We come from different backgrounds and bring different skillsets, but we are interested in solving similar problems.

Principal Investigator

Dr. Andreea Diaconescu

Dr Andreea Diaconescu is an Independent Scientist at the Krembil Centre for Neuroinformatics at CAMH and Associate Professor in the Department of Psychiatry at the University of Toronto with cross-appointments with the Institute of Medical Science and the Department of Psychology at the University of Toronto and member of the Max Planck-University of Toronto Centre for Neural Science and Technology.

Before this, she was a Swiss National Foundation Ambizione fellow and Junior Group Leader at the University in Basel, Department of Psychiatry leading a project on early detection and treatment of psychosis using mathematical modelling. After completing her PhD in cognitive neuroscience at the Rotman Research Institute, University of Toronto, Dr. Diaconescu held a postdoctoral position at the Translational Neuromodeling Unit, University of Zurich and ETH Zurich. There, she developed and applied computational models of learning and decision-making to understand the emergence and persistence of delusions in psychoaffective disorders such as schizophrenia.

Areas of Research:
Dr. Diaconescu has developed and validated mathematical models that infer subject-specific disturbances of information processing in neuronal circuits from neuroimaging, electrophysiological, and behaviour measures. She also has expertise in whole-brain, multimodal neuroimaging analysis methods. Dr. Diaconescu’s research is centered on the clinical validation of (neuro)computational models of learning and decision-making for predicting psychosocial functioning and treatment response in individuals at clinical high risk for psychosis. Moreover, Dr. Diaconescu is engaged in the identification of cognition- and neuroimaging-based transdiagnostic predictors of suicidality.

Research Analyst

Diya Shah

Diya Shah is a Research Analyst in the CogNeMo lab. Her work focuses on integrating gamified cognitive tasks, mobile EEG, and computational modelling to support earlier detection and triage of severe mental illness. She holds an MHSc in Medical Physiology from the University of Toronto, with training spanning neuroscience, data analytics, science commercialization, and healthcare innovation. Her prior work includes developing commercialization strategies and business plans for biomedical technologies, as well as co-developing an AI-enabled referral automation concept for primary care. In the CogNeMo lab, Diya coordinates multimodal mental health studies involving digital platforms, wearable EEG, gamified cognitive assays, clinical workflows, and participant-facing research operations.

Research Methods Specialist

Dr. Zheng Wang

Zheng Wang is a Research Method Specialist working with both Dr. Andreea Diaconescu and Dr. John Griffiths. Following the completion of his Ph.D. in Control Theory from the Department of Mechanical Engineering at the University of Calgary, Dr. Wang undertook several postdoctoral positions at the University of Toronto and the Stevens Institute of Technology. His research at these institutions was primarily focused on consensus mechanisms in large network systems and bifurcation phenomena in power networks. Introduced to the field of brain network modeling by Dr. Randy McIntosh, Dr. Wang transitioned into this field after assuming a role as a research technician at Baycrest Hospital. His current research interests are centered on identifying neuro-behavioral biomarkers through the integration of neural and behavioral computational models. This multidisciplinary approach seeks to elucidate the underlying mechanisms responsible for brain disorders, thereby aiding in both diagnosis and the identification of potential intervention targets.

Master Student

Denisa Lazar

Denisa Lazar joined the CogNeMo lab in February 2024 as a Research Analyst, while also providing support for the centralized recruitment team within the Slaight Family Centre for Youth in Transition at CAMH. With an Honours Bachelor of Science specializing in Mental Health Studies from the University of Toronto, Denisa additionally completed a co-operative work term as a Research Participant Assistant at the Rotman Research Institute of Baycrest Hospital. Since then, Denisa has cultivated a diverse background in psychological assessment, research methods, ethics, as well as clinical and neuropsychological test administration. In terms of their research interests, Denisa is focused on understanding risk factors associated with the development of conditions such as psychosis and mild cognitive impairment, with a particular emphasis on identifying and implementing preventative strategies aimed at reducing their onset in at-risk populations.

PhD Student

Pamina Laessing

Pamina Laessing received her B.Eng. and M.Sc. in Engineering Physics with a focus on Machine Learning and Optimization from the University of Oldenburg in Germany in 2018 and 2021, respectively. Now, she is a doctoral student at the Max Planck – University of Toronto Centre (MPUTC) for Neural Science and Technology under the joint supervision of Dr. Andreea Diaconescu in the CogNeMo group at the Krembil Centre for Neuroinformatics and CAMH and Dr. Peter Dayan at the Max Planck Institute for Biological Cybernetics. Pamina is broadly interested in employing Machine Learning concepts to understand human behaviour and decision making. In the CogNemo group, Pamina works on formulating and validating biologically informed computational models of suicidality. Integrating Active Inference and Reinforcement Learning frameworks with fMRI data, the research aims to advance our theories about pathological behaviours observed in individuals with suicidal tendencies as well as the involvement of underlying neuromodulatory systems. The larger goal of the project is to develop a refined mechanistic understanding of suicidal thoughts and behaviours with the potential to improve early detection in individuals at risk through the identification of biomarkers and develop customized interventions.

PhD Student

Milad Soltanzadeh

Milad Soltanzadeh earned his bachelor’s and master’s degrees in electrical engineering from the University of Tehran and Concordia University, respectively. His research background includes development of biophysical models for brain metabolism and application of machine learning algorithms on EEG signal’s features in patients with epilepsy. Currently pursuing a Ph.D. at the University of Toronto’s Institute of Medical Science under the supervision of Dr. Andreea Diaconescu, Milad is engaged in developing large-scale neurocomputational models that incorporate conductance-based and Jansen-Rit models to study brain activity and connectivity in the context of psychosis and the antidepressant effects of psychedelics. The final goal of the project is to provide a computational framework to advance our understanding of underlying mechanisms and to predict treatment response in treatment-resistant depression and schizophrenia.

PhD Student

Onjoli Krywiak

Onjoli graduated from McMaster University with an Honours Bachelor of Science in Integrated Science, earning a double minor in Biology and Psychology. She is currently pursuing a Master of Science degree at the University of Toronto’s Institute of Medical Science, with plans to transition into the PhD program next year. Under the joint supervision of Dr. Sakina Rizvi and Dr. Andreea Diaconescu, Onjoli is investigating the efficacy of a novel psychotherapy, Brief Skills for Safer Living (Brief-SfSL), in reducing suicide risk. Her research focuses on uncovering the underlying mechanisms of this therapy through gamified behavioral tasks and computational modeling.

PhD Student

Peter Bedford

Peter Bedford is a PhD student in the Institute of Medical Science at the University of Toronto. His research sits at the intersection of computational psychiatry, neuroimaging, and mathematical modelling, with a particular focus on Bayesian and active-inference approaches to altered belief updating, cognitive flexibility, and treatment response. He has contributed to work on whole-brain functional and effective connectivity under LSD, computational prediction of treatment response in major depression, and theoretical models of psilocybin-assisted therapy for treatment-resistant depression in autism. He previously worked as a Research Analyst in the CogNeMo Lab, where he developed expertise in fMRI, dynamic causal modelling, EEG-related computational methods, and translational mental health research. His background in physics and mathematics provides a strong quantitative foundation for his current work on mechanistic models of psychiatric symptoms and psychedelic treatment effects.