جست‌وجوی استاد و پژوهشگر

Michael Blackمشاهده پروفایل

استاد

• Computer Vision
• Computer Graphics
• Human Body Shape Modeling
• +۷ مورد دیگر

Michael Black is a leading researcher in computer vision and human body modeling. He is a founding director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he leads the Perceiving Systems department. He holds concurrent academic appointments as Honorarprofessor at the University of Tübingen, Adjunct Professor (Research) in Computer Science at Brown University, and Visiting Professor of Electrical Engineering at Stanford University. B.Sc., University of British Columbia (1985) M.S., Stanford University (1989) Ph.D., Yale University (1992) His research centers on the mathematical representation of human body shape, with applications in computer vision, graphics, and neuroscience. He has pioneered methods for analyzing body shape variation using statistical models derived from 3D body scans and has developed techniques for estimating body shape from commodity sensors. His work bridges geometry, perception, and machine learning to enable machines to understand human form and behavior. His publications and research have significantly influenced the field of computer vision, particularly in shape modeling and pose estimation, with long-standing contributions to both theoretical and applied aspects. His work integrates broad disciplines such as machine learning, imaging, and human-computer interaction. IEEE Computer Society Outstanding Paper Award (1991) Honorable Mention for the Marr Prize (1999) Honorable Mention for the Marr Prize (2005) 2010 Koenderink Prize for Fundamental Contributions in Computer Vision Michael Black has advised numerous students and researchers through his roles at Brown University and the Max Planck Institute, though specific names are not listed. He has led major research initiatives and secured significant funding through his leadership in the Perceiving Systems department. His work continues to drive innovation in intelligent systems that perceive and interpret human behavior. He leads the Perceiving Systems department at the Max Planck Institute for Intelligent Systems, a multidisciplinary team focused on vision, learning, and human-centered computing. The group integrates computer vision, machine learning, and 3D modeling to develop systems that understand human shape, motion, and behavior.

Houmam Saadمشاهده پروفایل

پژوهشگر

• Archaeology
• Digital Humanities
• Cultural Heritage Preservation
• +۴ مورد دیگر

Houmam Saad is a Syrian archaeologist and researcher affiliated with the École Pratique des Hautes Études - PSL , where he is supported by the PAUSE program. He previously led archaeological efforts in Syria as head of the Excavations and Archaeological Studies Department at the General Directorate of Antiquities and Museums since 2019. His work focuses on Syrian cultural heritage, particularly in Damascus and Palmyra. Education: Master’s in Islamology from EPHE-PSL (2016), International Diploma in Heritage Studies (INP-Paris, 2012), PhD in Archaeology (Damascus, 2013). Dr. Saad’s research spans archaeology , cultural heritage preservation , and Digital Humanities , including 3D digitization and satellite-based damage monitoring. His current projects involve publishing 20 years of Damascus excavations, documenting conflict-related heritage loss, and teaching at the Master’s program in "History of Art and Archaeology." He has contributed to high-profile initiatives like the 3D digitization of Palmyra and the Krak des Chevaliers with ICONEM, showcased in exhibitions at the Grand Palais and Cité de l'Architecture. His scientific work emphasizes the universal value of cultural heritage amid war, regime changes, and looting. Scientific Awards: PAUSE fellowship (2025). Dr. Saad is actively involved in heritage safeguarding, leading the PROCLAC unit, and preparing publications on Byzantine mosaics and Crusades-era artifacts. He also trains students on conflict-induced challenges in archaeology.

Christian Weinheimerمشاهده پروفایل

استاد

• Neutrino Physics
• Dark Matter Research
• Astroparticle Physics
• +۵ مورد دیگر

Professor Christian Weinheimer is a leading experimental physicist at the University of Münster's Institute of Nuclear Physics, where he holds a full professorship and serves as the Managing Director of the Institute. His research focuses on fundamental questions in particle and astroparticle physics, particularly neutrino mass measurements and the search for dark matter. He plays key roles in major international collaborations including KATRIN (neutrino mass experiment at Karlsruhe Institute of Technology) and XENONnT (dark matter search experiment at the Italian LNGS underground laboratory). Weinheimer's research interests span neutrino physics , dark matter detection , precision measurement techniques , and detector development . His group develops cutting-edge technologies for the KATRIN experiment's precision high-voltage system and electrode components, while also pioneering cryogenic distillation techniques for the XENON experiments to remove radioactive contaminants. His work extends to medical applications through the BOLD-PET project, developing novel detectors using trimethylbismuth for positron emission tomography. Analysis of his recent publications reveals a strong focus on pushing the boundaries of neutrino mass measurements, developing next-generation dark matter detectors capable of reaching the 'neutrino fog' sensitivity limit, and exploring innovative detector technologies. His work consistently combines theoretical insight with experimental ingenuity to address fundamental questions about the universe's composition and fundamental particles. Scientific awards: ERC Advanced Grant (2022) Helmholtz-Preis (2001) Dissertationspreis from Vereinigung der Freunde der Universität Mainz (1993) CERN Fellowship (1995-1996) Weinheimer actively mentors PhD students working on KATRIN background reduction, dark matter searches with XENON, precision energy measurements, and novel PET detector development. His research is supported by major grants including the ERC Advanced Grant LowRad project (2022-2027), multiple DFG-funded Collaborative Research Centers, and international collaborations with CERN, DESY, and research institutions worldwide. He also leads the development of technologies for the future DARWIN/XLZD observatory, which aims to be the most sensitive dark matter detector ever built. His laboratory operates specialized facilities including a large xenon purification system, detector development labs for the BOLD-PET project, and precision measurement equipment for high-voltage and low-background applications. Weinheimer's group collaborates extensively with other research teams at Münster University, particularly with the Cells in Motion initiative and the European Institute for Molecular Imaging.

Kyle Dawsonمشاهده پروفایل

استاد

• Cosmology
• Observational Astronomy
• Dark Energy
• +۵ مورد دیگر

Kyle Dawson is a Professor of Physics and Astronomy at the University of Utah, where he has been employed since 2009. He currently serves as both a full Professor and Director of Graduate Studies in the Department of Physics and Astronomy, having progressed from Assistant Professor (2008-2015) to Associate Professor (2015-2019) before achieving his current position in 2019. His institutional affiliation places him within the College of Science at the University of Utah, a major research university in the western United States. Dawson earned his BA in Physics from Cornell University in 1998, followed by a PhD in Physics from the University of California, Berkeley in 2004. After completing his doctoral studies, he served as a postdoctoral researcher at the Lawrence Berkeley National Laboratory before joining the University of Utah faculty. His educational background in physics provided the foundation for his transition into observational cosmology, where he has made significant contributions through large-scale spectroscopic surveys. Professor Dawson's research focuses on observational cosmology through large spectroscopic surveys designed to measure the fundamental properties of the universe. He is currently the co-Spokesperson for the Dark Energy Spectroscopic Instrument (DESI), a major cosmological survey that has produced numerous high-impact publications in 2024-2025. Previously, he served as Principal Investigator for the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), which concluded in 2020 with final cosmological measurements. His work centers on measuring baryon acoustic oscillations to constrain cosmic expansion history, dark energy properties, neutrino masses, and to test General Relativity. His research group employs techniques including galaxy clustering analysis, quasar astrophysics, and large-scale structure mapping to address fundamental questions in cosmology. The analysis of Dawson's recent publications reveals a strong focus on extracting cosmological constraints from the DESI survey data. His work spans multiple aspects of cosmological analysis, including baryon acoustic oscillation measurements, full-shape power spectrum analysis, imaging systematics mitigation, and cross-correlation studies with cosmic microwave background data. The publications demonstrate collaborative work with large international teams and contribute to increasingly precise measurements of cosmological parameters, with particular attention to dark energy equation of state, neutrino masses, and potential deviations from General Relativity. Professor Dawson has secured significant research funding throughout his career, including multiple grants from the Department of Energy (DOE), NASA, and the National Science Foundation. His grant portfolio includes leadership roles in major cosmological surveys like DESI and eBOSS, as well as support for postdoctoral researchers and graduate students. His research group has mentored numerous students who have gone on to successful careers in academia, industry, and data science fields. Dawson leads a vibrant research group at the University of Utah focused on cosmological data analysis from large spectroscopic surveys. His current team includes two postdoctoral researchers (Angela Berti and Sarah Eftekharzadeh) and a graduate student (Allyson Brodzeller). The group specializes in galaxy clustering analysis, quasar astrophysics, and machine learning applications to spectroscopic data. The research environment fosters collaboration with international teams working on DESI and related cosmological surveys, providing students with opportunities to engage with cutting-edge cosmological research and large-scale data analysis techniques.

Sahani Pathirajaمشاهده پروفایل

مدرس

• Data Science
• Bayesian Inference
• Stochastic Analysis
• +۷ مورد دیگر

Dr. Sahani Pathiraja is a Lecturer (tenure track assistant professor) at UNSW Sydney , specializing in Data Science . Her research bridges mathematical and statistical foundations with practical applications in environmental and biomedical sciences. Research Focus : Sequential Bayesian inference, Monte Carlo methods, stochastic analysis of non-linear filtering, uncertainty quantification, and real-time parameter estimation. Current Projects : Co-investigator in the ARC Industrial Transformation Training Centre: Data Analytics for Resources and Environment (DARE) and the Next Generation Graduate Program (NGGP) in Sports Data Science and AI . Research Supervision : Dr. Pathiraja supervises PhD students in areas including: Bayesian inference Stochastic differential equations Data assimilation Non-linear filtering Scientific Collaborations : Her work intersects with environmental science, biomedical applications, and machine learning. Projects include stochastic hydrology, SDEs, and operator learning for environmental systems. Contact Information : Email: s.pathiraja@unsw.edu.au Phone: +61 2 8065 0836 Office: Room 2070, Level 2, The Red Centre, UNSW Sydney

• Neural Engineering
• Global Health
• Infectious Diseases
• +۷ مورد دیگر

Dr. Steven Schiff is the Harvey and Kate Cushing Professor of Neurosurgery and Professor of Epidemiology at Yale University, serving as Vice Chair for Global Health in the Department of Neurosurgery. He is affiliated with multiple Yale institutions including the Yale Institute for Global Health, Yale Program for Biomedical Ethics, Yale Interdepartmental Program in Neuroscience, and the Yale Center for Brain and Mind Health. Dr. Schiff's educational background includes an MD and PhD in Physiology from Duke University, followed by General Surgery Internship, Neurosurgery Residency, and Pediatric Neurosurgery Fellowship at Children's Hospital of Philadelphia. His training established the foundation for his dual expertise in clinical neurosurgery and biomedical engineering. Dr. Schiff is a pioneer in neural control engineering and sustainable health engineering with a strong focus on global health applications. His research spans neural engineering, infectious disease modeling, hydrocephalus treatment in low-resource settings, and the development of low-cost medical imaging technologies. He has made significant contributions to understanding neonatal infections in Africa, particularly the discovery of Neonatal Paenibacilliosis. His work on Predictive Personalized Public Health (P3H) integrates engineering principles with public health practice. His recent publications demonstrate a strong focus on applying AI and engineering solutions to global health challenges, particularly in neurosurgery and infectious disease. Key themes include the development of low-field MRI technology for resource-limited settings, understanding Paenibacillus infections causing hydrocephalus in African infants, and applying computational modeling to infectious disease dynamics. NIH Director's Pioneer Award (2015) NIH Director's Transformative Award (2018) Fellow of American Association for the Advancement of Science (2012) Fellow of American Epilepsy Society (2016) Honorary African Name: Musaazi, Fumbe Clan (2018) Dr. Schiff leads the Center for Global Neurosurgery at Yale University, building on his previous work founding the Center for Neural Engineering at Penn State. His NIH-funded research has enabled significant advances in understanding and treating infant infections in the developing world. He serves on multiple editorial boards including the Journal of Computational Neuroscience and Journal of Neural Engineering, and has contributed to FDA scientific committees. His work bridges engineering, neuroscience, and global public health to address critical healthcare disparities worldwide.

• Biophysics
• Theoretical Physics
• Developmental Biology
• +۲ مورد دیگر

Edouard Hannezo is a Professor at the Institute of Science and Technology Austria (ISTA) since 2022, focusing on the application of theoretical physics to elucidate design principles of self-organization in biological systems. His research spans multiple scales, from cytoskeletal dynamics to tissue architecture, with key collaborations in developmental and cell biology. The Hannezo Group integrates solid/fluid mechanics, statistical physics, and soft materials engineering to address fundamental questions in morphogenesis. Current Research: Investigates stochastic branching in mammalian organs, active fluids in cytoskeletal organization, and fate selection during development. Key Contributions: Developed biophysical frameworks for intestinal organoid morphogenesis, branching morphogenesis, and mechanochemical feedback loops. Scientific Awards: EMBO Young Investigator Award (2019) ERC Starting Grant (2019) Wellcome Trust Fellowship (2015) Bettencourt-Schuller Young Researcher Prize (2014) Team: Leads a multidisciplinary team including PhD students (Zuzana Dunajova, Andreas Ehrmann, Kasumi Kishi) and postdocs (Oliver Drozdowski, Yuting Li), with technical support from image analyst Suyash Naik.

Eric Barthمشاهده پروفایل

استاد

• Dynamic systems and control
• Mechatronic systems
• Fluid power systems
• +۷ مورد دیگر

Eric Barth is Professor of Mechanical Engineering and Professor of Neurological Surgery at Vanderbilt University's School of Engineering. He serves as Director of the C* Control laboratory (also known as the Laboratory for the Design and Control of Energetic Systems) and is affiliated with the Vanderbilt Institute for Surgery and Engineering (VISE), an interdisciplinary entity bringing engineers and physicians together to impact healthcare. His educational background includes: Ph.D. in Mechanical Engineering from Georgia Institute of Technology M.S. in Mechanical Engineering from Georgia Institute of Technology B.S. in Engineering Physics from University of California - Berkeley Professor Barth's research focuses on dynamic systems and control with applications spanning multiple domains. His primary interests include the design, modeling and control of mechatronic and fluid power systems, free-piston internal combustion and free-piston Stirling engines, energy storage and harvesting systems, and MRI compatible pneumatic robots for medical applications. His work applies a system dynamics and control perspective to problems involving the control and transduction of energy, encompassing multi-physics modeling, control methodologies formulation, and model-based design. His recent publications reveal a strong trajectory connecting mechanical engineering principles with medical applications, particularly in neurosurgery. The research spans energy systems (especially Stirling engines and novel energy storage approaches) and advanced medical robotics for MRI-guided interventions. This dual focus demonstrates his ability to bridge theoretical control systems with practical applications in both energy and healthcare domains. Professor Barth actively advises several doctoral students including David Comber, Joshua J Cummins, Alexander V. Pedchenko, and E. Bryn Pitt. His research is supported by significant funding, notably from the Center for Compact and Efficient Fluid Power, an NSF Engineering Research Center. The C* Control laboratory he directs occupies approximately 1000 square feet and contains specialized equipment including an 8-camera high-bandwidth optical tracking system, mechanical breadboard tables, pneumatic equipment with high-bandwidth servo-valves, specialized pressure sensors, a thermographic camera, high-speed video equipment, 3D printers, and a 2D laser cutter. Computational facilities include a network of approximately 20 machines running MATLAB/Simulink and SolidWorks, with access to additional CNC machining resources through the School of Engineering and the University.

John A. Goreeمشاهده پروفایل

استاد

• Experimental Plasma Physics
• Statistical Physics
• Soft Condensed Matter Physics
• +۴ مورد دیگر

John A. Goree is a Professor in the Department of Physics and Astronomy at the University of Iowa. His research focuses on experimental plasma physics, statistical physics, and soft condensed matter physics, with particular emphasis on dusty plasma and strongly-coupled plasma systems. His work involves optical diagnostics of plasmas and wave analysis, combining interdisciplinary approaches from condensed matter, statistical physics, and plasma physics. Education: PhD in Physics from Princeton University. Goree leads experimental research in university labs and analyzes data from International Space Station experiments. His labs (Van Allen Hall offices 553, 555, 518, 501) support advanced equipment for vacuum, electronic, optical, and laser systems. Students gain skills in experimental design/operation and programming. Collaborations include international researchers and cross-departmental faculty. His group emphasizes hands-on training in image analysis and interdisciplinary problem-solving.

• Genomics
• Computational Biology
• Cell Biology
• +۵ مورد دیگر

Gioele La Manno is an Assistant Professor (tenure track) at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences (SV) and the Department of Life Sciences Engineering. He leads the Unité du Prof. La Manno (UPLAMANNO) and holds roles in teaching and doctoral education within the SSV-ENS and Programme doctoral Biologie computationnelle et quantitative . His research focuses on single-cell genomics, cellular heterogeneity, and systems biology approaches to study developmental and neural systems. La Manno’s educational background includes a strong foundation in computational and quantitative biology, reflected in his teaching of courses like Biological data science I: statistical learning and Scientific literature analysis in neuroscience . His lab explores interdisciplinary methods combining genomics, epigenomics, and machine learning to dissect cell fate decisions and tissue organization. He actively contributes to doctoral programs, supervising over a dozen PhD students and advising theses on topics ranging from nervous system development to drug sensitivity profiling. His work bridges basic research with translational applications, such as clinically compliant cell cryopreservation for regenerative medicine. La Manno collaborates across EPFL’s Life Sciences Engineering ecosystem, leveraging state-of-the-art technologies like mass spectrometry imaging and single-cell RNA velocity modeling. His lab’s recent studies address lipidome dynamics, glial diversity in the CNS, and the molecular regulation of midbrain dopaminergic neurons.

Bastian Pfauمشاهده پروفایل

پژوهشگر

• X-ray Holography
• Magnetic Imaging
• Ultrafast Dynamics
• +۵ مورد دیگر

Dr. Bastian Pfau serves as Department Head of the “Imaging and Coherent X-rays” (B2) division and Project Coordinator for “Transient Structures and Imaging with X-rays” at the Max Born Institute in Berlin, where he has conducted postdoctoral research since 2016. His work pioneers nanoscale magnetic imaging using coherent X-ray techniques, with significant contributions to ultrafast magnetization dynamics and topological spin structures. His academic foundation includes a Dr. rer. nat. (PhD) in Physics from Technical University Berlin (2013) with thesis “Imaging magnetic nanostructures using soft x-ray Fourier transform holography,” and a Diplom (MSc) in Physics from Technical University Dresden (2006) focused on “Combining photon correlation spectroscopy and fluctuation analysis for investigating diffusion dynamics.” Additional research experience spans Lund University (2014-2015), Technical University Berlin (2010-2013), and Helmholtz Center Berlin (2006-2010). Dr. Pfau’s research centers on developing and applying X-ray holography and coherent diffraction imaging to visualize magnetic nanostructures at nanometer-femtosecond scales. His group specializes in ultrafast magnetization dynamics , skyrmion imaging , and element-specific magnetic probing using soft X-rays. Key innovations include achieving 5 nm resolution magnetic imaging and demonstrating all-optical helicity-independent switching via plasmonic nanostructures, with applications in next-generation spintronic devices and magnetic storage technologies. Analysis of his 15 most recent publications reveals dominant themes in nanoscale magnetic imaging (particularly skyrmions and topological textures), ultrafast opto-magnetic effects using extreme ultraviolet radiation, and advanced X-ray methodologies for capturing transient magnetic states. His work consistently bridges fundamental physics with practical instrumentation development, as evidenced by contributions to laser-driven plasma sources and tabletop X-ray setups. As Department Head of B2, Dr. Pfau leads a multidisciplinary team operating cutting-edge X-ray microscopy facilities at MBI. The group maintains strong collaborations with international synchrotron facilities (including BESSY II) and free-electron laser centers, focusing on developing MHz-repetition-rate pump-probe capabilities and high-resolution magnetic imaging techniques. Current projects emphasize real-time visualization of light-induced phase transitions and magnetic switching phenomena in functional materials.

Hoda Eldardiryمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Hoda Eldardiry is an Associate Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), and Director of the Machine Learning Laboratory. Her research focuses on artificial intelligence, machine learning, data mining, and their applications in policy, ethics, and complex systems analysis. Education: Ph.D., Computer Science, Purdue University M.S., Computer Science, Purdue University B.E., Computer and Systems Engineering, Alexandria University, Egypt Research Interests: Eldardiry’s work spans machine learning modeling (e.g., hypergraph neural networks, time-series forecasting), AI ethics and policy education, and interdisciplinary applications in transportation, healthcare, and cybersecurity. She emphasizes socially responsible AI development through curriculum design and policy frameworks. Recent Research Themes: Her 2025 publications highlight advancements in graph-based learning, policy-aware AI education modules, and novel techniques in multi-modal data analysis. Key areas include zero-shot learning, sparse control systems optimization, and collaborative machine learning frameworks. Labs & Teams: Directs the Machine Learning Laboratory at Virginia Tech, fostering innovation in ethical AI systems and data-driven decision-making.

Rudie P.J. Kunnenمشاهده پروفایل

دانشیار

• Turbulent Flow
• Multiphase Flow
• Heat Transfer
• +۷ مورد دیگر

Rudie P.J. Kunnen is an Associate Professor at the Faculty of Applied Physics and Science Education , Eindhoven University of Technology, leading the Turbulent and Multiphase Flows group. His research focuses on heat, mass, and particulate transport in turbulent flows, with applications in geophysics and industry. Active in UN Sustainable Development Goals related to environmental protection Collaborator in projects like Active Contamination Control for Equipment and SubstrateS Research Interests : Turbulent flow dynamics, rotating convection, vortex structures, thermophoresis, plasma-liquid interactions, and geostrophic turbulence. His work combines experimental and numerical approaches (e.g., direct numerical simulation, particle image velocimetry). Scientific Awards : NWO Vici Prize (2024) Advising and Collaborations : Supervised multiple BSc and MSc theses at TU/e. Collaborates with researchers like F. Toschi and H.J.H. Clercx on turbulence projects.

Yinqiu Heمشاهده پروفایل

استادیار

• High-dimensional statistical inference
• Large-scale data analysis
• Mediation pathway analysis
• +۵ مورد دیگر

Yinqiu He is an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison. They hold affiliations with the School of Computer, Data & Information Sciences and the Data Science Institute at Columbia University (2021-2022 postdoc). Their research focuses on developing statistical methodologies for high-dimensional and complex data, with applications in genomics, metabolomics, and network analysis. Key areas include mediation pathway analysis, asymptotic theory for U-statistics, and functional connectivity modeling. Education includes a B.S. in Statistics from the University of Science and Technology of China (2016) and a Ph.D. in Statistics from the University of Michigan-Ann Arbor (2021), advised by Professors Gongjun Xu and Xuming He. They were awarded the ProQuest Distinguished Dissertation Award (2022) and received multiple travel grants from the Institute of Mathematical Statistics and ASA. Teaching includes core Ph.D. courses like STAT 849 (Regression Analysis) and applied courses like STAT 456 (Multivariate Statistics). Current mentoring includes MS student Yuhan Zheng (now pursuing UW-Madison Ph.D.) and Xiangyi Liao (Ph.D. in Educational Psychology). Research outputs include foundational work on adaptive U-statistics testing frameworks and scalable methods for large-scale genomic data analysis. Active in methodological contributions to biostatistics, their work bridges statistical theory and computational efficiency. Recent projects involve dynamic functional connectivity estimation from fMRI data and latent space modeling in heterogeneous networks. GitHub repository 'Adaptive-U-stats' hosts open-source implementations of high-dimensional testing algorithms developed in their research.

Yannis Stylianouمشاهده پروفایل

استاد

• Speech Signal Processing
• Audio Signal Processing
• Biomedical Signal Processing
• +۳ مورد دیگر

Yannis Stylianou is Professor of Speech Processing at University of Crete and Senior Research Scientist at Apple. Former positions include AT&T Labs Research, Bell-Labs, and Toshiba Cambridge Research Lab. IEEE Fellow with PhD from ENST-Paris and over 200 publications. Research spans: Adaptive speech/audio modeling Neural speech synthesis/enhancement Biomedical signal processing Awards include: IEEE Fellowship French Ministry Research Fellowship ENST Graduate Scholarship Recent work focuses on neural TTS architectures, intelligibility enhancement, and multimodal synthesis. Organizes annual International Summer School on Speech Processing.