جست‌وجوی استاد و پژوهشگر

• Complex Networks
• Neuroscience
• Functional Brain Connectivity
• +۳ مورد دیگر

Tommaso Gili is an Assistant Professor at the IMT School for Advanced Studies Lucca, specializing in interdisciplinary research at the intersection of network science, neuroscience, and biomedical engineering. His work focuses on analyzing complex systems through network theory and advanced imaging techniques, with emphasis on brain connectivity, functional networks, and their disruptions in neurological disorders. Key research areas include: Functional and structural brain network integration Multi-scale Laplacian analysis in heterogeneous systems Applications of network theory to medicine (e.g., schizophrenia, epilepsy, inflammatory bowel disease) Development of novel methods for network coarse-graining and renormalization His research leverages fMRI, MRI, and computational modeling to study: Cerebral blood flow dynamics Neurological connectivity alterations Biomedical signal processing Complex disease biomarker discovery Notable contributions include innovations in: Functional connectivity analysis of C. elegans Topological symmetry-breaking in brain networks Machine learning approaches for brain digital twins

Federico Raimondoمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Computational Neuroscience
• Machine Learning
• +۳ مورد دیگر

Dr. Federico Raimondo is a post-doctoral researcher at the Institute of Neuroscience and Medicine (INM-7) , Research Center Jülich, Germany. His work focuses on developing trustworthy AI algorithms to address clinical challenges in neurosciences, particularly in understanding consciousness and cognition. He holds a PhD in Computer Science (University of Buenos Aires) and Cognitive Neurosciences (Sorbonne University), followed by a postdoc at the University of Liège, Belgium. Education: Bachelor’s/Master’s in Computer Science, University of Buenos Aires PhD in Computer Science (joint program with Sorbonne University’s Cognitive Neurosciences) Research Interests: Machine Learning, Clinical AI Transparency, EEG/MEG Signal Processing, Disorders of Consciousness, and Neuroimaging Analytics. He develops tools like NICE-EEG Cleaner and contributes to libraries such as Julearn and JuHarmonize to ensure reproducible and bias-free AI models. Labs/Teams: Applied Machine Learning Group at INM-7, collaborating on projects like the CONNECT-ME Study for ICU patient prognosis.

Ralph Weidnerمشاهده پروفایل

دانشیار

• Visual System
• Perception
• Attention
• +۵ مورد دیگر

PD Dr. Ralph Weidner serves as a researcher at the Jülich Research Centre, specifically within the Institute of Neuroscience and Medicine (INM), where he leads the Cognitive Neuroscience department (INM-3). His work is situated at the intersection of experimental psychology and advanced neuroimaging techniques. Dr. Weidner's research focuses on understanding how the human brain processes and integrates visual information from our environment. His laboratory investigates the cognitive mechanisms underlying perception and attention, examining how the brain selects relevant information while filtering out irrelevant stimuli. Using a multimodal approach, his team combines experimental psychological paradigms with functional magnetic resonance imaging (fMRI), Dynamic Causal Modeling (DCM), transcranial direct current stimulation (tDCS), and transcranial magnetic stimulation (TMS) to uncover the neural foundations of healthy brain function and potential pathological mechanisms in damaged brains. His work has significant implications for understanding both normal brain function and neurological disorders where information processing is impaired. The research group maintains strong methodological expertise in functional brain imaging and causal modeling of neural networks, contributing to the broader understanding of how brain structures interact during cognitive processes.

Sarah Assmannمشاهده پروفایل

استاد

• Plant Biology
• Molecular Biology
• Signal Transduction
• +۴ مورد دیگر

Sarah Assmann is the Waller Professor of Biology at Pennsylvania State University, affiliated with the IEE Research Themes in Climate and Natural Systems. Her research focuses on molecular and electrophysiological mechanisms of plant signal transduction, particularly in guard cells and responses to environmental stimuli like light, hormones, and abiotic stress. She leads the Assmann Lab in the Mueller Lab facility, contributing to interdisciplinary projects bridging plant biology, genomics, and computational modeling. Her work spans topics such as stomatal physiology, heterotrimeric G protein signaling, RNA structure dynamics, and climate adaptation in plants. Notable contributions include the development of Oryza CLIMtools for rice genomic-environmental associations and studies on riboSNitches' roles in climate-driven plant adaptation. She has been recognized with the Masatoshi Nei Innovation Prize in Biology (2025) and collaborates with researchers like Réka Albert, advancing systems biology approaches to plant stress responses. Her recent publications (2021–2025) highlight advancements in understanding G protein variants' agronomic impacts, RNA structural dynamics under stress, and computational modeling of signal transduction networks. Her research bridges fundamental biology with applications in crop improvement and climate resilience.

Chinmoy Bhattacharjeeمشاهده پروفایل

استاد

• Stochastic Geometry
• Distributional Approximations
• Stein's Method
• +۴ مورد دیگر

Prof. Chinmoy Bhattacharjee is a Professor of Mathematical Stochastics at the University of Hamburg's Department of Mathematics, part of the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on stochastic geometry, distributional approximations via Stein's method, stochastic analysis, random graphs, continuum percolation, and nonparametric inference. He holds a PhD from the University of Southern California and previously held postdoctoral positions at the University of Luxembourg and University of Bern. His work is supported by the German Research Foundation (DFG) through Project No. 531540467. Education: PhD in Applied Mathematics, University of Southern California (2013–2018) M.Stat. and B.Stat. (Hons.), Indian Statistical Institute, Kolkata (2008–2013) Teaching: Recent courses include Stochastic Processes, Probability and Statistics, and seminars on percolation theory and probabilistic methods. Research Highlights: Prof. Bhattacharjee's work bridges stochastic geometry and Stein's method, with applications in spatial random structures, non-Gaussian limit theory, and noise sensitivity in percolation models. Key contributions include Gaussian and Dickman approximation techniques, spectral analysis of Poisson functionals, and statistical applications in random forests. Upcoming Events: Co-organizing session on Spatial Stochastics at German Probability and Statistics Days (2025) Invited talks at Aarhus University and workshops in Germany/Croatia (2025)

Sung Jae Junمشاهده پروفایل

استاد

• Econometrics
• Causal Inference
• Quantile Regression
• +۵ مورد دیگر

Sung Jae Jun is a Professor of Economics and Associate Department Head in the Department of Economics at Pennsylvania State University. His research focuses on econometrics, causal inference, quantile regression, and auction models. He holds a Ph.D. from Brown University (2006). His work emphasizes methodological advancements in causal effect estimation under partial identification, weak identification, and outcome-based sampling. Key themes include persuasion effects, treatment heterogeneity, and structural estimation in triangular models. Recent contributions address policy evaluation frameworks using difference-in-differences and counterfactual prediction in game-theoretic contexts. Publications span topics like risk aversion in auctions, semiparametric testing, and software development (e.g., R package 'ciccr'). His research integrates econometric theory with applied problems in labor economics, industrial organization, and political economy. No academic awards or advisees are listed in available materials. He maintains a Google Scholar profile and a website at https://sites.google.com/view/suj14/.

Kevin Inglesمشاهده پروفایل

استادیار مدعو

• Particle Physics
• Nuclear Physics
• Theoretical Physics
• +۳ مورد دیگر

Kevin Ingles is an Adjunct Assistant Professor in the Department of Physics at Ohio State University. His research focuses on theoretical and computational studies of high-energy nuclear collisions, particularly involving charm mesons and their molecular structures in expanding hadronic environments. He also develops advanced Bayesian analysis tools for nuclear dynamics modeling, contributing to frameworks like Taweret and BANDFramework. Ingles has explored causality constraints in dissipative magnetohydrodynamics and the role of spin in relativistic plasma systems through his work. His research interests span particle physics, nuclear physics, and fluid dynamics, with emphasis on understanding hadron gas dynamics, quarkonium physics, and relativistic hydrodynamic simulations. Recent studies include examining shear-stress response functions in massless particle systems and thermal energy calculations of charm-meson molecules in pion gases. Ingles received a MPS-Ascend Postdoctoral Fellowship in 2023 focusing on Spin Magnetohydrodynamics. His advisory and grant activities include managing the BANDFramework project and advancing computational methods for nuclear dynamics analysis. He is affiliated with the Physics Research Building at Ohio State University's Department of Physics. Key contributions include developing Python-based Bayesian model mixing tools and investigating contact interactions that produce exotic particle states. His work integrates theoretical plasma physics with computational fluid dynamics to address challenges in modern collider experiments and nuclear collision simulations.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Shwai He is a PhD student and Affiliate Assistant Professor at the University of Maryland, advised by Ang Li. Their research focuses on advancing AI systems through innovative approaches in large language models (LLMs), fairness in machine learning, and efficient neural network architectures. Key areas include multi-agent systems, causal modeling for bias mitigation, and optimization techniques for mixture-of-experts models. Research interests span artificial intelligence, machine learning, and natural language processing with emphasis on practical applications like healthcare diagnostics and social pairing systems. Notable work includes developing GNWT-based multi-agent digital twins for social platforms and improving LLM transparency through token analysis. Publications highlight contributions to counterfactual fairness, dynamic-depth transformers, and parameter-efficient methods. Current efforts explore computational efficiency in vision-language models and bio-inspired antibody prediction systems. No scientific awards have been mentioned. Advising and grants: Currently a PhD student under Ang Li's supervision. Research involves collaborations across computer science and bioinformatics domains.

• Epilepsy Research
• Neurodevelopmental Disorders
• Genetic Epilepsies
• +۳ مورد دیگر

Professor Andreas Brunklaus is a Senior Clinical Research Fellow at the University of Glasgow, specializing in neurology and pediatric neurodevelopmental disorders. His research focuses on understanding the genetic and clinical aspects of epilepsy syndromes, particularly Dravet syndrome and SCN1A-related epilepsies. He leads studies investigating genotype-phenotype correlations, comorbidities, and quality of life impacts in patients with these conditions. Key areas of investigation include the molecular mechanisms underlying sodium channelopathies, the psychosocial burdens faced by caregivers, and the development of precision therapies. His work combines genomic analyses with clinical data to inform diagnostic approaches and therapeutic strategies. Active collaborations with international teams enhance his research impact. Brunklaus has secured grants from organizations such as Biocodex and the UK Office of the Chief Scientific Adviser, supporting natural history studies of SCN1A-related epilepsy. His publications emphasize longitudinal patient outcomes, epilepsy classification advancements, and the identification of genetic modifiers influencing disease severity. Notable contributions include refuting SCN9A's role as an epilepsy gene, analyzing autism comorbidity in Dravet syndrome, and developing predictive models for early epilepsy diagnosis. These efforts aim to improve care pathways and patient management strategies for rare neurological disorders.

Vince D Calhounمشاهده پروفایل

استاد مدعو

• Image and Signal Processing
• Multimodal Brain Imaging
• Machine Learning/Deep Learning
• +۵ مورد دیگر

Dr. Vince D Calhoun is an Adjunct Professor in the Department of Electrical and Computer Engineering at Georgia Institute of Technology. He is the founding director of the tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), collaborating with Georgia State University and Emory University. His work focuses on advancing neuroimaging analytics for mental and neurological disorder biomarkers through big data and neuroinformatics tools. Dr. Calhoun holds a BSEE from the University of Kansas (1991), M.S. in Biomedical Engineering and Information Systems from Johns Hopkins University (1993, 1996), and a Ph.D. in Electrical Engineering from the University of Maryland Baltimore County (2002). He previously served as Director of Medical Image Analysis at the Olin Neuropsychiatry Research Center, Associate Professor at Yale University, and Distinguished Professor at the University of New Mexico. His research interests span machine learning, multimodal neuroimaging (MRI/EEG/MEG), neurogenomics, and functional connectivity analysis. Notable contributions include developing NeuroMark ICA frameworks and federated analytics via COINSTAC, advancing schizophrenia and addiction biomarker research. Awards: Fellowships: IEEE, AAAS, AIMBE, ISMRM, ACNP 2016 Outstanding Alumnus (UMBC) 2014 IEEE Engineer Award & 2015 Educator Award Professional Roles: Chair, Organization for Human Brain Mapping Chief Section Editor, Frontiers in Brain Imaging Methods Dr. Calhoun’s work emphasizes translational research, bridging neuroimaging innovation with clinical applications. His current efforts include large-scale collaborative initiatives like TReNDS to address brain health challenges through interdisciplinary approaches.

Sarah M. Assmannمشاهده پروفایل

استاد

• Plant Physiology
• Cell Signaling
• Abiotic Stress Response
• +۴ مورد دیگر

Sarah M. Assmann is the Waller Professor of Biology at the Department of Biology, Pennsylvania State University. She is affiliated with the Huck Institutes of the Life Sciences. Her research focuses on plant electrophysiology, cell signaling, and abiotic stress responses, particularly in guard cell function and G-protein signaling pathways. She leads projects aimed at improving crop resilience through genetic and environmental studies in rice and Arabidopsis. Research Interests: Dr. Assmann investigates how plants perceive and respond to environmental stresses like drought and heat. Key areas include guard cell signaling, heterotrimeric G-protein regulation, and RNA structure-function relationships. Her work integrates molecular, physiological, and systems biology approaches. Grants & Projects: NIFA-USDA 2019-67013-29234: Rice G-protein signaling for drought tolerance NIH-R01GM126079: G-protein phosphorylation mechanisms NSF-MCB 1715826: G-protein networks in stomatal closure Labs & Teams: The Assmann Lab focuses on plant stress biology, employing advanced techniques like Structure-seq for RNA structural analysis. Collaborations include interdisciplinary teams addressing crop improvement and environmental adaptation.

Michael Krammمشاهده پروفایل

استادیار

• Behavioral Public Economics
• Political Economy
• Industrial Organization
• +۳ مورد دیگر

Michael Kramm is an Assistant Professor at the Technical University Dortmund's Faculty of Business and Economics. He specializes in microeconomic theory with research spanning behavioral public economics, political economy, and strategic interactions. His work combines game theory, information economics, and causal inference methods to analyze policy design and market competition. Research interests focus on corrective policies in behavioral economics, political platform competition, evolutionary game dynamics, and measurement of competitiveness. His interdisciplinary approach incorporates psychological insights into economic modeling. Publication trends show consistent focus on game-theoretic modeling of economic behavior, with recent work exploring stability in cooperative games and taxation frameworks. Methodologically, articles demonstrate advanced applications of mathematical optimization and dynamic systems analysis. Awards include the IIPF Young Economist Award for innovative work on tax policy design. Current projects investigate energy efficiency policies, consumer learning in markets, and political signaling strategies.

• Statistics
• Probability
• Copulas
• +۲ مورد دیگر

Johanna Genest Nešlehová is a Professor at the Institute for Statistics and Mathematics at Vienna University of Economics and Business. She previously served as Assistant Professor at McGill University (2009-2020) and holds a PhD in Mathematics from Carl von Ossietzky University of Oldenburg. Her research spans statistics, probability theory, and financial mathematics with focus areas including multivariate analysis, dependence modeling, copulas, and statistical methods for financial applications. She serves as editor for the Canadian Journal of Statistics and Statistics and Risk Modeling. Research publications demonstrate focus on multivariate statistical methods, dependence modeling, and applications in financial mathematics. Recent work includes stochastic decomposition methods, causal inference techniques, and rank-based estimation. Carrie M. Derick Award for Graduate Supervision and Teaching CRM-SSC Prize

• Psychiatry
• Psychotic Disorders
• Adolescent Psychiatry
• +۵ مورد دیگر

Dr. Scott Woods is a Professor of Psychiatry and holds a secondary appointment in the Child Study Center at Yale School of Medicine. He specializes in assessing and treating youth at risk for psychosis through the PRIME Clinic, focusing on early intervention and prevention strategies. His research interests include prodromal symptoms, clinical high-risk psychosis, and suicidality, with a strong emphasis on translational research and biomarker development. Dr. Woods earned his MD from Baylor College of Medicine (1978) and completed residency training at Massachusetts General Hospital (1984). He leads the ProNET clinical trial and is a key investigator in the Accelerating Medicines Partnership for Schizophrenia (AMP SCZ) Program, contributing to large-scale studies on psychosis prediction and treatment. His work has been supported by major grants, including a $76M NIH award (2024), and he has published extensively on topics like predictive modeling, neuroimaging, and sociocultural factors influencing psychosis risk. Key achievements include developing tools for early detection (e.g., Mini-SIPS adaptation) and advancing understanding of mechanisms underlying delusions/hallucinations. He collaborates with institutions globally, emphasizing interdisciplinary approaches to mental health challenges. Dr. Woods' clinical and research activities are centered at Yale's Psychiatry Department and the Connecticut Mental Health Center.

• Psychiatric epidemiology
• Comorbid mental and substance use disorders
• Classification systems in psychiatry
• +۴ مورد دیگر

Associate Professor Matthew Sunderland is a leading researcher at the Matilda Centre for Research in Mental Health and Substance Use at the University of Sydney. He holds concurrent appointments as an External Fellow at the Black Dog Institute and Visiting Fellow at the Australian National University . His work focuses on psychiatric epidemiology, comorbidity between mental and substance use disorders, and the development of psychometric tools to improve classification systems in mental health. Current research explores data harmonisation , machine learning applications, and school-based eHealth interventions like Health4Life to address lifestyle risk factors in youth. Major projects include NHMRC-funded trials , implementation of eHealth portals for co-occurring disorders, and international collaborations on alcohol-dementia relationships. His publications span psychopathology modeling , dimensional assessment tools , and implementation science , with over 150 peer-reviewed articles and $22M+ in research funding . Awards include NHMRC Fellowships and Rising Star recognitions from UNSW and the Society for Mental Health Research.