Marta M. Maslej
PhD
Qualification
- PhD, Psychology, Department of Psychology, Neuroscience & Behaviour, McMaster University
- BA, Psychology (Honours), Department of Psychology, York University
- BA, English (Honours), Department of English, York University
- BEd, Junior/Intermediate (Concurrent Program), Faculty of Education, York University
Professional Memberships
- Fellow, Associated Medical Services (AMS), Fellowship in Compassion and AI
- Member, Temerty Centre for AI Research and Education in Medicine (T-CAIREM)
Marta Maslej is a Staff Scientist with the Krembil Centre for Neuroinformatics (KCNI) at The Centre for Addiction and Mental Health. She completed her PhD in the Department of Psychology, Neuroscience & Behaviour (PNB) at McMaster University, with a focus on understanding the emotional and cognitive symptoms of depression, and implications for treatment. Dr. Maslej started her clinical research training at CAMH in 2018, by evaluating the feasibility and efficacy of an online psychosocial intervention for depression. She joined KCNI as a CIHR-funded postdoctoral fellow in 2019, with an interest in the potential of technology to improve mental health assessment and care. Her postdoctoral research involved analyzing data from electronic health records, including clinical notes, to predict mental health outcomes and risks, as well as experimental work into AI-based clinical decision support.
Dr. Maslej’s current research at KCNI involves using computational methods to derive insights from clinical data, with the aim of improving assessment, informing treatment decisions, and identifying and mitigating bias. She is interested in human-AI teaming, or the ways AI can supplement limitations in human cognition and prognostication to enhance mental health assessment and care. As part of this work, she explores aspects of the clinical setting that impact teaming performance, with the aim of supporting AI implementation. At KCNI, Dr. Maslej co-leads the Predictive Care team, which draws on interdisciplinary methods to anticipate impacts of AI applications on patients and providers, with a focus on promoting compassionate and equitable care. Her other research initiatives involve increasing access to sociodemographic data to support fair AI applications in mental health, as well as collaborations on large cohort studies, which aim to understand functional trajectories in schizophrenia spectrum disorders and how to improve suicide prevention efforts at CAMH. Her research has received funding from CIHR, SSHRC, Google Research, and AMS Healthcare.