She’s a research associate in the Department of Psychiatry at Dalhousie University, where she focuses on integrating mobile sensing technology and AI for anxiety diagnosis. She also serves as a scientific consultant at The Centre of Excellence in Youth Mental Health — Douglas Mental Health University Institute — McGill University, contributing to the development of machine learning and deep learning models for mental health conditions, particularly schizophrenia and psychosis. Passionate about applying AI to improve diagnosis and treatment of mental and neurodegenerative disorders, her research has been published in respected journals including Artificial Intelligence Review, Alzheimer’s & Dementia, and BMC Medical Informatics and Decision Making. 

Dr. Graciela Gonzalez-Hernandez is the Vice Chair of Research and Education in the Department of Computational Biomedicine at Cedars-Sinai Medical Center. She is a recognized expert and leader in natural language processing, artificial intelligence, and machine learning methods for knowledge discovery. Her team has created state-of-the-art methods for extracting unstructured information from clinical records, journal articles, and social media postings to elucidate data patterns, trends, and relationships that can aid the discovery process in areas such as pharmacoepidemiology, clinical research, or public health monitoring and surveillance. She has organized numerous shared tasks, including seven iterations of #SMM4H, plus tasks at BioNLP, Biocreative, and SemEval. Her work has been funded by NSF, NIH/NLM, NIH/NIAID, NIH/NIA, the FDA, and the CDC. She serves as a member of the Advisory Council for Intramural Research at the National Library of Medicine, has mentored more than 50 students, medical residents, post-doctoral researchers and junior faculty, and has published over 210 papers in top scientific journals over her 23 years in academia. 

Google scholar: https://scholar.google.com/citations?user=ER8VZlwAAAAJ&hl=en Institutional homepage: https://www.cedars-sinai.edu/research/labs/gonzalez-hernandez.html 

James Green received his PhD degree from Queen's University in 2005 for research in the areas of computational genomics and proteomics. In 2000-2001, Dr. Green worked at Molecular Mining Corporation, a bioinformatics start-up company in Kingston Ontario, where he helped to develop novel analysis methods for the interpretation of gene expression data. In September 2005, Dr. Green joined the Department of Systems and Computer Engineering at Carleton University where he is now a Full Professor and a Senior Member of the IEEE. His research focuses on machine learning challenges in biomedical informatics, particularly in the presence of class imbalance and the prediction of rare events. Current research projects include unobtrusive and non-contact patient monitoring; the prediction of protein/RNA structure, function, and interaction; and the acceleration of scientific computing using parallel computing. Most recently, Dr. Green has collaborated with the Children's Hospital of Eastern Ontario to collect and analyze a multimodal patient dataset from the neonatal intensive care unit. His lab has developed non-contact patient monitoring methods for patient segmentation, pose estimation, movement detection, vital sign estimation, and the detection and classification of clinical interventions. He is now exploring the use of LLMs for summarizing these various data sources to produce coherent patient summaries for diverse audiences including both clinicians and parents.

Dr. Ali Ayub is an Assistant Professor at Concordia Institute for Information Systems Engineering (CIISE), Concordia University where he directs the PaInt (Personal and Interactive Autonomous Systems) lab. Before joining Concordia, he was a postdoctoral fellow at the University of Waterloo, and received his MS and PhD in Electrical Engineering from Penn State in 2017 and 2021, respectively. His research interests include interactive machine learning, continual learning, personalization and assistive robotics. He aims to develop autonomous systems that can continually adapt to individual user preferences and unique environments to provide personalized assistance over the long term. His research is supported by the NSERC Discovery grant, Mitacs Accelerate, and Concordia’s Facility Optimization program. He is a recipient of the Petro Canada Young Innovator award, DAAD research fellowship, and Google’s Diversity, Equity, and Inclusion (DEI) award.