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نمایش ۱۶-۳۰ از ۷۲۵ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Daniel B. Neillمشاهده پروفایل

استاد

New York UniversityUnited States

Daniel B. Neill is a Professor of Computer Science, Public Service, and Urban Analytics at New York University (NYU), jointly appointed across the Courant Institute of Mathematical Sciences, Robert F. Wagner Graduate School of Public Service, and the Center for Urban Science and Progress (Tandon School of Engineering). He also serves as the Director of the Machine Learning for Good Laboratory (ML4G) and is affiliated with NYU's Center for Data Science and Tandon Department of Computer Science and Engineering. Education: Ph.D. in Computer Science, Carnegie Mellon University M.S. in Computer Science, Carnegie Mellon University M.Phil. in Computer Speech, Cambridge University Research Interests: Dr. Neill's research focuses on developing novel machine learning methods for social good, with applications in disease surveillance (e.g., early outbreak detection), healthcare (e.g., anomalous care patterns), and urban analytics (e.g., predicting citizen needs). He also explores algorithmic fairness , causal inference , and pre-syndromic surveillance using unstructured data. His work bridges theoretical machine learning with real-world policy challenges, collaborating with health departments, hospitals, and city governments to deploy data-driven tools that enhance public health, safety, and security. Scientific Awards & Honors: NSF CAREER Award NSF Graduate Research Fellowship IEEE Intelligent Systems' "Top Ten AI Researchers to Watch" Yelp Dataset Challenge Winner Hidden Signals Challenge Runner-Up (DHS) Grants & Funding: He has received significant funding from the National Science Foundation (NSF), including grants on fairness in AI (IIS-2040898), bias in urban analytics (IIS-1926470), and others. He also acknowledges support from UPMC, MacArthur Foundation, and Richard King Mellon Foundation. Laboratory & Leadership: He directs the Machine Learning for Good Laboratory (ML4G) at NYU, focusing on AI for social impact. He previously co-directed NYU's Urban Initiative (2019-2022) and led the Event and Pattern Detection Laboratory at Carnegie Mellon University.

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Anup Basuمشاهده پروفایل

استاد

University of AlbertaCanada

Anup Basu is a Professor in the Department of Computing Science at the University of Alberta. His research focuses on computer graphics, computer vision, and multimedia communications. He holds an B.S. in Math & Statistics from the Indian Statistical Institute (1980), an M.E. in Computer Science from the Indian Statistical Institute (1983), and a Ph.D. in Computing Science from the University of Maryland (1990). His work emphasizes Quality of Service (QoS) in multimedia delivery for e-commerce and telelearning, adaptive bandwidth monitoring, and 3D visualization tools. He pioneered foveated image compression and stereo visualization techniques, contributing to MPEG-4 coding standards. He leads major initiatives like the ASRA/TelePhotogenics/IBM 3D Medical Imaging project ($2M+ funding) and developed patented SHR Stereo/3D scanning technologies. Awards include the American Neurological Association Fellowship. He has held leadership roles as General Chair for IEEE International Conferences on SMC (2017), Multimedia & Expo (2013), and SMC (2014). His research integrates interdisciplinary collaborations across universities and industry partners, leveraging advanced equipment like the CAVE system for immersive visualization.

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Abdulkadir C. Yucelمشاهده پروفایل

استادیار

Nanyang Technological UniversitySingapore

Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.

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Teng-Fong Wongمشاهده پروفایل

استاد پژوهش

Stony Brook UniversityUnited States

Teng-Fong Wong is a Research Professor in the Department of Geosciences at Stony Brook University, where he has been a faculty member since 1982. His research focuses on the intersection of rock mechanics, earthquake processes, and environmental applications, making significant contributions to understanding deformation mechanisms in geological materials. Education: Sc.B., Brown University, 1973 M.S., Harvard University, 1976 Ph.D., Massachusetts Institute of Technology, 1981 Research Interests: Professor Wong's research centers on rock mechanics with emphasis on earthquake mechanics, energy resources, and environmental applications. He investigates both phenomenological and micromechanical aspects of rock deformation and fluid flow using an integrated approach combining high-pressure deformation experiments, quantitative microstructure characterization, and theoretical analysis. His work spans brittle-ductile transitions in porous rocks, permeability evolution, strength properties of fault zone materials from SAFOD and TCDP drilling projects, and submarine groundwater discharge systems. Publication Trends: Wong's recent publications (2006-2008) demonstrate a consistent focus on strain localization mechanisms in porous rocks, particularly examining compaction bands and deformation bands in sandstones. His work integrates advanced imaging techniques (X-ray radiography, CT scanning) with mechanical testing to understand the micromechanics of rock failure. A significant thread connects his research on fault zone properties from major drilling projects (SAFOD, TCDP) with fundamental rock deformation processes. Scientific Recognition: U.S. Patent 6,874,371 for Ultrasonic Seepage Meter (2005) U.S. Patent 7,107,859 for Ultrasonic Seepage Meter (2006) Co-author of "Experimental Rock Deformation - The Brittle Field" (2nd Edition, Springer-Verlag, 2005) Professional Activities: Professor Wong maintains an active international research profile with numerous visiting appointments including at Australian National University, MIT, ETH Zurich, and institutions in China and France. His work involves extensive collaboration with USGS and international research teams on major fault zone drilling projects. He has developed specialized equipment like the ultrasonic seepage meter for measuring submarine groundwater discharge. Research Infrastructure: Wong's laboratory utilizes advanced capabilities including high-pressure deformation equipment, 3D visualization through laser scanning confocal microscopy and synchrotron microCT, and integrates these with analytic modeling and numerical simulation techniques (finite element and discrete element methods) to investigate micromechanics of dilatant and compactant failure in geological materials.

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Amy Vaughan Van Heckeمشاهده پروفایل

استاد

Marquette UniversityUnited States

Amy Vaughan Van Hecke serves as Assistant Chair and Professor in Marquette University's Department of Psychology, leading research on autism spectrum disorder (ASD) and social development across the lifespan using neuroimaging and psychophysiological methods. She directs community initiatives to improve autism services access for underserved Milwaukee populations through the Next Step Clinic. Her academic foundation includes: B.A. in Psychology from Smith College Ph.D. in Developmental Psychology from the University of Miami Dr. Van Hecke's research centers on brain activity, heart rate regulation, and social behavior in individuals with and without ASD, utilizing high-density EEG and MRI to examine neural responses to interventions like PEERS ® . Current projects investigate pandemic impacts on social interaction in autistic adults and neural mechanisms of social isolation remediation. Her work bridges laboratory neuroscience with community-based clinical applications. Publication analysis reveals consistent focus on intervention efficacy (particularly PEERS ® ), neural plasticity measurement, and comorbid conditions across developmental stages. Recent work emphasizes gender-specific outcomes, family impacts, and pandemic-related mental health, demonstrating methodological diversity from EEG asymmetry to community-based participatory research. Her distinguished scientific recognition includes: Kirschstein National Research Service Award (NRSA) from the National Institute of Mental Health Dr. Van Hecke mentors students through her Marquette Autism Project lab and the Next Step Clinic training program while securing major grants from Marquette University, Johnson Controls Foundation, and the Greater Milwaukee Funders’ Collaborative. She teaches undergraduate/graduate courses in developmental psychology and statistics. Advising: Clinical psychology graduate mentorship (excluding 2025 intake); undergraduate research supervision Grants: $500k+ secured for Next Step Clinic serving underserved Milwaukee children She co-directs the Marquette Interdisciplinary Autism Initiative and the Next Step Clinic, which employs a Family Navigation model in Milwaukee's Metcalfe Park neighborhood to provide autism screening, diagnosis, and therapy for children aged 15 months-10 years facing systemic barriers to care.

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Sean Anderssonمشاهده پروفایل

استاد

Boston UniversityUnited States

Sean Andersson is a Professor in Mechanical Engineering and Systems Engineering at the College of Engineering, Boston University, and serves as Director of the BU Robotics Lab. His research bridges systems and control theory with applications in nanotechnology , atomic force microscopy , and robotics . His work in nanobioscience focuses on single molecule tracking and high-speed imaging in atomic force and fluorescence microscopy, leveraging control theory to enhance imaging capabilities. In robotics, he develops stochastic control methods for autonomous systems operating in complex environments, emphasizing multi-agent systems , sparsely sampled data , and symbolic control frameworks . Recent publications highlight trends in receding horizon control , persistent monitoring , neural style transfer for imaging , and stochastic policy optimization . The Andersson Lab also explores compressive sensing and optimal control for sensor networks and nanoscale fluid dynamics.

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Yu Lanمشاهده پروفایل

پژوهشگر ارشد

Yale UniversityUnited States

Yu Lan is a Research Fellow at the Yale School of Public Health , specializing in spatial epidemiology and health geography . Her work integrates genomic data (e.g., WGS) with geographic information systems (GIS) to analyze transmission patterns of infectious diseases like COVID-19 and tuberculosis . Education: PhD in Geography, University of North Carolina at Charlotte MA in Geography, University of North Carolina at Charlotte Research Interests focus on space-time disease modeling , infectious disease transmission , and data-driven public health tools . She develops web-based systems for real-time disease surveillance and environmental risk assessment, including tools for private well contamination and urban neighborhood dynamics . Scientific Awards include the SISMID Scholarship (2024) , Student Honors Paper Competition Finalist (2023) , and David Woodward Digital Map Award (2021) . Collaborations include work with the Ted Cohen Lab and researchers like Joshua Warren and Eric Delmelle . Her publications emphasize genomic-spatial integration and cluster detection algorithms for diseases such as tuberculosis and SARS-CoV-2.

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Joshua Frenchمشاهده پروفایل

دانشیار

University of Colorado, DenverUnited States

Joshua French is an Associate Professor and Director of Data Science Programs at the University of Colorado Denver, housed within the Department of Mathematical and Statistical Sciences in the College of Liberal Arts and Sciences. His research focuses on spatial statistics, epidemiology, environmental health, and statistical methodology with applications to public health and climate science. He leads initiatives in data science education and collaborates on projects addressing disease surveillance, environmental risk assessment, and geospatial modeling. Key research areas include developing statistical methods for cluster detection, analyzing NTM infection risks linked to environmental factors like water quality and trace metals, and evaluating climate model projections. His work integrates spatial analysis with real-world applications in public health and environmental science. French’s contributions span algorithmic innovations (e.g., PreCoG scan method) and interdisciplinary projects such as the MORACS clinical trial and aerosol impact studies on Arctic climate change. He advises the Data Science Programs at UC Denver and maintains active collaborations with institutions like National Jewish Health Hospital. His research has been published in top-tier journals and methodological packages, including 'smerc' and 'smacpod', reflecting his commitment to advancing statistical tools for spatial and environmental data analysis.

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Charles Dossمشاهده پروفایل

دانشیار

University of Minnesota Twin CitiesUnited States

Charles Doss is an Associate Professor in the School of Statistics at the University of Minnesota. He earned his PhD from the University of Washington in 2013 under Jon Wellner and holds a B.S. in Mathematics from the University of Chicago. His research focuses on empirical process theory, nonparametric estimation/inference for functions with shape constraints (e.g., concavity, log-concavity), and applications to causal inference, birth-death processes, and unlinked regression. His recent publications address problems such as doubly robust estimation for continuous treatments, heteroscedasticity detection, and convex stochastic optimization. He has received significant funding, including NSF grants DMS-2210312 and DMS-1712664, as well as institutional awards. Warwick Mid-Career Faculty Research Award (2022–2023) NSF DMS-2210312 Grant NSF DMS-1712664 Grant He has served as an Associate Editor for The Electronic Journal of Statistics (2022–present) and The American Statistician (2020–2024). He mentors students such as Guangwei Weng, Daeyoung Ham, and Oliver VandenBerg and contributes to outreach programs like Run the World, a Machine Learning summer camp for high school students.

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Gaël Kermarrecمشاهده پروفایل

پژوهشگر

Leibniz University HannoverGermany

Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections

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Laura Bruckmanمشاهده پروفایل

دانشیار

Case Western Reserve UniversityUnited States

Laura Bruckman is a Climo Associate Professor in the Department of Materials Science and Engineering at Case Western Reserve University's Case School of Engineering. Her research focuses on predictive lifetime modeling for materials degradation, quantitative spectroscopic characterization of materials, and applying statistical analytics and data science to solve challenges in photovoltaic systems and long-lived engineering materials. Her work emphasizes understanding degradation mechanisms in photovoltaic materials (e.g., backsheets, encapsulants, and silicon cells) under environmental stressors, with applications in improving reliability and service life through advanced data-driven approaches. Dr. Bruckman has contributed to the development of machine learning methods for material characterization (e.g., ToF-SIMS analysis) and spatiotemporal models for predicting degradation patterns in field-deployed PV systems. Her research also extends to curriculum design for applied data science, emphasizing industry-relevant training in statistical modeling and interdisciplinary problem-solving. Her expertise bridges materials science, data science, and energy systems, with over 50 peer-reviewed publications and a patent in classification using multivariate optical computing. Key technical contributions include analyzing environmental impacts on solar module performance, quantifying crack propagation in polymers, and developing predictive frameworks for material aging. Her work has been supported by collaborations with industry partners and federal research initiatives.

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Peter B. Noëlمشاهده پروفایل

استادیار

Technical University of MunichGermany

Peter B. Noël is an Assistant Professor of Radiology at the Hospital of the University of Pennsylvania and holds academic affiliations with the Technical University of Munich (TUM). He is a member of the Institute for Diagnostic and Interventional Radiology and chairs the 'image reconstruction & x-ray' research group under Prof. Navab's Medical Informatics Applications department. His work integrates advanced imaging technologies with clinical applications. Education: PhD in Computer Science (2009, SUNY Buffalo), MSc in Computer Science (Toshiba Stroke Research Center), BEng in Biomedical Engineering (University of Applied Sciences Giessen, Germany). Research focuses on CT imaging advancements including diagnostic/interventional x-ray imaging, tomographic reconstruction algorithms, spectral imaging with photon-counting detectors, and GPU-based high-performance computing. He leads efforts to reduce radiation exposure in CT scans and translate cutting-edge imaging techniques into clinical practice. Awards: Behnker-Berger Foundation Research Award for contributions to CT radiation exposure reduction. Current projects include the 'image reconstruction & x-ray' group, DHM (Deutsches Herzzentrum München) collaborations, and leadership in labs like NARVIS (NArvIs) and RobUSt (Robotics and Ultrasound). His work is supported by national and industrial grants.

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Johannes Kinderمشاهده پروفایل

استاد

Ludwig Maximilian University of MunichGermany

Prof. Dr. Johannes Kinder is a Professor and Chair of Programming Languages and Artificial Intelligence at the Institute of Informatics , Ludwig Maximilian University of Munich. His research focuses on software security through program analysis and machine learning, particularly targeting malware detection , vulnerability analysis , and reverse engineering . He has held faculty positions at Royal Holloway, University of London, and Bundeswehr University Munich. Research Interests include: Securing software systems via program and machine learning techniques Detection of software vulnerabilities and malware Preventing exploitation through binary analysis Applications of formal methods in systems security Recent Publications highlight advancements in binary function embedding , malware detection in npm , and speculative execution attack modeling . His work appears in top venues like USENIX Security and IEEE S&P . Education : Diplom from TU Munich (2005), Doctorate from TU Darmstadt (2010). Professional Roles : General Chair, ACM CCS 2019 Doctoral Symposium Chair, ESSoS 2016 Program Committee member for NDSS 2026, IEEE S&P 2022-2025

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Karin H. Jamesمشاهده پروفایل

استاد

Indiana University BloomingtonUnited States

Karin H. James is a Professor and Director of Graduate Studies in the Department of Psychological and Brain Sciences at Indiana University Bloomington's College of Arts and Sciences, actively reviewing applications for Fall 2026 admissions. Her research program investigates how self-generated actions shape cognitive development through neural mechanisms. Her educational background includes: Post Doctoral Fellow, Vanderbilt University (2001-2003) Ph.D. in Psychology, University of Western Ontario (2001) M.A. in Psychology, University of Western Ontario (1998) B.S. in Psychology, University of Toronto (1996) B.A. in History, University of Toronto (1991) Dr. James' research centers on the neural correlates of learning, specifically how motor experience influences visual recognition across domains including object recognition, reading acquisition, language learning, and mathematical understanding. Using fMRI and behavioral methods, she examines how sensorimotor interactions during symbol production (e.g., handwriting) reconfigure brain networks for perception. Her work demonstrates that visual-motor contingency during learning creates lasting neural changes that enhance recognition, with implications for educational practices in literacy and numeracy development. Analysis of her 2018-2021 publications reveals consistent focus on embodied cognition principles, particularly how action-perception loops establish neural representations. Key themes include gesture-based learning in mathematics, visual-motor integration in symbol recognition, and category formation mechanisms, bridging cognitive neuroscience with developmental psychology through innovative methodologies like the MRItab neuroimaging tool. Dr. James maintains active membership in the Society for Research in Child Development (2007-present), Cognitive Neuroscience Society (2004-present), and Vision Sciences Society (2000-present), reflecting her interdisciplinary approach to learning mechanisms. As Director of Graduate Studies, she oversees program development and student mentorship while leading the Cognition and Action Neuroimaging Lab. Her research infrastructure includes specialized neuroimaging tools developed for naturalistic experimental paradigms, supporting investigations into how embodied experiences shape cognitive architecture from childhood through adulthood. The Cognition and Action Neuroimaging Lab develops cutting-edge methodologies like the MRItab touchscreen system to study naturalistic learning during fMRI scanning, focusing on how sensorimotor experiences during symbol production reconfigure neural networks for perception and recognition across developmental stages.

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Mahdi Soltanolkotabiمشاهده پروفایل

استاد

Brown UniversityUnited States

Mahdi Soltanolkotabi is a Professor in the Departments of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering at the University of Southern California's Viterbi School of Engineering. He serves as the inaugural Director of the USC Center on AI Foundations for Science (AIF4S). His academic journey includes a Ph.D. in Electrical Engineering from Stanford University (2014) under Emmanuel Candes, followed by a postdoctoral position at UC Berkeley's AMPLAB mentored by Ben Recht and Martin Wainwright. Dr. Soltanolkotabi's research spans both theoretical and applied dimensions of data science. On the theoretical side, he develops mathematical foundations for modern data science, focusing on generative AI, deep learning, machine learning, signal processing, and computational imaging. His work draws upon nonconvex optimization, high-dimensional probability, statistical estimation, empirical processes, and learning theory. On the applied side, he develops reliable AI systems for healthcare and scientific applications, collaborating with physicians and domain scientists to enhance AI reliability, develop new architectures, and create rigorous evaluation frameworks. His recent publications demonstrate strong focus on medical AI applications, image reconstruction, and theoretical foundations of deep learning. His work bridges the gap between theoretical guarantees and practical implementations, particularly in medical imaging where reliability is critical. His research group has made significant contributions to understanding the behavior of nonconvex optimization algorithms in high-dimensional settings. David and Lucile Packard Fellow Information Theory Society Best Paper Award NIH Director's new innovator award Sloan Research Fellowship NSF Career award Airforce Office of Research Young Investigator award (AFOSR-YIP) Viterbi school of engineering junior faculty research award Faculty awards from Google and Amazon Dr. Soltanolkotabi has received multiple research grants including Amazon Research Awards for projects on "Artificial intelligence for fast and portable medical imaging" and "Reliable AI for Generation of Medical Reports from MRI Scans." He actively collaborates with medical professionals and leads educational outreach initiatives with local schools through USC's Viterbi Adopt-a-School program. His work demonstrates a strong commitment to translating theoretical advances into practical healthcare solutions while maintaining rigorous mathematical foundations.

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