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2019.06 Aris Sotiras

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Aristeidis Sotiras: Advancing Big Neuroimaging Data Analysis for Precision Diagnostics

Location: Anna Spiegel, Seminar Room Level 3
Date: June 17th 2019, 2pm


Modern neurotechnologies produce massive, complex imaging data from multiple modalities that reflect brain structure and function in disease and health, leading neuroimaging to the “big data” era. Big data provides unprecedented opportunities to develop computational approaches that can deliver personalized, quantitative disease indexes of diagnostic and prognostic value. Such biomarkers have the potential to quantify the risk of developing a disease, track the disease progression or the effect of pharmacological interventions in clinical trials, and deliver patient specific diagnosis before measurable clinical effects occur. However, big data analyses also pose important challenges. Specifically, i) the high dimensionality of the data may hinder the extraction of interpretable and reproducible information; while ii) heterogeneity, which is increasingly recognized as a key feature of brain diseases, limits the use of current analytical tools. In this talk, I will discuss novel computational approaches that leverage advanced machine learning techniques to address these challenges. First, I will describe an unsupervised multivariate analysis technique based on non-negative matrix factorization that optimally summarizes high dimensional neuroimaging data through a set of highly interpretable and reproducible imaging patterns. Second, I will discuss a semi-supervised multivariate machine learning technique that aims to reveal disease heterogeneity by jointly performing disease classification and clustering of disease sub-groups. Applications of these approaches in diverse settings will be discussed to highlight their broad impact as well as their role in future directions toward precision medicine.

Short Bio:

Aristeidis Sotiras, PhD, is an Assistant Professor at the Institute for Informatics and the Department of Radiology at Washington University School of Medicine in St. Louis. His research interests are at the intersection of medical image analysis, machine learning, and computational neuroscience. Dr. Sotiras focuses on developing novel computational tools to extract quantitative information from imaging data and delineate patterns in large heterogeneous data sets with the goal of improving patient-specific diagnosis and advancing our understanding of brain structure and function in health and disease.

Dr. Sotiras completed his postdoctoral training at the Department of Radiology at the University of Pennsylvania, where he worked on multivariate pattern techniques for quantitative image analysis. He received his PhD, with the highest distinction and the committee compliments, in applied mathematics from Ecole Centrale Paris, where his research focused on developing novel algorithms for deformable image alignment. Dr. Sotiras also received his graduate degree in applied mathematics from Ecole Polytechnique, and his undergraduate degree in electrical and computer engineering from the National Technical University of Athens.