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Collaboration Hub

The T-CAIREM Collaboration Hub is a service to connect researchers, clinicians, healthcare practitioners, students and mentors with each other. Please fill out this Collaboration Hub submission form. Once approved, we'll post your listing as soon as possible.

U of T DNA researcher seeks advanced skills in time series prediction models

Researcher: Karim Mekhail seeks skills from another member
Institution: University of Toronto
Website: https://www.mekhaillab.com
Project aims: DNA mobility can contribute to genome stability. Although initially believed to be random, this mobility can exhibit non-random motions consistent with gaining access to repair-conducive factors. The current goal is to predict the motion of DNA and the outcome of repair on a single-cell level.
Desired skills in collaborators: Advanced knowledge of time series prediction models.

POPLAR seeks EMR data research collaborations

Researcher: Dr. Michelle Greiver
Organization: POPLAR (Primary care Ontario Practice-based Learning and Research Network)
Contact: dfcm.utopian@utoronto.ca
Website: UTOPIAN Data Safe Haven
Project aims: Collaboration between seven networks across Ontario that extract and manage primary care EMR data. Data sets provided in secure environment. To request access to data, please see https://www.dfcm.utoronto.ca/getting-utopian-support
Research team currently has these skills: Data managers, epidemiologists, data analysts, primary care clinician-scientists, primary care researchers
Desired skills in potential collaborators: Data science. We welcome applications for use of primary care EMR data. (Linkage with ICES currently underway.)
Learn more and see project collaborators: https://www.poplarnetwork.ca

UHN's DateLab seeks collaborator(s) with skills in machine learning, video analysis, computer vision

Contact: Dr. Arlene Astell
Organization: University Health Network
Website: DATE Lab
Project title and goals: Measuring movement confidence in people with dementia
Movement confidence describes the comfort and confidence people during physical activities, such as exercise. Movement confidence is linked to fall risk, which increases with age, and is even greater for people with dementia. We have collected video data of 66 people with dementia during a 20 session virtual bowling game and conducted observational analysis of their movement confidence. We wish to further analyze the data to create a measure of movement confidence that can be used to identify risk and measure the impact of exercise rehabilitation.
Research team currently has these skills: Human data collection, observational analysis of video data, psychology, kinesiology, rehab sciences, physiotherapy, occupational therapy.
Desired skills in potential collaborators: Machine learning, video analysis, computer vision. At this stage we want to know if the data we have are suitable for using machine learning to train a model to detect/assess movement confidence. If so would like to work with collaborators to develop the funding proposal to conduct the modelling and develop the measure of movement confidence.
Other collaborators on this project:
• Erica Dove, PhD candidate, Rehabilitiation Sciences Institute, University of Toronto
• Dr. Kara Patterson, Physical Therapy, University of Toronto
• Dr. Amy Hwang, Occupational Sciences & Occupational Therapy, University of Toronto

St. Mike's Genetics researcher seeks NLP specialist/assistant for new digital tool

Contact: Yvonne Bombard
Organizations: University of Toronto and Unity Health Toronto
Website: http://stmichaelshospitalresearch.ca/researchers/yvonne-bombard/
Funding available: Yes
Project title and background: Genetics Navigator: A novel digital tool to advance quality and equity in genomic medicine
Our Genetics Navigator + NLP Chatbot will provide the full spectrum of clinical service for pediatric and adult populations that is culturally sensitive and customized to all levels of health literacy. The key feature of the final product is Nat, the NLP agent which can detect distress among patients using the platform and direct patients requiring psychological support to genetic counsellors. 
Project objectives:
1) Co-develop the Genetics Navigator platform and Nat chatbot with the ability to detect patient distress and support patients in their genetic testing journey.
2) Refine and evaluate the usability of the Genetics Navigator platform and Nat chatbot.
Team currently has these skills: Clinical and methodological skills, except NLP specialist/assistant
Desired skills in potential collaborators: NLP specialist/assistant
Other collaborators on this project: Bombard Y (Co-PI), Hayeems R (Co-PI), Aronson M, Costain G, Liston E, Shuman C, Ungar W, Brudno M, Faghfoury H, Mamdani M, Silver J, Carroll J, Jobling R, Marshall C, Smith M, Chad L, Lerner-Ellis J, Seto E, Thorpe K.
Notes: NLP specialist/assistant to work collaboratively with coIlaborators

Sunnybrook Health Sciences Centre researcher seeks data analysts for neuroimaging project

Contact: Dr. Fa-Hsuan Lin
Organization: Sunnybrook Health Sciences Centre
Website: https://linbrainlab.org
Funding available: NSERC, MITACS
Project title and aims: Encoding and decoding the human brains under complex naturalistic stimuli
We are interested in measuring neuronal responses under complex and naturalistic stimuli, such as movie clips and musical pieces. The goals include the modeling of brain responses with sensory inputs (brain coding) and estimating the behavioural responses and sensory inputs based on brain activity (brain decoding). We hypothesize that relationships between brain activity, sensory input, and behavioural responses implicate the mental states, which are different between age groups, lifestyles, and disorders. We aim at classifying and predicting their subjective feelings, behaviours, and treatment responses based on neuroimaging measurements.
Research team currently has these skills: Our lab is specialized in neuroimaging data acquisition and analysis. The modality we used includes magnetic resonance imaging (MRI) and electroencephalography (EEG). We are specialized in recording MRI and EEG with high spatiotemporal resolution and sensitivity on healthy adults, schizophrenic patients, and teenagers suffering from attention deficit disorders.
Desired skills in potential collaborators: We are enthusiastic in collaborating with experts in data analysis. We are interested in learning data modeling methods capable of generating robust results with our sample size (tens to hundreds of participants) and analytic strategies that support adaptive models with evolving data samples.
Other project collaborators: We are collaborating with international industrial partners and major medical imaging vendors to provide infrastructure (AI computations) and technical development (parts and information for imaging hardware) supports.

Duchenne muscular dystrophy not-for-profit seeks AI advisors 

Contact: Andrew Semihradsky <andrew.sedmihradsky@utoronto.ca>
Telephone number: 289-527-5608
Website: https://www.maxsbigride.com/
Project Aims: I hope to create an intelligent online platform that captures information about completed, ongoing and recently funded Duchenne research from online databases, journals and various other sources. This platform will utilize the power of artificial intelligence to find synergies between existing research projects and suggest connections between researchers with the aim of promoting communication, collaboration and promising new directions for research.
Desired skills in collaborators: At the moment I am just interested in talking through this project to see if it is possible — I do not know enough about AI to determine its feasibility or potential.

UHN's Ajmera Transplant Centre is offering a Postgraduate Fellowship in Machine Learning (Supervisors: Drs. Mamatha Bhat and Rahul G. Krishnan)

We seek postgraduate applications for a project involving the application of cutting-edge ML tools to healthcare. The project will focus on how to leverage ideas from deep learning, causal inference and predictive models to develop software for clinicians from large patient datasets with longitudinal clinical, laboratory, and molecular data. The candidate would be co-supervised by both Dr. Mamatha Bhat (clinician) and Dr. Rahul G. Krishnan (computer scientist).

Required qualifications
Minimum MSc in computational biology, computer science, engineering, or statistics. Expertise in Python, C/C++ and Unix programming environments. We encourage PhD applicants interested in exploring this intersection space to apply (the opportunity can be converted into a postdoctoral fellowship). Exceptionally strong undergraduate students with a publication record will be considered for this opportunity.

Preferred qualifications
Hands-on experience using machine learning tools such as scikit-learn as well as developing custom machine learning models in PyTorch/JAX/Tensorflow in high performance python computing environments.

Application process
Applications will be accepted until the position is filled. Please submit a CV, a copy of your most relevant paper, and the names, email addresses, and phone numbers of three references as a PDF to Mamatha.bhat@uhn.ca and rahulgk@cs.toronto.edu. The subject line of your email should start with "Postgraduate ML position".