جستجوی استاد راهنمای دانشگاهی

Ali Bazziمشاهده پروفایل

استاد

• Fault-tolerant power electronics
• Electric motor drives
• Distributed generation (solar photovoltaics, wind)
• +۳ مورد دیگر

Dr. Ali Bazzi is the Madonna Professor in Power Engineering at the University of Connecticut (UConn), within the Department of Electrical and Computer Engineering. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (UIUC) and B.E./M.E. degrees from the American University of Beirut (AUB). He co-founded Valcon Labs Inc., a deep-tech startup focused on aircraft electrification, and previously worked at Delphi Corp. and Bitrode Corporation. His research focuses on fault-tolerant power electronics, electric motor drives, renewable energy integration (solar/wind), and WBG semiconductor applications. Education: Ph.D., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign M.E., Electrical and Computer Engineering, American University of Beirut B.E., Electrical and Computer Engineering, American University of Beirut Research Interests: Dr. Bazzi's work emphasizes reliability, efficiency, and fault mitigation in power systems, including extraterrestrial microgrids, electric vehicle propulsion, and high-voltage insulation technologies. His contributions span real-time control algorithms, fault diagnosis, and advanced materials for medium-voltage drives. Publications & Patents: With over 120 peer-reviewed publications and 10 U.S. patents, his work includes innovations in active EMI filters, insulation breakdown prediction, and cyber-physical testbeds for space habitats. Recent trends highlight space microgrid reliability, WBG semiconductor applications, and fault-tolerant drive systems. Awards & Grants: Awarded the Madonna Professorship, he has led grants from UTC and industry partners. His patents include methods for fault diagnosis, data-driven anomaly detection, and insulation material optimization. Labs & Teams: Director of the Power Electronics and Drives Advanced Research Laboratory at UConn, he collaborates with industry partners like Valcon Labs on aircraft electrification and propulsion systems.

• Computer Science
• Telecommunications Engineering
• Mobile and Wireless Networks
• +۳ مورد دیگر

Dr Shahedur Rahman is a Senior Lecturer in Computer Science at Middlesex University , with extensive contributions to telecommunications engineering, image processing, and bioinformatics. His research focuses on wireless network optimization, perceptual distortion metrics for video coding, and molecular biology database integration. Research Interests : Interference management in LTE/5G networks Perceptual quality assessment in multimedia systems Computer vision for accessibility and mobility aids Integration of biological and medical databases Wavelet-based image compression Genomic data analysis Selected Scientific Publications highlight trends in: Dynamic channel allocation techniques for LTE networks SSIM/SATD hybrid distortion metrics Gene mutation data modeling Image processing for visual impairment assistance Database interoperability in medical systems Optimization algorithms for hardware systems

• Image and Signal Processing
• Computer Vision
• Artificial Intelligence
• +۶ مورد دیگر

Dr. Deepayan Bhowmik is a Senior Lecturer and Director of Research at Newcastle University's School of Computing. He holds a PhD in Electronic and Electrical Engineering from the University of Sheffield. His research focuses on image/signal processing, AI, neuromorphic vision systems, and their applications in media security, remote sensing, and space research. He actively contributes to UN Sustainable Development Goals related to clean water, environmental sustainability, and responsible consumption. Grants & Collaborations: PI of £2M EPSRC-funded North East Space Communications Accelerator (2025-2029) Co-I in Airbus-funded Smart Earth Observation Satellite Constellation project (2024-2028) Lead on JPEG Trust international standard for media authenticity Research Interests: Combines theoretical advancements in signal processing with practical applications in media forensics, environmental monitoring, and heterogeneous computing. Recent work addresses AI-generated media manipulation detection and satellite imagery analysis for ecological challenges like water hyacinth infestation. Publications: Over 28 peer-reviewed articles spanning watermarking techniques, FPGA optimization, and remote sensing applications. Recent trends show increasing focus on AI-driven media security and space-based earth observation systems.

Hamidreza Ahady Dolatsaraمشاهده پروفایل

استادیار

• Health Care Analytics
• Finance
• Transportation
• +۷ مورد دیگر

Hamidreza Ahady Dolatsara is an Assistant Professor at Clark University's School of Business, specializing in data science applications across health care analytics, finance, and transportation. His research focuses on machine learning-based decision-support systems, blockchain adoption, and optimization techniques. Ph.D. in Industrial & Systems Engineering, Auburn University (2019) M.S. in Information Systems Management, Auburn University (2019) M.Eng. in Industrial & Systems Engineering, Auburn University (2015) M.S. in Civil-Transportation Engineering, West Michigan University (2014) B.S. in Railroad Operation and Management Engineering, IUST (2008) His work bridges cutting-edge technologies with societal impact, including applications of generative AI for fraud detection, blockchain in financial health analysis, and eco-efficiency evaluation using hybrid optimization models. Recent publications (2017-2026) demonstrate expertise in cryptocurrency trading algorithms, heart transplant survival modeling, and transportation safety. Scientific recognition includes Clark University Faculty Development Fund grants, Academic Innovation Fund awards, and the Business Edge Technologies Lab. His research spans healthcare analytics, financial technology, and transportation systems, with a strong focus on interdisciplinary collaboration.

• Signal Processing
• Medical Imaging
• Computer Vision
• +۴ مورد دیگر

Benoît Macq is a distinguished professor at the ICTEAM Institute , Université catholique de Louvain, Belgium. With over 350 publications spanning 1990–2025, his work bridges signal processing , medical imaging , machine learning , and data security . Key collaborators: Christophe De Vleeschouwer Simon K. Warfield Jean-François Delaigle Mathieu De Craene Research interests include: Adaptive algorithms for real-time model deployment and domain adaptation 3D reconstruction and medical image segmentation for radiation therapy Security in blockchain-based distributed learning systems Transformers for EEG classification and proton radiography Recent publications focus on foundation models in cytology and remote sensing, holonic multi-agent systems for VR, and secure codestreams for UAV object detection. His work emphasizes scalable architectures and privacy-preserving techniques in dynamic environments. Advising: Dani Manjah Antoine Aspeel Maxime Zanella

Yang Guoمشاهده پروفایل

دانشیار

• Advanced Manufacturing
• Materials Processing
• Tribology
• +۳ مورد دیگر

Yang Guo serves as an Associate Professor in the Department of Mechanical Engineering at Michigan State University's College of Engineering. His research laboratory is based in Engineering Building Room 2440, with contact email yguo@msu.edu. Dr. Guo's academic credentials include: Ph.D. in Industrial Engineering from Purdue University (2012) M.S. in Production Engineering from RWTH Aachen University, Germany (2006) M.S. in Industrial Engineering from Tsinghua University, Beijing (2006) B.E. in Mechanical Engineering from Tsinghua University, Beijing (2003) His research program focuses on Advanced Manufacturing , Materials Processing , and Tribology , with specialized expertise in large-strain plastic deformation phenomena. Key investigations include plastic instability mechanisms, ductile fracture behavior, plastic flow control strategies, and deformation-microstructure-property relationships. These fundamental studies directly inform applications in machining processes, surface engineering techniques, frictional welding, micro forming operations, and nanostructured materials processing. Dr. Guo's work bridges theoretical mechanics with industrial manufacturing challenges to develop innovative process technologies that enhance efficiency and product quality. From 2020-2024, his publication record demonstrates consistent contributions to manufacturing science, with eight articles in premier journals including Tribology International and Journal of Manufacturing Science and Engineering . His research trajectory shows increasing integration of data-driven methods (topological data analysis, wavelet transforms) with traditional manufacturing mechanics, while maintaining core focus on wear reduction, surface characterization, and deformation control across turning, drilling, and additive manufacturing processes. Scientific Recognition: No specific awards or honors were documented in the source material Professional Activities: While the source text does not specify graduate student mentorship or active grant funding, Dr. Guo's publication record indicates ongoing research collaborations with colleagues across mechanical engineering and materials science disciplines. His work with industry-relevant materials (nodular graphite iron, stainless steel 17-4) suggests strong connections to manufacturing sector challenges.

• Bioengineering
• Biotechnology
• Renewable Energy
• +۳ مورد دیگر

Dr. Patricia A Relue is a Professor and Associate Dean for Research at the College of Engineering , Bioengineering department, University of Toledo. Her research spans bioengineering , biotechnology , and biomedical optics , with a focus on: Biomass conversion for renewable fuels Simultaneous isomerization and fermentation processes Biomedical imaging techniques using fluorescence spectroscopy Computational modeling of gene expression and light-tissue interactions Key publication trends include: Biofuel production from biomass sugars Optical diagnostics for skin lesions Wavelet transforms for cancer classification Techno-economic analysis of biorefinery processes Patents and technologies developed: High-yield furan production at mild conditions Reactive extraction methods for glycerol recovery

David Atienza Alonsoمشاهده پروفایل

استاد

• Embedded Systems Design
• Internet of Things (IoT)
• Edge Computing
• +۱۳ مورد دیگر

David Atienza Alonso is a Full Professor in the Department of Electrical and Electronics Engineering at the School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He leads the Embedded Systems Laboratory (ESL) and serves as Associate Vice President for Centers and Platforms, overseeing major research infrastructure. His work spans embedded systems, IoT, edge AI, and sustainable computing. His research interests focus on system-level design for high-performance and low-power computing systems. Key areas include thermal-aware design of multi-processor systems-on-chip (MPSoC), energy-efficient embedded machine learning, wireless body sensor networks, and electronic design automation (EDA). His lab develops novel methodologies for hardware-software co-design, memory optimization, and edge computing architectures. The analysis of his recent publications reveals a strong trajectory in intelligent, energy-efficient computing systems. His work integrates machine learning with traditional EDA techniques for data center optimization, applies ultra-low power heterogeneous architectures to healthcare wearables, and advances thermal modeling for 3D ICs. Themes of sustainability, real-time processing, and edge intelligence are consistent across his research. Dr. Atienza has received numerous accolades, including: ERC Consolidator Grant (2016) DAC Under-40 Innovators Award (2018) IEEE TCCPS Mid-Career Award (2018) ACM SIGDA Outstanding New Faculty Award (2012) ICCAD 10-Year Most Influential Paper Award (2020) Best paper awards at top-tier conferences He has advised over 40 PhD students, many of whom have gone on to successful academic and industry careers. His research has been supported by major grants, including the ERC grant, and he has co-authored over 450 publications and 14 licensed patents. He plays a significant leadership role in the academic community, having served as Editor-in-Chief of IEEE Transactions on CAD, President of IEEE CEDA (2018–2019), and currently as Chair of the European Design Automation Association (EDAA). He is a Fellow of both IEEE and ACM. His laboratory, the Embedded Systems Laboratory (ESL), is a leading center for research in embedded and cyber-physical systems, fostering interdisciplinary collaboration and innovation in sustainable computing technologies.

Tatiana A. Bubbaمشاهده پروفایل

استادیار

• Computational Inverse Problems
• Medical Imaging
• Spent Nuclear Fuel Imaging
• +۲ مورد دیگر

Tatiana A. Bubba is an Assistant Professor in Numerical Analysis at the Department of Mathematics and Computer Science, University of Ferrara (Italy), and a Docent in Inverse Problems at the Department of Mathematics and Statistics, University of Helsinki (Finland). Her research bridges computational inverse problems with practical applications in medical imaging and nuclear fuel diagnostics.

• Financial Mathematics
• Computational Finance
• Mathematical Modeling
• +۷ مورد دیگر

Prof. Dr. ir. C.W. (Kees) Oosterlee is a Professor of Mathematical Modeling at the Mathematical Institute , Faculty of Science, Utrecht University . He is also a part-time professor at TU Delft since 2007 and a scientist at Centrum Wiskunde & Informatica (CWI) in Amsterdam, where he was part of the management team. Education : Ingenieur (ir) from TU Delft (1989), Doctor (dr) from TU Delft (1993), Habilitation from Cologne (1999). Career : PhD in 1993, worked at GMD (now Fraunhofer SCAI) in Germany until 2002, then TU Delft and CWI. Research Interests include computational finance, financial mathematics, and machine learning in finance. He develops advanced numerical methods like the COS method , SWIFT (Shannon Wavelet Inverse Fourier Transform), SGBM (Stochastic Grid Bundling Method), SCMC (Stochastic Collocation Monte Carlo), and the Seven-League scheme (7L). His work bridges Applied Data Science and Foundations of Complex Systems . Scientific Awards and Ancillary Activities: Member of the Mathematics Board Lorentz Center (Leiden University, unpaid). Advisory Board Member for the SeMA Journal (Springer Verlag, unpaid) and World Scientific’s Annual Reviews in Modern Quantitative Finance (unpaid). Member of the NIAS-Lorentz Fellowship Board . Teaching and Outreach include guest lectures at Oxford University , Hitotsubashi University , and University of A Coruna . He chairs the International Conference on Computational Finance (ICCF24) in 2024.

Paul Pearsonمشاهده پروفایل

دانشیار

• Machine Learning
• Topological Data Analysis
• Algebraic Topology

Dr. Paul Pearson is an Associate Professor in the Department of Mathematics and Statistics at Hope College, where he has taught since 2012. His educational background includes a Ph.D. in Mathematics (Algebraic Topology) from Northwestern University (2006) and a B.A. in Mathematics from St. Olaf College (2001). Research Focus: His interdisciplinary research integrates machine learning and topological data analysis to address challenges in mathematical biology, computational chemistry, and high-dimensional data science. Specific applications include analyzing biological time-series data, protein energy predictions, and pattern recognition. Publication Trends: Recent works demonstrate expertise in topological methods for machine learning (neural network visualization, election analysis), algebraic topology (K-theory, Hopf rings), and educational applications (chemistry performance prediction). His R package (TDAmapper) enables topological data analysis through Morse theory implementations. Awards & Grants: Key funding includes a Howard Hughes Medical Institute grant for first-year research curricula and NSF-supported MAA grants for developing WeBWorK educational tools. Student researchers under his mentorship earned the Janet L. Andersen Award (2016) and MAA MathFest presentation honors (2015). Academic Leadership: He directs undergraduate research programs and co-developed course-based projects analyzing Great Lakes ecosystems. As a WeBWorK contributor, he authored 1,500+ problems and pioneered interactive macros used globally. Additional roles include Executive Committee representation for the Midstates Consortium (Mathematics/Science), webmaster for MAA Michigan, and Phi Beta Kappa chapter support.

Pejman Bahramianمشاهده پروفایل

استادیار مدعو

• Energy Economics
• Time Series Analysis
• Applied Welfare Economics
• +۱ مورد دیگر

Dr. Pejman Bahramian is an Adjunct Assistant Professor at the Department of Economics, Queen's University. His research integrates economic theory with advanced analytical methodologies, focusing on energy economics, time series analysis, applied welfare economics, and machine learning applications in economics. His recent work explores the bidirectional relationships between clean energy consumption and economic growth in China, wind power investment dynamics in Ontario, and the macroeconomic implications of financial stress indices across global economies. Methodologically, he employs wavelet transforms, Granger causality, quantile-based econometrics, and feature selection algorithms to uncover nuanced economic patterns. Dr. Bahramian's publications span nonlinear economic modeling, international trade dynamics, and environmental policy evaluation. His empirical studies cover diverse regions including ASEAN-5 countries, Turkey, the UK, Germany, Nigeria, and the EU-12, demonstrating both technical rigor and cross-cultural economic insights. Email: bahramian@econ.queensu.ca

• Electrical Impedance Tomography
• Respiratory Monitoring
• Mechanical Ventilation
• +۳ مورد دیگر

Peter Somhorst is a researcher at Erasmus MC's Intensive Care department, specializing in advanced respiratory monitoring technologies. His work focuses on Electrical Impedance Tomography (EIT) and esophageal pressure measurement for optimizing mechanical ventilation in critical care settings. Key collaborations include international experts in critical care and biomedical engineering Active in developing standardized EIT protocols and clinical implementation frameworks Research interests center on: Biomedical signal processing for critical care applications Development of solid-state sensors for physiological monitoring Filtering methods to improve EIT data quality Addressing cardiovascular contamination in respiratory measurements Translating EIT into clinical practice through validation studies International perspectives on advanced respiratory monitoring adoption His recent publications highlight methodological advancements in EIT data acquisition and processing, with applications in ARDS , COVID-19 , and general intensive care management. Collaborative projects include dataset contributions to open-access repositories like Zenodo.

Miao-Jung Yvonne Ouمشاهده پروفایل

استاد

• Inverse Scattering Theory
• Poroelastic Materials
• Partial Differential Equations
• +۱۲ مورد دیگر

Miao-Jung Yvonne Ou is a Professor in the Department of Mathematical Sciences at the University of Delaware, with affiliated faculty status in the Department of Biomedical Engineering. She is based in 508 Ewing Hall, Newark, DE, and actively contributes to research and graduate education in applied mathematics. Research Interests: Her work centers on inverse and direct scattering in composite and poroelastic materials, partial and integral differential equations, homogenization theory, and the application of Herglotz-Nevanlinna functions in systems with memory. She applies these to material sciences, particularly in modeling wave propagation and effective properties in complex media such as bones and porous composites. Her recent publications (2023–2015) show a strong trend in numerical methods (e.g., discontinuous Galerkin, RKHS regularization), wave modeling in poroelastic and dispersive media, and inverse problems in biomedical and geophysical contexts. She frequently employs advanced mathematical tools like fractional calculus, spectral analysis, and integral representations to address real-world physical systems. Scientific Engagement: She is currently part of the EU RISE EXPOWER project (2021–2025), promoting international research exchange in mathematics. She teaches graduate courses such as Math 508: Introduction to Complex Variables and Applications. Advising and Grants: She supervises graduate student research projects in areas like efficient quadrature for fractional derivatives, RKHS frameworks, spectral analysis of poroelastic waves, and NMR imaging. Funding includes the EU RISE grant, supporting collaborative research and student involvement. Laboratories and Teams: She collaborates with interdisciplinary teams in applied mathematics, biomedical engineering, and computational science. Her work with the MADNESS project and poroelastic wave modeling indicates active participation in high-performance scientific computing and modeling environments.

Sven Johanssonمشاهده پروفایل

مدرس ارشد

• Computer Science
• Signal Processing
• Internet of Things (IoT)
• +۳ مورد دیگر

Sven Johansson is a Senior Lecturer at the Department of Technology and Aesthetics , Blekinge Institute of Technology, Faculty of Engineering. His work spans interdisciplinary domains, with a focus on signal processing, IoT applications, and educational technology. Current affiliation: Blekinge Institute of Technology Academic role: Senior Lecturer Research Interests Johansson's research primarily intersects Computer Science , Signal Processing , and Health Informatics . Key areas include: Wavelet Transform and Neural Network integration for noise cancellation in medical diagnostics IoT-based solutions for agricultural and industrial applications Innovations in remote laboratory education and flipped classroom methodologies Industry 4.0 implementations for safety and health preservation systems His publications demonstrate a consistent focus on applying computational techniques to real-world challenges in healthcare and automation. Scientific Contributions Johansson has contributed to advancements in: Adaptive denoising algorithms for phonocardiography Smart sensor networks in agricultural supply chains Remote diagnostics education frameworks Control algorithm validation in distributed systems