"Knowledge is Speaking, Wisdom is Listening" - Jimi Hendrix
Daniel Felsky, PhD
Dr. Daniel Felsky is an Independent Scientist and Head of Whole Person Modelling in the Krembil Centre for Neuroinformatics at CAMH and Assistant Professor in the Department of Psychiatry and Institute of Medical Science (IMS) at the University of Toronto.
Dr. Felsky completed his PhD in neuroimaging and genetics of Alzheimer’s disease at IMS in 2015. Following this, Dr. Felsky completed postdoctoral fellowships at the Anne Romney Center for Neurologic Diseases at Brigham and Women’s Hospital, Harvard Medical School, in Boston, and the Centre for Translational and Computational Neuroimmunology at Columbia University Medical Centre in New York.
Dr. Felsky has experience across several areas including structural brain imaging, human genomics and transcriptomics, neuroimmunology, biostatistics, psychiatric epidemiology, and study design. In addition to his research, Dr. Felsky is a passionate teacher, and has taught globally as a Fellow for the Harvard Global Initiative for Neuropsychiatric Genetics Education in Research, co-sponsored by the Stanley Centre for Psychiatric Research at the Broad Institute of MIT and Harvard.
Milos Milic is a Research Analyst working primarily with Dr. Felsky in developing standardised internal protocols for data curation.
Milos completed his first Masters in the department of Cell and Systems Biology at the University of Toronto under Dr. Ulrich Tepass where he focused on successfully characterising the function of Drosophila gene CG34347 via confocal microscopy and genetic recombination. He has recently completed a Masters of Data Science at the University of British Columbia where he helped develop a machine learning model that can identify iron ore veins from images.
He is passionate about machine learning, especially leveraging labelled image data in their application to healthcare diagnostics.
Peter Zhukovsky, PhD
Primarily supervised by Dr. Aristotle Voineskos
Dr. Peter Zhukovsky (PhD, Experimental Psychology and Psychiatry, University of Cambridge, UK) is a postdoctoral fellow. Peter’s work focuses on the neurobiological mechanisms of major depressive disorder, particularly on late-life treatment resistant depression. In particular, he uses whole brain structural and functional connectivity to identify depression “biotypes” in large scale databases (UK Biobank) and predict cognitive outcomes in a depression treatment trial (OPTIMUM dataset). He combines functional connectivity measures from resting-state fMRI and structural connectivity measures from morphometric similarity mapping and diffusion weighted images with advanced statistical approaches such as partial least squares, clustering and deep neural networks. He hopes to reveal the associations between brain connectivity, cognition and clinical symptoms.
Sejal Patel, PhD
Dr. Sejal Patel (PhD, Bioinformatics and Neuroimaging-Genetics, IMS, University of Toronto) is a postdoctoral fellow. Sejal’s work focuses on integrating neuroanatomical and genetics data (GWAS and gene expression) to better understand the etiology of neurodegenerative diseases such as Alzheimer’s disease, including a focus on ageing and major depressive disorder. Sejal works with large datasets such as CLSA to integrate clinical and genetic data in statistical models primarily in R to identify associations between biomarkers and clinical symptoms in Alzheimer’s disease and major depressive disorder. Her research passion is finding ways to use existing tools to analyze data and apply these tools in a wide range of disorders.
Mohamed Abdelhack, PhD
Mohamed is a Postdoctoral Fellow working on using machine and deep learning to model psychiatric disorders.
He previously worked as a Postdoctoral Researcher in Washington University in St. Louis where he was building machine learning models to predict post-surgical medical complications. He also worked as a researcher in Kyoto University Hospital studying neural activity markers of Schizophrenia using brain decoding techniques. His doctoral work in Kyoto University focused on using deep learning models to understand robustness of human brain in recognizing degraded visual input.
He possesses a wide range of skills in computational neuroscience, neural imaging, machine and deep learning, and electronic design.
PhD student, Department of Physiology
Co-supervised with Dr. Evelyn Lambe
Jonas is a graduate student at the University of Toronto, and a member of the Collaborative Program in Neuroscience. He is working toward his PhD in the laboratory of Dr. Evelyn Lambe and is working with Dr. Felsky on a project focusing on cholinergic signaling in Alzheimer’s disease.
Following the completion of his bachelor studies in Biomedical science at the University of Dundee, Jonas went to Boston as a visiting student in a systems biology laboratory at the Massachusetts Institute of Technology and since then has also completed a research assistantship focused on multiple sclerosis, at the University of Munich.
Jonas is passionate about research in neurological disorders and wants to implement a multi-disciplinary approach in his thesis work, combining bioinformatics, electrophysiology, and behavioral techniques.
MSc student, Biostatistics, Dalla Lana School of Public Health
Amin Kharaghani is a graduate student at University of Toronto, studying Biostatistics with an emphasis on machine learning at Dalla Lana School of Public Health. As part of his graduate studies, he is working with Dr. Felsky as a practicum student, focusing on Alzheimer Disease.
Amin completed his undergraduate studies at University of Toronto Mississauga majoring in Mathematics and Statistical Sciences. Amin is passionate about education; he works at University of Toronto as a teaching assistant at Mathematical and Computational Sciences department.
Amin is interested in machine learning and deep learning and their applications in genetics and, behavioural sciences.
MSc Student, Institute of Medical Science
Earvin Tio is a graduate student with the Institute of Medical Science and a member of the Collaborative Program in Neuroscience. His research focuses on uncovering neuroimmune mechanisms that underlie suicidal ideation by leveraging large cohort studies and machine learning.
Earvin graduated from the University of Waterloo with a Bachelor of Applied Science in Systems Design Engineering and a specialization in Artificial Intelligence. During his undergraduate studies, Earvin also had the opportunity to study abroad in Switzerland at the École polytechnique fédérale de Lausanne, studying computer science.
Earvin is passionate about destigmatizing mental illness. He actively works with GradMinds, the University of Toronto's graduate student mental health committee, and regularly contributes to Elemental Magazine, the University of Toronto's official mental health magazine.
MHSc Student, Medical Physiology, Department of Physiology
Alyssa Cannitelli is a graduate student at the University of Toronto in the
Medical Physiology master’s program, which exposes students to
healthcare from diverse lenses: namely, clinical physiology, big data,
commercialization, research, and industry. Alyssa recently worked on a
literature review on maternal-fetal origins of child neuropsychiatric risk. As
part of the program, she is working with Dr. Felsky as a practicum student,
with a focus on whole-person modelling approaches in psychiatry.
Alyssa also completed her undergraduate studies at the University of
Toronto, majoring in Human Physiology and Italian language/literature with
a minor in Immunology. Alyssa is passionate about destigmatizing and
disintegrating the ‘blame’ associated with mental and chronic illnesses
such as obesity. She is interested in learning more about precision,
person-centred approaches to clinical medicine and hopes to pursue a
career in psychiatry or family medicine.