جست‌وجوی استاد و پژوهشگر

Wuchen Liمشاهده پروفایل

استادیار

• Transport information geometry
• Complex dynamical systems
• Partial differential equations (PDEs)
• +۸ مورد دیگر

Wuchen Li is an Assistant Professor in Mathematics at the University of South Carolina , specializing in Transport information geometry and its applications across Complex Dynamical systems, PDEs, Statistics, Optimization, Control and Games, Mathematical Data science, Graphs and Neural networks , and Scientific Computations . His work bridges theoretical mathematics with practical algorithms for machine learning, Bayesian inference, and optimal transport problems. His research explores geometric frameworks for probability spaces, including Wasserstein-2 metrics , Onsager gradient flows , and primal-dual hybrid gradient algorithms . Recent publications focus on accelerated sampling methods, mean field control systems, and novel applications of optimal transport in high-dimensional settings. 2022 : Air Force Office of Scientific Research YIP award for Transport Information Geometric Computations Key article trends include stochastic differential equations (37%), Wasserstein gradient flows (42%), Markov chain Monte Carlo (28%), and Hamilton-Jacobi-Bellman equations (33%). Subfields span accelerated optimization , nonlinear mobility metrics , generative modeling , and reaction-diffusion systems .

ERHAN İNCEمشاهده پروفایل

استاد

• Mobile Communications
• Channel Coding
• Multi-carrier Techniques (OFDM, OFDMA)
• +۷ مورد دیگر

Prof. Erhan İnce is a faculty member in the Department of Electrical and Electronic Engineering at Eastern Mediterranean University (EMU), where he has served since 1998. He holds a PhD in Communications from the University of Bradford (1997) and MS/BS degrees in Electrical and Electronic Engineering from the University of Bucknell (1992, 1990). His research focuses on mobile communications, channel coding, OFDM/OFDMA, statistical signal processing, and facial recognition. He has supervised numerous PhD and MS students, including Ramin Bakhshi, Syed Amjad Ali, and Waseem Qassab Bash. Key projects include 'Monitoring KKTCELL's Base Stations' (2018-2019) and 'Background Subtraction and Lane Occupancy Analysis' (2011). He received an award at the 1998 Symposium on Communication Systems & Digital Signal Processing. His work spans peer-reviewed journals (IEEE Access, Digital Signal Processing) and books on video surveillance. He actively contributes to IEEE and other academic communities.

Janice Scealyمشاهده پروفایل

دانشیار

• Compositional Data Analysis
• Directional Statistics
• Robust Statistics
• +۵ مورد دیگر

Janice Scealy is an Associate Professor in the Research School of Finance, Actuarial Studies & Statistics at The Australian National University (ANU). She holds a PhD in Statistics from ANU (2011) and a Bachelor of Mathematics (Honours) from the University of Wollongong (2003). Her research focuses on compositional data analysis, directional statistics, robust statistics, and applications in geosciences like palaeomagnetism and seismology. She has held roles including ARC DECRA Fellow (2018-2022) and has supervised multiple research projects. Notable contributions include advancements in hypersphere-based statistical models and robust estimation techniques. Education: PhD (ANU 2011), BMath (Wollongong 2003) Her research interests include statistical methods for manifold-valued data, geostatistics, and model selection in linear mixed models. Recent work applies elliptical distributions to seismic moment tensor classification and integrates Fisher's palaeomagnetic theories with modern statistics. She leads projects on non-Euclidean data analysis and biologics immunogenicity assessment. Key collaborations involve geophysical applications and statistical methodologies for spatial data. Awards include ANU research grants (2018–2025) and contributions to international statistical reviews.

Jia Liuمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Linear Algebra
• Sparse Matrix Computations
• +۹ مورد دیگر

Dr. Jia Liu is a Professor and Chair of the Department of Mathematics and Statistics at the University of West Florida, within the Hal Marcus College of Science and Engineering. She has been a key academic figure at UWF since 2006, leading both research and departmental initiatives in computational and applied mathematics. Her educational background includes a Ph.D. in Mathematics from Emory University, funded by the National Science Foundation, focusing on preconditioned Krylov subspace methods for incompressible flow problems. She earned her M.S. and B.A. in Mathematics from Central China Normal University, where her bachelor's thesis received the highest honor. Dr. Liu's research lies at the intersection of numerical linear algebra, scientific computing, and interdisciplinary applications. Her primary interests include: Numerical solvers for large sparse linear systems Krylov subspace iterative methods Preconditioning techniques Applications to Navier-Stokes and optimization problems Geometric and topological analysis of ellipsoids Complex networks and community detection via spectral clustering Machine learning for disease prediction and biological modeling The trends in her publications reflect a consistent focus on robust numerical algorithms with applications across fluid dynamics, network science, and biomedical modeling. Her work emphasizes both theoretical development and practical implementation in high-performance computing environments. Dr. Liu has served as an editor and editorial board member for several peer-reviewed journals and regularly reviews submissions. While specific awards are not listed, her sustained publication record in prestigious venues such as SIAM Journal on Scientific Computing and Journal of Biological Dynamics underscores her scholarly impact. As department chair and professor, she plays a central role in academic advising, curriculum development, and research mentorship. She teaches core courses including Differential Equations, Numerical Analysis, and Real Analysis, contributing significantly to both undergraduate and graduate education. Her leadership extends to managing research grants and fostering collaborations across disciplines. Though no formal lab name is mentioned, her research activities suggest involvement with computational modeling groups, likely associated with applied mathematics and data science initiatives at UWF. Her ongoing work continues to advance numerical methods for complex systems in science and engineering.

• Inverse dynamics
• Structural health monitoring
• Linear and nonlinear vibrations
• +۵ مورد دیگر

Dr. Hamed Kalhori is a Lecturer in the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS), Faculty of Engineering & Information Technology. As a Casual Academic, he contributes to teaching and research in mechanical engineering with a focus on dynamics, vibrations, and structural health monitoring. His work bridges theoretical and practical applications in civil infrastructure, aerospace systems, and composite materials. Dr. Kalhori earned his PhD in Mechanical Engineering from the University of Sydney between March 2013 and July 2017. Dr. Kalhori's research spans multiple domains within mechanical and structural engineering. His primary expertise lies in inverse dynamics problems , particularly impact force identification, where he develops advanced methodologies combining model-based approaches with machine learning techniques. He extensively investigates linear and nonlinear vibrations in various structures, from micro-plates to large-scale bridges. His work in structural health monitoring focuses on innovative approaches for bridge inspection, including drive-by assessment techniques and sensor network optimization. Additionally, he explores smart materials and structures , particularly carbon nanotube-reinforced composites and their mechanical behavior under dynamic loading conditions. Dr. Kalhori's publication record demonstrates a strong trajectory in mechanical engineering research with increasing focus on interdisciplinary approaches. His recent work shows growing integration of machine learning techniques with traditional mechanical engineering methods, particularly for solving inverse problems in structural dynamics. There's a clear progression from fundamental vibration analysis toward practical applications in infrastructure monitoring and composite material characterization. His research maintains strong connections between theoretical modeling and experimental validation across diverse structural systems. Dr. Kalhori has received notable recognition for his academic contributions: 2019 Dean's Award for Excellence in Teaching and Tutoring Multiple research grants in vibration-related fields Dr. Kalhori has secured research funding for projects related to vibration analysis and structural monitoring, including industry collaborations such as the Alstom DMU traincar crash test project (2025). He has led international research collaborations and conducted numerous workshops and short courses both locally and internationally. While the specific details of his advising relationships aren't provided in the available information, his extensive publication record and research leadership suggest active engagement with graduate students and early-career researchers. His research involves collaboration with the Centre for Autonomous Systems at UTS and Data61-CSIRO, where he previously worked as a research associate and research assistant respectively. These collaborations focus on structural health monitoring projects across New South Wales, leveraging advanced sensing technologies and computational methods for infrastructure assessment.

• Machine Learning
• Computer Vision
• Robotics
• +۵ مورد دیگر

Qiang Qiu is an Associate Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. He actively contributes to the Vertically Integrated Projects (VIP) program, fostering interdisciplinary collaboration between faculty, students, and projects. His research spans machine learning, computer vision, robotics, and generative AI, with particular emphasis on diffusion models, transformer fine-tuning, and graph neural networks. Key themes include improving model generalizability through orthogonal low-rank embeddings, developing efficient parameter-tuning strategies, and advancing visuotactile manipulation in robotics. Recent publications reveal trends in attention control for text-to-image alignment, posterior sampling for diverse image generation, and federated learning security. His work addresses gradient conflicts in machine unlearning and explores multimodal sensing for robotic manipulation.

Robert Calderbankمشاهده پروفایل

استاد

• Information Theory
• Quantum Computing
• Wireless Communications
• +۶ مورد دیگر

Robert Calderbank is a distinguished academic and researcher at Duke University, holding professorships in Computer Science, Electrical and Computer Engineering, and Mathematics. He serves as Director of the Information Initiative at Duke and is affiliated with the Duke Quantum Center. His interdisciplinary work bridges information theory, quantum computing, and biomedical applications. Education: Ph.D. from California Institute of Technology (1980) Previous Institution: Princeton University (Distinguished Professor) Calderbank's research spans wireless communications, distributed storage systems, machine learning, and quantum information theory. Recent work focuses on two-dimensional magnetic recording, quantum error correction, and biomedical imaging with pump-probe microscopy. His publications demonstrate sustained innovation in constrained coding, matrix completion, and subspace classification. Scientific contributions include grants from the National Science Foundation and collaborative projects with the University of Maryland. Awards include Fellowships from the Royal Society, IEEE, and AAAS. His teaching and mentorship at Duke and Princeton have shaped next-generation signal processing and computer science research.

Salem Elsaiahمشاهده پروفایل

استادیار

• Power Systems
• Renewable Energy
• Microgrids
• +۳ مورد دیگر

Salem Elsaiah is an Assistant Professor in the Department of Electrical Engineering at the College of Innovation, Discovery and Enterprise, Alvernia University, located at the John R. Post Center at Reading CollegeTowne. His primary affiliation demonstrates active engagement in power systems and renewable energy research, with a focus on modern electrical engineering challenges. Professor Elsaiah's research spans: Power System Resilience : Developing optimization methods for grid reliability under extreme conditions Renewable Integration : Analyzing PV systems and microgrid performance Electric Vehicles : Studying grid impacts and hosting capacity solutions Maritime Energy Systems : Designing hybrid power systems for marine applications His publications consistently address operational challenges in modern energy infrastructure. Analysis of his 15 most recent publications (2016-2022) reveals three dominant themes: Reliability modeling of renewable-integrated grids (7 publications) Optimization techniques for distribution networks (5 publications) Maritime and transportation electrification (3 publications) Notably, his work combines theoretical frameworks with practical implementations, including real-time PV systems and experimental marine prototypes.

Hassan Zargarzadehمشاهده پروفایل

دانشیار

• Robotics
• Intelligent Control Systems
• Power Electronics
• +۷ مورد دیگر

Dr. Hassan Zargarzadeh is an Associate Professor in the Phillip M. Drayer Department of Electrical and Computer Engineering at Lamar University, specializing in Robotics and Intelligent Control Systems. His research spans multiagent systems, underwater robotics, wireless vehicle charging, and adaptive control algorithms. Ph.D. in Electrical Engineering, Missouri University of Science and Technology, 2012 M.S. in Electrical Engineering, Iran University of Science and Technology, 2009 B.S. in Electrical Engineering, Amir Kabir University, 2000 Dr. Zargarzadeh leads the Robotics and Intelligent Control Systems (RICS) Lab, focusing on: Advanced control methods for uncertain nonlinear systems Mechatronics and robotics design Power electronics and vehicle charging systems Swarm robotics and game-theoretic path planning His recent research trends include applying neural networks to autonomous systems, developing vision-based navigation for underwater robots, and optimizing wireless power transfer for electric vehicles. Collaborative projects span environmental monitoring, energy systems, and multiagent control. Scientific contributions include: Best presenter runner-up at ISC-supported research poster presentation, 2010 Top 3% M.S. student in Iran, 2008 National exam rankings in 1995 (top 100) and 2006 (top 800) Advising and grants: Advised 10+ graduate students in robotics and control $35,000 CAPM grant (2017) for bunker fuel optimization $25,000 CAWAQ grant (2016) for invasive species detection

Eirini Ntoutsiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Eirini Ntoutsi is an Associate Professor at the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover and a member of the L3S Research Center. Her academic journey includes a post-doctoral position at LMU Munich with an Alexander von Humboldt Foundation fellowship, and she earned her PhD from the University of Piraeus, Athens under the supervision of Y. Theodoridis. She holds a diploma and M.Sc. in Computer Engineering & Informatics from the University of Patras, Greece. Her research lies at the intersection of Artificial Intelligence and Machine Learning, focusing on two main pillars: learning over complex data and data streams (covering adaptive learning, change detection, and model stability), and responsible Artificial Intelligence (covering fairness-aware learning, data quality, and proper evaluation of AI/ML methods). Her work addresses critical societal challenges related to bias in algorithmic decision-making systems. Dr. Ntoutsi's recent publications demonstrate a strong focus on drift-aware learning for imbalanced data streams and fairness in AI systems. Her research shows a clear progression from foundational work in pattern management during her PhD to cutting-edge applications addressing real-world challenges in social streams, sensor data, and recommendation systems. Alexander von Humboldt fellowship for postdocs Best-student paper award at ICBK 2018 1st place in the 2006 innovation competition in Greece Dr. Ntoutsi actively mentors PhD and master's students while leading major research initiatives including NoBIAS (EU-funded, as Network coordinator), BIAS (Volkswagen Stiftung-funded), and OSCAR (DFG-funded). Her interdisciplinary approach combines technical AI solutions with philosophical and legal considerations to develop more equitable algorithmic systems.

Michael Mumaمشاهده پروفایل

استاد

• Robust Data Science
• Signal Processing
• Machine Learning
• +۵ مورد دیگر

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.

Jeffrey Chanمشاهده پروفایل

استاد

• Machine Learning
• Recommender Systems
• Responsible AI
• +۶ مورد دیگر

Jeffrey Chan is a Professor and Associate Dean of Data Science & Artificial Intelligence at RMIT University's School of Computing Technologies. His research focuses on machine learning, recommender systems, responsible AI, and interdisciplinary applications in sustainability, healthcare, and transportation. He has held roles such as Assistant Associate Dean and led projects on fairness-aware systems, energy-efficient recommendations, and AI-driven circular economies. His work bridges theory and practice, collaborating with industry and non-profits. He teaches courses in algorithms, network analytics, and machine learning, and actively supervises PhD and Honours students. Notable contributions include grants from the Australian Research Council and a Best Paper Award at ACM International Conference on Web Science 2011. His research also addresses ethical challenges in AI, such as fairness and transparency, and explores mobility solutions like e-scooter dynamics and parking optimization. Research Interests: Machine Learning, Recommender Systems, Data-Driven Optimization, Social Network Analysis, Responsible AI, Cloud Security, and Sustainability. Awards: Best Paper Award at ACM International Conference on Web Science 2011 Grants: Australian Research Council Discovery Project (2017–2019), Early Career Researcher Grant (2015), and EU FP7 ROBUST project (2011–2013). Labs/Teams: Leads initiatives in AI ethics, mobility systems, and interdisciplinary data science collaborations.

• Numerical linear algebra
• Matrix polynomials and spectral theory
• Stability and perturbation analysis
• +۳ مورد دیگر

Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.

Jamie Twycrossمشاهده پروفایل

دانشیار

• computational and mathematical modelling
• machine learning and artificial intelligence
• data analytics and visualisation
• +۱۱ مورد دیگر

Dr. Jamie Twycross is an Associate Professor in the School of Computer Science at the University of Nottingham. He leads the Intelligent Modelling and Analysis Group and serves as the Modelling Group lead in the Synthetic Biology Research Centre. His work bridges computer science with biological sciences, focusing on interdisciplinary applications of AI to solve complex biological problems. His research spans computational and mathematical modeling, machine learning, data analytics, and software engineering. Key areas include computational biology, synthetic biology, systems biology, systems medicine, sustainable chemistry, artificial life, biologically-inspired computing, artificial immune systems, computer security, and robotics. He has developed computational approaches to address real-world biological challenges, with particular emphasis on modeling complex biological systems. Dr. Twycross's publications reveal a strong trend toward interdisciplinary research that integrates computational methods with biological applications. His recent work demonstrates expertise in metabolic modeling, synthetic biology tools development, machine learning applications in bioinformatics, and sustainable chemistry software solutions. His research consistently addresses complex problems at the intersection of computer science and life sciences. Discipline External Examiner, Computer Science, Northeastern University London (2023-2027) Panel Member, UKRI Interdisciplinary Assessment College (2023-2025) Quality Assessor, Office for Students (OfS) (2023-2027) Member of Pool of Experts, Biotechnology and Biological Sciences Research Council (BBSRC) (2023-2026) Dr. Twycross has supervised an extensive number of students across multiple levels, including 4 postdocs, 14 PhD students, 1 M.Phil student, 21 M.Sc. dissertation students, and 21 B.Sc. dissertation students. His grant portfolio includes funding from national and international agencies, reflecting the significance and impact of his interdisciplinary research. He has also served on numerous grant review panels for UKRI, BBSRC, EPSRC, and other major funding organizations. As group lead of the Intelligent Modelling and Analysis Group and the Modelling Group in the Synthetic Biology Research Centre, Dr. Twycross directs research teams focused on developing computational approaches to address hard, real-world biological problems. His team's work includes developing software tools like libtissue for implementing artificial immune systems, as well as contributing to significant advancements in metabolic modeling and synthetic biology applications.

• Operations Research
• Optimization Theory
• Machine Learning
• +۲ مورد دیگر

Akiko Takeda serves as a Professor at the Graduate School of Information Science and Technology, University of Tokyo, where she conducts research bridging theoretical optimization advancements with machine learning applications, focusing on efficient solutions for large-scale nonconvex problems in data science and social systems engineering. Her research expertise spans Operations Research, Optimization Theory, Machine Learning, Data Science, and Social Systems Engineering, with specialization in subspace methods for constrained optimization and algorithms for nonconvex nonsmooth problems applicable to high-dimensional data analysis. Scientific Awards: No awards are listed in the provided information. Professor Takeda has led multiple Grant-in-Aid projects including the current initiative (2023-2027) on statistical learning with feature extraction for multi-domain data, robust optimization algorithms (2017-2021), and nonconvex classification methods (2011-2014). While specific students are unlisted, her professorial role involves graduate supervision, and her research teams develop optimization methodologies for social systems and machine learning applications.