جست‌وجوی استاد و پژوهشگر

Joshua Lukemireمشاهده پروفایل

استاد پژوهشی

• Imaging Bayesian Analysis
• High-Dimensional Imaging Data Analysis
• Brain Network Analysis
• +۳ مورد دیگر

Joshua Lukemire is an Assistant Research Professor at Emory University's Department of Biostatistics and Bioinformatics. His research focuses on developing statistical methods for analyzing high-dimensional imaging data, particularly in pediatric applications like near-infrared spectroscopy for monitoring red blood cell transfusion efficacy in preterm infants. He also explores brain network differences between clinical groups using Bayesian techniques and hierarchical modeling. Education: B.S. from University of Georgia; M.S. and Ph.D. from Emory University (Department of Biostatistics). Research interests include Imaging Bayesian Analysis, longitudinal brain network connectivity studies, and statistical methods for healthcare applications. His work bridges neuroimaging analysis, pediatric medicine, and advanced computational statistics. Recent publications highlight innovations in functional brain network generation with graph neural networks (e.g., Fbnetgen and Braingb tools), as well as clinical studies on transfusion efficacy in preterm infants and electroconvulsive therapy outcomes. His methodological contributions include sparse Bayesian modeling, optimal experimental designs, and nonparametric vector autoregression approaches. No advising or grant details are listed in the provided text. His work is supported by collaborations across disciplines, though specific lab affiliations are not mentioned.

• High-Dimensional Data Modeling
• Multivariate Time Series
• Sparse Principal Components Analysis
• +۱ مورد دیگر

Dr. Victoria Harris is a researcher at King's College London specializing in modeling high-dimensional data. Her doctoral work focused on modeling multivariate spatio-temporal structure in ecological data and responses to climate change. Current research involves latent variable modeling and latent transition analysis, contributing to UN Sustainable Development Goals.

Justin Rombergمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Optimization
• +۳ مورد دیگر

Justin Romberg is the Schlumberger Professor and Associate Chair for Research in the School of Electrical and Computer Engineering at Georgia Tech, and serves as Associate Director for the Center for Machine Learning. He holds a B.S.E.E., M.S., and Ph.D. from Rice University, followed by postdoctoral work at Caltech and visiting roles at UCLA and the Laboratoire Jacques-Louis Lions. His research bridges signal processing, machine learning, optimization, and applied probability, with notable contributions to imaging inverse problems, data compression, and broadband beamforming. Dr. Romberg has received prestigious awards including the ONR Young Investigator Award (2008), PECASE (2009), Packard Fellowship (2009), and IEEE Fellowship. His recent work emphasizes real-time RF emulation, neural adjoint methods for sound speed learning, and decentralized optimization frameworks. Current projects include radar signal processing, multi-agent reinforcement learning, and hardware-friendly sparse neural networks. His lab focuses on translating theoretical insights into practical systems, such as low-bit beamforming accelerators and medical monitoring devices using MEMS sensors.

Fang Liuمشاهده پروفایل

دانشیار مدعو

• High Performance Computing
• Parallel/Distributed Scientific Computing
• Software Interface Design
• +۵ مورد دیگر

Fang Liu is an Adjunct Associate Professor in the School of Computational Science and Engineering (CSE) at Georgia Institute of Technology. She holds a Ph.D. in Computer Science from Indiana University (2009) and serves as a Research Scientist at the Partnership for Advanced Computing Environment (PACE) center. Her primary roles include diagnosing complex technical issues in HPC systems, developing HPC software stacks, teaching courses on Linux and Python, and conducting research on big data analytics. She has also served in leadership roles for HPC conferences since 2014, including program chair for HPC2012 and HPC2013. Education: Ph.D. in Computer Science, Indiana University Bloomington (2009) Dissertation: "Building Sparse Linear Solver Component for Large Scale Scientific Simulation and Multi-physics Coupling" Advisor: Professor Randall Bramley Research Interests: Her work focuses on High Performance Computing (HPC) , parallel/distributed scientific computing , multi-physics coupling , big data infrastructure , and data management systems . She actively explores Hadoop/Spark-based solutions for graph databases and streaming data security, collaborating with Prof. Polo Chau's group. Recent projects include optimizing HPC cluster operations through big data analytics and developing provenance-capturing frameworks. Technical Contributions: She has pioneered tools like ProvBench for performance tracking and Phoenix for cost model modernization at Georgia Tech. Her work on automated storage cleanup and hybrid software deployment workflows has improved research computing efficiency across heterogeneous systems. Professional Activities: Served as program committee member for HPC, ICCS, and ICCSA conferences. Currently chairs the HPC conference steering committee since 2014. Labs & Teams: Works within the PACE center and collaborates closely with CSE faculties on computational science projects. Leads instructional initiatives via the ICE federated cluster environment.

Vincent Vuمشاهده پروفایل

دانشیار

• High-Dimensional Data Analysis
• Statistical Learning
• Statistical Computing
• +۳ مورد دیگر

Vincent Vu is an Associate Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics. He holds a PhD from the University of California, Berkeley (2009). His research focuses on high-dimensional statistics, statistical machine learning, and their applications in neuroimaging, particularly fMRI. He previously worked as a software engineer in Silicon Valley for over six years, blending computational and statistical expertise. His expertise includes statistical computing, multivariate analysis, and computational sufficiency. He has been funded by the NSF and received the 2012 AISTATS Best Paper Award. He serves as an associate editor for the Journal of the American Statistical Association and The American Statistician. His work bridges statistical theory and computation, addressing challenges in high-dimensional data analysis. Collaborations involve neuroscientific applications, combining algorithmic innovation with real-world problem-solving.

• Statistics
• Mathematical Statistics
• Functional Analysis
• +۱ مورد دیگر

Tomas Masak is an Assistant Professor at the Department of Statistics and Mathematics, Vienna University of Economics and Business (WU). He holds a PhD in Mathematics from EPFL Lausanne (2018–2022) and an MSc in Mathematical Statistics from Charles University. His academic roles include Bernoulli Instructor at EPFL (2022–2024) and Research and Teaching Assistant at Technical University of Munich (2017–2018). Education Mathematics PhD, EPFL Lausanne (2018–2022) Mathematical Statistics MSc, Charles University (completed 2017) His research focuses on functional data analysis, covariance estimation, and statistical computing. Recent work includes the Functional Graphical Lasso and methods for sparsely observed random surfaces. Publications span journals like the Annals of Statistics , Journal of the American Statistical Association , and Biometrika , emphasizing open-access venues. Keywords across his work include functional analysis, covariance modeling, and high-dimensional statistics. He actively participates in scientific lectures and peer review, serving as a reviewer for the Annals of Statistics and Journal of Machine Learning Research in 2024. His collaborations span Europe and focus on data science applications.

Stéphane Azouمشاهده پروفایل

استاد

• Optical Communications
• Radio-over-Fiber
• Signal Processing
• +۴ مورد دیگر

Stéphane Azou is a Full Professor at the Ecole Nationale d'Ingénieurs de Brest (ENIB), part of the University of Brest, France. He holds a Master's and PhD in Electronic Engineering from the University of Brest (1993 and 1997), and an HDR (Habilitation à Diriger des Recherches) from the same institution in 2007. His career includes roles as an Associate Professor at the University of Brest (2000–2012) and leading the Signal Processing for Communications group at Lab-STICC from 2007–2012. Currently, he heads the Microwave Photonics & Photonics Architectures and Systems (ASMP) team within Lab-STICC. His research focuses on optical communications, fiber-wireless systems, signal processing, and hardware impairments compensation. He has authored over 60 articles and serves as an Associate Editor for the International Journal of Electronics and Communications and is a Senior Member of the IEEE. Education: PhD in Electronic Engineering, University of Brest (1997) Master of Science in Electronic Engineering, University of Brest (1993) HDR (Accreditation to Supervise Research), University of Brest (2007) Research Interests: His work spans optical fiber communication systems, radio-over-fiber technologies, advanced modulation formats, and signal processing techniques for improving system performance. Key areas include physical layer monitoring, compensation of hardware impairments (e.g., IQ imbalance, laser phase noise), and experimental validation of novel architectures like C-RAN and 5G fronthaul systems using semiconductor optical amplifiers (SOAs). Professional Contributions: Azou has led teams in Lab-STICC, a CNRS laboratory, and contributed to projects on parametric networks for joint channel estimation and symbol detection. His recent work addresses challenges in UWB radar-based detection, nonlinear compensation techniques, and stochastic modeling of optical components like SOAs. Awards and Roles: Senior Member, IEEE (2017–present) Associate Editor, International Journal of Electronics and Communications

Alejandro Luis Callaraمشاهده پروفایل

پژوهشگر

• Biomedical Signal Processing
• Neuroimaging
• Brain Connectivity
• +۶ مورد دیگر

Alejandro Luis Callara serves as a postdoctoral researcher at the University of Pisa's Research Center "E. Piaggio," specializing in biomedical signal and image processing methodologies for neuroscience applications. His work bridges engineering and cognitive science through advanced computational approaches to physiological data analysis. His academic foundation includes: Bachelor’s Degree in Biomedical Engineering (2012), University of Pisa Master’s Degree in Biomedical Engineering (2015), University of Pisa PhD in Information Engineering (2019), University of Pisa Callara's research centers on developing analytical pipelines for EEG and fMRI data to map brain connectivity networks, with particular expertise in autonomic nervous system interactions during emotional processing. He actively contributes to the Brain matters team's development of neuronal segmentation tools for confocal microscopy data, extending his methodological focus to multi-scale tissue imaging. His technical proficiency spans directed coherence analysis, dynamic causal modeling, and thermal response quantification. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research trajectories: (1) autonomic physiology investigations examining parasympathetic-sympathetic coupling during emotional tasks using EDA and HRV metrics; (2) innovative neuroimaging techniques for brainstem fMRI analysis and sparse connectivity mapping; and (3) multimodal integration studies exploring olfactory-visual-auditory interactions in affective disorders through VR/AR paradigms. Methodologically, his work consistently employs advanced signal processing frameworks like PCA-based partial correlation and ThermICA for multivariate analysis. Within the Research Center "E. Piaggio," Callara collaborates across bioengineering and robotics initiatives, particularly through the Brain matters team where he applies computational neuroscience to neuronal morphology analysis. His current projects focus on refining contactless stress classification systems using thermal imaging and developing immersive VR scenarios for anxiety disorder research, demonstrating strong translational potential for clinical applications.

• Computational Imaging
• Compressed Sensing
• Signal Processing
• +۵ مورد دیگر

Dr. Karen Eguiazarian is a Professor of Signal Processing at the Department of Computing Sciences , Tampere University . He leads the Computational Imaging research group and has served as head of the Signal Processing Research Community (SPRC) at Tampere University of Technology (2016-2018). Education: M.Sc. in Mathematics, Yerevan State University, Armenia (1981) Ph.D. in Physics and Mathematics, Moscow State University, Russia (1986) Doctor of Technology in Signal Processing, Tampere University of Technology, Finland (1994) His research focuses on Computational Imaging , Compressed Sensing , and Efficient Signal Processing Algorithms , with significant contributions to Image/Video Restoration and Compression . Recent work includes AI-driven phase imaging, hyperspectral reconstruction, and noise-robust algorithms for remote sensing and biomedical applications. Scientific Awards: Service Award from the Society for Imaging Science and Technology (IS&T) (2014) Honorary Doctoral Degree from Don State-Technical University, Russia (2015) Dr. Eguiazarian has supervised 25 doctoral theses and published over 650 papers. He serves as Editor-in-Chief of the Journal of Electronic Imaging and associate editor of the IEEE Transactions on Image Processing , while co-founding Noiseless Imaging Oy , a Tampere University spin-off.

Justin Petrovichمشاهده پروفایل

استادیار

• Applied Statistics
• Functional and Longitudinal Data Analysis
• Multiple Imputation
• +۳ مورد دیگر

Dr. Justin Petrovich is an Assistant Professor of Statistics and Business Analytics at Saint Vincent College, where he also serves as Chair of the Marketing, Analytics, and Global Commerce Department since 2022. He holds a B.S. in Finance and Mathematics from Saint Vincent College (2014) and a Ph.D. in Statistics from Pennsylvania State University (2018). His research focuses on functional and longitudinal data analysis, multiple imputation, and statistics education, with applications in mental health, pediatrics, marketing, and economics. Education: B.S., Saint Vincent College (2014); Ph.D., Pennsylvania State University (2018) Teaching: Courses include Business Statistics, Econometrics, and Data Science. His research interests emphasize applied statistical methodologies, particularly in handling complex data structures such as longitudinal and functional datasets. He has collaborated across disciplines, including health sciences and economics, and has contributed to studies on suicide risk prediction in college students and labor market analysis. Dr. Petrovich has published in high-impact journals like the Journal of the Royal Statistical Society and Journal of Counseling Psychology. His work bridges theoretical statistical advancements with practical applications, aiming to make complex analyses accessible to applied scientists. Outside academia, he is an active member of Saint Vincent Basilica Parish and maintains ties to his alma mater through teaching and community involvement. He resides in Latrobe with his family and remains an avid runner.

• Signal and image processing
• Inverse problems
• Optimization
• +۶ مورد دیگر

Argheesh Bhanot is a Researcher at Savoie Mont-Blanc University, affiliated with Polytech Annecy-Chambéry and the LISTIC laboratory. His email is bhanota@univ-smb.fr , and he is located in office A127 at LISTIC’s campus in Annecy-le-Vieux, France. Research Interests : Bhanot’s work focuses on advanced signal and image processing techniques, including inverse problems, optimization, Markov Chain Monte Carlo (MCMC) methods, artificial intelligence (AI), and graph analysis. His research applications span environmental monitoring (e.g., avalanche dynamics) and medical imaging (e.g., fMRI connectivity analysis). Key Research Contributions : His recent work explores functional connectivity in neuroimaging through graph theory and independent component analysis (ICA), as well as dictionary learning algorithms for signal separation in fMRI and other domains. He also develops energy-efficient remote sensing systems for environmental studies. Labs & Teams : Bhanot is a core member of the LISTIC laboratory, which specializes in interdisciplinary research combining signal processing, AI, and applied mathematics.

Remco W. van der Hofstadمشاهده پروفایل

استاد

• Probability theory
• Statistics
• Complex networks
• +۷ مورد دیگر

Remco W. van der Hofstad is a Full Professor and Chair of Probability at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). He also serves as Scientific Director of EURANDOM (Dutch center for statistics, probability theory, and stochastic operations research) and co-directs the 'Random Spatial Structures' program at EURANDOM. His research focuses on probability theory, statistics, and their applications to complex networks including social media systems, with specific interests in percolation, random graphs, self-interacting random processes, and interdisciplinary applications in electrical engineering, computer science, and theoretical physics. Key Affiliations: Eindhoven University of Technology (TU/e) EURANDOM Gravitation Program NETWORKS Platform Wiskunde Nederland (spokesman) Research Highlights: He investigates probabilistic models for complex networks, combining theoretical mathematics with real-world applications. His work connects statistical physics concepts (e.g., percolation theory) with network science challenges in areas like glioma patient neuroimaging and pandemic transmission modeling. Scientific Recognition: Recipient of prestigious awards including the Prix Henri Poincaré (2003), Rollo Davidson Prize (2007), and NWO VIDI/VICI grants. His research contributes to UN Sustainable Development Goals related to scientific innovation. Collaborative Leadership: Active in interdisciplinary projects like the Gravitation Program NETWORKS and the Dutch Mathematics Platform. He also contributes to public dissemination through roles as Editor-in-Chief of the Network Pages community website.

Jonas Striaukasمشاهده پروفایل

استادیار

• Econometrics
• Statistical/Machine Learning Methods
• Mixed Frequency Data Econometrics
• +۳ مورد دیگر

Jonas Striaukas is an Assistant Professor of Statistics and Finance at the Copenhagen Business School (CBS), Department of Finance, and a Marie Skłodowska-Curie Action fellow. His research focuses on econometrics, statistical/machine learning methods, and mixed frequency data modeling. He holds a PhD from Université Catholique de Louvain (2022), supervised by Professors Andrii Babii and Eric Ghysels. Prior to CBS, he was a research fellow at Fonds de la Recherche Scientifique—FNRS in Belgium. Research Interests: High-dimensional econometric modeling, nowcasting techniques, factor-augmented regressions, and applications of machine learning to financial and macroeconomic data. His work emphasizes robust statistical methods for time series analysis and sparse/dense model comparisons. Key Projects: Developed the 'midasml' and 'FAS' R packages for econometric analysis. Conducted a 2022 project at the National Bank of Belgium on machine learning-based nowcasting models. Affiliations: Department of Finance, Center for Statistics, CBS. Member of the Marie Skłodowska-Curie fellowship program.

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

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

Ali Shojaieمشاهده پروفایل

استاد

• Biostatistics
• Statistical Learning
• Machine Learning
• +۴ مورد دیگر

Ali Shojaie is a Professor of Biostatistics & Statistics at the University of Washington , currently serving as Interim Chair of Biostatistics. His research bridges statistical learning, network analysis, and high-dimensional data modeling with applications in biological and health sciences. Research Focus: Shojaie develops advanced methodologies for causal inference, spatial statistics, and semi-supervised learning. His recent work includes network-based gene set analysis, Granger causality estimation, and regularization techniques for complex data structures. Scientific Contributions: Received the 2022 Leo Breiman Award for innovative statistical learning research Elected as Fellow of the Institute for Mathematical Statistics (IMS) and American Statistical Association (ASA) Secured major NIH grants for projects on gene-phenotype associations and explainable AI in neuroscience Advising & Leadership: His students have won multiple awards at ASA and AISTAT conferences. He co-developed the netgsa and spacejam R packages for network-based analysis and spatial modeling. Current Projects: Shojaie leads NIH-funded research on prefrontal brain stimulation and gene knockout studies, combining statistical theory with interdisciplinary applications in biology and bioengineering.