About me

I am a postdoctoral research fellow in the Cognitive Network Modelling Lab at the Krembil Centre for Neuroinformatics at the Centre for Addiction and Mental Health, in Toronto, Canada.

I work at the intersection of computational neuroscience, psychometrics, and biostatistics, developing methods and tools to advance prediction models in precision psychiatry. My work spans multiple scales - from developing computational models and gamified cognitive assessments for specific disorders to addressing methodological bottlenecks in research practices.

At the disorder level, I have developed and experimentally tested reinforcement learning and (hierarchical) Bayesian models (Karvelis et al., 2024) for multiple conditions including psychosis, autism (Karvelis et al., 2018), and suicidality (Karvelis et al., 2022). Such models could provide novel computational markers for diagnosis, prognosis, and treatment – yet their promise depends on overcoming multiple methodological barriers.

At the field level, I have shown that these novel computational assays often suffer from poor psychometric (reliability and validity) properties due to inadequate research designs, and provided a roadmap for achieving clinical utility (Karvelis et al., 2023). I have further shown that measurement reliability challenges apply not only when analyzing individual differences but also group differences, and derived a set of formulae to account for these effects (Karvelis & Diaconescu, 2025). I have also detailed common methodological problems when applying machine learning for treatment response prediction, highlighting the benefits of developing generative models for feature extraction (Karvelis et al., 2022).

To address these methodological and translational bottlenecks, I have developed an open-source simulation engine, www.e2p-simulator.com. E2P Simulator is designed for evaluating biomarkers and prediction models through the lens of real-world predictive utility by accounting for measurement reliability and outcome base rates – two crucial but often overlooked factors. Its interactive nature makes it accessible and educational, helping fight methodological inertia and steer researchers towards best practices.

In general, I am interested in using computational tools to solve real-world problems while simultaneously exploring fundamental questions about the mind, intelligence, and knowledge.