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Rafal Bogacz graduated in computer science at Wroclaw University of Technology in Poland. Then he did a PhD in computational neuroscience at the University of Bristol, and next he worked as a postdoctoral researcher at Princeton University, USA, jointly in the Departments of Applied Mathematics and Psychology. In 2004 he came back to Bristol where he worked as a Lecturer and then a Reader. He moved to the University of Oxford in 2013.
Ramon Llull University (URL, Barcelona) Research Methods and Statistics full professor since 2017, my main field of research is brain's connectivity using fMRI, specifically working with non-linear methods to explore the brain's dynamics in neurodegenerative (Alzheimer and Parkinson) and chronic (Fibromyalgia) diseases. I've also worked on chronic pain characterization and psychological instruments validation. Since 2020, I'm also vice dean of Quality, Students and Employment in the Faculty of Psychology, Education and Sports Sciences in URL.
Ernesto Estrada is Full Research Professor of the National Research Council of Spain at the Institute of Cross-Disciplinary Physics and Complex Systems (IFISC) in Palma de Mallorca, Spain. He is active in the field of mathematics applied to networks theory as well as its interdisciplinary applications. He has published more than 240 papers in this field as well as two textbooks with Oxford University Press (OUP). He is the founding Editor in Chief of the Journal of Complex Networks (OUP). For his researches he has been elected Member of the Academia Europeae, as a SIAM Fellow, as Fellow of the Institute of Mathematics and its Applications and of the Academy of Sciences of Latin America. He received the Wolfson Research Merit Award of the Royal Society in the U.K. He is well known for many of his works concerning "network communicability", "subgraph centrality", "d-path Laplacians", "degree-biased Laplacians", and their applications in many different fields.
Ben Fulcher is a Senior Lecturer in The School of Physics at The University of Sydney. His research uses physical modeling, dynamical systems, genetics, and statistical machine-learning approaches to understand organizational principles of complex systems. Applications of his methods have focused on explaining patterns in neural structure and function in terms of underlying physical mechanisms.
My primary research focuses on the study of the dynamics of recurrent neuronal networks trained for bio-inspired tasks such as working memory and decision-making. I have published works on models used for representing brain areas that perform sensory responses. You can find more on my work and publications at:
Also, my secondary research area includes studying complex systems in general. Last but not least, I work in the analysis and implementation of time-series algorithms and software. Specifically, I am working on characterizing spatio-spectral subject differences in fMRI, MEG and EEG data using kernel mean embeddings of distributions. I have a strong background in statistics, electronics and analysis of large data sets from my experience in high-energy physics.
I am a professor at UNQ for the Degree and PhD level (on leave this year utill 2024) doing an extended research stay at Aarhus University at CFIN (https://cfin.au.dk/).
Fabian Sinz is a Professor for Machine Learning at University of Göttingen since 2021. He is also an Independent CyberValley Research Group Leader for Neuronal Intelligence at the Wilhelm-Schickard-Institute for Computer Science at the University of Tübingen and an Adjunct Assistant Professor at the Center for Neuroscience and Artificial Intelligence at Baylor College of Medicine in Houston, Texas. He has held various positions in research, including a postdoctoral associate and research assistant professor in the Department of Neuroscience at Baylor College of Medicine. He earned his PhD from the Max Planck Institute for Biological Cybernetics/Graduate School for Neural and Behavioral Sciences at the University of Tübingen. Prior to that, he studied Philosophy and Bioinformatics at Eberhard-Karls Universität Tübingen. His research interests include system identification models for the visual cortex, machine learning for computational neuroscience, machine learning in closed/frozen loop experiments, and inductive biases of biological neuronal systems.
Sebastiano Stramaglia is Full Professor of Applied Physics at the University of Bari, Italy. He received MD in Physics (1991) on models of strongly correlated electronic systems and PhD in Physics (1994) on Statistical Mechanics of random surfaces. Dean of the Center of Excellence “Innovative Technologies for Signal Detection and Processing”, funded by the Italian Ministry for Scientific Research; member of the V National Scientific Commission of INFN-Istituto Nazionale di Fisica Nucleare, Italy. He chaired several international events, including “Modeling Migraine: from nonlinear dynamics to clinical neurology” July 2009, Berlin, the series of workshop “QBIO – Quantitative Biomedicine in Health and Disease”. Editor of the books “Modelling Biomedical Signals”, World Scientific 2002, and “Emergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences”, Springer 2017. He has been visiting scientist at the Institute for Theoretical Physics NORDITA and at the Department of Data Analysis of the University of Gent, Belgium. Visiting Professor at Biocruces Health Institute, and External Scientific Member of the Basque Center of Applied Mathematics (BCAM), Bilbao, Spain. Since 2003 he is the team leader of the INFN project “Biological applications of Theoretical Physics Methods”. Sebastiano has published more than 150 scientific articles and his research focuses on models for data analysis in complex systems and network neuroscience.