to assess power in biodiversity monitoring programs.
Overview: Detecting biodiversity change in natural systems with confidence is a critical challenge for reaching the goals of the Kunming-Montreal Global Biodiversity Framework. Power analyses for biodiversity change detection are possible but require complex subsampling simulations. We are developing a software tool that will allow users to conduct our simulation-based power analysis for detecting differences in species richness in any custom (user-supplied) biodiversity monitoring dataset. This will increase the impact of our research and better inform ongoing efforts for monitoring and interpreting changes in biodiversity.
Core Team
Jennifer Sunday
Department of Biology, Faculty of Science
Dr. Eden Tekwa
Department of Biology, Faculty of Science
Jake Lawlor
PhD Candidate, Department of Biology, Faculty of Science
Emily Rubidge
DFO Pacific, Science Branch
Ryan Stanley
DFO Atlantic, Science Branch
Matt Lemay
Hakai Institute
2025 Exploratory | Building a Canadian Machine Learning-Assisted PFAS Inventory to Guide Public Health, Mitigation, and Remediation Strategies
Overview: We will develop a framework to automatically estimate a PFAS inventory for Canada, providing a baseline to compare with public health data, and potentially efficiently guide remediation strategies. The reconstructed flows will provide estimates of PFAS emission locations and magnitudes.
Core Team
Prof. Sidney Omelon
Department of Mining and Materials Engineering, Faculty of Engineering
Dr. Edgar Mart铆n Hern谩ndez
Department of Mining and Materials Engineering, Faculty of Engineering
Dr. Gerardo Ruiz Mercado
Center for Environmental Solutions and Emergency Response (CESER), US Environmental Protection Agency (United States)
Prof. Mariano Martin
Department of Chemical Engineering, University of Salamanca (Spain)
Ms. Faezeh Pazoki
Civil Engineering Ph.D. student, Faculty of Engineering
2025 Exploratory | Lights Out: Examining Power Outages and Vulnerability Across Quebec
Overview: This project examines power outages across Quebec to uncover patterns of energy vulnerability and inequity. By analyzing high-resolution data on outages alongside socio-economic and demographic factors, we aim to identify communities most at risk. Our findings will provide critical insights to inform policies and interventions that enhance resilience and ensure equitable access to reliable energy. Ultimately, this work addresses the growing challenge of energy insecurity in a changing climate, benefiting vulnerable populations and strengthening societal preparedness.
Core Team
Grant McKenzie
Department of Geography, Faculty of Science
Myl猫ne Riva
Department of Geography, Faculty of Science
2024
2024 Proof-of-Concept | Development and integration of Intelligent Personal Assistant platform (IPA) in biomedical high-risk environment (HRE)
Overview: This 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
J茅r么me Waldisp眉hl
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 秀色直播
PhD students Juan Camargo Tavares, George Tarabain
Beno卯t 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, NB
Hannele Lindqvist-Kreuze
Leader of the Genetics, Genomics and Crop Improvement at International Potato Center - a non-profit CGIAR center, Lima, Peru
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. Our 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
Universit茅 du Qu茅bec 脿 Trois-Rivi猫res (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
2023
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, 秀色直播 Health Centre
Carolyn Freeman
Radiation Oncology, 秀色直播 Health Centre
Shirin Enger
Medical Physics Unit, Gerald Bronfman Department of Oncology, 秀色直播.
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
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
2023 Exploratory | Applications of natural language processing in clinical care at 秀色直播
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
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 Dor茅
Marketing, Desautels Faculty of Management
Brendan Johns
Psychology, Faculty of Science
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
J茅r么me Waldisp眉hl
Computer Science, Faculty of Science
Silvia Vidal
Human Genetics, Faculty of Medicine
Elena Nazarova
Computer Science, Faculty of Science
