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Joseph BentsmanView profile

Professor

• Control of nonlinear systems
• Control of distributed parameter systems
• Nonlinear oscillations
• +7 more

Joseph Bentsman is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. He also holds affiliate appointments in the Department of Aerospace Engineering (since 2015) and the Department of Electrical and Computer Engineering (since 2018). His academic journey began with an M.S. from Byelorussian Polytechnic Institute in Minsk, USSR (1979), followed by a Ph.D. in Electrical Engineering from Illinois Institute of Technology (1984). Professor Bentsman's research focuses on control of nonlinear and distributed parameter systems, nonlinear oscillations, network control, stability theory, and stochastic multiscale methods. He pioneered a new class of dynamical systems with active singularities that admit control actions during singular phases of motion, which represent a novel category of hybrid systems characterized by impulsively controlled discrete transitions. His recent work has expanded into biomedical applications, particularly thermophysical modeling of tissue during electrosurgery and control of phase change processes. His recent publications (2021-2024) reveal a strong trend toward biomedical applications of control theory, particularly in modeling heat conduction in biological tissues, electrosurgical processes, and phase change phenomena. Approximately 60% of his recent work focuses on biomedical applications, while the remainder continues his foundational work on nonlinear control systems, distributed parameter systems, and systems with active singularities. Key subfields include Stefan problems, enthalpy-based control, telegraph equation modeling, and PDE-based control of complex physical processes. NSF Presidential Young Investigator Award (1989) Life Fellow of American Society of Mechanical Engineers Life Senior Member of IEEE IEEE Control Systems Society Technical Committee Chair on Power Generation (2015-2019) International Society of Automation POWID Achievement Award (2014) 2018 AIST Computer Applications Best Paper Award Featured in 'People in Control', IEEE Control Systems Magazine (2018) Professor Bentsman has been instrumental in developing educational approaches that integrate signal processing, instrumentation, control, and machine learning, as evidenced by his two textbooks. His work on the steel continuous casting process, particularly the mold oscillation system, has led to practical industrial applications. He has also made significant contributions to power plant control systems and boiler/turbine control. His research group appears to focus on both theoretical control systems development and practical implementation in industrial and biomedical settings, with strong connections to steel manufacturing, power generation, and medical device industries.

John CotterView profile

Professor

• Finance
• Quantitative Finance
• Volatility Modelling
• +5 more

John Cotter is a Full Professor of Finance and Chair in Quantitative Finance at University College Dublin's Smurfit School of Business. He holds a PhD from Queen's University Belfast and prior academic roles include Associate Professor (2006-2012) and Senior Lecturer (2004-2006). His research focuses on volatility modeling, risk management, and asset pricing with applications in equity, real estate, and derivative markets. Cotter directs the Centre for Financial Markets and the Financial Mathematics Computation Cluster (FMC2), a multi-university research initiative funded by Science Foundation Ireland. Education: BComm and MEconSc from University College Cork, PhD in Finance from Queen's University Belfast. Research interests span asset pricing, volatility modeling, risk management, and financial market integration. His work has been published in top journals like Journal of Banking and Finance and Journal of International Money and Finance . He has secured grants including the ADAPT Phase 2 project (2021-2026). Cotter advises the European Securities Markets Authority (ESMA) and has consulted for numerous organizations globally. Notable awards include the UCD Outstanding Educator Teaching Award and UCD School of Business Research Contribution Award. He serves as Associate Editor for three journals and has supervised numerous PhD students through FMC2.

• Medical Microwave Imaging
• Microwave Engineering
• Millimeter-wave Engineering
• +3 more

Professor Amin Abbosh is a faculty member at the School of Electrical Engineering and Computer Science, University of Queensland. His research focuses on Medical Microwave Imaging and Millimeter-wave Engineering, with contributions to advanced imaging systems, antenna design, and communication technologies. He leads projects in electromagnetic medical sensing, including portable brain scanners and wearable diagnostic systems. His work integrates applied electromagnetics with AI-driven algorithms, addressing challenges in stroke detection, liver health monitoring, and deep vein thrombosis diagnosis. With over 16 patents and collaborations across biomedical and engineering domains, his research bridges clinical needs with cutting-edge electromagnetic techniques. Key projects include the development of low-cost healthcare monitoring systems and reconfigurable antennas for satellite communications. Research interests span medical imaging systems, antenna array design, and signal processing for healthcare applications. His team innovates in areas like phased arrays, dielectric property analysis, and non-invasive diagnostics. Recent advancements include synthetic microwave focusing techniques and self-supervised deep learning models for clutter removal in imaging. Publications highlight contributions in IEEE journals and conferences, emphasizing clinical applications and device prototyping. Collaborations with institutions like the University of Queensland’s medical faculty and industry partners ensure practical implementation of his research.

Edouard OyallonView profile

Researcher

• Machine Learning
• Deep Learning
• Distributed Systems
• +5 more

Edouard Oyallon is a CNRS Researcher at Sorbonne University's MLIA team within the Institute of Intelligent Systems and Robotics (ISIR). His research focuses on machine learning foundations, particularly the symmetries of deep neural networks, and large-scale distributed/decentralized training algorithms. He has contributed to frameworks like Kymatio for wavelet scattering transforms and collaborates on projects such as SHARP (Frugal Learning) and ADONIS (ANR-funded). He advises multiple PhD and postdoctoral researchers and teaches advanced deep learning courses at Institut Polytechnique de Paris (IPP). Grants include the ADONIS project (ANR/Sorbonne) and participation in VHS and CoCa4AI initiatives. His work spans theoretical and applied aspects, with recent emphasis on optimizing LLM training at exascale. He maintains active roles in academic service, including organizing workshops on federated learning and graph machine learning.

Raphael SznitmanView profile

Professor

• Computer Vision
• Probabilistic Methods
• Statistical Learning
• +2 more

Prof. Dr. Raphael Sznitman serves as Director of the ARTORG Center for Biomedical Engineering Research and Head of the Artificial Intelligence in Medical Imaging group at the University of Bern, Switzerland, holding a Full Professor position in AI for Medical Imaging since 2015. Education: PhD in Computer Science, Johns Hopkins University (2011) MSc in Computer Science, Johns Hopkins University (2009) BSc in Cognitive Systems, University of British Columbia (2007) Research Interests: Sznitman's work centers on computational vision , probabilistic methods , and statistical learning applied to medical imaging challenges. His group develops AI algorithms for ophthalmic diagnostics, surgical robotics, and medical image analysis, with emphasis on OCT, surgical phase recognition, and domain adaptation techniques. Key application areas include retinal disease detection and cataract surgery automation. Publication Trends: His 2021-2025 publications reveal concentrated efforts in deep learning for medical imaging , particularly in ophthalmology (OCT analysis) and surgical video understanding. Emerging themes include LLM applications for clinical monitoring, unsupervised out-of-distribution detection for surgical safety, and physics-informed AI for multimodal medical data fusion. Research Leadership: As ARTORG Center Director, Sznitman oversees interdisciplinary research bridging computer science and clinical medicine. His group collaborates extensively with Bern University Hospital clinicians on translational projects, securing funding for AI-driven diagnostic tools and surgical assistance systems. Current initiatives focus on real-time intraoperative guidance and spaceflight ophthalmology applications. Laboratory: The Artificial Intelligence in Medical Imaging group operates within ARTORG's dedicated facilities, maintaining partnerships with surgical robotics labs and ophthalmology departments for clinical validation of AI systems. Their work integrates multimodal data streams including OCT, VR perimetry, and surgical video feeds.

Vikas SinghView profile

Professor

• Image Analysis
• Machine Learning
• Computer Vision
• +3 more

Vikas Singh is a Professor in the Department of Biostatistics at the University of Wisconsin-Madison, with appointments in Computer Sciences and Statistics. He also serves as a part-time Faculty Researcher at Google DeepMind. His research focuses on image analysis, machine learning, and medical imaging applications, particularly in neuroimaging and Alzheimer's disease studies. Singh holds a Ph.D. in Computer Science from SUNY Buffalo and has taught courses such as BMI/CS 767 (Medical Image Analysis) and CS 766 (Computer Vision). Affiliations: UW Computer Vision Group, Wisconsin Alzheimer's Disease Research Center (W-ADRC), Machine Learning@UW. Research: Develops algorithms for medical image analysis, including tools for neuroimaging and longitudinal biomarker studies. Grants: Collaborates on grants related to Alzheimer's progression modeling and imaging techniques. His work emphasizes interdisciplinary applications, bridging statistics, geometry, and optimization to solve real-world problems in healthcare and engineering.

Hamid KrimView profile

Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +5 more

Hamid Krim is a Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He leads the Vision, Information and Statistical Signal Theories and Applications (VISSTA) group, focusing on statistical signal/image analysis, data science, and machine learning. His prior roles include Research Scientist at MIT’s Laboratory for Information and Decision Systems and Member of Technical Staff at AT&T Bell Labs. He holds a Ph.D. in Electrical Engineering from Northeastern University, and degrees from the University of Washington and University of Southern California. Education: Ph.D., Electrical Engineering, Northeastern University (MA), 1990s Master's, Electrical Engineering, University of Washington Bachelor's, Electrical Engineering, University of Southern California and University of Washington Research Interests: Machine Learning, AI, Signal Processing, Communications, and Control Systems . His work bridges formal mathematical frameworks with applied problems, emphasizing generative AI, adversarial robustness, and subspace-driven data analysis. Recent innovations include Volterra neural networks and expansive synthesis techniques for data generation. Awards & Recognition: 2000 NSF CAREER Award 2008 IEEE Fellow 2019 IEEE SPS Sustained Impact Paper Award Multiple extended research invitations at top institutions globally Grants & Advising: Leads the VISSTA Lab, collaborating on projects like medical algorithm development (e.g., lung wheeze analysis) and hurricane activity prediction. His work spans interdisciplinary applications in healthcare, robotics, and defense systems. Labs & Teams: Director of the VISSTA Lab, fostering research in signal theory and machine intelligence. Collaborates with academia and industry on cutting-edge AI and sensor fusion technologies.

• Computer Graphics
• 3D Reconstruction
• Deep Learning
• +5 more

Maks Ovsjanikov is a Professor in the Computer Science Department at École Polytechnique, France , and a Visiting Research Scientist at Google DeepMind. His research focuses on mathematically principled approaches for geometric data analysis and synthesis, including learning on surface meshes, 3D point clouds, and graphs. Key Collaborations: Google DeepMind, Sanofi, Dassault Systèmes Research Themes: Non-rigid shape matching, 3D reconstruction, transfer learning, learning on geometric data, functional maps, deep learning for scientific discovery Recent Article Trends emphasize geometric deep learning, with publications at top venues like SIGGRAPH Asia, ICCV, and CVPR. Topics include surface reconstruction, functional maps, 3D keypoint detection, and diffusion models for shape matching. Scientific Honors include: ERC Consolidator Grant (VEGA Project, 2023) ERC Starting Grant (2017) ACM SIGGRAPH 2023 Test-of-Time Award Best Paper Awards at 3DV 2021 and 3DV 2022 Student Advisees have received prestigious awards, such as the IP Paris Best PhD Thesis Award (Souhaib Attaiki, 2023) and GdR IG-RV Runner-Up (Nicolas Donati, 2024). The GeomeriX Team at École Polytechnique drives his group's research, supported by the VEGA and AIGRETTE projects.

Olufemi A. OmitaomuView profile

Adjunct Professor

• Energy Infrastructure Siting and Analysis
• Artificial Intelligence in Critical Infrastructure Systems
• Anomaly Detection in Complex Systems
• +4 more

Olufemi A. Omitaomu is an Adjunct Professor at the Department of Industrial and Systems Engineering within the Tickle College of Engineering at the University of Tennessee, Knoxville. He serves as a Group Leader and Distinguished R&D Staff at Oak Ridge National Laboratory (ORNL), leading the Computational Urban Sciences Group in the Computational Sciences and Engineering Division. Ph.D., Industrial Engineering (Information Engineering concentration), University of Tennessee, Knoxville M.S., Mechanical Engineering, University of Lagos, Nigeria B.S., Mechanical Engineering, Lagos State University, Nigeria Dr. Omitaomu’s research focuses on artificial intelligence in energy systems , cognitive coupling of human-machine systems , anomaly detection in complex systems , energy infrastructure siting and analysis , and disaster risk analysis with urban systems resilience . His work integrates computational models, optimization techniques, and geospatial frameworks to address challenges in critical infrastructure systems. The 15 most recent publications highlight trends in renewable energy integration , climate adaptation strategies , and emergency resource allocation . Key methodologies include agent-based modeling , multicriteria decision analysis , and wavelet shrinkage , applied to domains like energy systems , disaster management , and urban sustainability . Scientific recognition includes: Distinguished R&D Staff, Oak Ridge National Laboratory Senior Member, Institute of Industrial and Systems Engineers (IISE) Senior Member, Institute of Electrical and Electronics Engineers (IEEE) He actively mentors MS and PhD students with expertise in Python programming , game theory , and human-machine systems . His research is supported by collaborations with ORNL and interdisciplinary grants.

• Image Compression
• JPEG2000
• Signal Processing
• +5 more

Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.

• Numerical Analysis
• Computational Fluid Dynamics
• Applied Mathematics
• +3 more

Jennifer Ryan is a Professor of Numerical Analysis and Division Head of Numerical Analysis, Optimization, and Systems Theory at the Department of Mathematics, KTH Royal Institute of Technology. Her research focuses on designing and developing numerical schemes to extract accuracy from simulations, particularly through superconvergence properties and computational efficiency improvements. She applies these techniques to applications such as imaging, fluid visualization, and plasma dynamics. Education: PhD in Applied Mathematics, Brown University; MS in Mathematics, Courant Institute; BA in Applied Mathematics, Rutgers University. Professional Activities: Member of editorial boards for BIT Numerical Mathematics, ESAIM:M2AN, and Communications on Applied Mathematics and Computation; Steering committee member of AWM's Women in Numerical Analysis and Scientific Computing (WINASc). Her publications emphasize discontinuous Galerkin methods, SIAC filtering, and applications in fluid dynamics. She has served on multiple grant review panels and received awards for diversity and inclusion initiatives. Grants: Principal Investigator for projects funded by the Swedish Research Council, NSF, and US Air Force Office of Scientific Research. Awards: Fellow of UK Higher Education Academy, DAAD Fellowship, and Householder Fellowship.

Galatia CleanthousView profile

Lecturer

• Mathematical Analysis
• Probability
• Statistics
• +6 more

Dr. Galatia Cleanthous is a Lecturer in the Department of Mathematics and Statistics at Maynooth University, Ireland, affiliated with the Faculty of Science & Engineering and the Hamilton Institute. She joined Maynooth in 2020 after postdoctoral positions at Trinity College Dublin, Newcastle University, and University of Cyprus, and holds a PhD in Pure Mathematics from Aristotle University of Thessaloniki (2014). Education PhD in Mathematics, Aristotle University of Thessaloniki, Greece (2014) MSc in Mathematics, Aristotle University of Thessaloniki, Greece Diploma in Mathematics, Aristotle University of Thessaloniki, Greece Research Interests Her research bridges pure and applied mathematics, focusing on Mathematical Analysis , Probability , and Statistics . Specifically, she explores Geometric Analysis , Geometric Function Theory , and Harmonic Analysis on manifolds and metric spaces. In statistics, she works on Nonparametric , Spatial , and Environmental Statistics , developing adaptive estimation techniques and studying Gaussian random fields on spheres and other domains. Publication Trends From 2025 back to 2013, her work has consistently appeared in top journals such as Annals of Statistics , Bernoulli , Journal of Nonparametric Statistics , and Transactions of the American Mathematical Society . A clear trend emerges: early publications concentrate on pure analytic topics like Fourier multipliers and function spaces, while recent outputs integrate these theoretical tools into modern nonparametric statistics, density estimation on manifolds, and stochastic modeling of environmental and seismological data. Scientific Awards Master’s degree ranked first with grade 9.8/10, Aristotle University of Thessaloniki (2011) Diploma ranked first among ~200 students, grade 9.7/10, Aristotle University of Thessaloniki (2009) Undergraduate merit awards for three consecutive academic years (2005-2008), State Scholarship Foundation of Greece National first place in Cypriot high-school mathematics entrance exams (2005), Ministry of Education, Cyprus Advising & Outreach Dr. Cleanthous has supervised BSc and MSc students, including Ultán Doherty (BSc, 1st Class Honors, 2021) and Anush Harish (MSc, 2022). She serves as Chair of the Department PR Committee, Member of the University STEM Promotions Committee, and Member of the departmental Equality, Diversity & Inclusion committee. Beyond campus, she trains young mathematicians at the North Kildare Maths Problem Solving Club and organizes public engagement events for Science Week. Labs & Teams She is associated with the Hamilton Institute at Maynooth University, a multidisciplinary research institute fostering collaboration between mathematics, computer science, and engineering.

Ina Fiterau BrosteanView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Health Informatics
• +3 more

Ina Fiterau Brostean is an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where she leads the Information Fusion Lab. Previously, she was a Postdoctoral Fellow at Stanford University's Mobilize Center (2015–2018) and earned her PhD in Machine Learning from Carnegie Mellon University (2015). Her research focuses on hybrid systems for multimodal data integration, particularly in healthcare, aiming to develop predictive models for clinical outcomes using time series, text, and images. Key areas include disease trajectory modeling, weakly-supervised transfer learning, and adaptive representation learning. Education: PhD in Machine Learning (Carnegie Mellon, 2015), MSc in Machine Learning (Carnegie Mellon, 2012), BEng in Computer Engineering (Politehnica Timisoara, Romania, 2009). Professional roles include teaching COMPSCI 651 (Optimization in Computer Science) and organizing NeurIPS workshops on Machine Learning in Healthcare. Research interests span machine learning methodologies for healthcare applications, including interpretable models, time series analysis, and dimensionality reduction. Notable achievements include the Marr Prize (ICCV 2015) and Star Research Award (SCCM 2016). Her lab collaborates on projects like predicting Alzheimer's disease progression and surgical outcomes using Bayesian networks and deep learning. Awards and recognitions include Rising Stars Workshop (2016), Manning IALS Research Award (2019), and GE Foundation Scholar Leader Award (2007). She actively contributes to the ML4Health community through leadership roles and workshop organization.

Christopher GoyneView profile

Associate Professor

• Hypersonics
• Scramjets
• Instrumentation and Laser Diagnostics
• +5 more

Christopher Goyne is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia (UVA) and Director of the UVA Aerospace Research Laboratory. He holds a B.Eng. (1991) and Ph.D. (1999) in Mechanical Engineering from the University of Queensland, Australia. His research focuses on hypersonic propulsion, scramjet technology, instrumentation development, and advanced manufacturing. He leads the UVA Hypersonics Research Complex and is a key figure in the University Consortium for Applied Hypersonics. Goyne’s work includes contributions to NASA’s Hyper-X Program and the HyShot scramjet flight test program. He is an Associate Fellow of the AIAA and serves on editorial and advisory boards for journals and organizations such as the Shock Waves journal and Virginia’s Aerospace Advisory Council. Education: B.Eng. (Mechanical Engineering, University of Queensland, 1991); Ph.D. (Mechanical Engineering, University of Queensland, 1999). Research Interests: Hypersonics and scramjet propulsion Diagnostic techniques (e.g., laser-based methods, optical emission spectroscopy) Wind tunnel and flight testing Controls and adaptive systems for hypersonic flow paths Advanced manufacturing for aerospace components Awards: Recipient of the 2023 James C. McDaniel Fellow Award and 2022 Outstanding Researcher Award. Holds leadership roles in AIAA committees, including past Chair of the HyTASP Program Committee. Recognized with the Sigma Gamma Tau Outstanding Aerospace Professor Award (2006) and multiple research fellowships. Grants and Projects: Funded by NASA, the Air Force Office of Scientific Research, and industry partners. Leads UVA’s contributions to hypersonic ground and flight testing, including sensor development and combustion efficiency studies. Labs and Teams: Directs the UVA Aerospace Research Laboratory, collaborating on projects such as the UVA Hypersonics Research Complex and the University Consortium for Applied Hypersonics. Advises student chapters of AIAA and Sigma Gamma Tau.

Navid BayatiView profile

Associate Professor

• Renewable/Hybrid Power Systems
• Protection and Transient Analysis of Power Grids
• Microgrids and Active Energy Networks
• +5 more

Navid Bayati is an Associate Professor at the University of Southern Denmark, affiliated with the Institute of Mechanical and Electrical Engineering and the Centre for Industrial Electronics. He leads the Control and Protection of Smart Grids (CAP-SG) group and focuses on renewable/hybrid power systems, microgrid protection, and grid code compliance. Education: Ph.D. in Power Systems & Microgrid Protection (2020, Aalborg University); M.Sc. in Power Systems (2017, Amirkabir University of Technology) His research spans renewable energy integration , transient analysis , grid interconnection , and digital twin applications . Recent work includes machine learning for carbon emission prediction, fault localization in DC microgrids, and supercapacitor resilience in hybrid systems. Collaborations include projects like IEA Wind Task 50 and RePoSys , addressing grid renovation, life cycle assessment, and digital twin resilience. His teaching portfolio covers power electronics , energy management , and microgrid control .