جست‌وجوی استاد و پژوهشگر

Jan-Frederik Pietschmannمشاهده پروفایل

استاد

• Nonlinear Partial Differential Equations
• Gradient Flows
• Parameter Identification in PDEs
• +۳ مورد دیگر

Jan-Frederik Pietschmann is a Professor for Inverse Problems at the University of Augsburg's Faculty of Mathematics, Natural Sciences and Technology. He previously held the same position at TU Chemnitz (2018–2023) and served as a Research Associate at institutions including the University of Münster and TU Darmstadt. University of Cambridge (PhD, Mathematics, 2008–2011) University of Münster (Diploma, Mathematics and Physics, 2008) Habilitation in Mathematics, University of Münster, 2018 His research focuses on the analysis of nonlinear PDEs , gradient flows , and parameter identification in PDEs . Recent work explores applications to cell invasion , pedestrian dynamics , neurite growth , and organic semiconductors . Publications highlight his expertise in optimal transport , cross-diffusion systems , and uncertainty quantification . Notable trends in his 15 most recent articles include Nonlinear PDEs for biological and physical systems Optimal transport on networks and metric graphs Computational methods for inverse problems His current team includes Gianna Götzmann, Carmen Tretmans, and Team Assistant Juliane Krautz. Contact details: jan-f.pietschmann@uni-a.de , +49 821 598-3926.

Jarvis Hauptمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Learning Theory
• Compressed Sensing
• +۵ مورد دیگر

Jarvis Haupt is an Associate Professor and Associate Department Head in the Department of Electrical and Computer Engineering at the University of Minnesota - Twin Cities. His work bridges theoretical foundations with practical applications in signal processing, machine learning, and data science. He leads the Haupt Research Group, which focuses on developing novel methodologies for efficient data acquisition and analysis. Education: B.S. in Electrical Engineering from the University of Wisconsin-Madison (2002) M.S. in Electrical Engineering from the University of Wisconsin-Madison (2003) Ph.D. in Electrical Engineering from the University of Wisconsin-Madison (2009) Postdoctoral Research Associate at Rice University (2009-2010) Professor Haupt's research centers on statistical signal processing and learning theory, with particular expertise in compressed sensing and adaptive sampling techniques. His work develops theoretical foundations for high-dimensional statistical inference while addressing practical challenges in communications, remote sensing, data science, and medical imaging applications. He has made significant contributions to understanding how to efficiently extract information from large datasets through carefully designed measurement processes. His recent publications demonstrate a shift toward neural network theory and applications, particularly examining convergence properties of gradient flow in homogeneous neural networks. Alongside this theoretical work, he continues to apply signal processing techniques to medical imaging challenges, particularly in MRI reconstruction. His interdisciplinary approach bridges theoretical machine learning with practical applications in imaging systems. Scientific Awards: DARPA Young Faculty Award (2014) and Director's Fellowship extension (2016) UMN ECE Russell J. Penrose Excellence in Teaching Award (2015) Multiple Best Paper Awards including at GlobalSIP (2015) and IWSHM (2015) Wisconsin Academic Excellence Scholarship and other undergraduate honors Three U.S. Patents related to adaptive data acquisition and channel estimation Professor Haupt has supervised numerous graduate students and collaborated extensively across disciplines, particularly with researchers in civil engineering, medical imaging, and astrophysics. His research has been supported by multiple grants from NSF, NIH, DARPA, and the U.S. Army Research Laboratory, totaling millions of dollars. Current projects include work on multi-messenger astrophysics, plenoptic imaging, and brain imaging with minimal mobility restriction. He leads the Haupt Research Group, which brings together students and collaborators from electrical engineering, computer science, and various application domains. The group maintains strong connections with industry partners and national laboratories, facilitating the translation of theoretical advances into practical systems.

• Food Safety Management
• Dairy Herd Optimization
• Agricultural Economics
• +۲ مورد دیگر

Monique Mourits is an Associate Professor in Business Economics at Wageningen University & Research, specializing in food safety management, agricultural economics, and dairy herd optimization. Her work integrates economic analysis with biological systems modeling. Core Research Areas: Food safety risk drivers, dairy cow longevity optimization, BVDV transmission dynamics Methodologies: Dynamic stochastic simulations, bio-economic modeling, multi-criteria decision analysis Recent publications focus on emerging food safety risks (2025), dairy herd economics (2024), and cross-disciplinary approaches to agricultural systems. Her research collaborations span 12 similar profiles and involve 19 projects including GIANT LEAPS and APP outbreak modeling. She supervises PhD candidates and contributes to datasets related to Fusarium mycotoxin management. No email addresses or scientific awards are explicitly listed in available sources.

Amjad Anvari-Moghaddamمشاهده پروفایل

استاد

• Renewable Energy Systems
• Power Systems Engineering
• Microgrid Technology
• +۶ مورد دیگر

Amjad Anvari-Moghaddam is a Full Professor at AAU Energy , Aalborg University's Faculty of Engineering and Science. As Vice-Head of Department (Head of Research) and leader of the iGRIDS Research Group , he coordinates the Integrated Energy Systems Laboratory (IES-Lab) . His work focuses on: Renewable/Hybrid Power Systems Planning and Control Microgrids and Active Energy Networks Power Electronic Control & System Optimization Energy Markets and Economic Modeling Operations Research Applications in Energy His recent research trends show strong emphasis on AI applications in energy systems, grid-forming controls for renewable-dominant networks, and decentralized energy markets. He serves as Editor-in-Chief for Academia Green Energy and associate editor for multiple IEEE journals. Scientific recognitions include: World's Top 1% Highly-Cited Researcher (Clarivate, 2020-2023) Stanford's Top 2% Scientists (2020-2023) IEEE Outstanding Leadership & Service Awards He has organized 10+ courses on energy optimization and advanced control systems since 2013, and participates in 35+ research projects including COPPER (electricity grid resilience) and BidX (PtX market strategies).

• Nonlinear Dynamics
• Atomic Force Microscopy
• Nanomechanics
• +۴ مورد دیگر

Prof. Pierpaolo Belardinelli is a researcher at the Department of Civil, Architectural and Environmental Engineering of Marche Polytechnic University (UNIVPM), Italy. His academic career focuses on nonlinear dynamics and micro/nano electromechanical systems , with significant contributions to atomic force microscopy (AFM) and nanomechanics. Research Interests: Explore dynamical systems, viscoelastic characterization, and machine learning applications in mechanical analysis. Key Techniques: Sparse regression, dynamical integrity measures, and data-driven modeling. His recent publications highlight hybrid dynamic systems , carbon nanotube bistability , and machine learning integration in AFM. He employs high-performance computing for basin stability analysis and investigates nonlinear energy sinks for vibration control.

• Power Systems
• Renewable Energy Integration
• Smart Grids
• +۷ مورد دیگر

Professor Tapan Saha is a faculty member at The University of Queensland's School of Information Technology and Electrical Engineering. His research focuses on modern power systems, including renewable energy integration, smart grid technologies, transformer condition monitoring, and distributed energy resources management. He leads investigations into dynamic grid operation, cybersecurity for power networks, and sustainable energy transitions. His core research explores: Power system stability and control under high renewable penetration Advanced diagnostics for electrical assets using AI and signal processing Peer-to-peer energy markets and transactive control systems Dynamic operating envelopes for distribution networks Cyber-physical security for critical infrastructure Recent publications demonstrate strong emphasis on grid modernization challenges, particularly in voltage regulation, demand response, and distributed resource integration. His work frequently combines theoretical modeling with hardware validation.

Peter Seilerمشاهده پروفایل

دانشیار

• Robust Control Theory
• Linear Time-Varying Systems
• Wind Energy Systems
• +۴ مورد دیگر

Peter Seiler is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan. His work focuses on robust control theory, particularly analyzing the impact of model uncertainty on system design. He contributes to the Robust Control Toolbox in MATLAB and develops algorithms for finite-time horizon robustness assessment. His research spans wind turbine control, fault detection in safety-critical systems, and disk drive stabilization, with prior industry experience at Honeywell Research Labs on aerospace and automotive systems. Education: Not explicitly mentioned Seiler's research interests center on robust control theory, covering applications in aerospace (Boeing 787 redundancy management, NASA Orion flight control), wind energy (turbine and farm control), and safety-critical systems (UAVs, disk drives). He explores connections between robust control and optimization algorithms, including model-free reinforcement learning. His work often employs Integral Quadratic Constraints (IQCs) and Linear Parameter-Varying (LPV) techniques for stability and performance analysis. His recent publications (2019-2018) emphasize robust control of time-varying systems, optimization algorithms, wind energy systems, and fault-tolerant UAVs. Trends include IQC applications for stability analysis, LPV synthesis for aerospace and wind turbines, and robustness in machine learning. Scientific Awards Best Energy Paper Award, ASME Dynamic Systems and Control Conference (2016) Seiler has advised numerous PhD, MS, and undergraduate researchers across topics including wind turbines, UAVs, disk drives, and supercavitating vehicles. His collaborations span institutions like the University of Minnesota, German Aerospace Center, and University of Campinas. His lab at the University of Michigan integrates robust control theory with applications in aerospace, renewable energy, and fault-tolerant systems, working with teams on projects like UAV reliability, wind farm modeling, and adaptive control algorithms.

• Robotics
• Human-Robot Interaction
• Autonomous Systems
• +۳ مورد دیگر

Dirk Wollherr is a Senior Researcher and Lecturer (Privatdozent) at the Institute of Automatic Control Engineering, Faculty of Electrical Engineering and Information Technology, Technical University of Munich (TUM). He received his Dipl.-Ing. (2000), Dr.-Ing. (2005), and Habilitation (2013) from TUM. His career includes research at TU Berlin and a JSPS Fellowship at the University of Tokyo. He led the Cluster of Excellence 'Cognition for Technical Systems' (2006-2008) and is a TUM Carl-von-Linde Junior Fellow (2010-2013). Research Interests: Autonomous mobile robots, human-robot interaction, SLAM, real-time trajectory planning, stochastic model predictive control, and robotic manipulation under uncertainty. His work bridges control theory and practical applications in urban robotics, agriculture automation, and autonomous vehicles. Publication Trends: Recent articles (2021-2025) focus on robust control under uncertainty, human-robot collaboration, agricultural automation, and probabilistic motion planning. Key themes include constraint violation minimization, data-driven control, and ethical human-robot interaction. Awards & Recognition: Japanese Society for the Promotion of Science (JSPS) Research Fellowship (2004) TUM Carl-von-Linde Junior Fellowship (2010-2013) Projects & Leadership: Principal investigator in EU projects (Robot@CWE, IURO), General Chair of Robotik 2008, and Co-Chair of ARSO 2013. Leads research on the Autonomous City Explorer and human-robot interaction frameworks.

Matthew C. Valentiمشاهده پروفایل

استاد

• Wireless Communications
• Communication Theory
• Statistical Signal Processing
• +۳ مورد دیگر

Matthew C. Valenti is a Professor and coordinator for the National Center of Academic Excellence in Cyber Defense Education and Research at West Virginia University's Lane Department of Computer Science and Electrical Engineering. He also serves as Site Director for the WVU Center for Identification Technology Research (CITeR). Ph.D., Electrical Engineering, Virginia Tech (1999) M.S., Electrical Engineering, Johns Hopkins (1995) B.S., Electrical Engineering, Virginia Tech (1992) Valenti's research focuses on wireless communications, including communication theory, statistical signal processing, interference analysis, and cooperative communications. His work leverages computational methods for network optimization and error-control coding, particularly in millimeter-wave systems and secure biometrics. Key article trends include wireless network optimization, secure biometric systems, millimeter-wave communication advancements, and cooperative diversity techniques. His publications span topics from 5G virtualization to interference modeling in ad hoc networks. 2019 MILCOM Award for Sustained Technical Achievement Outstanding Researcher, WVU College of Engineering and Mineral Resources (3x: 2001, 2002, 2009) Valenti leads the Wireless Communications Research Laboratory (WCRL) with a 396-core cluster funded by the NSF CRI program. He contributes to open-source tools like the Coded Modulation Library (CML) and has mentored research in space-time coding and iterative processing techniques.

Sergio Antonio Cruces Álvarezمشاهده پروفایل

استاد

• Signal Processing
• Communications
• Brain-Computer Interfaces (BCI)
• +۳ مورد دیگر

Sergio Antonio Cruces Álvarez is a Professor of Signal Processing and Communications at the University of Seville, Spain, affiliated with the Signal Theory and Communications Department and leading the TIC-246 Research Group (Machine Learning Technologies and Digital Information Processing). His work bridges statistical signal modeling, data-driven learning, and applications in brain-machine interfaces and nonlinear behavioral modeling .

Yushan Liمشاهده پروفایل

پژوهشگر ارشد

• Control Systems
• Networked Dynamical Systems
• Multi-Robot Systems
• +۳ مورد دیگر

Yushan Li is currently a Postdoc Researcher in the Division of Decision and Control Systems at KTH Royal Institute of Technology, Sweden. He received his Ph.D. in Control Science and Engineering from Shanghai Jiao Tong University (SJTU) in 2024 and a B.E. in Automation from Huazhong University of Science and Technology (HUST) in 2018. His research focuses on cooperation and inference in networked systems, with applications to robotic networks and control security. Education: B.E. in Automation (2018) from HUST Ph.D. in Control Science and Engineering (2024) from SJTU His research interests include networked dynamical systems, multi-robot cooperation, topology inference, and cybersecurity in control systems. He has contributed to adversarial strategies against formation control, secure collaboration algorithms, and topology preservation under attacks. Recent publications highlight his work on network topology inference, secure control mechanisms, and adversarial strategies. His research has been accepted in prestigious journals such as IEEE Transactions on Automatic Control and IEEE Transactions on Robotics , with presentations at international conferences like CDC and IFAC World Congress. Scientific Awards: 2025 SJTU Outstanding Doctoral Dissertation Award 2023 SJTU Academic Star National Scholarships (2023, 2022, 2019) 2023 Best Oral Presentation Award at ZJU Swarm Intelligence Forum Li has also served as a teaching assistant for undergraduate courses on intelligent optimization of network systems and as a reviewer for leading journals including IEEE Transactions on Automatic Control and Automatica . He co-founded the Distributed Control, Optimization, and Security Zhizhen Forum and contributed to organizing major international conferences.

• Control Systems
• Machine Learning
• Networked Systems
• +۳ مورد دیگر

Siva Seetharaman is an Assistant Professor in the Department of Industrial Engineering at Purdue University's College of Engineering, joining the faculty in 2022. His research bridges control theory, machine learning, and networked systems with critical applications in energy infrastructure and transportation electrification. Affiliated with Purdue's top-ranked engineering programs, he focuses on safety-critical control solutions for real-world cyber-physical challenges. His educational trajectory includes a Ph.D. in Electrical Engineering from the University of Notre Dame, followed by postdoctoral research at Texas A&M University. This foundation supports his interdisciplinary approach to complex system dynamics. Dr. Seetharaman's research centers on risk-aware control synthesis for safety-critical systems, with three core thrusts: (1) Developing risk-tunable control barrier functions for human-robot collaboration and autonomous systems; (2) Creating data-driven energy management frameworks for virtual power plants and grid reliability; (3) Advancing distributed learning methods for stability assessment in large-scale networked systems. His work uniquely integrates formal verification techniques with machine learning to address nonlinear dynamics in transportation electrification and decarbonized energy grids. Analysis of his 15 most recent publications (2021–2025) reveals a decisive shift toward causal safety engineering —evident in risk-tunable control barrier functions and sampling-based safe reinforcement learning—and energy-transportation convergence , particularly in electric roadway charging and heavy-duty vehicle grid impacts. Key technical trends include compositional verification of neural networks (ECLipsE), multi-scale energy datasets, and dissipativity-based modeling for nonlinear systems. His work consistently targets real-world deployment challenges in Texas grid case studies and transportation networks. No scientific awards were documented in the provided materials. No student advisement records or grant funding details were included in the source texts. His collaborative publication pattern suggests engagement with interdisciplinary teams across control theory, machine learning, and power systems domains, though specific mentoring activities remain unreported. While no dedicated laboratory is specified, his research on cyber-physical systems, electric roadways, and grid-transportation integration implies active participation in Purdue's energy and transportation research ecosystems. His focus on synthetic Texas case studies indicates strong ties to regional energy infrastructure initiatives.

Arash Aminiمشاهده پروفایل

استاد

• Theoretical Signal Processing
• Statistical Signal Processing
• Graph Signal Processing
• +۵ مورد دیگر

Arash Amini is a Professor at the Electrical Engineering Department of Sharif University of Technology, Tehran, Iran. He received dual B.Sc. degrees in Electrical Engineering (Communications) and Petroleum Engineering (Reservoir) in 2005, followed by M.Sc. and Ph.D. degrees in Electrical Engineering (Signal Processing) in 2007 and 2011, respectively, all from Sharif University of Technology. During his doctoral studies, he spent a year (2009–2010) as a visiting scholar at the Biomedical Imaging Group (BIG), EPFL, Switzerland. Current Position: Professor (since May 2025) Previous Roles: Assistant Professor (2013–2018), Associate Professor (2018–2025), Researcher at BIG, EPFL (2011–2013) Editorial Role: Associate Editor for IEEE Signal Processing Letters (2014–2018) Research Interests : Theoretical and Statistical Signal Processing Graph Signal Processing Large Language Models Signal Processing for Communications Compressed Sensing Recent Publication Trends focus on graph signal processing, compressed sensing, biomedical imaging, and mathematical optimization. Key subfields include harmonic retrieval, subspace-informed matrix completion, sparsity-driven algorithms, and AI alignment benchmarks. Scientific Awards : Silver medal at the International Mathematical Olympiad (IMO2000) Advising includes 15+ Ph.D. and Master’s students, with collaborative projects involving institutions like EPFL and researchers such as Prof. F. Marvasti and Prof. S. Rini.

• Management
• Operations Research
• Industrial Management
• +۴ مورد دیگر

Prof. Dr. Jasmina Vesić Vasović is a Full Professor in the Department of Industrial Management at the Faculty of Technical Sciences, University of Kragujevac, Serbia. With over 15 years of academic experience, she specializes in Management and Operations Research, teaching courses including Industrial Management, Operations Research, Product Development, Project Management, and Multi-Criteria Decision Making. Education: Bachelor's Degree (1998): Technical Faculty in Čačak, Pedagogical-Technical Department Master's Degree (2003): "Contribution to the development of a model for multi-criteria optimization" PhD (2006): "Multi-criteria approach to optimization of enterprise programming" Dr. Vesić Vasović's research focuses on the application of mathematical and computational methods to solve complex decision-making problems in industrial and business environments. Her work spans multiple domains including production optimization, project management, and strategic decision making. She has made significant contributions to the field of Multi-Criteria Decision Making (MCDM), developing innovative approaches that integrate fuzzy logic, PROMETHEE methods, and other advanced techniques to address real-world business challenges. Her research has practical applications in areas such as supplier selection, production cycle optimization, and enterprise resource planning. Her recent publications demonstrate a strong trend toward integrating advanced computational methods with practical business applications, particularly in Product Lifecycle Management (PLM), Lean manufacturing, and innovation management. She has been increasingly focusing on fuzzy logic applications, digital transformation, and the integration of Industry 4.0 technologies with traditional operations research methods. Scientific Awards: Best Paper Award at the 24th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS2011) Dr. Vesić Vasović has been involved in numerous research projects funded by the Ministry of Education and Science of the Republic of Serbia, including "Production Management through Orders" (2008-2010) and "Development of Stochastic Models for Determining Production Cycle Time and Their Optimization for Serial Production" (2011-2019). She has also participated in international projects such as the EU-funded "Support to Enterprise Development and Entrepreneurship Programme." Her collaborative work extends to developing several technical solutions including software for multi-criteria decision making support and production cycle optimization. She is actively involved in the academic community through her participation in scientific conferences and as a co-author of multiple monographs including "Industrial Management" (2011) and "Application of optimization methods in the function of improving performance of organizational systems" (2013).

Henrik Zimmerمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Geometry Processing
• 3D Modeling
• +۵ مورد دیگر

Dr. Henrik Zimmer is a researcher at RWTH Aachen University specializing in computer graphics and geometry processing. His work bridges theoretical computational geometry with practical applications in digital fabrication, medical visualization, and industrial manufacturing. Affiliated with the Department of Computer Science, he has made significant contributions to mesh processing, 3D modeling, and optimization algorithms for real-world applications. His research focuses on geometry optimization for fabrication constraints, including Zometool-based approximation of freeform surfaces and rationalization of point-folding structures. Key interests include Mesh processing and topology preservation Variational methods for planar polygonal meshing Mixed-integer optimization for quadrangulation Medical visualization techniques for diffusion fiber tracking Industrial vision systems for laser welding process control His publication record shows consistent innovation from 2006 to 2014, with recent work emphasizing digital fabrication and practical optimization for consumer-level manufacturing. Notable scientific contributions include: Efficient algorithms for shortest path-concavity computation in 3D meshes Interactive volume-based visualization for diffusion MRI data Rationalization methods reducing fabrication costs by over 90% Novel approaches to melt pool monitoring in industrial welding Zimmer's collaborative work with Leif Kobbelt demonstrates strong industry-academia connections, particularly in translating geometric algorithms to manufacturing applications. His research spans both theoretical computer graphics (SIGGRAPH, Eurographics) and industrial applications (laser welding process control), reflecting a unique interdisciplinary approach. Current work focuses on making advanced geometric modeling accessible through personal fabrication technologies.