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I am a Professor in the Department of Psychiatry where I lead the Systems Neuroscience Lab—an interdisciplinary team at the intersection of psychiatry, neuroscience, and applied mathematics. Our research integrates computational modelling, large-scale data analysis, and systems thinking to uncover the biological principles underlying brain organization, function, and dysfunction. We are particularly interested in the mechanisms of neurodevelopmental disorders, investigating how they arise and manifest across scales—from genes and cells to brain networks and behaviour. Alongside human studies, we explore simpler model organisms such as C. elegans, as well as cerebral organoids, which serve as testbeds for methodological innovation and provide insights into fundamental aspects of brain organization, development, network dysfunction, and repair. Our goal is to develop a deeper, mechanistic understanding of the brain—one that can ultimately inform novel approaches to diagnosing and treating psychiatric and neurological disorders.

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Professor Nikolaos Koutsouleris serves as Chair of Precision Psychiatry at the IoPPN, King’s College London and Ludwig-Maximilian University Munich (LMU). He is Fellow of the Max-Planck-Society at the Max Planck Institute for Psychiatry Munich. Professor Koutsouleris coordinated the EU-FP7 funded project PRONIA (“Personalised Prognostic Tools for Early Psychosis Management”; http://proniapredictors.eu/pronia/index.html ) and heads the Centre for Transitional Youth Mental Health and the Section for Precision Psychiatry at the Department of Psychiatry, LMU. Prof Koutsouleris studied medicine at LMU between 1996 and 2003 as scholar of the German National Academic Foundation. He took his first medical & academic appointment in 2004 at the Department of Psychiatry and Psychotherapy, where he finished his doctorate thesis in 2005. Since 2008, Prof Koutsouleris has advanced the use of multivariate pattern recognition methods for the identification and validation of diagnostic and prognostic prediction models in at-risk and early stages of affective and non-affective psychoses. His work was awarded with several national and international prizes and led so far to 231 peer-reviewed, highly cited papers (h-index: 64). In addition, he strived to make robust machine-learning methods available to researchers in the clinical neurosciences in order to improve the methodological rigour of their use based on the proper use of validation and model sharing approaches. These efforts have lead to the publication of the open-source NeuroMiner machine learning platform available at https://neurominer- git.github.io/NeuroMiner_1.3/intro.html.

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Guido van Wingen is Professor of Neuroimaging in Psychiatry at Amsterdam UMC, The Netherlands. He studied psychology at Utrecht University and obtained his PhD at the Donders Institute for Brain, Cognition and Behavior in Nijmegen. The general aim of his research is to understand the neural basis of psychiatric disorders and their treatment, and to develop clinical tools to enable precision psychiatry. To address these questions, he combines neuroimaging techniques with psychological, pharmacological and neurostimulation treatments and machine learning analysis. He published over 200 research articles, obtained several prestigious personal grants, and is PI of the BOOST Depression consortium that aims to develop clinical decision support systems for the treatment of depression.

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Daniel B. Neill, Ph.D., is Professor of Computer Science, Public Service, and Urban Analytics at New York University, jointly appointed at NYU's Courant Institute Department of Computer Science, Robert F. Wagner Graduate School of Public Service, and Center for Urban Science and Progress, where he directs the Machine Learning for Good Laboratory. Dr. Neill's research focuses on developing novel machine learning methods for social good, with applications ranging from medicine and public health to urban analytics and fairness in criminal justice. Dr. Neill works extensively on developing new analytical methods to improve population health through predictive modeling, early event detection, causal inference, and targeting of interventions to reduce disparities. He received his M.Phil. from Cambridge University and his M.S. and Ph.D. in Computer Science from Carnegie Mellon University.

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Andre Marquand is Professor of Computational Psychiatry at the Donders Institute in the Netherlands. He is widely recognized as a pioneer in the fields of neuroscience and psychiatry and is internationally recognized for his contributions to: (i) developing groundbreaking machine learning tools for analysing population level clinical, brain imaging and remote sensing data and (ii) the application of these methods to clinical datasets to stratify cohorts and predict the onset, course and outcome of mental disorders. In short, he aims to change the way people think about mental disorders, to challenge the prevailing view of clinical conditions and develop technologies to improve the lives of individuals with mental disorders. He is the recipient of prestigious personal and collaborative grants including an ERC consolidator grant and VIDI fellowship from the Dutch organization for scientific research. Previously, he led neuroimaging analytics at King’s College London; one of Europe’s leading institutions for research in psychiatry and neuroscience. His influence is reflected in his publication record in high impact journals including some of the most influential and widely cited machine learning papers in psychiatry. He has won multiple awards for his work, including replication and technology awards. He is recognized as an opinion leader and holds positions of trust on scientific advisory boards, grant review panels and editorial boards and is involved in multiple initiatives to inform policymakers in the Netherlands and internationally.

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Professor Claudia Clopath is based in the Bioengineering Department at Imperial College London. She is heading the Computational Neuroscience Laboratory. Her research interests are in the field of neuroscience, especially insofar as it addresses the questions of learning and memory. She uses mathematical and computational tools to model synaptic plasticity, and to study its functional implications in artificial neural networks. Prof. Clopath holds an MSc in Physics from the EPFL and did her PhD in Computer Science under Wulfram Gerstner. Before joining Imperial College, she did postdoctoral fellowships in neuroscience with Nicolas Brunel at Paris Descartes and in the Center for Theoretical Neuroscience at Columbia University

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Dr. Adam Chekroud is the President and Co-founder of Spring Health, based in New York City, and an Assistant Professor of Psychiatry at Yale University. His research seeks to improve treatment outcomes in mental health, and has been featured in the Lancet, JAMA Psychiatry, Lancet Psychiatry, and PNAS, and covered in the Wall Street Journal, Financial Times, BBC, CNN, and NPR.

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I am J.Z. Liang Associate Professor in the School of Interactive Computing at Georgia Institute of Technology. I Co-Lead the Patient-Centered Care Delivery research pillar at the Children's Healthcare of Atlanta Pediatric Technology Center at Georgia Tech (PTC). Given the diversity of my intellectual interests, I am also affiliated with the Institute for People and Technology (IPaT), the Machine Learning Center (ML@GT), the Institute for Data Engineering and Science (IDEaS), and the Petit Institute for Bioengineering and Bioscience (IBB) at Georgia Tech. Research Background and Interests. Trained as a computer scientist studying internet phenomena, my career has been dedicated to research that tackles some of the most pressing challenges of the 21st century. Sitting at the intersection of the fields of computer science and social science, my work employs large-scale data analytics, machine learning, and artificial intelligence techniques to understand human behavior and dynamics in online environments. Core to these investigations is a highly interdisciplinary approach, blending insights from psychology, sociology, medicine, and public health with advanced computational methods.

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I am an Early/Mid Career Researcher currently employed as a Robinson Fellow at the Brain and Mind Center at the University of Sydney, Australia. In my work, I use multimodal neuroimaging techniques (most notably functional MRI) to explore the systems-level mechanisms that govern cognitive function, both in health and disease. I have a particular interest in tracking brain activity patterns that reflect communication between regions of the brain, allowing for the characterization of dynamic patterns of brain activity that reflect both normal function and impairments in neuropsychiatric disease. After graduating with a medical degree from the University of Sydney in 2007, I completed my medical residency and obtained medical registration in Australia. I have since successfully finished my PhD, in which I explored the brain network abnormalities associated with symptoms of Parkinson’s disease, including freezing of gait and visual hallucinations. In 2014, I was awarded a prestigious CJ Martin fellowship by the National Health and Medical Research Council, which I used to work with Professor Russell Poldrack at Stanford University. I have now returned to Australia, where I am working on the neural basis of cognition and dementia.

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