جستجوی استاد راهنمای دانشگاهی

Mingzhou Ding is Distinguished Professor and J. Crayton Pruitt Family Professor in the J. Crayton Pruitt Family Department of Biomedical Engineering at the University of Florida, within the College of Engineering. His research bridges physics, engineering, and neuroscience to investigate brain function through advanced neuroimaging and signal processing techniques. His research interests include cognitive neuroscience , multimodal neuroimaging , signal processing , affective neuroscience , and computational modeling of brain networks . He employs methods such as EEG-fMRI integration, multivariate pattern analysis, and effective connectivity modeling to study how emotional and cognitive processes are represented in the brain. Recent publications highlight a strong trend in decoding emotional content in visual cortex, attentional control mechanisms, and neuromodulation of brain networks. His work frequently involves analyzing neural representations of affective scenes, conflict processing in psychiatric conditions, and the role of reentrant feedback from limbic structures in shaping visual perception. Specialty Chief Editor, Frontiers in Human Neuroscience Guest Associate Editor, Frontiers in Neural Circuits Guest Associate Editor, Cognitive Neuroscience , Frontiers in Human Neuroscience Prof. Ding has contributed to numerous editorial roles and has collaborated widely across neuroscience and biomedical engineering. He advises several researchers and has been involved in studies supported by significant NIH and NSF funding, though specific grants are not detailed here. His lab focuses on brain imaging and stimulation, with a strong emphasis on experimental design and computational analysis of neural data. He leads a research team specializing in brain network dynamics, neuroimaging methodology, and cognitive neuroscience experiments, particularly in the domain of emotion and attention.

• Biomedical Engineering
• Neuroscience
• Cognitive Neuroscience
• +۷ مورد دیگر

Carlos Amendola is an Assistant Professor (tenure-track) of Algebraic and Geometric Methods in Data Analysis at the Institute of Mathematics of the Technical University of Berlin. Previously, he was a postdoc at TU Munich in Mathematical Statistics and Optimization and Data Analysis groups, and served as a substitute professor at Ulm University. His academic journey includes a PhD from TU Berlin under Bernd Sturmfels and Christian Haase, an M.Sc. from NYU's Courant Institute, and dual B.Sc. degrees from Mexican institutions. B.Sc. in Applied Mathematics, Instituto Tecnológico Autónomo de México B.Sc. in Mathematics, Universidad Nacional Autónoma de México M.Sc. in Mathematics, Courant Institute at New York University Ph.D. in Mathematics, Technical University of Berlin (2017) Amendola's research centers on Algebraic Statistics, with significant contributions to Applied Algebraic Geometry and Nonlinear Algebra. His work explores the intersection of algebraic methods with statistical modeling, particularly focusing on Gaussian mixtures, likelihood geometry, moment varieties, graphical models, and path signatures. He has developed novel approaches connecting tropical geometry with max-linear graphical models for extreme value theory, and has made important contributions to understanding maximum likelihood estimation through algebraic and geometric perspectives. His recent publications demonstrate a strong focus on applying algebraic techniques to statistical problems, with particular emphasis on path signatures, tropical geometry, and graphical models. The research shows a clear trajectory toward developing computational methods for complex statistical models using algebraic geometry, with applications spanning machine learning, causal inference, and extreme value analysis. Amendola serves as an editor for the Algebraic Statistics (AStat) journal since 2022 and has organized major conferences including Algebraic Statistics 2025. He has successfully secured significant research funding through Berlin Mathematics Research Center MATH+ Project AA3-16, DFG Collaborative Research Center CRC/TRR 388, and DFG Priority Program SPP 2458. As an advisor, Amendola currently supervises four PhD students (Janike Oldekop, Kamillo Ferry, Francesco Nowell, and Gabriel Riffo) and two postdocs (Rosa Preiß and Leonard Schmitz). His teaching portfolio includes courses on Algebraic Statistics, Discrete Geometry, and various mathematics subjects at both undergraduate and graduate levels.

• Algebraic Statistics
• Applied Algebraic Geometry
• Nonlinear Algebra
• +۵ مورد دیگر

Themistocles (Tim) Assimes is an Associate Professor of Medicine (Cardiovascular Medicine) at Stanford University School of Medicine and holds a courtesy appointment in Epidemiology and Population Health. He is a clinical cardiologist with expertise in genomic determinants of coronary heart disease (CHD), precision medicine, and multi-omic data analysis. Born in Montreal, Canada, he earned his MD from McGill University, an MS in Epidemiology & Biostatistics, and a PhD in Epidemiology & Biostatistics (McGill). His research focuses on genomic factors underlying CHD risk, including polygenic scores, Mendelian randomization, and integrative omics. He leads major consortia like CARDIoGRAMplusC4D and chairs the Women's Health Initiative (WHI) Genetics, Proteomics & Biomarkers group. He is also a key investigator in the Million Veteran Program (MVP) and Precision Health Service at the VA Palo Alto Healthcare System. Education: MD (McGill, 1994), MS (McGill, 2001), PhD (McGill, 2008), Board Certifications in Internal Medicine and Cardiovascular Medicine. His work spans over 30 years of clinical practice and 25 years of research. Research Interests: Genomics of CHD, cardiometabolic traits, multi-omic integration (genetic, epigenetic, proteomic), and translational precision medicine. He explores genetic architecture across diverse populations and develops population-specific risk prediction tools. His studies leverage large consortia like WHI, MVP, and TOPMed to address health disparities in genomic medicine. Publications & Collaborations: Over 200 peer-reviewed articles, including high-impact studies in Nature Genetics , Circulation , and Science . Key projects include genetic analysis of CAD in African ancestry populations, proteomic biomarkers for insulin resistance, and causal inference using Mendelian randomization. He collaborates internationally with institutions like Vanderbilt, Fred Hutchinson Cancer Research Center, and the University of Pennsylvania. Awards: AAAS Fellow, American Society of Clinical Investigation, American Heart Association Mid-Career Research Award Leadership: Co-chair of MVP P&P Committee, Director of Medical Genomics at VA Palo Alto, Steering Committee member for Project Baseline Grants: NIH-funded projects on polygenic scores, WGS in CAD, and epigenetic aging mechanisms Labs/Teams: Coordinates the Precision Health Service at VA Palo Alto and leads the Stanford Cardiovascular Institute’s genomic medicine initiatives. His team integrates clinical, genetic, and omics data to advance personalized therapies for cardiovascular diseases.

• Genomics
• Cardiovascular Medicine
• Epidemiology
• +۶ مورد دیگر

Stephen John Eglen is a Professor in Computational Biology at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge. He holds the position of Reader, focusing on computational neuroscience with expertise in retinal and visual pathway development. His research explores structural and functional aspects of neural circuit formation, including positional information in retinal neurons and synaptic connectivity mechanisms. Eglen is affiliated with the Computational Biology research group and has contributed to open science initiatives like CODECHECK and DeepClean. His academic career includes roles as a Wellcome Trust Fellow (1997–2000), Lecturer at the University of Edinburgh (2000–2001), and Senior Lecturer at DAMTP (2006–2015). His work bridges computational methods with experimental neuroscience, emphasizing reproducible research practices and computational tools for data analysis. Eglen's research interests span computational modeling of neural development, open science advocacy, and neurophysiological data analysis. He has published extensively on topics like retinal mosaics, neural network topology, and Bayesian statistical methods. His lab develops tools for analyzing neuronal activity in vitro and promoting transparency in scientific workflows.

• Computational Neuroscience
• Neurodevelopment
• Retinal Development
• +۳ مورد دیگر

Kathryn Paige Harden is a Professor and Director of Clinical Training in the Department of Psychology at the University of Texas (UT Austin), within the College of Liberal Arts. She leads the Developmental Behavior Genetics lab and co-directs the Texas Twin Project. Her research focuses on genetic influences on complex human behaviors, including cognitive development, academic achievement, risk-taking, mental health, and reproductive behaviors. Harden earned her Ph.D. in Clinical Psychology from the University of Virginia and completed her clinical internship at McLean Hospital/Harvard Medical School. She has authored over 100 scientific articles and books like The Genetic Lottery: Why DNA Matters for Social Equality (2021) and Original Sin (forthcoming). Her work bridges genetics and social science, emphasizing how genetic factors interact with socioeconomic contexts. She teaches Introduction to Psychology in a synchronous online format. Harden's research has been featured in major outlets like the New York Times and New Yorker . Key Contributions: Over 150 peer-reviewed articles on behavioral genetics and sociogenomics Co-development of the Texas Twin Project to study genetic and environmental influences Advocacy for integrating genetic insights into policies for social equity Awards: American Psychological Association Distinguished Scientific Contributions Award Advising & Grants: Harden mentors students in behavioral genetics and has secured grants from the National Institutes of Health (NIH) and other bodies to study gene-environment interactions. Her lab focuses on longitudinal designs and twin studies to disentangle genetic and environmental effects. Labs/Teams: Developmental Behavior Genetics Lab Texas Twin Project

• Behavioral Genetics
• Sociogenomics
• Cognitive Development
• +۴ مورد دیگر

Abhishek K. Umrawal is a Teaching Assistant Professor at the University of Illinois Urbana-Champaign, affiliated with the Department of Electrical and Computer Engineering and the Coordinated Science Lab. He holds a Ph.D. in Industrial Engineering from Purdue University, an MS in Economics, and an MS in Statistics from IIT Kanpur. His research focuses on AI/ML fundamentals (Generative AI, Causal AI, Reinforcement Learning), algorithmic theory (Combinatorial Optimization, Game Theory), and applications in social networks, transportation systems, and conversational AI. Umrawal teaches core courses like ECE 313 (Probability), ECE 120 (Intro to Computing), and CS/ECE 374 (Algorithms). He has published 10+ peer-reviewed articles in top venues (IEEE TETCI, ACM TDS, AAAI), focusing on influence maximization, reinforcement learning, and causal graph applications. His work bridges theoretical foundations with real-world problems in transportation and social networks. Recognitions include the Bilsland Dissertation Fellowship (2022), multiple mentorship awards, and research travel grants. He actively reviews for NeurIPS, ICML, and journals like IEEE T-ITS. Service roles include Scholarship Committees in Grainger College of Engineering and leadership in ACM/IEEE societies.

• Artificial Intelligence (AI)
• Machine Learning (ML)
• Algorithms and Computational Complexity
• +۵ مورد دیگر

Jad Beyhum is an Associate Professor in Economics at KU Leuven, affiliated with the Faculty of Economics and Business and its Economics Research Group. He specializes in econometrics and statistics, focusing on high-dimensional data analysis, instrumental variable methods, and survival analysis techniques. Current research themes include high-dimensional econometrics (e.g., LASSO-type procedures, factor models) and instrumental variables techniques (e.g., dynamic treatments, censored data). His methodological contributions target econometric challenges in nonlinear panel data, competing risks models, and duration outcomes. Key applications span corporate bankruptcy prediction, program evaluation, and macroeconomic forecasting. Recent publications highlight innovations in instrumental variable estimation under censoring, testing treatment effect homogeneity, and combining machine learning with traditional econometric frameworks. He actively supervises projects on topics like "Beyond Instrumental Variables" and "New methods to control unobserved heterogeneity" . Selected project leadership roles: "Predicting bankruptcy with machine learning methods" (Promotor, 2023-2027) "Partial identification methods for survival analysis" (Co-promotor, 2023-2027)

• Macroeconomic Forecasting
• Machine Learning
• Econometrics
• +۶ مورد دیگر

Karl Deisseroth is the D.H. Chen Professor of Bioengineering and Psychiatry and Behavioral Sciences at Stanford University, and an Investigator at the Howard Hughes Medical Institute. He holds dual appointments in the School of Medicine and School of Engineering. His research focuses on developing transformative neurotechnologies like optogenetics and CLARITY, pioneering causal systems neuroscience, and advancing understanding of psychiatric disorders such as depression and autism. He completed his A.B. at Harvard, and M.D. and Ph.D. at Stanford, followed by residency and postdoctoral training at Stanford. Deisseroth's lab has disseminated optogenetics and CLARITY to thousands of labs globally. His honors include the Japan Prize (2023), Lasker Award (2021), Kyoto Prize (2018), and membership in all three U.S. National Academies. He continues clinical practice in psychiatry, combining medications with neural stimulation therapies. Key research interests span optogenetic tools, 3D genome architecture in neurons, and neurobiological mechanisms of emotion and dissociation. He teaches courses on optogenetics and systems physiology, advising over 30 doctoral and postdoctoral researchers. His work bridges engineering, neuroscience, and psychiatry, with over 500 publications and numerous patents.

• Optogenetics
• Neural Circuit Analysis
• Hydrogel-Tissue Chemistry (CLARITY)
• +۴ مورد دیگر

Eric Y Ling is a Postdoctoral Researcher at the Department of Mathematical Sciences , University of Copenhagen. He is affiliated with research groups AG and GeoTop. Institution: University of Copenhagen Department: Mathematical Sciences Role: Researcher (Postdoc) Ling’s research focuses on Mathematical Physics , General Relativity , and Differential Geometry , particularly spacetime analysis and singularity theorems. His recent work explores causality, geometric rigidity, and quantum field theory in curved spacetime. His publications include studies on time separation functions in C0 spacetimes (2025), Dirac spectra for relativistic ions (2025), and Penrose’s singularity theorem (2025). These contributions span geometric analysis, cosmological models, and quantum gravity applications. Collaborations and trends in his work emphasize international partnerships in Mathematical Physics and General Relativity , with implications for theoretical cosmology and quantum field theory.

• Mathematical Physics
• General Relativity
• Differential Geometry
• +۳ مورد دیگر

Prof. Dr. Holger Brandt serves as Professor of Psychometrics at the Methods Center within the Department of Social Sciences, Faculty of Economics and Social Sciences, Eberhard Karls University of Tübingen since August 2021. Previously, he held Assistant Professor positions at the University of Zurich (2019-2021) and University of Kansas (2016-2019), following a postdoctoral fellowship at Tübingen's Hector Institute for Empirical Educational Research (2013-2016). His educational background includes a PhD (Promotion) from Goethe University Frankfurt's Institute of Psychology in 2013. Brandt's research pioneers advanced methodological frameworks at the intersection of psychometrics, statistics, and machine learning. He specializes in developing dynamic models for intensive longitudinal data, Bayesian estimation techniques, causal mediator analysis, and identification of inattentive response behaviors in surveys. His work rigorously addresses challenges in measurement invariance, structural equation modeling, and handling complex dependencies in social science data. Analysis of his recent publications reveals a dominant focus on Bayesian approaches for latent variable modeling, particularly spike-and-slab priors and latent class methods. His research consistently targets data quality issues in survey methodology while advancing causal inference techniques that relax traditional no-unmeasured-confounder assumptions. Applications span educational research, psychological assessment, and therapeutic alliance dynamics. As a core member of Tübingen's Methods Center, Brandt provides critical methodological infrastructure for social science research across the university, supporting researchers through statistical consulting and advanced methodology development.

• Psychometrics
• Structural Equation Modeling
• Bayesian Statistics
• +۵ مورد دیگر

Anne Therese Frederiksen is an Assistant Professor in the Department of Communication, Arts, Sciences, and Disorders at Brooklyn College, City University of New York (CUNY) . Her work bridges psycholinguistics and sign-language linguistics, with a particular focus on how bilingualism in signed and spoken languages illuminates broader questions about language and cognition. Education: Ph.D. in Linguistics and Cognitive Science, University of California, San Diego (Mayberry Lab for Multimodal Language Development) Postdoctoral fellowships: UC Irvine Chancellor’s Postdoctoral Fellow (2019–2020) and NSF-funded postdoc under Dr. Judith Kroll (2020–2022) Research Interests: Frederiksen’s research centers on bimodal bilingualism —the simultaneous use of a signed and a spoken language—examining how this unique bilingual experience shapes cognitive control, referential cohesion, and discourse processing. She investigates American Sign Language (ASL) pronouns and spatial grammar, gesture–language interaction, and the developmental trajectories of both native and late learners of ASL. Publications at a glance: Since 2015 she has published nine peer-reviewed articles and numerous conference presentations. The corpus spans high-impact journals such as Language, Cognition and Neuroscience , Frontiers in Psychology , and Lingua . Collectively, the work traces a trajectory from foundational descriptions of reference tracking in novice ASL learners to sophisticated models of bilingual language control and the semantics-pragmatics interface in sign languages. Scientific Awards & Funding: National Science Foundation research grant (2020–2022) UC Irvine Chancellor’s Postdoctoral Fellowship (2019–2020) Advising & Collaborative Networks: While the supplied text does not list individual advisees, Frederiksen has collaborated closely with Prof. Judith Kroll (UC Irvine) and Prof. Rachel Mayberry (UC San Diego), indicating active mentoring of graduate students and postdocs within large, federally funded research teams. Labs & Research Groups: During her doctoral training she was a member of the Mayberry Lab for Multimodal Language Development at UC San Diego, and she maintains collaborative ties with language-science groups at UC Irvine and CUNY.

• Deaf and bilingual language processing
• Sign language linguistics
• Bimodal bilingualism
• +۳ مورد دیگر

Alison M. Goate is the Jean C. and James W. Crystal Professor and Chair of the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. She serves as Director of the Ronald M. Loeb Center for Alzheimer’s Disease and faculty at the Icahn Genomics Institute. Her three-decade career has focused on neurodegenerative disease genetics, including pioneering discoveries of causal mutations in Alzheimer's and Frontotemporal Dementia. Current affiliations: Genetics & Genomic Sciences Chair, Neuroscience Professor, Neurology Professor, Icahn Genomics Institute Education: DPhil (Doctor of Philosophy) Her research spans: Alzheimer's disease genetics via GWAS and whole-genome sequencing Microglial enhancer networks and TREM2/SPI1 risk factors CRISPR-based iPSC modeling of tauopathies Therapeutic development targeting ApoE4 and MS4A variants Genetic overlaps between alcohol use disorder and neurodegeneration Recent publications highlight integrative approaches to 17q21.31 inversion effects, CASP8 intronic expansions in AD, and xenotransplantation models for myeloid studies. She leads multiple NIH-funded projects including U19AG069701 and U54NS123746. Scientific honors include: Potamkin Award (American Academy of Neurology) Khalid Iqbal Lifetime Achievement Award Rainwater Prize MetLife Award Elected Fellow, National Academy of Medicine (2016) Elected Fellow, AAAS (2012) Laboratory studies combine human iPSC-derived microglia with mouse models to validate genetic findings, supported by major foundations and institutions. Current team includes 24 members across graduate students, postdoctoral fellows, and associate researchers.

• Neurodegenerative Diseases
• Alzheimer's Disease Genetics
• Genomics
• +۶ مورد دیگر

Murilo da Silva Baptista is a Reader at the Institute for Complex Systems and Mathematical Biology , within the School of Natural and Computing Sciences at the University of Aberdeen . He has been with the university since 2009, initially as a Senior Lecturer and promoted to Reader in 2014. He is actively accepting PhD students in Physics, Mathematics, and Engineering, and his research is internationally recognized in the fields of complex systems and chaos theory. His research focuses on understanding the relationship between function—such as information processing, collective behavior, and synchronization—and structure in large networked complex systems. He applies analytical methods, data science, nonlinear time series analysis, and machine learning to model systems in neuroscience, smart engineering, and Earth sustainability. He is a leading scientist in chaos-based communication, demonstrating how chaotic signals can enable smart and secure wireless and underwater communication systems. His work includes theoretical developments in phase definition in chaotic oscillators, chaos-based cryptography using Poincaré return times, and the discovery of phenomena like Collective Almost Synchronization, which enhances machine learning for EEG signal prediction. His recent publications (2023–2025) span a wide range of applications, including chaotic image and 3D model encryption, UAV surveillance using chaotic paths, causal feature selection in health systems, modeling neurological disorders, and socio-environmental analysis in Brazil. These works reflect a strong trend toward applying nonlinear dynamics and network science to real-world engineering, biomedical, and societal challenges. Scientific Contributions and Recognition: Proved a conjecture on the analytical calculation of Poincaré first return times using unstable periodic orbits. Contributed foundational work to chaos-based cryptography. Proposed a formula linking mutual information to Lyapunov exponents, supporting the Infomax theory of brain evolution. Discovered the phenomenon of Collective Almost Synchronization in complex networks. Demonstrated that causality is a space-time phenomenon, not purely temporal. Advising and Research Support: He is currently supervising PhD students in Physics, Maths, and Engineering, indicating active mentorship. His research is supported by analytical developments and data-driven modeling. He leads work on optimal wireless chaos communication, synapse modeling, brain network changes post-surgery, and socio-economic causality in Brazil. His collaborations span institutions in the USA, Brazil, Germany, and Portugal. Labs and Research Groups: He is affiliated with the Institute for Complex Systems and Mathematical Biology at Aberdeen, a hub for interdisciplinary research in nonlinear dynamics, network theory, and their applications across physical, biological, and social systems.

• Complex Systems
• Chaos Theory
• Network Science
• +۱۶ مورد دیگر

Dr. Stephanie Noble is an Assistant Professor in the Departments of Psychology and Bioengineering at Northeastern University, with a joint appointment in the Center for Cognitive and Brain Health. Her research focuses on developing statistical and computational tools to enhance precision in human neuroscience inference and prediction, bridging data science, neuroscience, and open science. She co-founded the EEG startup goBlue Labs and has consulted for organizations like Source Signal Imaging and Elite Warrior Identification. Education: PhD, Interdepartmental Neuroscience Program, Yale University (2019) BSE, Chemical & Biological Engineering, Princeton University (2012) Research Interests: Dr. Noble's work addresses methodological challenges in neuroimaging, including data leakage, batch effects, and reproducibility. She emphasizes open science practices and inclusive scientific design. Her research explores functional connectivity, predictive modeling of brain-phenotype relationships, and applications in mental health and aging populations. Articles Trends: Her recent publications highlight advancements in connectome-based prediction, machine learning robustness, and neuroimaging reproducibility. Key themes include improving predictive models' generalizability, mitigating biases in datasets, and leveraging large-scale neuroimaging cohorts like the UK Biobank. Scientific Awards: No awards explicitly mentioned in the text. Advising & Grants: While specific grants are not detailed, her dual appointments and startup involvement suggest active research funding and collaboration. She advises on open science protocols and interdisciplinary projects. Labs & Teams: Affiliated with Northeastern's Center for Cognitive and Brain Health and goBlue Labs, focusing on translational neuroscience and EEG technology.

• Neuroscience
• Data Science
• Neuroimaging
• +۳ مورد دیگر

Uku Vainik is a Professor of Behavioural Genetics at the University of Tartu’s Institute of Psychology (since June 2024), concurrently holding an adjunct professorship at the Montreal Neurological Institute. He also serves as an Associate Professor in Genomics at the Faculty of Science and Technology (since 2022). His career spans postdoctoral research at McGill University and extensive interdisciplinary projects on personality-psychopathology links, neurobehavioral mechanisms of obesity, and genetic influences on behavior. Education: PhD in Psychology (2015) from University of Tartu, MA (2011) and BA (2008) in Psychology. He holds scholarships from the Kristjan Jaak Foundation, Erasmus Programme, and Estonian Research Council. Research focuses on behavioral genetics, health psychology, and personality-environment interactions. Key projects include studying how personality traits predict mental health outcomes, obesity-related behaviors, and genetic contributions to migration patterns. He developed the ionr R package for psychometric analysis. Awards include recognition for societal engagement (2023), best publication awards (2018, 2014), and national accolades for doctoral research (2015, 2013). Administrative roles include editorial board membership at Frontiers in Psychology and leadership in interdisciplinary doctoral programs.

• Behavioral Genetics
• Health Psychology
• Personality Psychology
• +۵ مورد دیگر