جست‌وجوی استاد و پژوهشگر

Marco A.R. Ferreiraمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Multi-Scale Modeling
• Spatial-Temporal Models
• +۴ مورد دیگر

Marco A.R. Ferreira is an Associate Professor in the Department of Statistics at Virginia Polytechnic Institute and State University (Virginia Tech), affiliated with the College of Science. He holds a Ph.D. in Statistics from Duke University (2002), with a dissertation on Bayesian multi-scale modeling under M. West. He also earned an M.Sc. (1994) and B.Sc. (1993) in Statistics from the Federal University of Rio de Janeiro. Research Interests: Ferreira specializes in Bayesian statistics, multi-scale modeling, spatial-temporal models, computational methods (e.g., MCMC), and applications in environmental science, genomics, and epidemiology. His work emphasizes hierarchical models, inverse problems, and high-dimensional data analysis. Publications Trends: His research spans advanced statistical methodologies for environmental monitoring, civil unrest modeling, and genomic data analysis. Key themes include Bayesian hierarchical models, spatiotemporal fusion, and computational algorithms for optimal experimental design. Awards & Honors: OBAYES Poster Prize (2009) CNPq Fellowship (2003–2006) WNAR/COBAL 2 Award (2005) Springer Poster Prize (2003) Finalist, Savage Award (2003) Best Contributed Paper (JSM 2000) Professional Activities: He serves as an Associate Editor for Bayesian Analysis and is a member of the American Statistical Association and the International Society for Bayesian Analysis.

Sarah Burke-Spolaorمشاهده پروفایل

دانشیار

• Gravitational Wave Detection
• Binary Supermassive Black Holes
• Fast Radio Bursts (FRBs)
• +۳ مورد دیگر

Dr. Sarah Burke-Spolaor is an Associate Professor in the Department of Physics and Astronomy at West Virginia University and a member of the Center for Gravitational Waves and Cosmology (GWAC) . Her research focuses on dynamic astrophysical phenomena , including binary supermass of mass black holes , pulsar timing arrays for gravitational wave detection , and Fast Radio Bursts (FRBs) . Key Research Areas : Gravitational wave detection via pulsar timing Observational studies of binary supermassive black holes Fast Radio Burst detection and classification Galaxy formation and merger dynamics Dr. Burke-Spolaor mentors a team of students and postdocs including Gregory Walsh, Jessica Sydnor, Reshma Thomas , and Kshitij Aggarwal , whose work includes projects like the BHBinary/Galaxy Evolution Survey and The Petabyte Project for Radio Transients . Her awards include the Alfred P. Sloan Fellowship and recognition as a CIFAR Azrieli Global Scholar . Selected Article Trends : Leading gravitational wave research through NANOGrav's 15-year data set Multi-messenger astronomy targeting binary black hole systems FRB host galaxy characterization across telescopes Exploring unconventional astrophysical transients in the Galactic bulge Advancing pulsar timing array methodologies Developing next-generation radio interferometry techniques Scientific Awards : Alfred P. Sloan Fellow CIFAR Azrieli Global Scholar

• Quality Inspection and Control
• Predictive Modeling
• Smart Manufacturing Systems
• +۳ مورد دیگر

Dr. Yu (Chelsea) Jin is an Assistant Professor in the Department of Industrial Engineering at the University at Buffalo, specializing in quality inspection, predictive modeling, and data analytics for advanced manufacturing systems. She holds a PhD in Industrial Engineering from the University of Arkansas, an ME from the University of Michigan, and dual BS degrees in Network Engineering and Finance from Jinan University. Her research focuses on integrating machine learning and physics-based models to optimize manufacturing processes, such as additive manufacturing, PCB assembly, and pharmaceutical distribution systems. She has developed frameworks like ReflowNet for reflow oven optimization and physics-informed neural networks for thermal profile prediction. Her work emphasizes both theoretical advancements and practical applications in smart manufacturing and healthcare logistics. Dr. Jin's recent publications highlight contributions to generative AI for knowledge retrieval, AGV system optimization, and multi-source transfer learning for pandemic modeling. She actively collaborates with industry partners to bridge academic research and real-world manufacturing challenges.

Ann Maharajمشاهده پروفایل

دانشیار مدعو

• Time Series Classification
• Wavelet Analysis
• Fuzzy Classification
• +۹ مورد دیگر

Ann Maharaj is an Adjunct Associate Professor in the Department of Econometrics and Business Statistics at Monash University's Caulfield Campus, within the Faculty of Business and Economics. She is an active researcher and educator with expertise in statistical computing and time series analysis. Department: Econometrics and Business Statistics Role: Adjunct Associate Professor Institution: Monash University Campus: Caulfield Her research focuses on advanced statistical methodologies, particularly in time series classification , wavelet analysis , fuzzy classification , and interval time series analysis . These methods are applied in diverse domains such as finance, environmental science, climatology, and human mobility. She has co-authored a book on time series clustering and classification and has published extensively in top-tier journals. The recent trend in her publications (2020–2024) shows a strong emphasis on clustering and classification of complex time series data using wavelet, cepstral, and fuzzy techniques. Her work integrates statistical theory with practical applications, particularly in financial and environmental datasets, contributing to sustainable development goals through data-driven insights. She has received recognition for her teaching excellence: Monash Business School Award for Teaching Excellence (2017) Ann Maharaj is actively involved in academic service and professional communities. She has supervised research students and contributed to statistical consulting and workshops. Her professional affiliations include: Elected member of the International Statistical Institute (ISI) Member of the International Association of Statistical Computing (IASC), serving on its Council (2013–2017) and Executive (2015–2017) Accredited statistician with the Statistical Society of Australia (SSA) Former Secretary and Academic Vice-President of the Monash Branch of the NTEU (2000–2014) She led a research project funded by the Collier Charitable Fund in 2005 on computational infrastructure, indicating early engagement with data-intensive research. Her ongoing scholarly output demonstrates sustained research activity and collaboration with international scholars in statistics and data science. She is associated with research groups and networks focused on statistical computing and time series analysis, contributing to both methodological advancement and real-world application through interdisciplinary collaboration.

Detlev Marpeمشاهده پروفایل

پژوهشگر

• Video Coding
• Image Compression
• Signal Processing
• +۳ مورد دیگر

Detlev Marpe is a leading researcher at the Fraunhofer Heinrich Hertz Institute (HHI), serving as Head of the Video Coding & Analytics Department and Head of the Image & Video Coding Group. His work focuses on advancing video compression standards, including HEVC (H.265) and its extensions. He has contributed significantly to tools like entropy coding, transform coding, and scalable video coding. His research emphasizes efficient compression techniques, such as adaptive context models and wavelet-based methods, with applications in multimedia communication and low-delay video encoding. Affiliations: Fraunhofer Institute for Telecommunications HHI, Berlin, Germany Roles: Department Head, Research Group Leader, and Adjunct Lecturer at TU Berlin (2013/14) Research Interests: Video coding standards (HEVC, H.264/AVC), entropy coding (CABAC), wavelet-based compression, scalable video coding (SVC), multiview video coding (MVC), and rate-distortion optimization. His work bridges theoretical advancements with practical implementations, addressing challenges in compression efficiency, scalability, and real-time applications. Publications & Awards: Over 200 publications in top-tier journals and conferences, including IEEE Transactions and SPIE. Notable awards include the Chester Sall Best Paper Award and multiple Best Paper Awards from IEEE journals. His contributions to video coding standards have been adopted in global specifications like MPEG and ITU-T. Grants & Labs: Involved in major research projects on HEVC extensions, 3D video coding, and low-delay applications. Collaborates with industry partners and academic institutions globally. His team at HHI develops reference software and test models for emerging standards.

Xiuyuan Chengمشاهده پروفایل

دانشیار

• Applied Mathematics
• High-Dimensional Data Analysis
• Signal Processing
• +۴ مورد دیگر

Xiuyuan Cheng is an Associate Professor of Mathematics at Duke University, affiliated with the Trinity College of Arts & Sciences. Her expertise lies in applied analysis, focusing on developing theoretical and computational techniques for high-dimensional data analysis, signal processing, and machine learning. She holds a Ph.D. from Princeton University (2013). Her research emphasizes graph-based methods, kernel techniques, and deep learning applications in manifold data analysis. Notable contributions include work on graph Laplacian convergence, optimal transport for single-cell data, and rotation-equivariant neural networks. She has received grants from the National Science Foundation, including a CAREER award (2023–2028) for learning graph diffusion from high-dimensional data. Recent publications explore topics such as bi-stochastic graph normalization, neural tangent kernels, and adversarial defense using basis transformations. Teaching includes advanced courses like Measure and Integration and High-Dimensional Data Analysis. She collaborates extensively in interdisciplinary projects, integrating mathematical theory with computational tools for real-world applications.

Ana Dyresonمشاهده پروفایل

استادیار

• Renewable Energy Systems
• Climate Change Mitigation in Energy Systems
• Electric Power Systems
• +۵ مورد دیگر

Ana Dyreson is an Assistant Professor in Mechanical and Aerospace Engineering at Michigan Technological University and Associate Research Director at the Center for Innovation in Sustainability & Resilience (CISR). She leads the Great Lakes Energy Group, focusing on climate change impacts on electric power systems , energy transitions in cold climates , and thermal power plant modeling . Her work bridges solar photovoltaic design , electricity grid operational modeling , and the energy-water nexus . Education : PhD in Mechanical Engineering, University of Wisconsin–Madison (2018) MS in Mechanical Engineering, Northern Arizona University (2014) BS in Engineering Mechanics, University of Wisconsin–Madison (2011) Research emphasizes climate-resilient energy systems , particularly solar energy in cold climates and grid-scale modeling . Her 2025-2022 publications investigate snow mitigation on PV panels , heat pump adoption , floating solar-hydropower hybrids , and climate stressor impacts on thermoelectric plants . She develops inclusive teaching methods and advises on renewable energy deployment through initiatives like the Tech Forward Initiative on Sustainability and Resilience . Her team collaborates on multisector dynamics and energy-water-climate research in the Great Lakes region.

Salvatore Ramponeمشاهده پروفایل

دانشیار

• Machine Learning
• Cybersecurity
• Internet of Things (IoT)
• +۷ مورد دیگر

Salvatore Rampone is an Associate Professor at the Department of Law, Economics, Management and Quantitative Methods (DEMM) of Università degli Studi del Sannio (Unisannio). His research focuses on Machine Learning , Internet of Things (IoT) , and Cybersecurity , with applications in intrusion detection, bioinformatics, and financial systems. Research Interests : Privacy-preserving Machine Learning Hybrid Cloud-IoT Architectures Genetic Programming Neural Network Applications Data Processing in Distributed Environments Recent Publication Trends : His work spans federated learning for IoT security ( 2025 ), DNA splicing site prediction ( 2020 ), meningitis classification ( 2019 ), RFID-based traceability systems ( 2015 ), and geological data analysis using neural networks ( 1995-1996 ). Key technologies include Apache Spark , MLlib , and Wavelet Transform . Teaching Activities : He teaches courses like Applied Machine Learning and Principles of Computer Sciences across multiple degree programs including Statistical and Actuarial Sciences and Economics and Management .

Risi Kondorمشاهده پروفایل

دانشیار

• Machine Learning
• Graph Theory
• Quantum Mechanics
• +۶ مورد دیگر

Risi Kondor is an Associate Professor in the Departments of Statistics and Computer Science at the University of Chicago. His research focuses on machine learning, group theory applications, and equivariant neural networks. He develops algorithms respecting geometric and physical symmetries, with contributions to graph learning, quantum mechanics modeling, and multiresolution matrix factorization. Key projects include the development of Covariant Compositional Networks (CCNs) for graph-structured data and N-body networks for molecular simulations. He has created software tools like GraphFlow, SnOB (FFT for symmetric groups), and Mondrian for high-performance computing. His work bridges algebraic methods (e.g., Fourier analysis on permutation groups) with machine learning, addressing challenges in multi-object tracking, computer vision, and materials science. Risi Kondor holds grants including a DARPA Young Faculty Award ($500K, 2016–2018) and NSF funding for non-commutative harmonic analysis in machine learning. His research emphasizes theoretical foundations and practical applications, advancing areas like equivariant architectures, multiscale analysis, and symmetry-aware machine learning systems.

Nicholas Kevlahanمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Fluid Dynamics
• Geophysical Fluid Dynamics
• +۵ مورد دیگر

Dr. Nicholas Kevlahan is a Professor in the Department of Mathematics and Statistics at McMaster University. He holds a BSc in Physics from the University of British Columbia (1985–1989), a PhD in Applied Mathematics from the University of Cambridge (1990–1994), and completed a Marie Curie postdoctoral fellowship at École Normale Supérieure in Paris (1998). He has held visiting positions at institutions including Université Grenoble-Alpes, INRIA, and the University of Cambridge. His research focuses on advanced computational and mathematical methods for fluid dynamics, including the development of the WAVETRISK code for adaptive climate modelling, data assimilation techniques, fluid-structure interaction, and compressive sampling. His work integrates interdisciplinary approaches across applied mathematics, geophysics, and engineering. Key research areas include geophysical fluid dynamics, numerical analysis, partial differential equations, and wavelet-based methods. He has published extensively on topics such as ocean circulation models, turbulence simulation, and adaptive numerical methods. Dr. Kevlahan teaches courses in numerical methods, differential equations, asymptotic analysis, and mathematical physics. He mentors a diverse group of PhD, MSc, and BSc students in his active research laboratory.

Amin Darvazehbanمشاهده پروفایل

پژوهشگر ارشد

• Electromagnetic Antenna Design
• Medical Imaging
• Microwave Engineering
• +۵ مورد دیگر

Dr. Amin Darvazehban is an Adjunct Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on electromagnetic antenna design for biomedical imaging systems, particularly targeting torso and liver diagnostics. Key Research Areas: Medical microwave imaging systems Metasurface and reconfigurable antenna technologies Dielectric property analysis for diagnostics Biomedical electromagnetic sensors Quantum signal processing applications Publication Trends: Recent work highlights electromagnetic solutions for non-invasive steatotic liver detection, torso imaging optimization, and metasurface-based antenna systems. His articles span IEEE journals in Antennas, Microwaves in Medicine, and Biosensors. Education: Completed his PhD thesis in 2021 on 'Reconfigurable antennas for electromagnetic torso imaging' at the School of Information Technology and Electrical Engineering, The University of Queensland. Patent: Co-inventor of an apparatus for electromagnetic characterization of internal features (US20230228917A1, 2023).

Noah Simonمشاهده پروفایل

دانشیار

• Machine Learning
• High-dimensional Estimation
• Clinical Trial Design
• +۴ مورد دیگر

Noah Simon is an Associate Professor in the Department of Biostatistics at the University of Washington School of Public Health. His research focuses on high-dimensional statistical methods, machine learning, and their applications in biomedicine. He develops computational tools for genomic and clinical data analysis, including penalized regression techniques and adaptive clinical trial designs. Education: B.A. Mathematics, Pomona College (2008) Ph.D. Statistics, Stanford University (2013), advised by Robert Tibshirani Research Interests: Dr. Simon specializes in high-dimensional estimation, algorithm optimization, and clinical trial methodology. His work addresses challenges in biomarker discovery, imaging-based diagnostics, and genomic data analysis. Key areas include sparse-group lasso regularization, adaptive enrichment designs for personalized medicine, and scalable computational methods for big data. Grants & Funding: NIH Director's Early Independence Award ($250k/year, 2014–2019) Amazon and Google Cloud Computing Grants for biomarker research Awards: Forbes 30 Under 30 in Science (2015) NSF Graduate Research Fellowship Honorable Mention (2010) Weiland Fellowship (2011–2013) Advising: He mentors PhD and MS students in biostatistical methodology and data science, with current advisees including Jean Feng, Brayan Ortiz, and Jeremy Roth. Notable collaborations include work on neural activity detection via calcium imaging (SCALPEL) and nonparametric variable importance assessment using neural networks. Lab & Affiliations: Based at the Hans Rosling Center for Population Health, his group develops open-source software (e.g., sgl , standGL ) and contributes to biomedical data science initiatives at UW.

Kimberlee Kearfottمشاهده پروفایل

استاد

• Radiation Protection
• Nuclear Medicine
• Medical Physics
• +۳ مورد دیگر

Kimberlee Kearfott, Sc.D., is a Professor in the Department of Nuclear Engineering and Radiological Sciences at the University of Michigan. Her primary affiliation is with the College of Engineering, and she holds an additional role as Affiliate Faculty in Biomedical Engineering (BME). Her research focuses on radiation protection, nuclear medicine, medical physics, and biomedical imaging. Key areas include radon gas dynamics, dosimetry techniques, environmental radiation monitoring, and the development of radiation-aware technologies like drones and weather stations. Her work spans theoretical and applied domains, including algorithm development for anomaly detection in radon time series data, advanced imaging systems, and radiation source mapping. She has contributed to the design of cost-effective radiation measurement instruments and systems for real-time environmental monitoring. Notable projects include the creation of an Intelligent Radiation Awareness Drone and a Low-cost Radiation Weather Station. Dr. Kearfott’s expertise also extends to radiation safety protocols, quality control in dosimetry calibration, and the application of machine learning to thermoluminescent dosimeter analysis. Her research has addressed critical issues such as earthquake prediction through radon gas analysis and sterilization techniques for SARS-CoV-2-contaminated equipment. Her laboratory focuses on interdisciplinary projects at the intersection of nuclear engineering, biomedical sciences, and environmental science. Collaborations involve both academic and industrial partners, emphasizing practical solutions for radiation-related challenges in healthcare, environmental safety, and homeland security.

Min Chenمشاهده پروفایل

استاد

• Statistical Genomics
• Bioinformatics
• Bayesian Methods
• +۵ مورد دیگر

Dr. Min Chen is a Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD), affiliated with the School of Natural Sciences and Mathematics. He holds an adjunct professorship at the University of Texas Southwestern Medical Center. His expertise spans statistical genomics, bioinformatics, Bayesian methods, and sampling techniques. He completed his Ph.D. in Statistics and Decision Science at the University of Texas at Austin and a postdoctoral fellowship in statistical genomics at Yale University. Education: B.S. in Computer Science (University of Science & Technology of China, 1994); M.A. in Statistics (University of Pittsburgh, 1999); Ph.D. in Statistics (UT Austin, 2006); Postdoc in Statistical Genomics (Yale University, 2008–2010). Research focuses on statistical methodologies in genomics, including genome-wide association studies, network-based modeling, and Bayesian integrative analysis. He also explores spatial modeling and ranked set sampling. His work addresses challenges in cancer genetics, epigenetics, and single-cell gene regulation. Notable awards include the NIH Career Development Award (2013), David Bruton Fellowship (2006), and R.L. Anderson Student Paper Award (2006). He is a member of the American Statistical Association and International Chinese Statistical Association. He advises graduate students in Data Science, Statistics, and Bioinformatics & Computational Biology (BCBM) programs. His teaching includes courses on advanced statistical methods and data science. Research contributions span over 40 peer-reviewed articles, with recent work in tumor pathology imaging, antibiotic resistance, and Alzheimer’s disease mechanisms.

Christopher Chenمشاهده پروفایل

دانشیار

• Space Plasma Physics
• Solar Wind
• Plasma Turbulence
• +۵ مورد دیگر

Dr. Christopher Chen is a Reader in Space Plasma Physics at Queen Mary University of London, holding a UKRI Future Leaders Fellowship. He is affiliated with the School of Physical and Chemical Sciences within the Department of Physics and Astronomy. Chen earned his PhD in Space Physics from Imperial College London in 2011 and has held research positions at the University of California, Berkeley and Imperial College before joining QMUL in 2017, where he progressed from Lecturer to Senior Lecturer in 2021 and then to Reader in 2022. Chen's research focuses on space plasma physics, particularly solar wind turbulence and its fundamental properties. His work bridges theoretical models, spacecraft data analysis, and laboratory experiments to understand complex plasma behavior. He is actively involved in several major space missions including Parker Solar Probe (as a member of FIELDS and SWEAP teams), Interstellar Probe mission study, and Magnetospheric Multiscale mission (as a member of the Science Working Team). His publication record shows a strong focus on solar wind turbulence, Alfvén wave interactions, and plasma dynamics in the inner heliosphere. The most recent work emphasizes forecasting techniques for solar wind parameters, laboratory experiments of wave interactions, and detailed analysis of turbulence properties using data from Parker Solar Probe. His research demonstrates a clear progression toward understanding the kinetic-scale processes that govern energy transfer in space plasmas. Royal Astronomical Society Fowler Award (2017) American Geophysical Union Macelwane Medal (2021) Conferred Fellow of the American Geophysical Union (2021) American Physical Society Landau-Spitzer Award (2022) Chen actively supervises a large research group including multiple postdoctoral researchers and PhD students, and has secured significant research funding through an STFC Ernest Rutherford Fellowship (2016-21), UKRI Future Leaders Fellowship (2022-26), and multiple STFC Consolidated Grants. His teaching responsibilities include organizing and lecturing the Astrophysical Plasmas module and supervising Masters and undergraduate research projects. He is also engaged in extensive public outreach through media interviews, public talks, and participation in science festivals.