جست‌وجوی استاد و پژوهشگر

Henry Pernickaمشاهده پروفایل

استاد آموزشی

• Astrodynamics
• Orbital Mechanics
• Spacecraft Design
• +۷ مورد دیگر

Dr. Henry Pernicka is a Curators’ Distinguished Teaching Professor in the Department of Mechanical and Aerospace Engineering at Missouri University of Science and Technology. He leads the Space Systems Engineering Laboratory and advises the Missouri S&T Satellite research team (M-SAT) and the Formula Society of Automotive Engineers (FSAE) team. Ph.D. in Aeronautical and Astronautical Engineering (1990) M.S. in Aeronautical and Astronautical Engineering (1986) B.S. in Aeronautical and Astronautical Engineering (1984) His research focuses on astrodynamics , libration point orbits , spacecraft formation control , and small satellite systems . He has pioneered the use of neural networks and genetic algorithms for spacecraft attitude control and trajectory optimization, advancing applications in swarm missions and precision formation flying . Recent publications highlight trends in spacecraft propulsion , vision-based navigation , and machine learning for formation missions. Key areas include plasmonic force propulsion , genetic algorithm trajectory design , and GPS/star tracker integration . Curators’ Distinguished Teaching Professor Managing Editor of The Journal of the Astronautical Sciences (1999-2011) As an advisor, Dr. Pernicka has mentored 8 Ph.D. and 34 master’s students. His work bridges spacecraft design , dynamics and control , and educational outreach through initiatives like the M-SAT program and high-altitude ballooning projects.

Rico Piconeمشاهده پروفایل

دانشیار

• Control Systems
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

Rico Picone is an Associate Professor of Mechanical Engineering and Director of the MSME program at Saint Martin's University's Hal and Inge Marcus School of Engineering. His research focuses on control systems, robotics, artificial intelligence, and real-time computing, with applications in magnetic resonance technologies and augmented human intelligence. He holds a Ph.D. from the University of Washington (2014), alongside an M.S.M.E. and B.S.M.E. from the same institution and the University of Nevada, Las Vegas, respectively. His teaching emphasizes hands-on exploration of dynamical modeling, control systems, and robotics, integrating real-world applications. Research highlights include advancing magnetic resonance force microscopy (MRFM) via hyperpolarization and exploring the philosophical dimensions of engineering's role in human-technology interaction. Recent work includes developing real-time computing frameworks for mechanical systems and investigating metastimuli in human learning through machine learning. Publications span 2024–2009, with a focus on real-time control systems, embedded software, and interdisciplinary engineering challenges. While no explicit awards are listed, his contributions reflect sustained innovation in both pedagogy and applied research. Advising and grants are not detailed in the provided text, though his role as program director suggests active engagement with student mentorship and academic leadership. His work bridges theoretical foundations with practical engineering solutions, emphasizing the ethical and societal implications of technological advancement.

Michael Hunterمشاهده پروفایل

استادیار

• Human Development
• Mathematical Modeling
• Statistical Computing
• +۷ مورد دیگر

Michael Hunter is an Assistant Professor in the Department of Human Development and Family Studies at Pennsylvania State University's College of Health and Human Development. His research integrates human development with mathematical and computational methodologies. Ph.D. in Psychology (2014), University of Oklahoma M.A. in Psychology (2011), University of Virginia B.S. in Mathematics and Psychology (2008), Michigan State University Hunter's research focuses on dynamical systems , structural equation modeling , and behavior genetics . His work emphasizes: Development of dynamical systems models for human behavior Creation of statistical software packages (OpenMx, dynr) Analysis of intensive longitudinal data and twin studies Applications to addiction relapse, cognitive development, and affect dynamics Recent publications highlight expertise in nonlinear modeling , state-space algorithms , and genetic-environmental interaction analysis. His methodological contributions bridge theoretical statistics with practical behavioral research implementations.

Kostas Giannopoulosمشاهده پروفایل

استاد

• Market Risk Management
• Option Pricing
• Volatility Modelling
• +۴ مورد دیگر

Professor Kostas Giannopoulos holds the position of Professor of Finance at the School of Economics, Business and Computer Science, Neapolis University, Pafos. He is a Doctor Laureate in Banking and Economics from the University of Siena, with additional degrees from the University of Wales (Masters in Banking and Finance) and London Guildhall University (PhD in Finance). His research focuses on volatility modeling, market risk analysis, option pricing, and international portfolio diversification. Notable contributions include the Filtered Historical Simulation (FHS) model, which revolutionized risk forecasting in finance. Key achievements include a 1999 Journal of Futures Markets publication ranked as the most influential in commodities and 5th overall in the journal's 40-year history, and a 1997 European Journal of Finance article ranked 13th in its 25-year history. He has advised financial institutions and regulatory bodies for over 30 years, and served as regional chapter director for PRMIA. His work spans commodity markets, derivatives risk, and systemic financial risk in global contexts. Active in academic speaking engagements and professional seminars across Europe and the U.S., he maintains strong industry ties. Research emphasizes volatility spillovers, risk modeling under uncertainty, and applying FHS to CCP default funds and waterfall mechanisms. His work bridges theoretical finance with practical applications in regulatory frameworks and portfolio management strategies. Ongoing interests include cryptocurrency risk analysis and long-horizon volatility forecasting.

• Acoustics
• Vibrations
• Active Noise Control
• +۵ مورد دیگر

Professor Anthony Zander is a faculty member in the School of Electrical and Mechanical Engineering at the University of Adelaide, holding the rank of Professor since 1999. He has held leadership roles, including Head of the School of Mechanical Engineering (2014–2022) and Acting Head (2013). His research focuses on acoustics, vibration control, ultrasonics, and biomedical engineering applications such as fluid-structure interaction (FSI) in cardiovascular systems. He teaches courses in vibrations, acoustics, signal processing, and machine dynamics. His work spans nanoscale materials, biomechanics, and industrial noise control. Education & Experience: Industry roles at United Technologies Research Center (1995–1999) and senior consulting engineer in acoustics/vibration (prior to academia). Research Highlights: Active noise control, assistive technologies, and FSI analysis of aortic aneurysms and coronary arteries. His recent publications emphasize cutting-edge studies in magnetic field dynamics, coronary artery biomechanics, and vibration isolation systems. He has contributed to patents in acoustic materials and noise reduction technologies. Grants & Funding: Not explicitly listed, but implied through research output. Labs/Teams: Acoustics, Vibration and Control group at the University of Adelaide.

Emma Wilsonمشاهده پروفایل

مدرس

• Systems and Control Theory
• Digital Health Applications
• Adaptive Treatments
• +۵ مورد دیگر

Dr Emma Wilson is a Lecturer in the Computing and Communications department at Lancaster University, focusing on systems and control theory applied to healthcare and biological systems. Her interdisciplinary research integrates engineering principles with biomedical challenges to improve health outcomes, including adaptive treatments, bio-inspired robotics, and muscle modeling. Her research spans multiple areas such as control theory, bio-inspired control mechanisms, brain-based robotics, and computational modeling of biological systems. A key theme is translating insights from biological systems into engineered solutions and vice versa, particularly in digital health applications like personalized treatment decisions and dynamic health modeling. Recent work includes developing robust control algorithms for anticoagulant therapies and neuro-inspired adaptive systems for robotics. She leads projects like DCQ (Cyber Security of Digital Medical Devices) and collaborates on initiatives like the Health Behavior Change research group. Her publications reflect a blend of theoretical control systems research with practical healthcare applications. Dr Wilson is affiliated with the Lancaster Intelligent, Robotic and Autonomous Systems Centre (LIRA) and the Data Science Centre (SCC), emphasizing her role in cutting-edge interdisciplinary research.

• Structural Health Monitoring
• Dynamics of Structures
• Civil and Structural Engineering
• +۹ مورد دیگر

Marco Civera is a Fixed-term Assistant Professor at the Department of Structural, Building and Geotechnical Engineering (DISEG), Polytechnic University of Turin, where he also contributes to the Interdepartmental Center SISCON – Safety of Infrastructures and Constructions. He is actively involved in teaching and research, with a strong focus on structural health monitoring, dynamics of structures, and civil infrastructure safety. His research interests include Structural Health Monitoring , Dynamics of Structures , Civil and Structural Engineering , Aerostructures , Critical Infrastructures , and Mechanics of Solids and Structures . He applies advanced techniques such as operational modal analysis, machine learning, and nonlinear modeling to assess structural integrity and performance. The recent publications highlight a strong trend in data-driven structural assessment, including the use of generative adversarial networks, meta-learning, clustering algorithms (e.g., DBSCAN), and Bayesian inference for real-time monitoring and damage detection. His work bridges civil and aerospace engineering, with applications in bridges, aircraft, and underground infrastructures. Scientific Awards: Top 2% most-cited scientist conferred by Stanford University published by Elsevier, Italy (2023) Marco Civera advises several PhD students and contributes to commercial research projects such as ISABHEL - Integrated Satellite and ground-based monitoring for BridgeHealth Lifetime assessment (2024–2026). He has also co-developed two patented medical devices: T_RESAP (Tendon Repair Surgical Applicator) and T–REM3DIE (Tendon Repair Medical Device) . His teaching spans multiple disciplines and includes courses in Structural Collapse and Monitoring, Dynamics of Structures, and Computational Mechanics across Civil, Petroleum, Biomedical, and Architectural Engineering programs. He is a member of research teams focused on Structural and Seismic Monitoring and Structural Engineering , contributing to both academic and industrial advancements in infrastructure safety.

Arnfinn Aas Eielsenمشاهده پروفایل

دانشیار

• Control Systems
• Mechatronics
• Signal Processing
• +۲ مورد دیگر

Arnfinn Aas Eielsen is an Associate Professor in Electrical Engineering at the University of Stavanger, Faculty of Science and Technology, Department of Energy and Petroleum Technology. His research focuses on control systems and signal processing in precision mechatronics, particularly digital-to-analog converters (DACs), nanopositioning devices, and piezoelectric actuators. Key research areas include: Glitch mitigation in DACs using dithering and model predictive control Thermal protection for piezoelectric actuators Nonlinear damping and robust repetitive control for nanopositioning State and parameter estimation for precision systems Low-noise transimpedance amplifier design Recent publications (2023-2025) emphasize error compensation in DACs, thermal modeling, and advanced control strategies for nanoscale positioning. His work often involves collaborations with researchers in Australia and Norway, with applications in atomic force microscopy and industrial precision engineering. Current affiliations: University of Stavanger Faculty of Science and Technology Department of Energy and Petroleum Technology

Daniel De Matos Silvestreمشاهده پروفایل

پژوهشگر

• Fault Detection and Isolation
• Distributed Systems
• Network Control Systems
• +۵ مورد دیگر

Daniel De Matos Silvestre is a researcher at COPELABS, Lusofona University, and the Institute for Systems and Robotics (ISR) at Instituto Superior Técnico, Lisbon. He holds a PhD in Electrical and Computer Engineering from IST (2017) with highest honors, an M.Sc. in Advanced Computing from Imperial College London (2009), and a B.Sc. in Computer Networks from IST (2008). His research focuses on fault detection and isolation, distributed systems, network control systems, and randomized algorithms. PhD in Electrical and Computer Engineering (IST, 2017) M.Sc. in Advanced Computing (Imperial College London, 2009) B.Sc. in Computer Networks (IST, 2008) His work spans critical areas such as state estimation , collision avoidance , and resilient control systems against adversarial attacks. Recent publications highlight advancements in constrained convex generators , flocking-based navigation , and gradient descent algorithm analysis . Collaborations include research stays at University of California, Santa Barbara, and joint works with Joao Hespanha. Key contributions include novel methods for set-membership estimation , autonomous vehicle coordination , and distributed control algorithms with applications in robotics and cybersecurity. He has published extensively in top journals like IEEE Transactions on Automatic Control , European Journal of Control , and Systems and Control Letters .

• Finance
• Macro-Finance
• Econometrics
• +۴ مورد دیگر

Andras Fulop is a Professor in Finance at ESSEC Business School, France, where he conducts advanced research at the intersection of finance, econometrics, and macroeconomic modeling. His work is recognized in academic circles, as evidenced by his guest lecture hosted by CY Advanced Studies. His research focuses on the development and application of sophisticated computational techniques for estimating high-dimensional, nonlinear, and non-Gaussian dynamic structural macro-finance models. He specializes in sequential Monte Carlo methods, approximate dynamic programming, and tempering procedures for likelihood estimation. The analysis of his recent publication reveals a strong emphasis on methodological innovation in econometric modeling, particularly in the context of macro-finance applications such as New Keynesian DSGE models and consumption-based long-run risk frameworks. His work contributes significantly to the advancement of quantitative tools in financial economics. No scientific awards mentioned There is no available information about student advising or research grants. Similarly, no details are provided about labs or research teams he may lead or participate in.

• Nonlinear Analysis
• Calculus of Variations
• Partial Differential Equations
• +۵ مورد دیگر

Dr. Joan Duran Grimalt is an Associate Professor of Applied Mathematics in the Department of Mathematics and Computer Science at the University of the Balearic Islands (UIB). He serves as a member of the Mathematical Image Processing (TAMI) research group and the Institute of Applied Computing and Community Code (IAC3). From July 2021 to June 2024, he held the position of deputy director of the Higher Polytechnic School and head of studies for the Degree in Mathematics program. His academic career at UIB began in 2015 following the completion of his PhD. Dr. Duran Grimalt earned his academic credentials at prestigious institutions: BSc in Mathematics (2010) from the University of the Balearic Islands (UIB) MSc in Advanced Mathematics and Mathematical Engineering (2011) from the Polytechnic University of Catalonia PhD in Mathematics (2016) from UIB with thesis on variational models for ill-posed inverse problems in digital imaging His research spans the intersection of mathematical theory and practical applications in imaging science. Dr. Duran Grimalt specializes in nonlinear analysis, calculus of variations, partial differential equations, and deep unfolding architectures. His work focuses on developing mathematical frameworks that bridge traditional variational methods with modern deep learning approaches, particularly for image processing and computer vision applications. This hybrid methodology allows for both the interpretability of model-based approaches and the performance benefits of data-driven techniques. Analysis of his recent publications reveals a clear research trajectory toward integrating classical mathematical models with deep learning architectures, particularly through the technique of deep unfolding. His work consistently addresses challenging problems in satellite image processing, pansharpening, hypersharpening, and low-light image enhancement. The publications demonstrate a progression from purely variational approaches to increasingly sophisticated hybrid models that incorporate attention mechanisms, nonlocal operations, and specialized network architectures designed specifically for imaging problems. His notable scientific recognition includes: Fellowship from the Govern de les Illes Balears for PhD research (2011-2015) Dr. Duran Grimalt has secured research funding for multiple projects, leading two major initiatives. He has established significant international collaborations with the National Centre for Space Studies (CNES) in France, where he contributed to the image restoration chain for Earth observation satellites, and with the Oceanographic Centre of the Balearic Islands, focusing on deep unfolding architectures for remote sensing data fusion and marine object detection. His academic mentorship includes supervising PhD candidates M. Francesc Alcover (working on nonlocal theory for variational problems) and Daniel Torres (researching the combination of variational models and deep learning for image processing). He has also been a visiting researcher at leading institutions including the Technical University of Munich, ENS Paris-Saclay, and New York University. His research is conducted through the Mathematics, Imaging and Learning (MIA) Consolidated R+D+I Group, where he is an active member, and leverages resources from the Institute of Applied Computing and Community Code (IAC3). These research structures provide the computational infrastructure and collaborative environment necessary for his work on advanced image processing algorithms and their applications in satellite imaging and computer vision.

Ralf Hielscherمشاهده پروفایل

استاد

• Mathematical Imaging
• Crystallography
• Texture Analysis
• +۷ مورد دیگر

Ralf Hielscher is a Professor at the Institute of Applied Analysis within the Faculty of Mathematics and Computer Science at the Technical University of Freiberg, Germany. His research lies at the intersection of applied mathematics, materials science, and imaging, with a strong focus on crystallographic texture analysis and electron backscatter diffraction (EBSD). He is a core developer and leading figure behind MTEX, a widely used open-source MATLAB toolbox for texture and orientation data analysis. Research Interests: His work centers on mathematical methods for analyzing crystallographic orientations, including spherical harmonic transforms, kernel density estimation on rotation groups, manifold-valued data processing, and inverse problems in tomography and texture reconstruction. He develops algorithms for parent grain reconstruction, orientation mapping, denoising, and visualization of microstructures. The recent publications reveal a consistent trend in advancing computational techniques for EBSD and texture analysis, particularly through the MTEX platform. His work bridges theoretical mathematics with practical materials characterization, enabling more accurate and efficient analysis of polycrystalline materials across geology, metallurgy, and engineering. Email: ralf.hielscher@math.tu-freiberg.de Scientific Contributions: While no formal awards are listed, his extensive publication record in high-impact journals such as SIAM Journal on Imaging Sciences , Journal of Applied Crystallography , and Inverse Problems underscores his significant contributions to the field. He has developed foundational algorithms now embedded in MTEX, which is used globally by researchers in materials science and geology. Teaching and Advising: He teaches courses such as Function Theory, Analysis 3, and Mathematics for Engineers. Although specific students are not mentioned, his leadership in MTEX and numerous collaborative publications suggest he mentors researchers and contributes to training the next generation of scientists in computational materials analysis. Labs and Teams: He is part of the team at the Institute of Applied Analysis and leads research efforts related to signal and image processing in crystallography. The MTEX project serves as a virtual research platform involving international collaborators in Germany, France, the UK, and beyond, facilitating open science in texture analysis.

• Signal Processing
• Speech Processing
• Audio Processing
• +۳ مورد دیگر

Dr. Turgay KOÇ is a Lecturer at the Department of Electrical and Electronics Engineering, affiliated with the Faculty of Engineering and Natural Sciences at Middle East Technical University. He holds a doctoral degree in Electrical-Electronic Engineering from the same institution (2012) and a bachelor's degree from Nigde University (2001). His research focuses on Signal Processing, Speech Processing, and applications of AI in healthcare, including lung disease detection and vocal fold analysis. He has contributed to advancements in CNN-based denoising, medical image segmentation with U-Net architectures, and forensic audio analysis. His work spans over 15 years, with notable publications on nonlinear source-filter models and glottal inverse filtering techniques. Dr. KOÇ's research interests also encompass audio, image, and video processing techniques, particularly in biomedical contexts. His recent work (2020–2025) emphasizes AI-driven solutions for post-COVID-19 lung disease detection and spoofing attack identification. He has explored hybrid modeling approaches for signal denoising and developed algorithms for vocal fold segmentation in high-speed video imagery. His contributions bridge theoretical signal processing with practical medical and cybersecurity applications. No scientific awards or grants are explicitly mentioned in the provided texts. He has advised no listed students, and lab affiliations remain unspecified.

Florian Holzapfelمشاهده پروفایل

استاد

• Flight System Dynamics
• Flight Control/Flight Guidance
• Trajectory Optimization
• +۴ مورد دیگر

Prof. Dr.-Ing. Florian Holzapfel holds the professorship in Flight System Dynamics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. His academic journey includes a doctorate at TUM under Prof. Gottfried Sachs, followed by industry experience at IABG before joining TUM in 2007. He is an Associate Fellow of the AIAA and a DGLR member. Research focuses on flight control systems, trajectory optimization, sensor technology, avionics, and safety-critical systems, with emphasis on collaboration with Bavarian aerospace SMEs. Key contributions include adaptive control strategies, eVTOL trajectory optimization, and urban air mobility applications. Education: Bachelor/Master in Aerospace Engineering from TUM Doctorate (Dr.-Ing.) at TUM's Chair of Flight Mechanics and Flight Control Awards include the AIAA 2004 Best Paper Award and the Willy Messerschmitt Prize. His work bridges industry-academia gaps, addressing practical challenges in flight dynamics and advanced air mobility. Labs/Teams: Leads research in eVTOL trajectory generation, flight control architectures, and safety-critical system design. Grants/Awards: Extensive contributions to AIAA conferences and industry-funded projects.

Marc BOCQUETمشاهده پروفایل

استاد

• Data Assimilation
• Inverse Problems
• Machine Learning in Geosciences
• +۳ مورد دیگر

Marc BOCQUET is a Professor and Senior Researcher at CEREA, a joint laboratory between École des Ponts ParisTech and Électricité de France (EdF) R&D. He serves as Deputy Director of CEREA and holds academic affiliations with Sorbonne Université, École Polytechnique, and other Paris-area institutions. His research focuses on data assimilation, inverse problems, and machine learning applications in geosciences, environmental statistics, and climate modeling. He has authored 110+ papers and co-edited books, and is a Fellow of the European Centre for Medium-Range Weather Forecasts. **Research Interests:** Development of mathematical methods for estimating atmospheric/oceanic states and improving geoscience models using observational data. Applications to atmospheric chemical transport models and dispersion modeling in urban environments. Integration of machine learning (e.g., deep learning, generative AI) with data assimilation for climate projections and emissions quantification. **Key Contributions:** Pioneered hybrid models combining machine learning and classical data assimilation for weather forecasting (e.g., IFS system). Advanced generative AI-based emulators for sea ice dynamics and wildfire spread. Developed neural network approaches for CO₂ plume inversion using satellite imagery (e.g., OCO-3 SAM data). **Awards:** Fellow of the European Centre for Medium-Range Weather Forecasts (ECMWF). **Labs/Teams:** Active in CEREA and collaborates with institutions like École Normale Supérieure and Université de Toulouse through projects like DRUIDS (PEPR Maths-Vives).