doi.bio/catherine_tong
Catherine Tong
Overview
Dr. Catherine Tong is a researcher with a focus on improving the health and well-being of older adults, particularly racialised and foreign-born older Canadians. Her work involves community-based and patient-oriented research, employing mixed and qualitative methodologies. In addition to her research, Tong teaches courses related to health and ageing at the university level.
Education
Tong completed her PhD in Interdisciplinary Studies at the University of British Columbia, with a focus on the physical activity and mobility of older adults from racial and ethnic minorities in Vancouver. Her prior degrees from the University of Victoria and Simon Fraser University were in languages, public policy, and intercultural communication.
Career and Research
Catherine Tong is currently affiliated with the University of Oxford, where she is a final-year PhD student working on Human Activity Recognition under the supervision of Dr. Nicholas Lane. She is also a visiting scholar at the Cambridge Machine Learning Systems Lab, University of Cambridge.
Tong has also been affiliated with the following institutions:
- McMaster University
- University of Cambridge
- University of Washington (Biostatistics School of Public Health)
Her research interests include:
- Ethnicity and aging
- Home and community care
- Mobility and physical activity
- Multilingual and accessible research design
- Human Activity Recognition models
- Zero-Shot Learning for IMU-Based Activity Recognition
- Large-scale networked mobile and appliance data
- Multimodal Deep Learning for Activity and Context Recognition
Awards and Achievements
- Google Generation Scholarship (2021)
- Runner-up Best Short Paper award at the AAAI workshop W3PHAI
Tong has also co-founded GirlsWhoML, an initiative that delivered an online Intro to Machine Learning course to over 80 female students.
Catherine Tong
Overview
Catherine Tong is a researcher and academic with expertise in human activity recognition, machine learning, and health systems. She has contributed to various fields, including biostatistics, public health, and gerontology.
Education
Tong completed her PhD at the University of British Columbia in the Interdisciplinary Studies Graduate Program, with a focus on the physical activity and mobility of older adults and the impact of the built and social environment. Her previous degrees from the University of Victoria and Simon Fraser University focused on languages, public policy, and intercultural communication.
Career and Research
Tong is currently affiliated with the University of Oxford, where she is a final-year PhD student working on Human Activity Recognition under the supervision of Dr. Nicholas Lane. She is also a visiting scholar at the Cambridge Machine Learning Systems Lab, University of Cambridge.
Her research interests include:
- Zero-Shot Learning for IMU-Based Activity Recognition: Tong has explored the use of zero-shot learning methods for recognizing new activities that were not seen during training, addressing the practical challenge of diverse user activities.
- IMUTube: Automatic Extraction of Virtual on-body Accelerometry: Tong has tackled the issue of limited labeled data sets in human activity recognition by proposing IMUTube, which automatically extracts virtual on-body accelerometry data from videos.
- Tracking Fatigue and Health State in Multiple Sclerosis Patients: Tong has investigated the use of ubiquitous sensing to track and predict fatigue and health status in Multiple Sclerosis patients, aiming to improve long-term disease management.
- Inference of Big-Five Personality Using Large-scale Networked Mobile and Appliance Data: In a large-scale study, Tong correlated behavioral and physical health features with personalities, successfully inferring each Big-Five personality trait.
- Multimodal Deep Learning for Activity and Context Recognition: This work explores the integration of data from various sensors, such as barometers, accelerometers, and microphones, for activity and context recognition.
In addition to her research, Tong has also founded GirlsWhoML, delivering an online introductory course in Machine Learning to over 80 female students.
Publications
Tong's notable publications include:
- "Zero-Shot Learning for IMU-Based Activity Recognition Using Video Embeddings"
- "IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition"
- "Tracking Fatigue and Health State in Multiple Sclerosis Patients Using Connected Wellness Devices"
- "Deterministic Binary Filters for Convolutional Neural Networks"
- "Inference of Big-Five Personality Using Large-scale Networked Mobile and Appliance Data"
- "Multimodal Deep Learning for Activity and Context Recognition"
Other Affiliations
Tong has also been affiliated with the following institutions:
- McMaster University: Research contributions while affiliated with McMaster University are listed on ResearchGate.
- University of Washington: Listed as part of the Biostatistics School of Public Health.
- Geriatric Health Systems Research Group: Involved in projects to improve the health and well-being of older adults, particularly racialized and foreign-born older Canadians, through community-based and patient-oriented research.
Awards and Recognition
- Google Generation Scholarship (2021)
- Runner-up Best Short Paper award at the AAAI workshop W3PHAI
Teaching
Tong teaches courses at the university level, including:
- Health and Aging
- Interdisciplinary Perspectives on Aging
- Sociology of Aging
- Active Bodies in Later Life
Catherine Tong
Overview
Dr. Catherine Tong is a researcher with a PhD in Interdisciplinary Studies from the University of British Columbia. Her research focuses on improving the health and well-being of older adults, particularly racialized and foreign-born older Canadians. She employs community-based and patient-oriented research methodologies and has expertise in areas such as ethnicity and aging, home and community care, and multilingual research design. In addition to her research, she teaches courses on aging and health at the university level.
Education
Catherine Tong received her PhD from the University of British Columbia's Interdisciplinary Studies Graduate Program, with a focus on Family Medicine. Her doctoral work involved a mixed-method exploration of the physical activity and mobility of Chinese and South Asian older adults in Vancouver, influenced by the local environment. Prior to her PhD, she studied languages, public policy, and intercultural communication at the University of Victoria and Simon Fraser University.
Career and Research
Catherine Tong is currently affiliated with the Geriatric Health Systems Research Group, where she is involved in projects such as the CFN-funded study "Transforming primary health care for frail elderly Canadians" and the CIHR-funded project "Developing strategies and resources to support patient and family engagement with racialized immigrant older adults." This project is being completed in nine languages to ensure accessibility. She has also served as the Social Science Section Editor for the Canadian Journal on Aging.
In addition to her work with the Geriatric Health Systems Research Group, Catherine Tong is also a final-year PhD student at the University of Oxford, supervised by Dr. Nicholas Lane. Her research at Oxford focuses on Human Activity Recognition. She is also a visiting scholar at the Cambridge Machine Learning Systems Lab, University of Cambridge.
Notable Works
- The Creating Brave Spaces workshop: A report on simulation-based faculty development to disarm microaggressions.
- The Certificate of Added Competence credentialling program in family medicine: A descriptive survey of the family physician perspective of enhanced skill practices in Canada.
- Zero-Shot Learning for IMU-Based Activity Recognition Using Video Embeddings.
- IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition.
- Are Accelerometers for Activity Recognition a Dead-end?
- Tracking Fatigue and Health State in Multiple Sclerosis Patients Using Connected Wellness Devices.
- Deterministic Binary Filters for Convolutional Neural Networks.
- Inference of Big-Five Personality Using Large-scale Networked Mobile and Appliance Data.
- Multimodal Deep Learning for Activity and Context Recognition.