جستجوی استاد راهنمای دانشگاهی

Michael Rudermanمشاهده پروفایل

استاد

• motion control
• robotics and mechatronics
• nonlinear systems with memory
• +۳ مورد دیگر

Michael Ruderman is a Professor in the Department of Engineering Sciences at the University of Agder, Norway, where he has been working since June 2020 (previously as Associate Professor from October 2015 to June 2020). He is currently on sabbatical from January 2025. Prior to his position at UiA, he held academic appointments at Nagaoka University of Technology, Nagoya Institute of Technology, and Technical University Dortmund. His research focuses on: Motion control and robotics Mechatronics systems Nonlinear systems with memory Nonlinear, hybrid, and robust control methodologies Ruderman's work centers on the analysis and compensation of kinetic friction in robotic and mechatronic control systems, culminating in his 2023 book 'Analysis and Compensation of Kinetic Friction in Robotic and Mechatronic Control Systems'. His research bridges theoretical control concepts with practical engineering applications, particularly in hydraulic systems, robotic actuators, and systems with hysteresis and friction effects. His recent publications (2024-2025) demonstrate a strong emphasis on advanced control techniques for complex mechanical systems, with particular focus on sliding mode control, nonlinear damping, hysteresis compensation, and oscillation control. Many of his papers address real-world challenges in hydraulic actuators, robotic systems, and mechanical interfaces with friction. Professionally, Ruderman serves on the editorial boards of IEEE Transactions on Control Systems Technology, IFAC Control Engineering Practice, IFAC Mechatronics, and IEEE/ASME Transactions on Mechatronics. He previously chaired the IEEE-IES Technical Committee on Motion Control (2018-2021) and served on the Management Committee of IEEE/ASME Transactions on Mechatronics (2020-2023). Ruderman has been actively involved in numerous research projects including DAAD mobility grants, H2020-MSCA-RISE projects on robust control, and several Research Council of Norway (RCN) projects focusing on fractional-order systems, nonlinear control methods, and compensators for non-minimum phase systems. He teaches courses including Control Theory (MSc), Advanced Control and Robotics (PhD), Electromagnetic Modeling (PhD), and Feedback Control Systems 1 (BSc), demonstrating his commitment to educating the next generation of control engineers and robotics specialists.

Naveed Ur Rehmanمشاهده پروفایل

دانشیار

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

Naveed Ur Rehman is an Associate Professor at Aarhus University's Department of Electrical and Computer Engineering, specializing in Signal Processing and Machine Learning. His research bridges mathematical methodologies with real-world applications in biomedical systems and environmental forecasting. His primary interests include: Multivariate signal analysis using Mahalanobis distance and detrended fluctuation analysis Graph signal processing for spatial-temporal data modeling Biomedical signal processing for clinical applications (e.g., IRRAflow® system evaluation) Machine learning integration with signal decomposition techniques Environmental monitoring systems for air quality forecasting Recent publications demonstrate his focus on developing adaptive signal processing frameworks that address both theoretical challenges and practical implementations. This includes: Time-varying graph processing methodologies Hybrid machine learning/signal decomposition techniques Statistical validation approaches for denoising algorithms Biomedical device performance analysis Multi-scale functional connectivity modeling His work spans interdisciplinary collaborations, particularly with medical researchers for intracranial pressure management systems and environmental scientists for air quality modeling.

• Planetary Dynamos
• Fluid Dynamics
• Exoplanetary Systems
• +۴ مورد دیگر

Yufeng Lin is an Associate Professor in the Department of Earth and Space Sciences at Southern University of Science and Technology's School of Science. He holds a B.S. in Geophysics from Wuhan University (2007), M.S. from Peking University (2010), and Ph.D. from ETH Zurich (2015), followed by postdoctoral research at the University of Cambridge. His research spans planetary dynamos , fluid dynamics of planetary interiors , and tidal interactions in exoplanetary systems . Key focus areas include magnetic field generation mechanisms, convection in rotating celestial bodies, and tidal dissipation processes. His work integrates theoretical modeling, numerical simulations, and experimental approaches to address fundamental questions about Earth and planetary evolution. Recent publications reveal strong trends in geomagnetic field analysis (using dynamic mode decomposition), Jovian system dynamics , and core-mantle interactions . His research consistently bridges geophysics and astrophysics, with increasing emphasis on computational methods for modeling complex fluid systems. IUGG SEDI Doornbos Memorial Prize (2022) National Special Expert (Youth) (2020) Pearl River Talents Plan Young Top Talent (2019) Swiss NSF Postdoctoral Fellowships (2014, 2016) Dr. Lin advises multiple graduate students (including J. Li and J.Y. Xu) and leads significant research initiatives including an NSFC Original Exploration Project (2023-2027) and a key sub-project in China's National Key R&D Program on Martian processes. His laboratory focuses on computational geodynamics and planetary fluid mechanics, with ongoing collaborations across international institutions.

Olena Tymoshenkoمشاهده پروفایل

پژوهشگر ارشد

• Stochastic Differential Equations
• Stochastic Analysis
• Mathematical Finance
• +۶ مورد دیگر

Olena Tymoshenko is a Research Fellow at the University of Oslo, Department of Mathematics, specializing in stochastic analysis and its applications. She is affiliated with the Risk and Stochastics research group and contributes to projects on robustness, asymptotic behavior, and stability of stochastic differential equation (SDE) models. Current Position: Research Fellow, Department of Mathematics, University of Oslo Previous Role: Associate Professor at National Technical University of Ukraine (2019–2023) Research Interests : Stochastic differential equations, asymptotic behavior of solutions, mathematical finance, limit theorems, pseudoregularly varying functions, and stochastic dynamics in biological systems. Recent Article Trends : Focus on partial differential equations driven by Lévy noise, McKean-Vlasov systems, and Brownian sheets. Applications span mathematical finance, epidemiology (monkeypox transmission), and control theory (Kalman filter algorithms). Teaching : Courses include Introduction to Mathematical Finance and Investment Theory (STK-MAT3700, Fall 2022) and Statistical Methods and Applications (STK4900, Spring 2023). Other Affiliation : Participant in the International Summer School as a student.

• Control Systems
• Stochastic Optimal Control
• Power Electronics
• +۴ مورد دیگر

Claes Breitholtz is a Professor of Automatic Control at Chalmers University of Technology , Sweden. His research focuses on modeling and control of distributed parameter systems and stochastic optimal control , with key applications in chemical reactor control and high-voltage direct current (HVDC) transmission systems . Key affiliations: Department of Signals and Systems, Chalmers University Email: claesbr@chalmers.se Research Interests His work addresses fundamental challenges in: Stability analysis of VSC-HVDC systems using Nyquist and eigenvalue methods Optimal control of stochastic systems with state constraints Model reduction techniques for complex industrial processes Gain scheduling in nonlinear systems like turbofan engines Publications span 1985–2017, covering both theoretical developments and industrial applications.

• Multi-agent systems
• Distributed artificial intelligence
• Autonomous unmanned systems
• +۳ مورد دیگر

Zhiyun Lin is Dean and Tenured Professor at the School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech). He holds affiliations as Foreign Academician of the Russian Academy of Engineering, Fellow of IEEE, IET, and AAIA. Previously, he served at Zhejiang University (2007-2017) and Hangzhou Dianzi University (2017-2021), with visiting positions at Yale University, ANU, and University of Tokyo. Education: PhD in Electrical and Computer Engineering, University of Toronto (2005) MSc in Control Theory, Zhejiang University (2001) BSc in Industrial Automation, Yanshan University (1998) Research Focus: Develops theoretical frameworks for multi-agent coordination, distributed AI algorithms, and cyber-physical systems. His work enables robust autonomous swarm behaviors in robotics and sensor networks through novel control strategies and optimization techniques. Publications: 230+ papers predominantly in IEEE Transactions journals demonstrate sustained contributions to formation control, distributed localization, and networked systems. Recent works (2021) advance resilient beamforming and finite-time stabilization for robotic formations. Awards: Natural Science Second Prize, Zhejiang Province (2020, 2013) Science and Technology Innovation First Prize (2020) Industry-University-Research Cooperation Innovation Award (2021) Prestigious fellowships: IEEE/IET/AAIA Leadership: Directs SUSTech's School of Automation and Intelligent Manufacturing. Formerly established the Artificial Intelligence Institute at Hangzhou Dianzi University and led research centers at Zhejiang University. Serves as Associate Editor for IEEE Systems Journal and IEEE Control Systems Letters.

• Epilepsy
• Neurology
• Signal Processing
• +۷ مورد دیگر

Dr. Wim Van Drongelen is a Professor at the University of Chicago in the Department of Pediatrics-Neurology. His research focuses on understanding the mechanisms underlying epilepsy through interdisciplinary approaches including advanced statistical and digital signal processing, non-linear dynamics, and modeling. His lab employs mathematical modeling, computational simulations, animal models, patient tissue analysis, and clinical recordings to improve epilepsy treatment.

Yifei Louمشاهده پروفایل

دانشیار

• Compressive sensing
• Image analysis
• Numerical analysis
• +۵ مورد دیگر

Yifei Lou is an Associate Professor jointly appointed in the Department of Mathematics and the School of Data Science and Society at the University of North Carolina at Chapel Hill . From 2014 to 2023 she served on the faculty of the University of Texas at Dallas, rising from Assistant to Associate Professor. Education: B.S., Peking University, 2005 Ph.D., Applied Mathematics, University of California, Los Angeles, 2010 (co-supervised by Prof. Andrea Bertozzi and Prof. Stefano Soatto) Postdoctoral Fellow, Georgia Institute of Technology, 2011–2012 (supervised by Prof. Allen R. Tannenbaum) Postdoctoral Fellow, University of California, Irvine, 2012–2014 (with Prof. Jack Xin and Prof. Hongkai Zhao) Research Interests: Dr. Lou’s work sits at the intersection of computational mathematics and data science . She develops efficient numerical tools to recover signals or images from limited measurements , exploiting sparsity, low-rankness, and tensor structures. Applications range from phase retrieval and hyperspectral imaging to seismic data completion and medical imaging . Core themes include compressive sensing , numerical optimization , and large-scale inverse problems . Recent Publication Trends: Her 2023–2025 corpus reveals a rapid pivot toward network-geometric approaches for pan-cancer genomics , neuroimaging , and immunotherapy response prediction . Papers leverage optimal mass transport , Ricci curvature , and multi-omics integration , translating sophisticated mathematical machinery into clinically actionable insights for breast cancer, melanoma, Alzheimer’s disease, and sarcoma. Scientific Awards: NSF CAREER Award (DMS-1846690), 2019–2024, “Mathematical Modeling from Data to Insights and Beyond” Grants & Workshops: NSF Workshop Grant (DMS-1821870), “Recent Developments on Mathematical/Statistical Approaches in Data Science,” May 31 – June 2, 2019 Laboratories & Collaborations: While no dedicated laboratory page is provided, Dr. Lou collaborates extensively across UNC Mathematics , the School of Data Science and Society , and interdisciplinary teams in medical imaging, oncology, and neuroscience.

Fabrice LEMOULTمشاهده پروفایل

دانشیار

• Wave Physics
• Metamaterials
• Acoustics
• +۴ مورد دیگر

Fabrice LEMOULT is an Associate Professor at Institut Langevin , ESPCI Paris - PSL University. His research focuses on experimental wave physics in complex media, particularly metamaterials, acoustics, and soft matter mechanics. Research Highlights : Wavefront shaping, Dirac cone manipulation, time-reversal applications, and subwavelength acoustic/elastic wave control. Key Collaborations : Mathias Fink, Geoffroy Lerosey, Sébastien Popoff, Claire Prada. Recent Work Trends : Studies on elastic wave dynamics in soft materials (2024), acoustic metasurfaces for noise isolation (2024), and microwave metamaterials for topological effects (2024). Earlier works explore superlensing (2015), phononic crystals (2016), and nonreciprocal wave propagation (2017). Advising & Outreach : Mentored 11 doctoral/postdoctoral researchers, including Samuel Croquette and Simon Yves. Advocates for frugal science and public engagement via platforms like @FabLemoult on scicomm.xyz .

• Signal Processing
• Image Processing
• Bayesian Inference
• +۳ مورد دیگر

Audrey Giremus is a Professor at the Institut de Mécanique et d'Ingénierie de Bordeaux (IMS Bordeaux) , affiliated with the Signal and Image Processing research group (SPECTRAL team). Her work bridges signal processing, statistical modeling, and interdisciplinary applications in environmental science and biomedical engineering. Key affiliations: IMS Bordeaux, BRGM (Bureau de Recherches Géologiques et Minières), CNRS (Centre National de la Recherche Scientifique) Collaborations: STMicroelectronics, Stellantis, Thales, CEA, Slb Research Interests focus on: Advanced signal/image processing algorithms Bayesian inference and particle filtering Applications in GPS navigation, fuel cells, and material science Statistical methods for high-dimensional data Thermal source localization and acousto-thermal coupling Coastal erosion detection using Markov chains Recent publications demonstrate expertise in GPS interference compensation , fuel cell degradation modeling , and biomarker validation . Collaborative projects with institutions like BRGM and CEA highlight her cross-disciplinary impact. Grants/Projects include: PEPR Risks (IRiMa) - CNRS OneWater PEPR - CNRS Technical platforms mentioned: Accelerated Aging Nanocomponent Testing (ATLAS, Cacyssee) Thermal Analysis (Elorga, Tamis) Terahertz and Organic Electronics

• Metrology
• Electromagnetic Field Measurement
• Power Quality
• +۴ مورد دیگر

Prof. Plamen Tsvetkov is a faculty member at New Bulgarian University (NBU) in the Department of Telecommunications since 2013. He previously held roles at Technical University of Sofia (1986–2019), including Head of the Department of Electrical Metrology (2004–2019). His academic career spans over 35 years in teaching and research. Education : PhD in Electrical Metrology from Technical University of Sofia (1998), specializing in robust structures for measurement systems. Research Interests : Metrology and calibration of measurement devices Power quality and electromagnetic field analysis Design of information-measurement systems Robust structures for measurement accuracy Applications in telecommunications and computer technology Recent Work Trends : His 15 most recent publications focus on power quality calibration methods, 4G/5G network protocols, and advanced signal processing techniques. Collaborations with international researchers emphasize standards like BDS EN 50160 and tools such as LabVIEW and MATLAB. Scientific Awards : Silver Medal, Technical University of Sofia Grants and Projects : Led 8 international projects and participated in over 30, including EU-funded initiatives on measurement standards and power quality. He also contributed to educational reforms in electrical engineering curricula. Publications and Leadership : Authored over 20 books and 130+ publications, with 15+ indexed in Scopus/Web of Science. Active in symposiums like International Symposium Metrology and Metrology Assurance as committee member.

Nicholas Landryمشاهده پروفایل

دانشیار

• Complex Systems
• Network Science
• Dynamical Systems
• +۵ مورد دیگر

Nicholas Landry is an Associate Professor of Biology at the University of Virginia with a courtesy appointment in the School of Data Science. His research examines contagion dynamics on complex systems, focusing on how higher-order network structures influence disease and information spread through interdisciplinary approaches combining network science, dynamical systems, and open-source development. His educational background includes a B.S. in Mechanical Engineering from the University of New Hampshire (2014), M.S. and Ph.D. in Applied Mathematics from the University of Colorado Boulder (2020, 2022), and postdoctoral training at the Vermont Complex Systems Center (2022-2024). Landry's core research investigates dynamical processes on higher-order networks, Bayesian inference for network structure reconstruction, and open-source software development. He maintains the XGI and HyperContagion libraries to advance cross-disciplinary research in network science, with applications spanning epidemiology, social contagion, and governance systems. His work emphasizes the critical role of group interactions in altering contagion dynamics compared to traditional pairwise models. Analysis of recent publications reveals accelerating focus on hypergraph structures, real-world dataset integration (particularly Bluesky social media), and computational frameworks for governance modeling. The research trajectory shows increasing methodological sophistication in handling higher-order interactions while maintaining strong connections to empirical social and biological systems. Scientific recognition includes: Small Data Analytics Resource Award from UVA's Data Analytics Center for "Analyzing large-scale social networks through the Bluesky social media platform" Landry actively mentors graduate researchers including Kuankuan Hu, Abhay Gupta, and Ahmed Ahmed through the Computing for Global Challenges program. His lab secured the Small Data Analytics Resource Award and participates in EXPAND fellowship collaborations with Professors Kasimatis and Brodie, integrating network methods into sexual conflict research and behavioral phenotyping. The Landry Lab, established August 2025, collaborates extensively with UVA's Biocomplexity Institute, Quantitative Collaborative, and School of Data Science. Current expansion includes postdoc Daniel Kaiser and multiple PhD students, with active development of open-source tools and participation in international conferences like NetSci 2025 where Landry organized the "Software and Data for Supporting Network Science" workshop.

Sufyan Ali Memonمشاهده پروفایل

استاد

• Target Tracking
• Unmanned Aerial Vehicles
• Data Association
• +۶ مورد دیگر

Sufyan Ali Memon serves as a Professor in the Department of Defense Systems Engineering at Sejong University, Republic of Korea. His academic journey includes Assistant Professor positions at Isra University (2016-2017), UNIST as Postdoc Research Associate (2017-2018), Indus University (2018-2019), and Mehran University of Engineering & Technology (2019-2021) before joining Sejong University in 2021. His research focuses on Tracking, Estimation, Guidance, Navigation, and Control systems, with particular expertise in multi-target tracking in cluttered environments, UAV trajectory estimation, and smoothing algorithms. His fingerprint analysis reveals strong activity in Target Tracking (97%) , Multi-Target Tracking (83%) , Unmanned Aerial Vehicles (37%) , and State Estimation (27%) . Analysis of his 42 research outputs (2015-2025) shows consistent growth in publication output, with 8 papers in 2025 alone. His work spans journals like Expert Systems with Applications , IEEE Access , and Sensors , demonstrating interdisciplinary applications from defense systems to social media analysis and pipeline inspection robotics. The publications reveal a clear evolution from fundamental tracking algorithms toward practical implementations in UAVs, industrial systems, and security applications. h-index 12 330 Citations Web of Science ResearcherID: AAD-9239-2019 His professional background includes significant contributions to smoothing algorithms for target tracking in cluttered environments, with major achievements including dynamic trajectory estimation for UAVs and multi-UAV tracking systems. Current research appears focused on integrating machine learning with traditional tracking systems and expanding applications to industrial inspection and security domains.

Erik Lindströmمشاهده پروفایل

استاد

• Financial Mathematics
• Energy Finance
• Power System Stability
• +۵ مورد دیگر

Erik Lindström is a Professor of Mathematical Statistics at Lund University's Centre for Mathematical Sciences, where he serves as Head of Department. He is also affiliated with several research initiatives including eSSENCE: The e-Science Collaboration, LTH Profile Area: The Energy Transition, and ELLIIT: the Linköping-Lund initiative on IT and mobile communication. His research spans financial mathematics, energy finance, and statistical methodology with applications to power system stability and financial markets. Dr. Lindström received his M.Sc. Eng. Phys. degree in 2000, B.Sc. and M.Sc. in Econ & Business in 2000 and 2001, and his Ph.D. in Mathematical Statistics in 2004, all from Lund University. He was appointed Docent in 2012, earned his ETP (Excellent Teaching Practitioner) qualification in 2013, and became a full professor in 2016. His research interests focus on financial mathematics, energy finance, statistical theory and testing, and development of algorithms for parameter estimation in continuous time processes, particularly discretely observed diffusion processes. His work contributes to UN Sustainable Development Goals related to energy and economic stability. The research fingerprint shows strong connections to models (100%), return analysis (76%), portfolio selection (64%), diffusion processes (54%), cryptocurrency (42%), nonlinear methods (39%), Hidden Markov Models (39%), and Maximum Likelihood estimation (36%). Lindström's publication record shows a clear progression from foundational statistical methods toward applied research in energy systems and cryptocurrency markets. Recent work (2023-2025) focuses on cryptocurrency returns using statistical jump models and energy load dynamics in apartment buildings, reflecting his dual expertise in financial mathematics and energy systems. Docent qualification (2012) Excellent Teaching Practitioner (ETP) award (2013) Professor Lindström serves as Assistant Director for the Program on Industrial Engineering and Management and chairs the nominating committee at the Faculty of Engineering for rector and prorector appointments. He has supervised doctoral research, including the project 'Scientific methods for integrating expert knowledge in Bayesian models.' His externally funded projects include Smart Cities Accelerator+ (2020-2022), eSSENCE@LU 6:5 on Bayesian spectroscopy (2020-2021), and the original Smart Cities Accelerator (2016-2020). As Manager of the Statistical Consulting Unit at the Centre for Mathematical Sciences, he oversees statistical support services for researchers across disciplines.

• Signal Processing
• Radar Systems
• Machine Learning Applications
• +۵ مورد دیگر

Thomas McKelvey is a Professor and Deputy Head of Department at Chalmers University of Technology, working within the Signal Processing research group. His research interests include: Signal Processing Radar Systems Machine Learning Applications Power Systems Automotive Control Systems Microwave Technology Wireless Power Transfer Professor McKelvey's research spans various domains where signal processing techniques are applied to solve complex engineering problems. His recent work has focused on developing advanced signal processing algorithms for radar systems, applying machine learning techniques to power system stability analysis, and creating innovative control strategies for automotive applications. His work demonstrates a strong interdisciplinary approach, bridging theoretical signal processing with practical engineering applications across multiple fields including aerospace, power systems, automotive engineering, and medical diagnostics. His extensive publication record shows a consistent research trajectory in signal processing with applications across multiple domains. His work on bistatic radar systems, power system inertia estimation, automotive control systems, and microwave-based medical diagnostics demonstrates both theoretical depth and practical relevance. The publications reveal a pattern of methodological innovation applied to domain-specific challenges, particularly in developing data-driven approaches to complex engineering problems. Professor McKelvey has collaborated extensively with researchers across various engineering disciplines. His work on automotive control systems, particularly related to catalyst control and engine management, has practical implications for reducing emissions in modern vehicles. His research on microwave technology extends to medical applications including stroke diagnosis and detection of intracranial bleedings, demonstrating the wide-ranging impact of his signal processing expertise.