My research interests center on imaging genomics, aiming to characterize the genetic underpinnings of neurobiological traits that further relate to phenotypic manifestations of various mental disorders. My research involves developing computational methods to mine high-dimensional complex data for better understanding of genetic effects on brain at network level, as well as data fusion to integrate different modalities, spanning omic (genetic, epigenetic, transcriptomic), neuroimaging (sMRI, fMRI, DTI) and phenotypic levels, to better characterize mental illnesses and facilitate improved patient stratification and more precise treatment planning.
I am interested in developing novel algorithms for tracking the dynamic brain connectivity and exploring the underlying physiological meaning of brain dynamics. Human brain is a highly dynamic system characterized by non-stationary neural activities and represented by changing psycho-physical states and mental functions. Identifying brain dynamics from complex neuroimaging signals and exploring the functional relevance of brain dynamics has gained increasing popularity for it is essential towards understanding how brain is organized to support cognitive and affective processes as well as utilization of brain dynamics in neural engineering and clinical applications. My current work could be divided into two parts: the first part focuses on developing new methods for tracking the dynamic brain connectivity; the second part focuses on identifying the brain dynamics and exploring their potential physiological mechanism.
H. Jeremy Bockholt works as the manager of application development at TReNDS. Mr. Bockholt is an enterprise data management professional and senior full stack software engineer working on commercialization of technology developed at TReNDS, including COINS, COINSTAC and other projects. Mr. Bockholt helps to lead software development activities at TReNDS and assists faculty and senior staff to develop proposals for external funding of various biomedical informatics research topics.
Dr. Abrol investigates the development of advanced machine learning and signal processing based deep data fusion frameworks to understand complex interactions in multimodal brain imaging data. Development of such end-to-end trained deep learning models is targeted to facilitate the discovery of crucial non-linear interrelationships between the data modalities, otherwise inaccessible to standard machine learning models. Leveraging this additional wealth of information can enable breakthrough advances in our pursuit of significant neuroimaging objectives such as identifying disease biomarkers at early stages, predicting progression to brain abnormalities and evaluating treatment effects of drugs on individuals with cognitive impairments. Motivated by this, his ongoing research includes assessing the effectiveness of engaging deep learning models to explain vital neuroimaging tasks, with a particular focus on sourcing superior lower-dimensional representations and finer methodical interpretations. Other research interests include exploring complex spatiotemporal associations in brain dynamics wherein his significant contributions include corroborating robustness and disease characterization/prediction utility of time-varying functional connectivity state profiles of the human brain at rest.
Nice to meet you! I am an assistant professor in the Department of Psychology at Georgia State University, and I am also affiliated faculty with the GSU Center for Research on the Challenges of Acquiring Language and Literacy and the GSU Neuroscience Institute. Prior to joining the faculty at GSU, I was an Associate Research Scientist in Pediatrics at Yale University. I also completed a postdoctoral fellowship at Haskins Laboratories, where I remain a Research Affiliate. My research focuses on the brain networks that support reading, spoken language processing, and attentional control. I use neuroimaging to study how these networks overlap, diverge, and change over the course of learning. I also examine how different biological, cognitive, and environmental factors shape the connectivity of these networks. In my research, I work with numerous populations of learners, including school-age children, adolescents, and adults; individuals with reading, language, and/or attention deficits; and individuals who speak or read more than one language. Over the past few years, I have had the pleasure of working with several collaborators in the GSU community to study reading development in children. Using fMRI, we are currently looking at the intersection between the brain networks underlying reading and attentional control (Arrington, Malins, et al., 2019, Developmental Cognitive Neuroscience). We are also following up on a recent study suggesting that a certain amount of variability in brain activity may be beneficial for reading growth (Malins et al., 2018, Journal of Neuroscience). In the future, I am particularly interested in examining how diverse experiences with language – such as bilingual language experience in children – help to shape the brain networks that support literacy skills. I look forward to continuing to build connections with the neuroimaging community in Atlanta and beyond. Together, I hope we can find ways to connect brain research with current practices in education in order to help individuals reach their learning potential. Representative Publications: Arrington, C.N., Malins, J.G., Winter, R., Mencl, W.E., Pugh, K.R., & Morris, R. (2019). Examining individual differences in reading and attentional control networks utilizing an oddball fMRI task. Developmental Cognitive Neuroscience, 38, 100674. DOI: https://doi.org/10.1016/j.dcn.2019.100674 Malins, J.G., Pugh, K.R., Buis, B., Frost, S.J., Hoeft, F., Landi, N., Mencl, W.E., Kurian, A., Staples, R., Molfese, P., Sevcik, R.A., & Morris, R. (2018). Individual differences in reading skill are related to trial-by-trial neural activation variability in the reading network. Journal of Neuroscience, 38(12), 2981-2989. DOI: https://doi.org/10.1523/JNEUROSCI.0907-17.2018 Links: GSU Psychology ResearchGate PubMed
I attended medical school at the University of Brescia (Italy), completing my residency program in Neurology at University of Brescia (Prof. Alessandro Padovani) in 2012. During this period, I had had the opportunity to increase my expertise in the field of neurodegenerative diseases (Prof. Barbara Borroni, Prof. Alessandro Padovani) from a clinical and research point of view. I focused my research interests on neuroimaging (2007: San Raffaele University, Milan, Italy: Prof. Perani; 2009: SPM course, UCL, London, UK; 2012: Neuroimaging Lab, Fondazione “S.Lucia”, Rome, Italy, Prof. Bozzali). I currently have a permanent contract as clinical neurologist in the Stroke Unit, ASST “Spedali Civili” (Brescia, Italy): my everyday clinical practise is focused on the clinical evaluation and treatment of patients with cerebrovascular diseases (ischemic and haemorragic stroke). In parallel, I continued my research activity in collaboration with the Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia. Since my residency period my research interests was focused on the application of advanced neuroimaging techniques to neurodegenerative diseases, in particular Frontotemporal Dementia (FTD) to explore structural and functional correlates of the disease. In the last years I explored in particular: i) the preclinical phase of FTD (studying presymptomatic carriers of FTD causative mutations), ii) the behavioral/neuroimaging correlates in FTD, iii) the neuroanatomical correlates of Cognitive Reserve in FTD, iv) the utilization of advanced statistical approaches to clinical neuroimaging in neurodegenerative diseases.
Dr. Rogers F. Silva is a multidisciplinary scientist with extensive experience developing algorithms for statistical and machine learning, image analysis, numerical optimization, memory-efficient large-scale data reduction, and distributed analyses of big multimodal, multi-subject neuroimaging data. Interests: His research interests include · heterogeneous multimodal data fusion · deep statistical and machine learning · image, video and data analysis · multiobjective, combinatorial and constrained optimization · image and signal processing · multimodal neuroimaging Research: As a modeling-oriented scientist actively conducting research on novel multimodal multidimensional learning (MML) methods for brain research, Dr. Silva leverages his multidisciplinary background to create new algorithms for deep unsupervised learning that can fully leverage the joint information and shared variability contained in heterogeneous multimodal (or multiview) datasets. He also develops new algorithms for federated learning of private multi-site data, seeking to enable collaborative research that can take advantage of decentralized datasets without requiring direct access to data stored in remote data centers. Education: He received the B.Sc. degree in Electrical Engineering in 2003 from the Catholic University (PUCRS), Porto Alegre, Brazil, the M.S. degree in Computer Engineering (with minors in Statistics and in Mathematics) in 2011, and the Ph.D. degree (with distinction) in Computer Engineering in 2017, both from The University of New Mexico, Albuquerque, NM, USA. Additional Training: He also received training in diffusion spectrum imaging at CMU and University of Pittsburgh in 2016, multimodal brain imaging at the MGH Martinos Center and Harvard/MIT in 2016, and deep learning at MILA, University of Montreal in 2017. Experience: Before joining the TReNDS Center, Dr. Silva developed novel approaches for multimodal medical image analysis as a Postdoctoral Fellow at the Mind Research Network, in addition to client-centric tools and processes using machine learning and statistical analysis as a Data Scientist with Datalytic Solutions. Previously, he also had experience as an engineer, lecturer, and consultant.
Vince D. Calhoun Dr. Calhoun is founding director of the tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) and a Georgia Research Alliance eminent scholar in brain health and image analysis where he holds appointments at Georgia State University, Georgia Institution of Technology and Emory University. He was previously the President of the Mind Research Network and Distinguished Professor of Electrical and Computer Engineering at the University of New Mexico. He is the author of more than 800 full journal articles and over 850 technical reports, abstracts and conference proceedings. His work includes the development of flexible methods to analyze functional magnetic resonance imaging data such as independent component analysis (ICA), deep learning for neuroimaging, data fusion of multimodal imaging and genetics data, neuroinformatics tools, and the identification of biomarkers for disease. His research is funded by the NIH and NSF among other funding agencies. Dr. Calhoun is a fellow of the Institute of Electrical and Electronic Engineers, The American Association for the Advancement of Science, The American Institute of Biomedical and Medical Engineers, The American College of Neuropsychopharmacology, and the International Society of Magnetic Resonance in Medicine. He served at the chair for the Organization for Human Brain Mapping from 2018-2019 is a past chair of the IEEE Machine Learning for Signal Processing Technical Committee. He currently serves on the IEEE BISP Technical Committee and is also a member of IEEE Data Science Initiative Steering Committee.
Dr. Liu’s research focuses on exploring and identifying genetic, epigenetic, transcriptomic effects on or association with brain anomalies related to various mental illnesses and disorders, as well as understanding normal brain development. Her research involves developing methods to unveil hidden relations between various phenotypes and genetic/epigenetic features across the whole genome. The brain-based phenotypes can be extracted from MRI images, EEG and MEG signals, while the genetic and epigenetic features are from single nucleotide polymorphisms (SNP), copy number variations (CNV), DNA methylation, and gene expression. Due to complexity of data, various types of methods/algorithms are being developed and extended, including multimodal feature extraction, multivariate association, multilayer networks, and statistical models. The disorders being studied include, but not limited to, schizophrenia, bipolar disorder, substance abuse, ADHD, mood disorders. “Everything should be made as simple as possible, but not simpler.” – Albert Einstein
Armin’s background is data science and signal processing including machine learning, data mining, image processing, and time-series analysis. With over 10 years of experience in medical imaging analyses such as MRI, CT, EEG, and photoacoustic imaging, Armin has devoted most of his research to developing analytical techniques to interpret brain signals with the goal of improving patient care and directly translating brain signals into actions.
Maria is currently pursuing a PhD in Psychology, and is a graduate research assistant in Jess Turner's Imaging Genetics and Informatics lab whose primary research focus is the application of neuroimaging techniques to aging and neurodegenerative diseases. She is excited to get the word out about the TReNDS center and its various achievements and research projects by assisting with web development, social media strategy, and graphic design.
Dr. Plis’s educational background is in engineering (MS), artificial intelligence (MS) and computer science (PhD). His research goals lie in developing computational instruments that enable knowledge extraction from observational multimodal data collected at different temporal and spatial scales. His focus is on understanding systems and processes formed by interactions of multiple “agents”. The human brain, his main application area, is an example of such system: neurons (or measured voxels) are the agents that interact and form networks that themselves are entities of interest with influence structure indicative of mental state, disorder and differences between individuals. Understanding the patterns, networks and interactions can improve our understanding of how the brain works but the data are complex, multidimensional, and neither modality alone carries enough information. The situation typical in many domains with complex incomplete observational measurements including climatology, social sciences, and others. The chosen methodology mainly draws from the fields of machine learning and data science. Specific developments are focused on multimodal pattern recognition, inference, predictive modeling, tracking, and causal learning. Ongoing work is focused on inferring multimodal probabilistic and causal descriptions of function-induced networks based on fusion of fast and slow imaging modalities. This includes feature estimation via deep learning-based pattern recognition and learning causal graphical models.
Interests: Krissy studies the joint analysis of diverse biomarkers, including (but not limited to) neuroimaging and genetic sources, with the aim of gaining a deeper understanding of the basis and progression of neuropathological illnesses. She loves implementing and comparing advanced statistical methods for the integration of big data sources. Krissy welcomes new, multi-disciplinary collaborations and is open to hearing interesting ideas and feedback from others in related fields. Research: Her PhD work focuses on the development of multi-modal data fusion techniques for the elucidation of Alzheimer’s Disease, utilizing structural and functional brain imaging, genetic, and clinical data derived from ADNI. Emphasis is placed on assessing the statistical reliability of decomposition techniques in order to preserve interpretability and generalizability for future studies. Education: Krissy received her Bachelor of Science in Mathematics with a concentration in Statistics at Austin Peay State University in Clarksville, TN in 2014. She received her Masters (2017) and her PhD in Statistics (2021) from the University of Georgia, Athens, GA.
After receiving a Ph.D. in cognitive neuroscience from the psychology department at UCLA in 2006, Dr. Uddin completed a postdoctoral fellowship at the Child Study Center at NYU. For several years she worked as a faculty member in Psychiatry & Behavioral Science at the Stanford School of Medicine. She joined the psychology department at the University of Miami in 2014. Within a cognitive neuroscience framework, Dr. Uddin’s research combines analyses of resting-state fMRI and diffusion weighted imaging data to examine the organization of large-scale brain networks supporting executive functions. Her current projects focus on understanding dynamic network interactions underlying cognitive inflexibility in neurodevelopmental disorders such as autism. Dr. Uddin’s work (over 125 publications) has been published in the Journal of Neuroscience, Cerebral Cortex, JAMA Psychiatry, Biological Psychiatry, PNAS, and Nature Reviews Neuroscience. She was awarded the Young Investigator award by the Organization for Human Brain Mapping in 2017. Website: https://bccl.psy.miami.edu/
Dr. Turner received her PhD in Psychology (Cognitive Sciences) from the University of California, Irvine, followed by a post-doctoral position at Rutgers the State University of New Jersey, learning single-cell recording and optical imaging techniques. Having determined that invasive measures were not her preferred techniques, she moved into functional and structural neuroimaging and was fascinated by the ability to measure brain function non-invasively. Since then, her research program uses neuroimaging of clinical populations to improve understanding of the structural and functional circuitry underlying mental illness and health, and integrates several approaches: The combination of imaging with genetics, to identify genotypes which might help individualize treatment and prognosis; structural and functional imaging across multiple institutions to develop robust clinical neuroimaging studies; use of these neuroimaging methods in schizophrenia and other disorders to determine the relationship between brain volume and functional characteristics with disease status and symptom profiles; and large-scale neuroimaging data sharing to support the international collaborations needed to perform imaging genetics analyses. Since 2013 she has been at Georgia State University as faculty in psychology and neuroscience, and the head of the Imaging Genetics and Informatics Laboratory.Website: https://psychology.gsu.edu/profile/jessica-turner/
Dr. Bandettini received his undergraduate degree in Physics from Marquette University in 1989, and his Ph.D. in Biophysics in 1994 at the Medical College of Wisconsin where he led the effort to carry out one of the first successful experiments in functional MRI. He completed his post doc at the Massachusetts General Hospital NMR Center in 1996. After spending three years as an Assistant Professor at the Medical College of Wisconsin he was recruited in 1999 to become Director of the Functional MRI Facility at and Chief of the Section on Functional Imaging Methods the National Institutes of Health. Recently, he has become the founding Director of the Center for Multimodal Neuroimaging at the National Institute of Mental Health and has started a Machine Learning group and a Data Sharing group. He also recently completed a 6 year tenure as Editor In Chief of the Journal, NeuroImage. He is the recipient of the 2001 OHBM Wiley Young Investigator Award, and in 2020 was awarded the ISMRM Gold Medal. His research focus over the past 29 years has been on advancing functional MRI in all ways, including novel fMRI methods in acquisition, processing, and paradigm design. He current research focus is high resolution layer fMRI, dynamic connectivity, understanding and mitigating physiologic noise in fMRI time series, and deriving individual specific information using fMRI. He has published over 175 papers and has presented over 390 invited lectures. Website: https://www.nimh.nih.gov/research/research-conducted-at-nimh/principal-investigators/peter-bandettini.shtml
Dr. Shella D. Keilholz received her B.S. degree in physics from the University of Missouri Rolla (now Missouri University of Science and Technology) and her Ph.D. degree in engineering physics at the University in Virginia. Her thesis focused on quantitative measurements of perfusion with arterial spin labeling MRI. After graduation, she went to Dr. Alan Koretsky’s lab at the NIH as a Postdoctoral Researcher to learn functional neuroimaging. She is currently a Professor in the joint Emory/Georgia Tech Biomedical Engineering Department, Atlanta, GA, USA and Program Director for the 9.4 T MRI. Her research seeks to elucidate the neurophysiological processes that underlie the BOLD signal and develop analytical techniques that leverage spatial and temporal information to separate contributions from different sources.
Russell A. Poldrack is the Albert Ray Lang Professor in the Department of Psychology and Professor (by courtesy) of Computer Science at Stanford University, and Director of the Stanford Center for Reproducible Neuroscience. His research uses neuroimaging to understand the brain systems underlying decision making and executive function. His lab is also engaged in the development of neuroinformatics tools to help improve the reproducibility and transparency of neuroscience, including the Openneuro.org and Neurovault.org data sharing projects and the Cognitive Atlas ontology.
Dr. Dante R. Chialvo received his diploma in 1982 from the National University of Rosario, in Argentina. In 1985 was appointed Professor of the Department of Physiology of the University of Rosario. From 1987 to 1992 was Associate Professor in the State University of New York (Syracuse, NY) in the Department of Pharmacology and latter in the Computational Neuroscience Program. Between 1992 and 1995 was associated with the Santa Fe Institute for the Sciences of Complexity, in Santa Fe, New México. Until 2010, he was Full Professor at Northwestern University (Chicago), and UCLA, when he returned to Argentina as Principal Investigator of Conicet (Argentina). Currently he is Full Professor and head of the Center for Complex Systems and Brain Sciences (Cemsc3) at the UNSAM (Universidad Nacional de San Martin) in Buenos Aires, Argentina. Throughout these years, he has been Visiting Professor at numerous universities including Wuerzburg University (Germany), University of Copenhagen (Denmark), The Rockefeller University (U.S.A.), University of the Balearic Islands, University of Barcelona, University Complutense of Madrid, (Spain), Naples (Italy) and University of Rosario, University of Cordoba (Argentina), Universidad Mayor de San Andres, La Paz, (Bolivia) , Jagellonian Univ. (Krakow) among others. Dr. Chialvo has published more than 100 scientific papers, all dedicated to understand natural phenomena from the point of view of Nonlinear Dynamics of Complex Systems. His work covers a wide range of topics, including the mathematical modeling of cardiac arrhythmias, the study of molecular motors as stochastic ratchets, neural coding, and self-organization and collective phenomena in ants swarms, brain and communities, among others. In 2005 he was the recipient of a Fulbright US Scholar Award (2005), in 2006 the Distinguished Visiting Professor of the University Complutense, (Psychology Department), Madrid, Spain, Visiting Professor Award of the Seconda Università degli Studi di Napoli, Aversa Italy and elected Fellow of the American Physical Society in 2007. Website: http://www.chialvo.net/ Recommended Articles Organization, Development and Function of Complex Brain Networks. Sporns O, Chialvo DR, Kaiser M & Hilgetag CC. Trends in Cognitive Sciences, 8 (9): 418– 425 (2004). (https://doi.org/10.1016/j.tics.2004.07.008) Scale-free brain functional networks. Eguiluz V, Chialvo DR, Cecchi G, Baliki M, Apkarian AV. Phys. Rev. Letters 92, 018102 (2005). () Emergent complex neural dynamics, Chialvo DR. Nature Physics 6, 744–750 (2010). (https://doi.org/10.1038/nphys1803) What kind of noise is brain noise: anomalous scaling behavior of the resting brain activity fluctuations. D Fraiman, DR Chialvo Frontiers in physiology 3, 307 (2012). (https://doi.org/10.3389/fphys.2012.00307) Criticality in large-scale brain fMRI dynamics unveiled by a novel point process analysis. E Tagliazucchi, P Balenzuela, D. Fraiman , DR Chialvo. Frontiers in physiology 3, 15 (2012). (https://doi.org/10.3389/fphys.2012.00015) Brain organization into resting state networks emerges at criticality on a model of the human connectome. A Haimovici, E Tagliazucchi, P Balenzuela, DR Chialvo. Physical review letters 110 (17), 178101 (2013). (https://doi.org/10.1103/PhysRevLett.110.178101). Life at the edge: Complexity and criticality in biological function. DR Chialvo, Acta Physica Polonica B, 49(12) 1955 (2018). (https://www.actaphys.uj.edu.pl/fulltext?series=Reg&vol=49&page=1955)
Dr. Juan Helen ZHOU is an Associate Professor at the Center for Sleep and Cognition, and the Deputy Director, Center for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore (NUS). She is also affiliated with Duke-NUS Medical School. Her laboratory studies selective brain network- based vulnerability in neuropsychiatric disorders using multimodal neuroimaging and machine learning approaches. She received Bachelor degree and Ph.D. from School of Computer Science and Engineering, Nanyang Technological University, Singapore. Dr. Zhou was an associate research scientist at Department of Child and Adolescent Psychiatry, New York University. She did a post-doctoral fellowship at the Memory and Aging Center, University of California, San Francisco, and in the Computational Biology Program at Singapore-MIT Alliance. Dr. Zhou is currently the Council Member and a previous Program Committee member of the Organization of Human Brain Mapping. She serves as an Editor of multiple journals including Human Brain Mapping, NeuroImage, and Communications Biology. Website: http://neuroimaginglab.org/members.html Recommended Articles Wang C, Ong JL, Patanaik A, Zhou J, Chee MW. Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states. Proc Natl Acad Sci U S A. 2016;113(34):9653-9658. doi:10.1073/pnas.1523980113 Chong JSX, Jang H, Kim HJ, et al. Amyloid and cerebrovascular burden divergently influence brain functional network changes over time. Neurology. 2019;93(16):e1514-e1525. doi:10.1212/WNL.0000000000008315 Zhang L, Biessels GJ, Hilal S, et al. Cerebral microinfarcts affect brain structural network topology in cognitively impaired patients. J Cereb Blood Flow Metab. 2021;41(1):105-115. doi:10.1177/0271678X20902187 Zhou J, Gennatas ED, Kramer JH, Miller BL, Seeley WW. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron. 2012;73(6):1216-1227. doi:10.1016/j.neuron.2012.03.004
I am a statistical physicist (MSc 2001, PhD 2007, University of Bari) who has always worked towards the characterization of the dynamics of complex systems, mainly the brain. From 2008 to 2011 I was a postdoc at CNRS, University Paris 5, performing in vivo electrophysiology and dynamic clamp experiments. Since 2011 I am Research Professor of Data Analysis at Ghent University, Belgium. I teach techniques of neuroimaging data analysis; I am member of the Belgian node of the International Neuroinformatics Coordinating Facility (INCF) and a mentor for Google Summer of Code on their behalf. I am co-editor in chief of Neurons, Behavior, Data Analysis, and Theory, deputy editor of PLOS Computational Biology, editor of NeuroImage, Network Neuroscience, Brain Topography, and PLOS One, editor of the PLOS complexity channel, and referee for many journals in the field of neuroscience and applied physics.
Dr. Kording obtained both a diploma degree and a PhD in physics at ETH Zurich in 1997 and 2001, respectively. He then worked as a postdoctoral fellow at the Collegium Helveticum in Zurich and at University College London, followed by a Heisenberg Fellow position at MIT. He joined the faculty at Northwestern University and the Rehabilitation Institute of Chicago where he was a professor of physical medicine and rehabilitation, physiology, and applied mathematics. In 2017, he joined the faculty at the University of Pennsylvania with joint appointments in the Department of Neuroscience and Department of Bioengineering.
Adeel Razi is an Associate Professor at the Turner Institute for Brain and Mental Health, Monash University, Australia, where he is the Head of the Computational Neuroscience Laboratory. His research is cross-disciplinary, combining engineering, physics, and machine-learning approaches, to model complex, multi-scale, network dynamics of brain structure and function using neuroimaging. He is currently Australian Research Council DECRA Fellow (2017-2020) and has also been awarded NHMRC Investigator (Emerging Leader) Fellowship (2021-2025). He is an `Honorary’ Senior Research Fellow at the Wellcome Centre for Human Neuroimaging of University College London, where he also worked from 2012 to 2018. He received the B.E. degree in Electrical Engineering from the N.E.D. University of Engineering & Technology, Pakistan, the M.Sc. degree in Communications Engineering from the University of Technology Aachen (RWTH), Germany, and the Ph.D. degree in Electrical Engineering from the University of New South Wales, Australia in 2012. Website: www.adeelrazi.org
Dimitri Van De Ville received the Ph.D. degree in computer science engineering from Ghent University, Belgium, in 2002. He was a post-doctoral fellow (2002-2005) at the lab of Prof. Michael Unser at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, before becoming responsible for the Signal Processing Unit at the University Hospital of Geneva, Switzerland, as part of the Centre d’Imagerie Biomédicale (CIBM). In 2009, he received a Swiss National Science Foundation professorship and since 2015 became Professor of Bioengineering at the EPFL, jointly affiliated with the University of Geneva, Switzerland. His main research interest is in computational neuroimaging to advance cognitive and clinical neurosciences. His methods toolbox includes wavelets, sparsity, deconvolution, graph signal processing. He was a recipient of the Pfizer Research Award 2012, the NARSAD Independent Investigator Award 2014, the Leenaards Foundation Award 2016, and was elected Fellow of the IEEE in 2020. Dr. Van De Ville serves as an Editor for the new journal NEUROIMAGE: REPORTS since 2020, as a Senior Editor for the IEEE TRANSACTIONS ON SIGNAL PROCESSING since 2019 and as an Editor for the SIAM Journal on Imaging Science from 2018 on. He served as an Associate Editor for the IEEE TRANSACTIONS ON IMAGE PROCESSING from 2006 to 2009, the IEEE SIGNAL PROCESSING LETTERS from 2004 to 2006. He was the Chair of the Bio Imaging and Signal Processing (BISP) TC of the IEEE Signal Processing Society (2012-2013) and the Founding Chair of the EURASIP Biomedical Image & Signal Analytics SAT (2016-2018). He is Co-Chair of the biennial Wavelets & Sparsity series conferences, together with Y. Lu and M. Papadakis. Website: miplab.epfl.ch
Eva Dyer is an Assistant Professor in the Coulter Department of Biomedical Engineering at the Georgia Institute of Technology and Emory University. Dr. Dyer works at the intersection of neuroscience and machine learning, developing machine learning approaches to interpret complex neuroscience datasets, and designing new machine intelligence architectures inspired by the organization and function of biological brains. Dr. Dyer completed all of her degrees in Electrical & Computer Engineering, obtaining a Ph.D. and M.S. from Rice University, and a B.S. from the University of Miami. She is the recipient of a Sloan Fellowship in Neuroscience, an NSF CISE Research Initiation Initiative Award, was a previous Allen Institute for Brain Science Next Generation Leader, and was recently awarded a McKnight Award for Technological Innovations in Neuroscience. Website: https://dyerlab.gatech.edu/
Dr. Tognoli is a Research Professor in Complex Systems and Brain Sciences at Florida Atlantic University. Her overarching scientific motivation is to understand brain function and dysfunction using the concepts and tools of complexity science. Her main research areas are spatiotemporal brain metastability, the neurophysiological basis of social behavior and the development of complex experimental systems for human-machine and neuro-technological interfaces. Her thinking has been enriched by numerous collaborations with psychiatrists, neurologists, neuropsychologists, ophthalmologists, physicists, mathematicians, behavioral and biological scientists, psychologists and engineers.
Dr. Kragel received his Bachelor of Science and Engineering (2006) and a Masters in Engineering Management (2007) at Duke University’s Pratt School of Engineering. He completed his Ph.D. in Psychology and Neuroscience (2015) at Duke University and subsequently trained as a postdoctoral fellow at the University of Colorado Boulder’s Institute of Cognitive Science. He is currently an Assistant Professor in the Psychology Department at Emory University. Dr. Kragel’s research explores the brain and computational basis of cognitive and affective behavior in humans, with a particular focus on understanding the nature of emotions – where they come from and what makes them unique from other mental phenomena. His research combines ideas from cognitive neuroscience and machine learning to build quantitative models that are both sensitive and specific to the engagement of individual mental processes.
Aapo Hyvarinen studied undergraduate mathematics at the universities of Helsinki (Finland), Vienna (Austria), and Paris (France), and obtained a Ph.D. degree in Information Science at the Helsinki University of Technology in 1997. After post-doctoral work at the Helsinki University of Technology, he moved to the University of Helsinki in 2003, where he was appointed Professor in 2008, at the Department of Computer Science. From 2016 to 2019, he was Professor at the Gatsby Computational Neuroscience Unit, University College London, UK. Aapo Hyvarinen is the main author of the books 'Independent Component Analysis' (2001) and 'Natural Image Statistics' (2009), Action Editor at the Journal of Machine Learning Research and Neural Computation, and has worked as Area Chair at ICML, ICLR, AISTATS, UAI, ACML and NeurIPS. His current work concentrates on unsupervised machine learning and its applications to neuroscience.
Dr. Ioulia Kovelman is an Associate Professor of Psychology at the University of Michigan. Bilingualism changes mind and brain. Dr. Kovelman is a developmental cognitive neuroscientist who uses optical fNIRS neuroimaging to understand the effects of bilingualism on children’s language, literacy, and brain development. Dr. Kovelan studies children within typical learners and those with language and reading difficulties. Dr. Kovelman holds a PhD from Dartmouth College and has completed post-doctoral training at the Massachusetts Institute of Technology. She is a recipient of NIH and NSF funding awards, among others. Dr. Kovelman welcomes cross-linguistic and cross-cultural collaborators, and students interested in the Bilingual Brain. To learn more, please visit Dr. Kovelman’s website at the University of Michigan.
Bertrand Thirion is the leader of the Parietal team, part of INRIA research institute, Saclay, France, that develops statistics and machine learning techniques for brain imaging. He contributes both algorithms and software, with a special focus on functional neuroimaging applications. He is involved in the Neurospin (CEA) neuroimaging center, one of the leading high-field MRI for brain imaging places. Bertrand Thirion is also head of the DATAIA Institute that federates research on AI, data science and their societal impact in Paris-Saclay University. He has recently been appointed as member of the expert committee in charge of advising the government during the Covid-19 pandemic.
Dr. Jessica Damoiseaux is an Associate Professor in the Institute of Gerontology and Department of Psychology at Wayne State University. Dr. Damoiseaux received her MSc in Psychology from Utrecht University and PhD in Cognitive Neuroscience from VU University Amsterdam. She then went on to do a postdoctoral fellowship at Stanford University. She currently heads the Connect Lab. Her research investigates the application of MRI-derived brain measures, with an emphasis on brain network approaches, to study typical aging and early detection of neurodegenerative disease.
Dr. Allen received his BS in Cellular and Molecular Biology from Tulane University and his MD and PhD in Neuroscience from Georgetown University School of Medicine. His thesis focused on the modulation of neuronal injury by metabotropic glutamate receptors using cell culture and animal models of trauma. He then completed neurology and diagnostic radiology residencies as well as a 2-year neuroradiology fellowship at New York University. Dr. Allen is currently an Associate Professor of Radiology and Imaging Sciences and Neurology at Emory University and an Associate Professor of Biomedical Engineering at Georgia Institute of Technology. He is the Director of the Neuroradiology Division and the Medical Director of the Center for Systems Imaging at Emory University. His laboratory currently focuses on defining the changes in structural and functional brain connectivity after concussion, particularly in patients with vestibular impairment, and developing diagnostic and prognostic imaging markers for this disorder. In addition, he is interested in developing and refining neurorehabilitation techniques for post-concussion vestibular impairment as well as understanding neural plasticity related to successful therapy.
Manish is a computational neuroscientist, who is trained in machine learning, neuroscience and psychiatry. The overarching goal of his research is to develop reliable computational methods that will allow for characterizing and modeling temporal dynamics of brain activity, without necessarily averaging data in either space or time at the outset. He firmly believes that the spatiotemporal richness in brain activity might hold the key to finding the person- and disorder-centric biomarkers. Manish received his bachelor’s degree from the Indian Institute of Information Technology (Allahabad) and Masters and PhD from the University of Texas at Austin, advised by Drs. Risto Miikkulainen and Clifford Saron. He did his Postdoctoral Fellowship at the Stanford University School of Medicine, mentored by Dr. Allan Reiss. He is currently an assistant professor in the Department of Psychiatry & Behavioral Sciences at Stanford University School of Medicine and directs the Brain Dynamics Lab.
Selin Aviyente received her B.S. degree with high honors in Electrical and Electronics engineering from Bogazici University, Istanbul in 1997. She received her M.S. and Ph.D. degrees, both in Electrical Engineering: Systems, from the University of Michigan, Ann Arbor, in 1999 and 2002, respectively. She joined the Department of Electrical and Computer Engineering at Michigan State University in 2002, where she is currently a Professor and Associate Chair for Undergraduate Studies. Her research focuses on statistical and nonstationary signal processing, higher-order data representations and network science with applications to neurophysiological signals. She has authored more than 150 peer-reviewed journal and conference papers. She is the recipient of a 2005 Withrow Teaching Excellence Award, a 2008 NSF CAREER Award and a 2021 Withrow Excellence in Diversity Award. She is currently serving on several technical committees of IEEE Signal Processing Society and is the vice-chair of IEEE Bioimaging and Signal Processing (BISP) technical committee. She is an Associate Editor for IEEE Open Journal of Signal Processing and Digital Signal Processing.
Gustavo Deco is Research Professor at the Institució Catalana de Recerca i Estudis Avançats (ICREA) and Professor (Catedrático) at the Pompeu Fabra University (UPF) where he leads the Computational Neuroscience group. He is also Director of the Center of Brain and Cognition (UPF). In 1987 he received his PhD in Physics for his thesis on Relativistic Atomic Collisions. In 1987, he was a postdoc at the University of Bordeaux in France. From 1988 to 1990, he obtained a postdoc of the Alexander von Humboldt Foundation at the University of Giessen in Germany. From 1990 to 2003, he leads the Computational Neuroscience Group at Siemens Corporate Research Center in Munich, Germany. He obtained in 1997 his Habilitation (maximal academical degree in Germany) in Computer Science (Dr. rer. nat. habil.) at the Technical University of Munich for his thesis on Neural Learning. In 2001, he received his PhD in Psychology at the Ludwig-Maximilians-University of Munich.
Amy Kuceyeski is an Associate Professor of Mathematics in the Radiology Department at Weill Cornell Medicine and an Adjunct Associate Professor in the Computational Biology Department at Cornell University. She was awarded her PhD in 2009 from Case Western Reserve University and spent her postdoctoral fellowship and early faculty years at Weill Cornell Medicine. For over a decade, Amy has been interested in understanding how the human brain works in order to better diagnose, prognose and treat neurological disease and injury. Quantitative approaches, including mathematical modeling and machine learning, applied to data from rapidly evolving neuroimaging techniques, have the potential to enable ground-breaking discoveries about how the brain works. Amy has particular interest in lesion-symptom mapping, non-invasive brain stimulation and pharmacological interventions, like psychedelics, that may be used to modulate brain activity and promote recovery from disease or injury.
Luiz Pessoa obtained a PhD in computational neuroscience at Boston University. After his PhD he returned to his home country, Brazil, and joined the faculty of Computer Systems Engineering at the Federal University of Rio de Janeiro. After a few years, he returned to the US as a Visiting Fellow at the National Institute of Mental Health. He then joined the Department of Psychology at Brown University as an Assistant Professor, the Department of Psychological and Brain Sciences at Indiana University, Bloomington, as an Associate Professor, and since 2011 has been at the Department of Psychology, University of Maryland, College Park, where he is full Professor and Director of the Maryland Neuroimaging Center. His research interests center around the interactions between emotion/motivation and perception/cognition. He published in 2013 the book 'The cognitive-emotional brain: from interactions to integration' and his book 'The Entangled Brain: How Perception, Cognition, and Emotion Are Woven Together', also by MIT Press, is scheduled for to be published later this year.
Dr. Bijsterbosch is an Assistant Professor in the Computational Imaging Research Center of the Department of Radiology at Washington University in St Louis. The Personomics Lab headed by Dr. Bijsterbosch aims to understand how brain connectivity patterns differ from one person to the next, by studying the “personalized connectome”. Using population datasets such as the UK Biobank, the Personomics Lab adopts cutting edge analysis techniques to study multivariate imaging measures associated with mental health symptomatology, heterogeneity, and resilience. In addition, Dr. Bijsterbosch is an advocate for Open Science serving as Editor of Open Data Replication Reports for the NeuroImage family of journals, and as Chair of the Open Science Special Interest Group within the Organization for Human Brain Mapping. Dr. Bijsterbosch is also engaged with international teaching efforts as Co-Chair of the FSL Course Organizing Committee and lead author of a textbook on functional connectivity analyses, which was published by Oxford University Press in 2017.
Psyche Loui is the Associate Professor of Creativity and Creative Practice in the Department of Music at Northeastern University. She graduated from University of California, Berkeley with her PhD in Psychology, and attended Duke University as an undergraduate with degrees in Psychology and Music. In the MIND (Music, Imaging, and Neural Dynamics) lab, Dr. Loui studies the neuroscience of music perception and cognition, tackling questions such as: What gives people the chills when they are moved by a piece of music? How does connectivity in the brain enable or disrupt music perception? Can music be used to help those with neurological and psychiatric disorders? Dr. Loui’s work has received multiple grants from the Grammy foundation, a young investigator award from the Positive Neuroscience Institute, and a Career award from the National Science Foundation, and has been featured by the Associated Press, New York Times, Boston Globe, BBC, CNN, the Scientist magazine, and other news outlets.
Benjamin Risk is an Assistant Professor in the Department of Biostatistics & Bioinformatics, Rollins School of Public Health, Emory University. He completed his PhD in Statistics at Cornell University (2015) and was a postdoctoral associate with the Statistical and Applied Mathematical Sciences Institute and the University of North Carolina, Chapel Hill (2017). Benjamin Risk’s research focuses on neuroimaging and aims to further scientific understanding and medical research by developing, improving, and disseminating statistical methodology. His research includes dimension reduction methods, multimodal data integration, and the statistical impacts of MRI acquisition methods.
Undergraduate in Physics at Oberlin College, Ohio. PhD at Indiana University in psychological and brain sciences/cognitive science with Olaf Sporns. Postdoc at University Pennsylvania in Bioengineering with Danielle Bassett. Started the “brain networks and behavior lab” at Indiana University in 2018. Our aim is to characterize the architecture of macro-scale brain networks and understand its roles in cognition/disease/development.
Dr. Tianming Liu is a Distinguished Research Professor (since 2017) and a Full Professor of Computer Science (since 2015) at University of Georgia (UGA). Dr. Liu is also an affiliated faculty (by courtesy) with UGA Bioimaging Research Center (BIRC), UGA Institute of Bioinformatics (IOB), UGA Neuroscience PhD Program, and UGA Institute of Artificial Intelligence (IAI). Dr. Liu’s primary research interests are brain imaging, computational neuroscience, and brain-inspired artificial intelligence, and he has published over 380 papers in this area. Dr. Liu is the recipient of the NIH Career Award (2007-2012) and the NSF CAREER Award (2012-2017). Dr. Liu is a Fellow of AIMBE (inducted in 2018) and was the General Chair of MICCAI 2019.
Catie Chang is an Assistant Professor of Electrical and Computer Engineering, Computer Science, and Biomedical Engineering at Vanderbilt University. She received her Ph.D. from Stanford University, and was a postdoctoral fellow in the NIH Intramural Research Program. Her lab, the Neuroimaging and Brain Dynamics Lab, seeks to advance understanding of human brain function through techniques for analyzing and interpreting neuroimaging data.
I am a Professor of Neurology and clinician scientist at Emory University. I am a clinical neurophysiologist and epileptologist, and I also have graduate and post-graduate degrees in computational neurosciences, clinical research and neuroimaging. Overall, my research is focused on improving the understanding of the mechanisms that underlie neurological impairments, epilepsy and language processing. I am directly involved in mechanistic research projects related to epilepsy or aphasia. My research is also related to clinical trials for aphasia treatments. In epilepsy research, I am the corresponding MPI on an R01 project to assess connectome markers of epilepsy surgical outcomes. Related to language and aphasia, I am the PI for an NIDCD supported R01 project on biomarkers of aphasia recovery using the brain connectome. I am the corresponding MPI for the phase II clinical trial speech entrainment for aphasia recovery (SpARc), and the PI of a core project related to brain health and aphasia recovery, as part of the ongoing P50 Center for the Study of Aphasia Recovery (C- STAR, PI Fridriksson).
Pierre Bellec is an associate professor at the department of Psychology of University of Montreal. His main research interest is to train artificial neural networks to mimic human brain activity and behavior, at the level of individuals.
Dr. Fan Lam graduated from Tsinghua University with his BS in Biomedical Engineering. He received his PhD in Electrical and Computer Engineering from the University of Illinois Urbana-Champaign (UIUC, 2015). Currently, he is an assistant professor in the Department of Bioengineering at UIUC, a full-time faculty member with the Beckman Institute for Advanced Science and Technology and a co- director of the Master of Science in Biomedical Image Computing program at UIUC. Lam's research focuses on developing advanced magnetic resonance-based molecular imaging and multimodal brain mapping methods, and their applications to the study of brain function at normal and diseased states. Dr. Lam is a Junior Fellow of ISMRM (International Society of Magnetic Resonance in Medicine), and a recipient of an NSF CAREER Award (2020). Other awards include a Best Student Paper Award from IEEE-ISBI (International Symposium of Biomedical Imaging, 2015), Robert T. Chien Memorial Award from ECE-UIUC (2015), an NIH-NIBIB Trailblazer Award (2020), and an NIH-NIGMS MIRA R35 Award (2021). Dr. Lam is a senior member of IEEE, serves as an Associate Editor for IEEE Transactions on Medical Imaging and a co-chair of the Young Scholar Committee at the World Association for Chinese Biomedical Engineers (WACBE).
Moo K. Chung, Ph.D. is an Associate Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison (http://www.stat.wisc.edu/~mchung). Chung is affiliated with the Waisman Laboratory for Brain Imaging and Behavior and the Department of Statistics. Chung received PhD from McGill University under Keith Wolseley and trained at the Montreal Neurological Institute. Chung’s research focuses on computational neuroanatomy, spectral geometry, and topological data analysis. Chung mainly concentrates on the methodological development required for quantifying and contrasting brain functional, anatomical shape and network variations in both normal and clinical populations using various mathematical, statistical, and computational techniques. He has published three books on neuroimage computation including Brain Network Analysis published through Cambridge University Press in 2019. Currently started writing a new book on Topological Data Analysis for Brain Imaging.
After graduating from the Scholar's Program at Whittier College in 1985, Dr Salisbury began studying Biological Psychology and human auditory neurophysiology with Prof. Nancy K Squires at Stony Brook. He began a post-doctoral fellowship in 1990 in Biological Psychiatry with Prof. Robert W McCarley at Harvard Medical School to examine auditory neurophysiology in schizophrenia. The 2 year post-doc turned into a 22 year career at Harvard, where Dr Salisbury worked with Dr McCarley, Prof. Martha E Shenton, and many others examining neurophysiological and MRI structural and functional measures of impaired sensation, perception, and basic memory function in first episode psychosis. The work conducted in his laboratory at McLean Hospital helped to change the conceptualization of schizophrenia as a static, perinatal encephalopathy. It pioneered the combined use of structural brain imaging and electroencephalographic (EEG) measurement of auditory cortex responses to demonstrate that progressive gray matter loss during the early disease course of schizophrenia was linked to progressive auditory impairment. In 2012, he left Harvard to join the faculty at Western Psychiatric Hospital at the University of Pittsburgh School of Medicine. The continuing multimodal imaging work in first episode psychosis individuals aims to identify local and distributed circuit abnormalities in early disease course and to develop biomarkers to facilitate early identification of the disorder.
He received his Ph.D. from the University of Michigan in Cognitive Psychology in 2003, and served as an Assistant (2004-2008) and Associate Professor (2009) at Columbia University, and as Associate (2010-2014) and Full Professor (2014-2019) at the University of Colorado, Boulder. Since 2004, he has directed the Cognitive and Affective Neuroscience laboratory, a research lab devoted to work on the neurophysiology of affective processes—pain, emotion, stress, and empathy—and how they are shaped by cognitive and social influences. Dr. Wager and his lab are also dedicated to developing analysis methods for functional neuroimaging and sharing ideas, tools, and scientific data with the scientific community and public.
Stefano Panzeri is full professor and director at the Department of Excellence for Information Processing at the Medical School in Hamburg (UKE). Stefano was trained and researched originally in theoretical physics (string theory) and has worked in computational neuroscience for more than 20 years. His research lies at the interface between theory and experiments and investigates how the functions of the brain originate from the interactions between its elements, the neurons.
Anqi Wu is an Assistant Professor at the School of Computational Science and Engineering (CSE), Georgia Institute of Technology. She was a Postdoctoral Research Fellow at the Center for Theoretical Neuroscience, the Zuckerman Mind Brain Behavior Institute, Columbia University. She received her Ph.D. degree in Computational and Quantitative Neuroscience and a graduate certificate in Statistics and Machine Learning from Princeton University. Anqi was selected for the 2018 MIT Rising Star in EECS, 2022 DARPA Riser, and 2023 Alfred P. Sloan Fellow. Her research interest is to develop scientifically-motivated Bayesian statistical models to characterize structure in neural data and behavior data in the interdisciplinary field of machine learning and computational neuroscience. She has a general interest in building data-driven models to promote both animal and human studies in the system and cognitive neuroscience.
Dr. Rahnev received his Ph.D. in Psychology from Columbia University in 2012. After completing a 3-year post-doctoral fellowship at UC Berkeley, he joined Georgia Tech in 2015 where he is currently Blanchard Early Career professor. His research focuses on perceptual decision making – the process of internally representing the available sensory information and making decisions on it. Dr. Rahnev uses a wide variety of methods such as functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS), psychophysics, and computational modeling. Dr. Rahnev’s work appears in high-impact journals such as Behavioral and Brain Sciences, PNAS, Nature Communications, and Nature Human Behavior. He has received over $3.5M in funding, including PI grants from NIH, NSF, and the Office of Naval Research.
Dr. Ying Guo is Professor in the Department of Biostatistics and Bioinformatics at Emory University, an appointed Graduate Faculty of the Emory Neuroscience Program and an Associate Faculty in Emory Department of Computer Science. She is a Founding Member and current Director of the Center for Biomedical Imaging Statistics (CBIS) at Emory University. Dr. Guo’s research focus on developing analytical methods for neuroimaging and mental health studies. Her main research areas include statistical methods for agreement and reproducibility studies, brain network analysis, multimodal neuroimaging and imaging-based prediction methods. Dr. Guo is a Fellow of American Statistical Association (ASA) and 2023 Chair for the ASA Statistics in Imaging Section. She is a Standing Member of NIH Emerging Imaging Technologies in Neuroscience (EITN) Study Section and has served on the editorial boards of several scientific journals in statistics and psychiatry.
Tonya White, MD, PhD moved in July 2022 from the Erasmus University in the Netherlands to head the Section on Social and Cognitive Developmental Neuroscience at the National Institute of Mental Health in Bethesda, Maryland. While in Rotterdam, she was professor of Pediatric Population Neuroimaging in the Department of Child and Adolescent Psychiatry and in the Department of Radiology and Nuclear Medicine at Erasmus University Medical Centre. Dr. White's has an eclectic educational background, having received a Bachelor's degree in electrical engineering (Magna Cum Laude) from the University of Utah and Master's degree in electrical engineering from the University of Illinois, Champaign/Urbana. She received her medical degree from the University of Illinois and later a Ph.D. from Erasmus University in Rotterdam, the Netherlands. Following a junior faculty position at the University of Minnesota, she joined the faculty at Erasmus University Medical Center in 2009 to set up and direct a pediatric population neuroimaging program within the Generation R study, which is a large epidemiological study of child development. While at the Erasmus University Medical Centre, her group acquired over 9000 brain imaging of children ranging from 6 to 17 years of age in four waves of data collection. Her primary focus lies in better understanding the underlying neurobiology in children with neurodevelopmental disorders, including autism spectrum disorders. The work that she will present during her talk will stem from her efforts in the Generation R Study.
Dr. Christos Davatzikos is the Wallace T. Miller Sr. Professor of Radiology at the University of Pennsylvania, and Director of the recently founded AI2D Center for AI and Data Science for Integrated Diagnostics. He has been the Founding Director of the Center for Biomedical Image Computing and Analytics since 2013, and the director of the AIBIL lab (AI in Biomedical Imaging). He holds a secondary appointment in Electrical and Systems Engineering and in the Division of Informatics at Penn as well as at the Bioengineering an Applied Mathematics graduate groups. He obtained his undergraduate degree by the National Technical University of Athens, Greece in 1989, and his Ph.D. degree from Johns Hopkins, in 1994, on a Fulbright scholarship. He then joined the faculty in Radiology and later in Computer Science, where he founded and directed the Neuroimaging Laboratory. In 2002 he moved to Penn, where he founded and directed the section of biomedical image analysis. Dr. Davatzikos’ interests are in medical image analysis. He oversees a diverse research program ranging from basic problems of imaging pattern analysis and machine learning, to a variety of clinical studies of aging and Alzheimer’s Disease, schizophrenia, brain cancer, and brain development. Dr. Davatzikos has served on a variety of scientific journal editorial boards and grant review committees. He is an IEEE fellow, a fellow of the American Institute for Medical and Biological Engineering, and member of the council of distinguished investigators of the US Academy of Radiology and Biomedical Imaging Research.
Danilo Bzdok is a medical doctor and computer scientist with a dual background in systems neuroscience and machine learning algorithms. After medical training at RWTH Aachen University (Germany), Université de Lausanne (Switzerland), and Harvard Medical School (USA), he completed one Ph.D. in brain-imaging neuroscience (Research Center Juelich, Germany, 2012) and one Ph.D. in computer science in machine learning statistics at INRIA Saclay and Neurospin (France, 2016). Danilo currently serves as Associate Professor at McGill's Faculty of Medicine and as Canada CIFAR AI Chair at Mila - Quebec Artificial Intelligence Institute, Montreal, Canada.
Andreas received an MD from Freiburg University and a PhD from Charité Berlin. He is Associate Professor for Neurology at Harvard Medical School and the director of deep brain stimulation research within the Center for Brain Circuit Therapeutics at Brigham & Women’s Hospital in Boston. He is further affiliated with the Massachusetts General Hospital in Boston and Charité Berlin. His lab studies how focal neuromodulation impacts the human connectome to refine clinical treatments for neurological and psychiatric disorders. A key question is which networks should be modulated for improvements of specific symptoms – in disorders such as Parkinson’s Disease, Obsessive Compulsive Disorder, Depression, or Alzheimer’s Disease. Further, the lab develops methods to segregate the human connectome into functional domains by combining brain stimulation with functional and diffusion-weighted MRI.