Jérémy Lefort-Besnard

I am a French PhD student with a psychology and neuroscience background. I am currently working on the Default Mode Network (DMN) in Schizophrenia. It has previously been segregated into functionally distinct subregions. Using group-sparse inverse covariance estimation on these subregion nodes, my project is about quantifying the importance of the dysfunction of each segregated subregions in the pathophysiology of schizophrenia. The proposed approach will help to formalize and predict complex relationships between the clinical exophenotype and neurobiological endophenotype in schizophrenia.


Christian Gerloff

I am currently a PhD candidate with a background in electrical engineering. I am fascinated by machine learning as well as computational statistics such as Bayesian methods, and it’s interdisciplinary applications in research and industry. The human brain inspired a variety of machine learning algorithms, such as the first neural networks. At the same time, machine learning plays a vital role in the discovery of the human brain. Connectivity analyses of intra- and interbrain signals are in the center of neuroimaging data analyses of diverse neuroimaging modalities, such as EEG, MEG, fMRI, and fNIRS. Understanding how different brain regions interact within a single subject at rest or during tasks as well as how brains synchronize while people interact has an enormous potential to advance our understanding of the brain’s functional organization: particularly, how real-life social interactions impact on functional brain organization and  how  it is altered during development or by disease. Hence, I aim in my interdisciplinary research to systematically explore the abilities of novel machine learning and Bayesian models as potentially important methods for neuroimaging in the discovery of the human brain and the prediction of human diseases. As a co-founder of EnergyCortex, I’m passionate to bring these methods from research to live industrial applications and vice versa for development technologies to foster the exchange between research and industry. Personally, I enjoy coding while hearing music with an unappropriated volume, but much more than that, I love to draw a line in the fresh powder of the alps with my snowboard.


Melisa Felek

I am a medicine student at RWTH Aachen University (scholarship holder of German National Merit Foundation) and currently working on my doctoral thesis. My project is about interpreting functional connectivity of ventral medial prefrontal cortex (vmPFC) and dorsal medial prefrontal cortex (dmPFC) resting state MEG spectral connectivity analysis. I am therefore examining different theories about neuronal processing, psychological models about the role of vmPFC and dmPFC in social cognition and the putative role of connectivity in different frequency bands. Examining connectivity in the unperturbated brain state might offer further understanding of the function of vmPFC and dmPFC.

Teresa Karrer

I am currently a PhD student at RWTH Aachen University with a background in clinical psychology and neuroscience (awarded a scholarship of the German National Merit Foundation and the Werner-Straub-Prize). My experience as a research assistant at Yale University deepened my interest in the intersection of psychiatry and neuroscience . My PhD project aims to forge links between both domains by focusing on statistical learning of candidate network stratifications in schizophrenia. We will rank candidate cognitive domains based on meta-analytic networks in their ability to predict diagnosis and clinical characteristics in a multi-site schizophrenia sample. The project will provide insight into which cognitive processes are impaired in schizophrenia and on which level (structure, functional connectivity, or both) they manifest in the brain. Furthermore, the agnostic ranking of schizophrenia-related cognitive domains generates new hypotheses to guide future research systematically. Outside of the lab, I enjoy hiking and kayaking.


Hannah Kiesow

I am a U.S. American PhD student with a background in psychology, neuroscience and clinical linguistics (awarded a full Erasmus Mundus scholarship). My interdisciplinary background has allowed me to pursue opportunities around the globe and gain valuable knowledge in the many aspects of research. Specifically, my research assistant positions in the BiG Project on language attrition at the University of Groningen and in the stress and social decision making lab at Heinrich Heine University in Düsseldorf have provided me with a fundamental basis to pursue a career in research. In my PhD project at RWTH Aachen University, I investigate complex social variables using a recently available social brain atlas. Extracted brain volumes will be looked at with data made available through the UK Biobank, one of the largest medical databases currently available. Areas of specialized interest are hemispheric asymmetry, gender, age and the combination of changes across the lifespan and major phenotypical aspects with other social variables. These multivariate brain-behavior associations will be charted and tested for extrapolation at the population level using Bayesian hierarchical approaches for multilevel modeling of structured variables and generalized additive models for complicated nonlinear relationships. Combining a recently emerged rich data resource and advanced data analytics, our work may open a new window into the brain basis underlying human social interaction. In my free time, I enjoy reading, the culinary arts and am in constant pursuit of a great cup of coffee.


Marc-Andre Schulz

I have a background in physics and have a longstanding interest in the neurosciences. My decision for a career in research was invigorated during a research stay at NUS Medical School in Singapore. In my PhD project, I systematically evaluate structured and mixed unsupervised-supervised prediction approaches for translation into the brain-imaging domain. They have the potential to improve model performance and interpretability when data of a psychiatric/neurological population is scarce, but large general-purpose datasets are available. Improving the model performance and neurobiological interpretability of clinical investigations by exploiting general-purpose neuroimaging databases could be an important cornerstone for personalized medicine in psychiatry and neurology. Such automatically revealed and formalized relationships between candidate disease endo-phenotypes and clinical exo-phenotypes could translate to medical practice. In my free time I pursue the art of debating (Grand Finalist at World Universities Debating Championships in 2016). He is currently on leave to work as a data scientist at QuantumBlack, London, UK.

Julius Kernbach

I am in the last part of my studies in medicine (scholarship holder of German National Merit Foundation, Dean’s List in 5 consecutive years). As visiting scholar at Harvard Medical School, I became interested in brain-imaging and predictive analytics. In my PhD project at RWTH Aachen University, I combine one of the currently largest, highest-quality medical datasets – UKBiobank – with state-of-the-art machine learning techniques. The goal is to reach a more comprehensive understanding of the human default mode network in health and disease. Given this major brain network’s implication in a majority of psychiatric and neurological brain disorders, a better understanding of the default mode network is likely to be an important cornerstone for tailoring medical care to individual patients. Apart from medicine and neuroscience, I am passionate about playing the piano and violin.

Daniel Alcalá-López

I’m a Spanish PhD student with a background in psychology and physiology. My current project is on brain networks for social interaction. It is focused on the characterization of the anatomical and functional patterns of organization of all brain regions involved in social cognition – the so-called social brain. All these regions are involved in a wide range of brain mechanisms and psychological functions, from face recognition to moral judgement. By using a data-driven approach and different connectivity techniques, we aim to quantitatively identify and study the brain networks underlying different social-cognitive processes, such as theory of mind or empathy. Therefore, we hope to make an integrative contribution by giving some structure to the vast literature on social and affective neuroscience.