جستجوی استاد راهنمای دانشگاهی

Stephan Raveمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Scientific Computing
• Machine Learning
• +۴ مورد دیگر

Dr. Stephan Rave is a Researcher in the Institute for Analysis and Numerics at the University of Münster. He is affiliated with the Applied Mathematics Münster cluster and serves as an Investigator in Mathematics Münster. His work focuses on numerical analysis, scientific computing, and machine learning, with a strong emphasis on model reduction techniques for complex systems. Education : PhD in Mathematics (2012), University of Münster, thesis on finitely summable K-homology. Master's and Bachelor's degrees in Mathematics from the University of Münster. Research Interests : Dr. Rave specializes in model order reduction (MOR) methods, including reduced basis techniques, localized orthogonal decomposition (LOD), and nonlinear approximation strategies. His work addresses challenges in multiscale modeling, domain decomposition, and parametrized partial differential equations. He also develops open-source software tools like pyMOR for MOR and contributes to initiatives like the MaRDI (Mathematical Research Data Initiative) to enhance interoperability in scientific computing. Projects : Key initiatives include the MaRDI project (2021–2026), EXC 2044 Cluster of Excellence (Geometry-based modeling), and MULTIBAT (lithium-ion battery simulation). His research bridges theoretical developments with practical applications in battery modeling, electrochemistry, and computational fluid dynamics. Grants & Awards : Funded by DFG, the German Federal Ministry of Research, and internal university grants, his work addresses strategic areas like sustainable research software and energy storage systems. He leads projects on distributed model reduction and communication-avoiding algorithms. Teaching : Dr. Rave teaches advanced numerical methods courses, including Model Order Reduction, Numerical Methods for PDEs, and Python-based computational labs. He co-organizes seminars and workshops on MOR and scientific software engineering.

• Cybersecurity
• Networking
• Wireless Security
• +۶ مورد دیگر

Thaier Hayajneh , Ph.D., is a University Professor at Fordham University's Department of Computer and Information Sciences. He serves as Founder Director of the Fordham Center for Cybersecurity and Program Director for the MS in Cybersecurity program. Previously, he held academic roles at New York Institute of Technology and Hashemite University in Jordan. Specializes in cybersecurity, networking, and applied cryptography Focus areas include blockchain, IoT security, and wireless network vulnerabilities Active in NSF cybersecurity review panels and as a CAE reviewer for NSA Recipient of peer-review awards from Publons His research spans secure protocols for medical IoT systems, lightweight cryptographic algorithms, and smart contract implementations in healthcare. Publications in IEEE IoT Journal, IEEE Systems Journal, and Journals like Future Generation Computer Systems demonstrate his expertise in securing emerging technologies. Dr. Hayajneh has received research funding from federal agencies including NSA, Department of Defense, and NSF. He has published over 80 papers and maintains active editorial roles in prestigious journals. Scientific Awards: Peer-review awards from Publons

Markus Listمشاهده پروفایل

استاد

• Systems Biology
• Data Science
• Bioinformatics
• +۳ مورد دیگر

Prof. Dr. Markus List is a Professor of Data Science in Systems Biology at the Technical University of Munich (TUM), affiliated with the TUM School of Life Sciences. His research focuses on integrating data science with systems biology to understand gene regulatory mechanisms across genomic levels. He leads the Big Data in Biomedicine group and has held roles including Group Leader at the Chair of Experimental Bioinformatics (2018–2023) and PostDoc at the Max Planck Institute for Informatics (2015–2018). Educational Background: BSc and MSc in Bioinformatics from Eberhard Karls University of Tübingen (2005–2011) PhD in Molecular Oncology from University of Southern Denmark (2011–2015) Research Interests: Prof. List’s work bridges biology, medicine, and informatics using machine learning and data integration. Key areas include: Systems Biology of gene regulation in cancer Epigenetic data analysis Computational methods for biomedical big data Awards & Recognition: Research Prize of the Hamburg Cancer Society (2023) TUM Teaching Award (2023) TUM School of Life Sciences Supervisory Award (2022) Labs & Teams: Leads the DaisyBio group, focusing on experimental and computational bioinformatics. Collaborates with interdisciplinary teams in bioinformatics and systems medicine.

Yanjun Zhangمشاهده پروفایل

پژوهشگر ارشد

• Cybersecurity
• Privacy-Preserving Technologies
• Federated Learning
• +۳ مورد دیگر

Dr. Yanjun Zhang is an Honorary Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on privacy-preserving technologies, federated learning, cybersecurity in IoT systems, and machine learning security. He holds a PhD in Privacy-Preserving Sharing for Genome-Wide Analysis from The University of Queensland (2021). Education: PhD in Information Technology, School of Information Technology and Electrical Engineering, The University of Queensland (2021) Research Interests: Designing secure collaborative machine learning frameworks Defending against adversarial attacks in cyber-physical systems Privacy preservation in distributed genomic and medical data analysis Compliance and ethics in virtual personal assistant applications Key Contributions: Developed privacy-preserving federated learning frameworks (AgrAmplifier, PrivColl) Conducted foundational studies on evasion attacks in IoT systems Created datasets for analyzing malicious browser extensions and Alexa skills Labs/Teams: Active contributor to UQ Cyber initiatives, including the 2021-2022 Seed Funding project on federated deep learning for medical imaging.

• Algorithmic Fairness
• Responsible AI
• Deep Learning
• +۵ مورد دیگر

Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science , an Adjunct Professor at Université de Montréal , and a Visiting Faculty Researcher at Google Research . She holds the Canada CIFAR AI Chair and is a core academic member of Mila – Quebec AI Institute . Her research focuses on algorithmic fairness , responsible AI , and optimization . She founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms) to address bias and discrimination in AI systems. Key publications explore fairness in kidney exchange programs, generative model geometry, multilingual LLM de-biasing, and prototype-based recommender systems. Her work bridges causal inference, adversarial robustness, and ethical AI. Google Award for Inclusion Research (2023) Women in AI Awards North America Finalist (2023) Facebook Privacy Enhancing Technologies Award (2021) IVADO Postdoctoral Fellowship (2018–2021) She has supervised over 15 PhD and Master's students, including Prakhar Ganesh (McGill) and William St-Arnaud (Université de Montréal). Her teaching includes Responsible AI and Machine Learning courses at McGill and HEC Montréal.

Melanie Campbellمشاهده پروفایل

استاد

• Biomedical Optics
• Retinal Imaging
• Alzheimer's Disease Diagnosis
• +۴ مورد دیگر

Melanie Campbell is a Professor at the University of Waterloo, cross-appointed to the School of Optometry and Department of Systems Design Engineering. Her research focuses on the optical properties of the eye, developing imaging systems for diagnosing Alzheimer's disease and diabetic retinopathy through polarization techniques and adaptive optics. PhD in Physics from Australian National University (1982) MSc in Physics from University of Waterloo (1977) BSc in Chemical Physics from University of Toronto (1975) Research interests include: Retinal amyloid detection for Alzheimer's diagnosis Adaptive optics for high-resolution imaging Polarimetry in ocular pathology Presbyopia and ophthalmic corrections Her publications (2012-2019) demonstrate interdisciplinary applications of optics in neuroscience and diabetes research, with key contributions to: Retinal polarization imaging Two-photon therapy systems Cone photoreceptor analysis Animal models of neurodegeneration Scientific honors include: 2019 Laird Lecturer 2014 CAP-INO Medal 2004 Rank Prize in Optoelectronics Fellow of Optical Society of America As director of Campbell Labs, she leads research on retinal imaging techniques and their applications in neurological disease diagnosis.

Esfandiar Mohammadiمشاهده پروفایل

دانشیار

• Privacy & security in machine learning
• Differential privacy
• Anonymous communication
• +۱ مورد دیگر

Esfandiar Mohammadi is an Associate Professor at the Institute for IT Security, University of Lübeck, leading the Privacy & Security (PrivSec) group and directing the AnoMed competence cluster. He has held tenured faculty positions since 2019 after postdoctoral research at ETH Zürich (2016-2019) and a PhD at Saarland University (2015). University of Lübeck (2015-present) ETH Zürich (2016-2019) Saarland University (2015) His research focuses on privacy-preserving technologies in machine learning, anonymous communication protocols, and formal verification of security properties. Recent work includes advancements in Mixnet scalability and federated learning with differential privacy guarantees. Key publication trends reveal a strong emphasis on privacy-preserving algorithms for machine learning (2024), cryptographic protocols for anonymous communication (2025), and security analysis of decentralized systems (2023-2025). Collaborations span institutions like ETH Zürich, Saarland University, and industry partners EnergieDock/NAECO Blue for the VeDS project. His group includes 11 researchers (5 PhD students) and software engineers working on topics like Differential Privacy Secure Multi-Party Computation Trusted Execution Environments

Daniel Royمشاهده پروفایل

استاد

• Machine Learning
• Statistical Learning Theory
• Foundations of Statistics
• +۱۱ مورد دیگر

Daniel Roy is a Full Professor at the University of Toronto, holding cross-appointments in the Department of Statistical Sciences, Computer Science, Electrical and Computer Engineering, and the Department of Computer and Mathematical Sciences at UTSC. He is also a Canada CIFAR AI Chair and Research Director at the Vector Institute, reflecting his leadership in AI and machine learning research. His educational background includes a PhD, MEng, and BSc in Computer Science from MIT, where his doctoral work earned the MIT EECS Sprowls Award. Prior to joining Toronto, he was a Newton International Fellow at the Royal Society and a Research Fellow at Emmanuel College, University of Cambridge. His research centers on foundational principles in machine learning, statistics, and probabilistic reasoning. Key interests include statistical learning theory, Bayesian nonparametrics, probabilistic programming, and information-theoretic generalization. His work bridges theoretical computer science, mathematical logic, and applied probability. His recent publications, appearing in ICML, NeurIPS, COLT, and JMLR, reflect a strong focus on theoretical advances in generalization, online learning, and stochastic optimization. Themes include minimax rates, conditional mutual information, and the role of data in PAC-Bayes bounds. His group has made foundational contributions to probabilistic programming, including work on Church and the computability of conditional probability. NSERC Discovery Accelerator Supplement Ontario Early Researcher Award Google Faculty Research Award Newton International Fellowship MIT EECS Sprowls Award Daniel Roy advises numerous PhD students and postdoctoral researchers, many of whom have gone on to prestigious positions in academia and industry. His group actively collaborates with leading researchers in machine learning and statistics. He is also an Action Editor for the Journal of Machine Learning Research and Transactions of Machine Learning Research, underscoring his role in shaping the field. He leads a vibrant research group focused on theoretical machine learning and probabilistic modeling, and maintains active collaborations with institutions such as MIT, Cambridge, and the Vector Institute. He is also the founder and maintainer of the probabilistic-programming.org wiki, a key resource in the community.

Craig A. Chinمشاهده پروفایل

دانشیار

• Digital Signal Processing
• Machine Learning
• Biomedical Signal/Image Processing
• +۷ مورد دیگر

Craig A. Chin is an Associate Professor in the Department of Electrical Engineering at Kennesaw State University. He holds a Ph.D. and M.S. in Electrical Engineering from Florida International University (2006, 2001) and a B.S. in Electrical and Computer Engineering from the University of the West Indies (1995). Research Interests: Digital Signal Processing for Biomedical Applications Machine Learning in Biomedical Signal/Image Analysis Wireless Body Area Networks (WBANs) for Healthcare Network Security in Resource-Constrained Environments Biometric Authentication for Wireless Sensor Networks Innovations in Engineering Education (Cooperative/Active Learning, Humanities Integration) Article Trends: His publications (2001–2019) focus on Biomedical Signal Processing (EMG/Eye-Gaze Integration, Stress Detection), WBANs for mHealth , and Engineering Education Pedagogy . Recent work (2013–2019) emphasizes Cooperative Learning Strategies and Wireshark-based Network Security Labs . Academic Contributions: Craig has taught courses such as Biometrics, Data Communications, Biomedical Instrumentation, and Medical Electronics. He actively explores future research on biometric-driven security for Body Area Sensor Networks and active learning strategies in online education.

Naoki Saitoمشاهده پروفایل

استاد

• Applied and Computational Harmonic Analysis
• Graph Signal Processing
• Multiscale Transforms
• +۶ مورد دیگر

Naoki Saito is a Professor in the Department of Mathematics at the University of California, Davis, and the Director of the UC Davis TETRAPODS Institute of Data Science (UCD4IDS). His research lies at the intersection of applied mathematics, signal processing, and data science, with a focus on multiscale analysis and harmonic analysis on graphs and networks. His research interests include Applied and Computational Harmonic Analysis , Graph Signal Processing , Multiscale Transforms , Wavelets , Spectral Graph Theory , and Mathematical Data Representation . He develops theoretical frameworks and practical algorithms for analyzing complex datasets, particularly through the use of Laplacian eigenfunctions and multiscale basis dictionaries. The recent publications reflect a strong trend toward graph-based signal processing , scattering transforms , and topological data analysis . His work emphasizes the construction of natural, adaptive bases for signals on graphs and simplicial complexes, enabling efficient and interpretable data analysis. The integration of harmonic analysis with machine learning techniques is a recurring theme. Although no specific scientific awards are listed in the provided texts, his sustained scholarly output and leadership in the field are evident. Dr. Saito advises a number of students and postdoctoral researchers, including J. Irion, Y. Shao, H. Li, and others. His research has been supported by various grants, though specific funding sources are not detailed in the provided materials. He leads the UCD4IDS, a research institute focused on data science, indicating active involvement in collaborative, interdisciplinary research and academic leadership.

Lin Caiمشاهده پروفایل

استاد

• Wireless Communications
• Networking
• Mobile Computing
• +۷ مورد دیگر

Lin Cai is a Professor in the Department of Electrical & Computer Engineering at the University of Victoria, Canada. She holds prestigious fellowships including NSERC Steacie, IEEE, CAE, and Royal Society of Canada. Her research focuses on wireless communications, networking, and mobile computing, with emphasis on protocols for multimedia and IoT systems. She has led projects in vehicular networks, UAV-assisted systems, and federated learning for edge intelligence. Dr. Cai has advised over 20 students, many of whom have received awards and prominent roles in academia and industry. She has authored numerous high-impact papers, secured grants from NSERC, CFI, and industry partners, and serves in leadership roles at IEEE and educational institutions. Notable contributions include work on congestion control, network security, and autonomous systems. Education: BEng (Nanjing U. of Sci. & Tech.), MASc/PhD (University of Waterloo) Affiliations: IEEE Vehicular Technology Society Board of Governors, IEEE ComSoc Distinguished Lecturer Awards: 2020 IEEE N2Women 'Star in Networking', RSC Fellow 2024, Best Paper Awards (ICC 2008, WCNC 2011) Research Labs: Connected Autonomous Vehicles (CAV) Lab, AI-driven Networking Group Her work integrates networking, AI, and control theory to address challenges in 6G, IoT, and smart transportation. She actively promotes diversity through initiatives like the 'Riko-chan' STEM manga series.

Psyche Louiمشاهده پروفایل

دانشیار

• Music Perception and Cognition
• Auditory Cognitive Neuroscience
• Creativity and Creative Practice
• +۳ مورد دیگر

Psyche Loui is an Associate Professor of Creativity and Creative Practice in the Department of Music at Northeastern University, leading the Music, Imaging, and Neural Dynamics (MIND) Lab. She holds a PhD in Psychology from UC Berkeley and dual bachelor’s degrees in Psychology and Music from Duke University. Her research focuses on the neuroscience of music perception, including how music influences attention, emotion, and neurological conditions like ADHD. Her work has been supported by major grants from the NSF and NIH, and she has received awards such as the Templeton Positive Neuroscience Award and a NSF CAREER Award. Education : PhD in Psychology (Cognition, Brain, and Behavior), University of California, Berkeley Bachelor’s in Psychology and Music, Duke University Research & Grants : Loui’s research explores how neural mechanisms underpin music perception and cognition, with recent studies investigating the efficacy of algorithmically generated music for improving focus in ADHD. Her grants include NIH funding for projects like ‘Gamma-Music-Based Intervention for Alzheimer’s’ ($2.7M) and NSF support for ‘Reward Prediction in Creative Perception.’ Awards : National Science Foundation CAREER Award (2020) Templeton Positive Neuroscience Award (2010) Multiple Grammy Awards (text reference) Labs & Teams : Loui directs the MIND Lab, which uses neuroimaging and electrophysiology to study music-brain interactions. Collaborations include work with Brain.fm on attention-focused music interventions and global studies on cross-cultural music patterns. Teaching : Courses include Music Cognition , Acoustics and Psychoacoustics , and Creative Cognition , bridging theory with practical applications in neuroscience and education.

Ian Abelمشاهده پروفایل

پژوهشگر

• Fusion Energy
• Plasma Physics
• Magnetic Confinement Systems
• +۳ مورد دیگر

Dr. Ian Abel is an Associate Research Scientist at the Institute for Research in Electronics & Applied Physics (IREAP) at the University of Maryland, where he has been since 2018. His expertise spans fusion energy, plasma physics, and computational modeling. Abel holds a B.A. in Mathematics (2006) and M.S. in Applied Mathematics (2007) from the University of Cambridge, followed by a Ph.D. in Theoretical Physics from the University of Oxford (2012). His research focuses on magnetically confined fusion systems, particularly edge dynamics in tokamaks and innovative centrifugal mirror concepts. He has contributed to the development of gyrokinetic simulation tools like the GX code and the MaNTA transport model. Abel’s work also explores machine learning applications in plasma turbulence analysis and centrifugal mirror fusion reactor design for space propulsion. His research leverages advanced numerical methods, including GPU-native algorithms and adjoint-based optimization techniques for plasma equilibria. Key projects include the Centrifugal Mirror Fusion Experiment (CMFX), where he investigates plasma confinement and transport phenomena. His publications emphasize interdisciplinary approaches, integrating computational fluid dynamics, statistical physics, and high-performance computing to address challenges in fusion energy and plasma dynamics. While no specific awards are listed, his contributions to gyrokinetic turbulence modeling and centrifugal confinement systems are central to current fusion research.

Rebecca L. Carrierمشاهده پروفایل

استاد

• Intestinal Tissue Engineering
• Retinal Regenerative Medicine
• Oral Drug Delivery
• +۴ مورد دیگر

Rebecca L. Carrier is a Distinguished Professor in the Department of Chemical Engineering at Northeastern University and affiliated faculty in Bioengineering and Biology. Her research focuses on biological systems-material interactions, spanning intestinal tissue engineering, retinal regenerative medicine, and oral drug delivery. Education: PhD in Chemical Engineering from MIT (2000), BS from Rensselaer Polytechnic Institute (1995) Research Interests: Carrier’s work advances understanding of compound transport in biological systems and develops biomimetic biomaterials. Key areas include lipid impact on oral absorption, mucus barrier mechanics, and retinal/intestinal tissue engineering. The Advanced Drug Delivery Research Lab employs engineering principles to create disease models and therapeutic delivery systems. Publication Trends: Recent articles highlight interdisciplinary approaches to drug transport modeling, mucosal barrier engineering, and biomaterials for organoid culture. Studies integrate chemical engineering, microbiology, and biomedical applications. Scientific Awards: Fellow, Controlled Release Society (2024) Distinguished Faculty Award (2024) AIMBE Fellow (2018) Søren Buus Outstanding Research Award (2017) NSF CAREER Award (2008) Advising & Grants: Carrier advises PhD and capstone design students, including Ronak Ansaripour’s award-winning team. She secured NIH grants for lipid absorption studies and a Spark Fund award for algorithm-driven drug delivery optimization. Collaborations include research with University College Dublin (2024). Labs & Teams: The Advanced Drug Delivery Research Lab (ADDRES) investigates retinal cell transplantation, gut microbiome interactions, and mucosal barrier dynamics. Lab values emphasize diversity, anti-racism, and ethical scientific collaboration.

• Numerical Linear Algebra
• Numerical Nonlinear Algebra
• Parallel Computing
• +۷ مورد دیگر

Prof. Zhong-Zhi Bai is a Professor of Computational Mathematics at the Institute of Computational Mathematics and Scientific/Engineering Computing, part of the Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing. He is based at the State Key Laboratory of Scientific/Engineering Computing, a leading institution in applied mathematics and high-performance computing in China. His research spans a broad range of topics in computational mathematics, with a strong emphasis on numerical linear and nonlinear algebra , parallel computations , numerical optimization , and sparse matrix techniques . He has made significant contributions to the development and analysis of preconditioners and numerical methods for differential-algebraic equations and partial differential equations . His work is foundational in advancing iterative solvers and large-scale scientific simulations. The available publications, including the notable book Milestones in Matrix Computation (2007), reflect a long-standing engagement with matrix computation and numerical algorithms. The research trends indicate a deep focus on theoretical and practical aspects of numerical methods, particularly in the context of high-performance and scientific computing environments. Prof. Bai serves on the editorial boards of several leading journals in the field, including: Applied Numerical Mathematics Communications on Applied Mathematics and Computation Computational and Applied Mathematics East Asian Journal on Applied Mathematics Electronic Transactions on Numerical Analysis Journal of Computational and Applied Mathematics Journal of Mathematical Study Journal of Scientific Computing Numerical Algorithms Numerical Linear Algebra with Applications He has advised students and mentored researchers, though specific names are not listed. His work is supported by institutional affiliations and likely national research grants, given his position in a State Key Laboratory. He has developed software tools and delivered lecture series, contributing to both research and education in computational mathematics. Prof. Bai is actively involved in the numerical linear algebra community and maintains a comprehensive academic presence, including publications, editorial service, and software contributions. He leads or participates in research teams focused on advancing computational methods within the State Key Laboratory framework.