جست‌وجوی استاد و پژوهشگر

• Field Programmable Gate Arrays
• Computer Architecture
• Artificial Intelligence hardware design
• +۳ مورد دیگر

Dr. Zainab Aizaz is a Research Fellow at the School of Engineering and Informatics , University of Sussex, specializing in Artificial Intelligence hardware design and Field Programmable Gate Arrays (FPGA) . Her work focuses on creating energy-efficient and high-performance hardware solutions for AI applications, particularly through innovative pipelined vector processor architectures extending RISC-V open-source frameworks. Her research interests include: Approximate computing for neural networks Hardware acceleration of machine learning Computer architecture optimization Energy-efficient FPGA design Medical imaging applications using AI Signal integrity in nanoscale circuits Dr. Aizaz's recent publications highlight her work on: Spiking neural network acceleration (2025) Approximate multiplier architectures (2022-2024) Hardware-software co-design for edge computing (2023) Cognitive radio antenna optimization (2016) Contact: Z.Aizaz@sussex.ac.uk

Vladimir Makarenkovمشاهده پروفایل

استاد

• Bioinformatics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Roles and Affiliations: Full Professor of Computer Science at Université du Québec à Montréal (UQAM), Director of the DESS in Bioinformatics program. Regular member of LaCIM and affiliate member of Mila (Quebec AI Institute). Served as Assistant Professor (2001–2006) and Associate Professor (2006–2011) before becoming Full Professor in 2011. Education: B.Sc./M.Sc. in Applied Mathematics and Computer Science, Moscow State University (1987–1992) DEA in Computer Science, University Paris V (1993–1994) Ph.D. in Computer Science and Mathematics, École des Hautes Études en Sciences Sociales (EHESS), Paris (1994–1997) Research Interests: Focuses on machine learning applications in bioinformatics, including clustering algorithms, phylogenetic analysis, and genomics. Specializes in CRISPR/Cas9 off-target prediction, SARS-CoV-2 origin analysis, and high-throughput screening (HTS) data correction. Develops tools like T-REX web server for phylogenetic analysis and DUNEScan for skin cancer uncertainty estimation. Integrates deep learning and reinforcement learning for clustering and medical diagnostics. Publications and Grants: Over 100 peer-reviewed articles spanning bioinformatics, AI, and computational biology. Key grants include projects on HTS data correction, phylogenetic networks, and machine learning for biomedical applications. Collaborates with institutions like Mila and Université de Montréal. Labs and Teams: Leads research teams at UQAM’s Department of Computer Science, focusing on interdisciplinary projects combining AI and life sciences. Maintains collaborations with international groups in computational biology and data science.

Felix Voigtlaenderمشاهده پروفایل

استاد

• Mathematics of Machine Learning
• Neural Networks
• Approximation Theory
• +۱۱ مورد دیگر

Felix Voigtlaender is a Professor for Mathematics with a focus on Reliable Machine Learning at the Mathematical Institute for Machine Learning and Data Science, KU Eichstätt-Ingolstadt. He has held previous academic positions at TU Munich as an Emmy Noether Group Leader, at the University of Vienna as a Senior Scientist, and at KU Eichstätt and TU Berlin in research and postdoctoral roles. PhD: RWTH Aachen University, 2015 Master: RWTH Aachen University, 2013 Bachelor: RWTH Aachen University His research lies at the intersection of mathematics and machine learning, with a strong emphasis on the theoretical underpinnings of neural networks. He investigates approximation properties, expressiveness, and the existence of adversarial examples. His work also extends into harmonic analysis, functional analysis, and multiscale systems such as wavelets and shearlets. He is deeply interested in the mathematical foundations of data science, including sampling theory and information-based complexity. The recent publications highlight a consistent focus on the mathematical analysis of neural networks, particularly approximation capabilities and universal approximation theorems in both real and complex domains. His work often provides sharp theoretical bounds and deep insights into the behavior of deep learning models in high-dimensional settings. Friedrich-Wilhelm-Award 2016 for his PhD thesis Felix Voigtlaender has supervised students and is actively involved in teaching and academic development, notably contributing to the launch of a new BSc program in Data Science at KU Eichstätt-Ingolstadt. He has collaborated with prominent researchers such as Götz Pfander, Gitta Kutyniok, and Hartmut Führ, and has been funded through competitive grants like the Emmy Noether program. He leads a research group focused on rigorous mathematical approaches to machine learning challenges. He is affiliated with the Mathematical Institute for Machine Learning and Data Science, a newly founded institute at KU Eichstätt-Ingolstadt, which will be based in Ingolstadt. His team is involved in both theoretical exploration and practical applications of data science methods.

• Partial Differential Equations
• Bayesian Statistics
• Ordinary Differential Equations
• +۷ مورد دیگر

Teo Deveney is a Researcher in the Department of Computer Science at the University of Bath, actively contributing to mathematical and computational research since completing his PhD in 2022. His work bridges theoretical mathematics with practical applications across medical imaging and climate science domains. His doctoral research focused on accelerating Bayesian inference using deep learning for physics-governed likelihoods, supervised by T. Shardlow and E. Mueller. Educational background centers on computational mathematics with strong foundations in differential equations and probabilistic modeling. Research interests prioritize mathematical frameworks for complex systems: Partial Differential Equations (100% fingerprint match), Bayesian Statistics (85%), and Stochastic Differential Equations (75%). Secondary emphases include Neural Networks (60%), Climate Modeling, and Medical Imaging applications. His methodology consistently integrates uncertainty quantification with deep learning architectures. Recent publications (2024-2025) reveal converging trends: applying score-based diffusion models to ODE-SDE gaps via Fokker-Planck equations, developing graph-adaptive finite element methods, and creating machine-learned climate parameterizations. Medical imaging work focuses on Bayesian MRI reconstruction with structured uncertainty distributions using autoencoders. No scientific awards documented in current records Collaborative networks include C. Budd, C. B. Schönlieb, and L. Kreusser across Bath's computational mathematics groups. Current projects involve physics-informed neural networks for atmospheric modeling and uncertainty-aware medical image reconstruction, leveraging the university's high-performance computing infrastructure.

• Electromagnetic Modeling
• Antenna Design and Optimization
• Computational Electromagnetics
• +۱۲ مورد دیگر

Nebojša S. Doncov is a Full Professor at the Faculty of Electronics, University of Niš, Serbia, where he is affiliated with the Department of Telecommunications. He has been a leading academic and researcher in computational electromagnetics and antenna systems since earning his PhD in 2002 from the same institution. His research interests include: Computational Electromagnetics, particularly the Cylindrical TLM Method Antenna Design and Optimization using AI and Neural Networks Wearable and Smart Textile Antennas Direction of Arrival (DoA) Estimation Electromagnetic Compatibility and High-Frequency Modeling Biomedical and UAV-based GPR Applications His recent publications show a strong trend toward integrating artificial intelligence with electromagnetic modeling, especially in wearable and conformal antenna systems. He has published extensively in top IEEE and Wiley journals, with a focus on numerical methods and practical antenna applications for wireless and biomedical systems. His scientific awards include: URSI Young Scientist Award (2002) Aleksandar Marinčić Award (2017) Best Diploma Thesis Award (1995) Early Graduation Charter (1995) He has led or participated in numerous national and international research projects, including NATO Science for Peace, Royal Society International Exchange, and multiple COST Actions. He has advised students through research collaborations and co-authored works, though no formal list is provided. He has also contributed to academic education through textbooks and problem collections. He is actively involved in research labs and teams, including the Serbian research teams in SmartBridges, Predictive Design of Wearable Antennas, and Advanced UAV-Based GPR Imaging. He collaborates with institutions such as WIPL-D, University of Nottingham, and Technical University of Munich.

Richard Kowarمشاهده پروفایل

پژوهشگر

• Inverse Problems
• Wave Propagation
• Medical Imaging
• +۳ مورد دیگر

Priv.-Doz. Dr. Richard Kowar is a Postdoctoral Researcher at the Department of Mathematics, University of Innsbruck. His research focuses on inverse problems, wave propagation, and mathematical modeling in medical imaging and physics. He specializes in photoacoustic tomography, regularization methods for ill-posed equations, and the analysis of causal wave equations. His work bridges theoretical mathematics with practical applications in biomedical imaging and signal processing. Key research areas include the analysis of dissipative systems, fractional calculus in wave equations, and iterative algorithms for image reconstruction. His studies on causality in wave equations and diffusion models have advanced the understanding of nonlocal operators and their applications. Kowar has contributed to projects like VASCAGE, integrating mathematical techniques with real-world engineering and medical challenges. Publications highlight advancements in photoacoustic imaging with attenuating media, numerical inversion techniques, and theoretical frameworks for causal dynamics. Despite no listed scientific awards, his extensive bibliography demonstrates impactful contributions to applied mathematics and engineering.

• Theoretical Computer Science
• Distributed Computing
• Approximation Algorithms
• +۴ مورد دیگر

Mohit Daga is a Researcher at KTH Royal Institute of Technology, Sweden, specializing in theoretical computer science, distributed computing, and algorithmic research. He holds an MS in Computer Science from IIT Madras (2018). His current role involves research engineering and supervising master thesis projects across Machine Learning, AI, and GPU Programming. Education: MS in Computer Science, Indian Institute of Technology Madras (2018) Research Interests: His work focuses on distributed algorithms, approximation methods, and dynamic systems. He has explored applications in network analysis, optimization, and medical imaging. Recent projects include developing consensus protocols for DAG-based systems and analyzing multi-shot broadcast stability in distributed networks. Awards: Google Summer of Code Scholarship (2020) Advising & Grants: Supervised 9 master’s theses since 2020. Research internships include Cystar Labs (IIT Madras), IMDEA Networks Lab, and Xerox Research. His work on resource allocation at Xerox led to a patented solution. Labs/Teams: Contributed to projects at KTH’s Department of Computer Science and collaborated with IMDEA Networks and Cystar Labs. Part of the team developing the AciSpace CubeSat payload integration system.

Süha Tunaمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Hyperspectral Imaging
• +۲ مورد دیگر

Assistant Professor Süha Tuna is affiliated with Istanbul Technical University at the Institute of Informatics in the Computational Science and Engineering department. She holds a postdoctoral degree from the University of Strathclyde and has been actively involved in academic roles since 2010.

Atousa Hajshirmohammadiمشاهده پروفایل

مدرس ارشد

• Engineering Pedagogy
• Digital Communications
• Multimedia Communications
• +۵ مورد دیگر

Atousa Hajshirmohammadi serves as a Senior Lecturer at Simon Fraser University's School of Engineering Science, where she has taught since 2004 after returning from industry experience at LSI Logic Inc. in Silicon Valley. Her academic journey includes B.Sc. and M.Sc. degrees in Electrical and Computer Engineering from Isfahan University of Technology and a Ph.D. in Communications Engineering from the University of Waterloo. Her research spans two primary domains: technical communications and engineering pedagogy. In digital communications, she focused on multimedia transmission, cognitive radio networks, and unequal error protection until approximately 2015. Her pedagogical research, which dominates recent work, investigates experiential learning, student wellbeing, time management strategies, and innovative teaching methods in engineering education. Her publication trend shows a clear shift from wireless communications research (2000-2015) to educational scholarship (2015-present), with significant contributions to IEEE Communications Magazine and Frontiers in Education conferences. Supported by multiple SFU Teaching and Learning Development Grants, she has developed tangible lab assignments, interactive online quizzes, and wellbeing-focused learning environments. Her outreach includes workshops like the Insect-Robot program for K-7 students and collaborations with SFU's Health Promotion unit. While her technical research involved graduate students, her primary role as teaching faculty emphasizes curriculum innovation rather than traditional graduate supervision.

Hassane Sadokمشاهده پروفایل

استاد

• Numerical Analysis
• Iterative Methods
• Linear Algebra
• +۵ مورد دیگر

Hassane Sadok is a Professor and President at the University of the Littoral Côte d'Opale (ULCO), France. He is affiliated with the Department of Mathematics within the College of Science, where he contributes to teaching and research in numerical analysis and scientific computing. Position: Professor Institution: University of the Littoral Côte d'Opale (ULCO) Administrative Role: President since 2016 Department: Mathematics School: College of Science His research focuses on numerical linear algebra, particularly iterative methods for solving large linear systems, Krylov subspace methods (such as GMRES, Arnoldi, and CMRH), and regularization techniques for ill-posed problems. He has made significant contributions to the convergence analysis of minimal residual and orthogonal projection methods. His work bridges theoretical numerical analysis with practical computational algorithms. The recent publications reflect a strong trend in developing and analyzing block and global iterative solvers for multiple linear systems, convergence theory for eigenvalue problems, and regularization strategies like the L-curve and greedy Tikhonov methods. These works are grounded in applied mathematics and computational science, with applications in engineering and data analysis. Although no formal awards are listed in the provided texts, his leadership as university president and sustained research output indicate recognition in both academic and administrative domains. He advises graduate students in numerical analysis and computational mathematics, though specific names are not listed. He has not disclosed information on research grants. His teaching includes Master’s level courses in interpolation, quadrature, and numerical resolution of nonlinear equations. While no specific research lab or team is named, his collaborations with researchers like K. Jbilou, Y. Saad, and L. Reichel suggest active participation in a research group focused on numerical methods, likely within the Laboratoire de Mathématiques Pures et Appliquées (L.M.P.A.) at ULCO.

• Spatial Statistics
• Bayesian Statistics
• Environmental Statistics
• +۳ مورد دیگر

Gunter Spöck is an Associate Professor at the Department of Statistics, University of Klagenfurt, Austria, holding this position since his 2011 Habilitation in Statistics. His academic qualifications include: PhD in Mathematics from University of Klagenfurt (2005) Habilitation and Venia Docendi for Statistics from University of Klagenfurt (2011) Dr. Spöck's research centers on Spatial Statistics and Bayesian methodologies, with significant applications in environmental systems and industrial quality control. His work develops advanced spatial prediction models and sampling designs for complex real-world data, particularly in atmospheric science and semiconductor manufacturing. Analysis of his publication record (2012-2021) reveals consistent innovation in spatial modeling techniques, including novel approaches for wind profile analysis, defect detection in silicon wafers, and groundwater contamination assessment. His research demonstrates strong interdisciplinary collaboration across environmental science, engineering, and biostatistics. Scientific recognition includes: 1st price award (1997) No information is available regarding student supervision, research grants, or laboratory affiliations in the provided materials.

John G. Flanneryمشاهده پروفایل

استاد

• Gene Therapy
• Molecular Biology
• Retinal Degenerations
• +۴ مورد دیگر

John G. Flannery serves as Professor of Molecular Therapeutics in the Department of Molecular and Cell Biology at the University of California, Berkeley. His research targets inherited retinal degenerations including retinitis pigmentosa and Usher syndrome, which collectively affect approximately 100,000 Americans and 1 in 3000 people globally. The Flannery Lab develops gene therapies using adeno-associated virus (AAV) and lentiviral vectors to deliver neuroprotective factors and correct genetic defects in photoreceptor and retinal pigment epithelial cells, with current emphasis on engineering vectors for efficient Müller glial cell transduction to enable broad retinal coverage. The lab investigates genetic and biochemical mechanisms of photoreceptor degeneration through animal models including rd mice, rhodopsin mutant rats, and Usher syndrome knockout mice. Key therapeutic strategies encompass viral-mediated gene replacement for recessive diseases, CRISPR/siRNA for dominant mutations, and neurotrophic factor delivery systems. Recent work focuses on overcoming viral delivery limitations through capsid engineering and addressing immune barriers to retinal gene therapy, particularly for Müller cell transduction which could revolutionize neuroprotective factor distribution. Analysis of Dr. Flannery's recent publications reveals consistent expertise in viral vector development for retinal applications, with significant contributions spanning AAV capsid evolution in primates, Müller cell-specific transduction, and optogenetic vision restoration. His research demonstrates progression from fundamental vector characterization to sophisticated in vivo applications, increasingly addressing translational challenges like immune responses and cell-type specificity through interdisciplinary collaborations with engineering and clinical teams. The Flannery Lab website explicitly states their mission as 'optimizing preclinical gene therapy and vision restoration with the therapeutic approach of using adeno-associated viruses to deliver gene replacements to models of inherited retinal degenerative diseases.' The team develops retinal cell-specific vectors and explores novel approaches for both dominant and recessive diseases, with particular emphasis on Müller glial cell transduction to overcome current limitations in achieving widespread therapeutic delivery across the retina.

Marcin Jurekمشاهده پروفایل

استادیار

• Spatial Statistics
• Computational Methods
• Gaussian Processes
• +۳ مورد دیگر

Marcin Jurek is an Assistant Professor in the Department of Statistics and Data Science at Southern Methodist University (SMU). He previously held a postdoctoral position at the University of Texas at Austin, working under Dr. Catherine Calder and Dr. Corwin Zigler, and completed his PhD in Statistics at Texas A&M University in August 2020 under Dr. Matthias Katzfuss. Research Focus: Computational methods in spatial statistics, scalable Gaussian process approximations, spatio-temporal modeling Software Development: Maintains the GPvecchia package for R/Python Collaborations: National Center for Atmospheric Research (NCAR), Texas A&M University His work addresses challenges in analyzing massive spatio-temporal datasets through hierarchical sparse Cholesky decompositions and Vecchia approximations. Current research includes statistical inference for mobility trajectories and high-dimensional data analysis. He organized an SMU workshop on high-dimensional data in 2024 and has contributed to applications in environmental monitoring and agricultural data science. Recent publications demonstrate his expertise in developing computationally efficient algorithms for complex statistical problems, including applications to groundwater contamination datasets and maize breeding programs. He actively engages in interdisciplinary research across climate modeling, movement ecology, and Bayesian inference.

Aliaksandr Hubinمشاهده پروفایل

دانشیار

• Statistics
• Artificial Intelligence
• Econometrics
• +۴ مورد دیگر

Aliaksandr Hubin is an Associate Professor at the University of Oslo's Department of Mathematics and Natural Sciences, affiliated with the Center for Computational Inference in Evolutionary Life Science (CELS) and Statistics and Data Science research groups. His career spans Bayesian methodology development, machine learning applications, and interdisciplinary research. PhD in Statistics (University of Oslo, 2018) MSc in Operations Research (Molde University College, 2014) Specialist degree in Economic Cybernetics (Belarusian State University, 2013) Research spans: Bayesian model selection and averaging Probabilistic machine learning Computational statistics Applications in genomics, clinical diagnostics, and aquaculture Weak supervision methods in NLP Medical decision support systems Recent publications focus on: Bayesian neural network compression Clinical score validation Outlier detection mechanisms Evolutionary optimization in inference Domain-informed deep learning Scientific recognition: NIMA 2014 award Multiple student research awards (2012-2013) Graybill poster competition second place (2017) Methodological contributions include: Genetically modified MCMC algorithms Adaptive simulated annealing EM Probabilistic framework development

Zeynep Özer Özcanمشاهده پروفایل

استادیار

• Ophthalmology
• Refractive Surgery
• Corneal Diseases
• +۳ مورد دیگر

Zeynep Özer Özcan serves as an Assistant Professor in the Department of Ophthalmology at Gaziantep University Faculty of Medicine since 2024. Her academic foundation includes a medical degree from Hacettepe University Faculty of Medicine (2008-2014) followed by ophthalmology specialization at Gaziantep University (2016-2020) within the Department of Surgical Medicine. Her educational background comprises: Medical Degree: Hacettepe University Faculty of Medicine (2008-2014) Ophthalmology Specialization: Gaziantep University Faculty of Medicine Department of Surgical Medicine (2016-2020) Dr. Özer Özcan's research spans ophthalmology with emphases on refractive surgery outcomes, corneal pathologies including keratoconus, and innovative applications of artificial intelligence in patient education. She investigates vascular dynamics through choroidal imaging techniques like OCT angiography, examining relationships between systemic conditions (Wilson's disease, preeclampsia, vitamin D deficiency) and ocular manifestations. Her work bridges clinical ophthalmology with bibliometric analysis of research impact in eye care literature. Analysis of her 13 publications (2020-2024) reveals three dominant research streams: bibliometric studies of high-impact ophthalmology literature (particularly refractive surgery and keratoconus), AI-driven patient education tools using chatbots, and clinical investigations of choroidal/vascular changes using advanced imaging. Approximately 60% of her work appears in Q2 SCI-Expanded journals, demonstrating consistent contributions to ophthalmology knowledge translation and technical innovation. No scientific awards were documented in the source material. While specific student mentorship details and grant funding aren't provided, her active publication record across multiple subspecialties suggests ongoing research supervision and potential grant involvement. Her collaborative patterns indicate regular teamwork with colleagues across ophthalmology subspecialties at Gaziantep University. As a core faculty member of Gaziantep University's Department of Ophthalmology, she contributes to both clinical care and academic missions. Her research collaborations span refractive surgery, corneal diseases, neuro-ophthalmology, and ocular imaging teams, leveraging departmental resources for comprehensive eye care research.