جست‌وجوی استاد و پژوهشگر

• Multimodal Perception
• Perception and Interaction in Social Networks
• Perception and Action with Digital Agents
• +۵ مورد دیگر

Markus Huff serves as a Full Professor of Applied Cognitive Psychology and head of the Applied Cognitive Psychology group at the Eberhard Karls University of Tübingen since 2020. He concurrently leads the Perception and Action research group at the Leibniz Institute for Knowledge Media (IWM). His research program investigates perception and action processes within digital environments through a perception-oriented framework that examines how media-mediated content influences human cognition and behavior. Dr. Huff earned his academic foundation in psychology, mathematics, and computer science at the University of Tübingen. Following his psychology degree, he completed his doctorate (Dr. rer. nat.) at the IWM and University of Tübingen with groundbreaking research on verbal influence on visual memory, which earned him the prestigious Leibniz Young Researcher Award in 2007. His academic trajectory includes postdoctoral positions at IWM, University of Tübingen, and Washington University in St. Louis, followed by a junior professorship in general psychology at Tübingen before heading the Department of Research Infrastructures at the German Institute for Adult Education. Professor Huff's research spans four primary interconnected domains. His Multimodal Perception research investigates how humans process and mentally organize media content across various formats including texts, images, comics, and videos. The Perception and Interaction in Social Networks focus examines the complex interplay between attitudes, knowledge, and metacognition during information reception and social interaction in digital spaces. His work on Perception and Action with Digital Agents explores human-AI communication dynamics, while his research on Risk Perception of Artificial Intelligence addresses the psychological dimensions of human responses to emerging digital technologies. Additionally, he conducts significant metascientific research examining policy effectiveness in the research process, with notable findings on research data policies. Analysis of Professor Huff's recent publication record reveals a clear trajectory toward increasingly sophisticated investigations at the intersection of cognitive psychology and digital technology. His work demonstrates methodological rigor across visual perception, audio-visual integration, event comprehension, and the cognitive impacts of digital environments. A particularly noteworthy trend is his expanding focus on AI-related perception and risk assessment, reflecting the growing importance of human-AI interaction in contemporary cognitive science. His research consistently bridges theoretical cognitive frameworks with practical applications in educational technology, health communication, and digital media design. Leibniz Young Researcher Award by the Leibniz Association (2007) Professor Huff directs a multidisciplinary research group comprising members from both the Applied Cognitive Psychology group at the University's Department of Psychology and the Perception and Action group at the Leibniz Institute for Knowledge Media. His research program has secured substantial funding, evidenced by his prolific publication record in high-impact journals spanning psychology, cognitive science, human-computer interaction, and educational technology. His grant portfolio reflects strong interest in understanding human cognition in digital contexts, with particular emphasis on educational applications, health communication, and human-AI interaction. His laboratory work employs advanced experimental methodologies including eye-tracking, behavioral experiments, and computational modeling to investigate how people process information across various media formats and interact with increasingly sophisticated digital agents. The research environment fosters collaboration between cognitive psychologists, computer scientists, and educational researchers, creating a rich interdisciplinary context for advancing understanding of human perception and action in our rapidly evolving digital landscape.

Hongxiao Zhuمشاهده پروفایل

دانشیار

• Bayesian Methods
• Functional Data Analysis
• Statistical Machine Learning
• +۶ مورد دیگر

Hongxiao Zhu is an Associate Professor in the Department of Statistics at Virginia Tech. She holds a Ph.D. in Statistics from Rice University (2009), an M.S. in Mathematics from the University of Arkansas at Little Rock (2004), and a B.S. in Finance from Wuhan University (2002). Her research focuses on Bayesian methods, functional data analysis, and statistical machine learning, with applications in medicine, bioinspired sensing, neuroimaging, finance, and genomics. She has developed methods for analyzing high-dimensional data, including biosonar signals, genomic loci, and medical imaging data. Zhu has been recognized with awards such as the Travel Award from the Sixth International Workshop on Statistical Analysis of Neuronal Data (2012) and the SBSS Student Paper Competition (2008). Her teaching includes advanced courses on regression, statistical inference, Bayesian statistics, and statistical computing. She is affiliated with the Institute of Mathematical Statistics and the American Statistical Association. Zhu collaborates with researchers in engineering, biology, and computer science, notably in bioinspired sonar sensing frameworks and UAV applications. Her work bridges statistical theory and interdisciplinary applications, emphasizing robust modeling and computational innovation. Zhu's lab explores sonar terrain analysis, functional mixed models, and sensor-based environmental sensing. Recent projects include simulating bat-inspired sonar systems and analyzing DNA methylation patterns in brain development. Her research trends emphasize integrating Bayesian methods with real-world data challenges in healthcare and robotics. Her awards highlight contributions to statistical methodologies and interdisciplinary research. Grants and collaborations are evident through her work with institutions like SAMSI and Duke University. She advises on advanced statistical techniques for diverse datasets, though no specific advisee list is provided here.

• Computer Science
• Cybersecurity
• Edge Computing
• +۸ مورد دیگر

Klara Nahrstedt is the Maybelle Leland Swanlund Endowed Chair and Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. She also serves as Director of the Coordinated Science Lab and holds professorships in Electrical and Computer Engineering, the Information Trust Institute, and the National Center for Supercomputing Applications (NCSA). Her expertise spans cybersecurity, edge computing, IoT, and multimedia systems. Her research focuses on immersive content delivery, network security, and AI-driven optimization for edge computing environments. Notable contributions include frameworks for robust 360-degree video analytics, cybersecurity in industrial networks, and energy-efficient video streaming. Awards include AAAS Fellow, ACM Fellow, and the Humboldt Research Award. Recent work emphasizes adaptive systems for IoT, reinforcement learning in multimodal applications, and generative models for intrusion detection. Key projects include Fire360 (firefighting video benchmarks) and AquaScope (underwater image transmission). Collaborations span academia and industry, addressing challenges in smart grids, telemedicine, and urban IoT. Awards: AAAS Fellow (2019), ACM Fellow (2012), Edward J. McCluskey Technical Achievement Award (2012) Labs/Teams: Coordinated Science Lab, NCSA, Information Trust Institute

Shuang Zhaoمشاهده پروفایل

دانشیار

• Physics-Based Computer Graphics
• Scientific Computing
• Inverse Rendering
• +۷ مورد دیگر

Shuang Zhao is an Associate Professor of Computer Science at UC Irvine, co-directing the Interactive Graphics & Visualization Lab (iGravi). He holds a PhD from Cornell University (2014) and a postdoc at MIT. His research focuses on physics-based computer graphics, scientific computing, and inverse rendering, with applications in material science, biomedicine, and robotics. Zhao's NSF CAREER Award (2023) supports his work on Physics-Based Differentiable and Inverse Rendering, enabling automated 3D reconstruction and medical imaging advancements. Education: Ph.D., Computer Science, Cornell University (2014); Postdoc at MIT. Research areas: Inverse rendering, differentiable rendering, Monte Carlo methods, and light transport modeling. Notable projects include Meta's digital twin creation for the Metaverse, non-line-of-sight imaging, and medical imaging applications. His lab develops algorithms for efficient inverse solutions and collaborates with industry (Meta, Nvidia, Adobe). Teaching includes advanced graphics courses like CS 114 and ICS 162. Awards include ACM programming contest championships and Best Paper recognitions. Students supervised include Cheng Zhang (Facebook Fellow), Kai Yan, and Zahra Montazeri. Hobbies include photography and Japanese anime/video games.

Beate Ehrhardtمشاهده پروفایل

پژوهشگر ارشد

• Hypothesis Testing
• Bayesian Statistics
• Machine Learning
• +۳ مورد دیگر

Dr. Beate Ehrhardt is a Mathematical Innovation Research Fellow at the University of Bath's Institute for Mathematical Innovation. Her research spans hypothesis testing, Bayesian statistics, machine learning, optimal experimental design, networks, and causality. She completed her PhD in Statistical Sciences at University College London and holds an MSc in Mathematics from Universität Bremen. Her research interests include: Statistical analysis of pharmaceutical and insurance data Design of pre-clinical experiments Community structure analysis in large networks Recent publications focus on electoral systems, digital health technologies, and clinical applications of machine learning. Her work on hip fracture classification demonstrates significant advancements in medical AI applications. Dr. Ehrhardt leads multiple research projects funded by UK Research Councils and health organizations, including studies on chronic pain mechanisms and fetal outcomes following antiepileptic exposure.

Jim Smithمشاهده پروفایل

استاد

• Bayesian statistics
• Decision theory
• Probabilistic graphical models
• +۷ مورد دیگر

Jim Smith is a Professor in the Department of Statistics at the University of Warwick. He actively leads research initiatives in Bayesian statistics, decision theory, and causal modeling with applications to food security, forensic science, and public health. His work bridges theoretical advancements in graphical models with real-world implementations through collaborations with government agencies and industry partners. PhD in Statistics (University of Warwick) Research focus: Bayesian networks, chain event graphs, expert judgment elicitation, causal inference Key collaborations: Martine Barons, Manuelle Leonelli, Ann Nicholson Research Interests Smith's work centers on high-dimensional Bayesian decision analysis, causal discovery in dynamic systems, and integrating expert judgments into formal models. He has developed novel Chain Event Graphs for complex system modeling and focuses on applications in: Food security risk assessment Forensic evidence evaluation Counter-terrorism strategies Medical imaging analysis Crisis management Energy security Recent Publications Trend His recent work demonstrates increasing focus on adversarial risk analysis (2025), secure data modeling for law enforcement (2024), and methodological improvements in Bayesian networks (2023-2024). Key themes include: Polynomial regression-based moment propagation Multi-agent decision frameworks Dynamic graphical model extensions Causal analysis in security contexts Mathematical foundations of Bayesian graphs Scientific Awards Fellow of the Alan Turing Institute (2017-2024) COST European Cooperation award for expert judgment research EPSRC-funded OxWaSP PhD training consortium co-director Grants & Collaborations Currently serving as PI in 6 Alan Turing Institute projects and CI on 7 others. Major grants include: European Food Standards Agency expert elicitation funding EPSRC OxWaSP initiative National Digital Twin Programme research Home Office policy evaluation projects Labs & Teams Co-leads a university research team studying cross-domain expert coherence. Member of the Food Global Research Priority team coordinating UK food security studies. Developed software tools in R/Python for Chain Event Graph analysis.

Phil Cumminsمشاهده پروفایل

استاد مدعو

• Earthquake Seismology
• Tsunami Modeling
• Geophysics
• +۳ مورد دیگر

Professor Phil Cummins is an Adjunct Professor at the Research School of Earth Sciences (RSES), The Australian National University (ANU), with a PhD in Geophysics from UC Berkeley (1988). He specializes in earthquake seismology, tsunami modeling, and seismic hazard assessment, focusing on Indonesia and the Asia-Pacific region. His research addresses earthquake mechanisms, tsunami generation, and basin effects on seismic waves. He collaborates with Geoscience Australia to enhance disaster risk reduction capabilities in developing nations. Research Interests: Earthquake source characterization and fault dynamics Tsunami forecasting and warning systems Basin amplification effects in seismic zones Historical seismicity and paleoseismic studies Seismic hazard assessment for urban areas like Jakarta and Bandung Key Projects: Finite Fault inversion using W-Phase data Improving tsunami warning systems via rapid source modeling Seismic microzonation studies in Indonesia Basin structure imaging via ambient noise tomography His work integrates geodetic, seismological, and numerical modeling techniques to advance understanding of geohazards. Recent studies include analyses of the 2022 Cianjur and 2018 Lombok earthquakes in Indonesia, and the 2013 Bohol earthquake in the Philippines.

Marta D'Eliaمشاهده پروفایل

استاد مدعو

• Scientific Machine Learning
• Nonlocal Modeling
• Fractional Calculus
• +۴ مورد دیگر

Marta D'Elia is an Adjunct Professor at Stanford's Institute for Computational and Mathematical Engineering (ICME), specializing in Scientific Machine Learning and nonlocal modeling. Her research develops data-driven algorithms for multiscale/multiphysics simulations, integrating numerical analysis, uncertainty quantification, and fractional calculus. Core applications include subsurface transport, turbulence modeling, image processing, and materials science. She leads innovations in nonlocal operator regression, physics-informed neural networks, and fractional Laplacian formulations. Current work focuses on embedded machine learning for constitutive modeling, Bayesian uncertainty frameworks, and computational homogenization. D'Elia pioneered approaches for nonlocal-to-local model coupling and fractional Helmholtz decompositions, advancing simulation capabilities for anomalous transport phenomena. Her Ph.D. in Applied Mathematics (Emory University) underpins rigorous mathematical foundations, while collaborations with national labs address high-performance computing implementations. Research contributes to open-source scientific software and computational mathematics education through ICME courses on numerical methods and machine learning.

Xueyu Songمشاهده پروفایل

استاد

• Theoretical Chemistry
• Computational Chemistry
• Electron Transfer
• +۱۱ مورد دیگر

Xueyu Song is a Professor of Chemistry at Iowa State University, affiliated with the Ames Laboratory of the U.S. Department of Energy. Their research focuses on theoretical and computational tools for chemical reactions in chemical and biochemical systems. Ames Laboratory of the U.S. Department of Energy Department of Chemistry, Iowa State University Education: Postdoctoral Fellow, University of California, Berkeley (1995-1998) Ph.D., California Institute of Technology (1995) Research Interests: The group develops theories for electron transfer in solutions and inhomogeneous materials, solvent effects on chemical reactions, solvation dynamics in protein environments and ionic fluids, phase behaviors of metallic systems, nucleation kinetics, protein-protein interactions, and protein crystallization. Recent work integrates machine learning with supercooled liquids and extends the Debye-Hückel theory beyond dilute solutions. Sub-diffraction imaging using heterodyne and entangled photons is also studied. Recent Article Trends: Publications span 2019-2024, focusing on solvation dynamics in ionic fluids, nanodomain analysis via single-molecule tracking, machine learning for heterogeneities in supercooled liquids, extensions of Debye-Hückel theory, and phase behavior in metallic systems. Computational methods like density functional theory, molecular dynamics, and Bayesian inference are key tools. Contact: Email xsong@iastate.edu , Phone: 515-294-4383, Office: 303 Wilhelm, 2332 Pammel Dr, Ames, IA 50011-1025.

Paul Barboneمشاهده پروفایل

استاد

• Theoretical Mechanics
• Biomechanical Imaging
• Inverse Problems
• +۷ مورد دیگر

Paul Barbone is a Professor in the Department of Mechanical Engineering at Boston University, with an affiliation to the Division of Materials Science & Engineering. He holds a PhD from Stanford University. His research focuses on theoretical and computational mechanics, acoustics, medical imaging, and inverse problems, with applications in biomechanical imaging and elastography. He has contributed to advancements in finite element methods, scattering theory, and multiple scales analysis. His honors include Fellowships from the American Institute for Medical and Biological Engineering (2018) and the Acoustical Society of America (2007). His work spans computational mechanics, inverse problems, and biomedical applications, with notable contributions to quantitative elasticity imaging and nonlinear tissue mechanics.

• Computer Vision
• Robotics
• 3D Vision
• +۴ مورد دیگر

Sangyun Shin is a Researcher in the Department of Computer Science at the University of Oxford, supervised by Professors Niki Trigoni and Andrew Markham. His work focuses on advancing object localization systems for robotics through 3D vision technologies. He specializes in integrating multimodal sensing (e.g., RGB-D cameras, acoustic arrays) to enhance robotic perception in challenging environments. His research interests include 3D motion capture for wildlife, domain-adaptive 3D detection, and self-supervised learning for nighttime vision. He has explored applications ranging from long-range wildlife tracking to autonomous drone navigation using reinforcement learning. Recent efforts emphasize sensor fusion and neural network architectures tailored for dynamic environments. Key technical contributions include the SoundLoc3D system for 3D sound localization and the WildPose framework for wildlife motion capture. His publications span topics like acoustic neural warping fields (SPEAR), spherical point cloud segmentation, and LiDAR-based object detection for urban driving. Shin's work bridges theoretical advancements in machine learning with practical robotics applications, addressing challenges such as cross-domain adaptation and low-resource sensor setups. His research has implications for autonomous systems, environmental monitoring, and human-drone interaction interfaces.

Fronefield Crawfordمشاهده پروفایل

استاد

• Pulsar Astronomy
• Gravitational Wave Detection
• Radio Transients
• +۳ مورد دیگر

Fronefield Crawford is the Charles A. Dana Professor of Physics and Astronomy at Franklin and Marshall College (F&M), serving as Director of Grundy Observatory since 2017. He holds a Ph.D. from MIT (2000) and B.A. from Williams College (1994). His research focuses on pulsar astronomy, gravitational wave detection via the NANOGrav consortium, and landmine detection technology. He leads the NANOStars program, enabling undergraduate research in pulsar timing arrays. Previously, he chaired F&M's Department of Physics and Astronomy (2020–2022) and served as a technical consultant for Raytheon and an investment firm. Research Highlights: - Pulsar surveys (e.g., PALFA, Parkes 70cm re-analysis) - Detection of fast radio bursts (FRBs) including four historic discoveries from archival data - Gravitational wave studies using millisecond pulsars as cosmic clocks He has co-authored over 50 publications and received prestigious awards including the 2025 Bruno Rossi Prize. His work bridges astrophysics and applied science, including NATO-sponsored landmine detection projects in collaboration with international teams.

Dmitry Vetrovمشاهده پروفایل

استاد

• Bayesian Methods
• Deep Learning
• Diffusion Models
• +۳ مورد دیگر

Prof. Dmitry Vetrov is a Professor of Computer Science at Constructor University Bremen, affiliated with the School of Computer Science and Engineering. His research focuses on integrating Bayesian methodologies with deep learning, particularly in diffusion models, generative adversarial networks (GANs), and domain adaptation. He leads the Bayesian Method Research Group and has contributed to advancements in areas such as neural optimal transport, unsupervised voice restoration, and genetic fine-mapping. Key research interests include probabilistic modeling, generative AI, and optimization techniques for neural networks. Notable work includes innovations in diffusion samplers, adaptive learning rate analysis, and encoder-based approaches for image and audio generation. His publications span top venues like NeurIPS, ICLR, and AAAI. Current projects emphasize scalable diffusion models, thermodynamic views of SGD, and Bayesian approaches in genetic analysis. He collaborates on applied areas like speech enhancement (HIFI++), protein sequence generation, and efficient parameterization of GANs. Labs/Teams: Leads the Bayesian Method Research Group, actively involved in Constructor University's AI and machine learning initiatives.

Matthew Kupilikمشاهده پروفایل

دانشیار

• Control Systems
• Renewable Energy
• Signal Processing
• +۳ مورد دیگر

Matthew Kupilik is an Associate Professor in the Department of Electrical Engineering at the University of Alaska Anchorage (UAA), within the College of Engineering. He holds a B.S., M.S., and Ph.D. in Electrical Engineering from the Colorado School of Mines. His research spans control systems, environmental modeling, and educational technology, with recent focus on Gaussian process regression for climate change studies and Arctic coastal erosion forecasting. He has taught multiple engineering courses at UAA including MATLAB for Electrical Engineers, Power Systems, and Control Systems. Educations: B.S. in Engineering Systems (Electrical Specialty), Colorado School of Mines, 2001 M.S. in Electrical Engineering, Colorado School of Mines, 2008 Ph.D. in Electrical Engineering, Colorado School of Mines, 2012 Professional Service: Chair of Curriculum Committee, UAA College of Engineering (2015–present) NSF GK-12 Fellow (2008–2010) Reviewer for Journal of the Acoustical Society of America, IEEE Conferences, and others His research trends show a progression from early work on fuel cell control systems to current interdisciplinary applications in environmental science and education. Collaborations include acoustic tracking of salmon migrations and development of pedagogical strategies for engineering education. Teaching and service contributions include developing energy curriculum for summer academies and leading instructional technology initiatives in eLearning at UAA.

• Structural Biology
• Cryo-Electron Tomography
• Membrane Trafficking
• +۳ مورد دیگر

Giulia Zanetti is a Professor of Structural Biology at Birkbeck, University of London's School of Natural Sciences. She specializes in studying membrane trafficking mechanisms, particularly focusing on COPII coat assembly and cargo recognition in the secretory pathway. Her research employs advanced imaging techniques like cryo-electron tomography and subtomogram averaging to visualize molecular complexes in situ. Her key research interests include structural biology, protein-protein interactions, and the dynamics of membrane-associated protein complexes. Notable contributions include elucidating the structure of the COPII transport vesicle coat and its role in membrane shaping, as well as advancements in cryo-EM data processing methodologies. Zanetti has supervised two doctoral students: Evgenia Markova (2021) and Joshua Hutchings (2020), both focusing on COPII-related studies. Her recent work (2025) explores mechanisms of COPII coat assembly and cargo recognition, while earlier studies (2013-2021) provided foundational insights into COPII structure and function. Her publications span high-impact journals like Nature Reviews Molecular Cell Biology, Nature Communications, and eLife, reflecting her expertise in structural biology and molecular imaging. She also contributes to teaching advanced cell biology and molecular biology modules at Birkbeck.