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Álvaro Hernández AlonsoView profile

Professor

• Mobile Robotics
• Multisensor Integration
• Ultrasonic Sensory Systems
• +5 more

Álvaro Hernández Alonso is a Full Professor in the Department of Electronics at the University of Alcalá (UAH). He previously served as Secretary of the Electronics Department and coordinated the GEINTRA research group (2021–2023), which includes 24 researchers and over 25 active projects. His research focuses on mobile robotics, multisensor integration, ultrasonic systems, NILM techniques, FPGA implementations, and electronic technologies for transport. Education: PhD in Electronics and Systems (University Blaise Pascal & UAH) Electronics Engineering Degree (UAH) Research interests include positioning systems (infrared, ultrasonic, visible light), energy monitoring for assisted living, and embedded hardware architectures. He has secured €1.9M in funding (public and private) over the past decade, leading 4 projects as Principal Investigator. His work has produced 41 indexed journal papers (25 Q1), 6 patents, and 65 conference contributions. Awards: Best Thesis Awards at UAH for two doctoral students under his supervision. He has advised 30+ student theses and 9 doctoral dissertations, many in international co-supervision programs. Funding highlights: 19 public projects (€900k total) and 10 private projects (€1.1M total). His research has achieved 4 consecutive positive evaluations (1997–2020) from Spain's National Research Evaluation Commission.

Huaping LiuView profile

Professor

• Wireless Communication Systems
• Signal Processing
• MIMO Systems
• +7 more

Huaping Liu is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University (College of Engineering). He holds a Ph.D. in Electrical Engineering from New Jersey Institute of Technology (1997) and previously worked at Lucent Technologies (1997–2001). His research focuses on wireless communication systems, including MIMO systems, ultrawideband technologies, spread spectrum systems, and channel coding. He has contributed to applications in healthcare monitoring and industrial automation. Education: Ph.D., Electrical Engineering, New Jersey Institute of Technology (1997) M.S., Electrical Engineering, Nanjing University of Posts and Telecommunications (1990) B.S., Electrical Engineering, Nanjing University of Posts and Telecommunications (1987) Research Interests: Wireless communication theories and systems Embodied intelligence and multi-agent robotics Tactile sensing and large-area robotic skin Autonomous navigation and vision-language models Awards and Recognition: 2017 IEEE UEMCON Best Paper Award 2015 OSU College of Engineering Research Collaboration Award 2011 DesignCon Best Paper Award 2010 IEEE VTS Best Paper Award 2007 & 2004 EECS IEEE Professor of the Year Advising and Grants: Dr. Liu emphasizes student-driven learning, fostering critical thinking through inquiry-based teaching. His work involves collaborative projects in robotics, cybersecurity, and sensor systems, supported by grants and industry partnerships. Labs/Teams: Active in developing embodied intelligence frameworks, robotic tactile sensing systems, and cybersecurity solutions for digital twins and vehicular networks. Collaborates on simulation platforms for human-robot collaboration (e.g., HumanTHOR).

Tetiana BogodorovaView profile

Researcher

• Power Systems
• Data Science
• Artificial Intelligence
• +4 more

Tetiana Bogodorova is a Senior Research Scientist in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI). Her research focuses on modeling, data science, and artificial intelligence for power systems and complex systems, with expertise in system identification and control. She holds a B.Sc. and M.Sc. from Igor Sikorsky Kyiv Polytechnic Institute and a Ph.D. in Electrical Engineering from KTH Royal Institute of Technology. Notable awards include the Program of Excellence Award from KTH's Doctoral Program in Electrical Engineering. Dr. Bogodorova's academic roles include serving as a docent (Associate Professor) at the Ukrainian Catholic University's Department of Computer Science and Information Technology. She also has industry experience as a System Analyst at NetCracker Technology Corp. and a Data Scientist at ABM Cloud. Her work emphasizes interdisciplinary approaches, particularly leveraging Modelica for multi-domain simulations in power systems, microgrids, and energy systems. Her research portfolio includes developing open-source tools like OpenIPSL, ModelicaGridData, and OpenIMDML, which address challenges in power system stability, renewable integration, and data-driven modeling. These tools combine machine learning techniques with traditional engineering methods to enhance predictive accuracy and system resilience. Key Research Themes: Renewable energy modeling, deep learning for grid stability, microgrid control, and Modelica-based multi-domain simulation frameworks. Awards: Program of Excellence Award (KTH, 2017). Industry Collaboration: Experience in system analysis and data science roles, bridging academic research with real-world applications.

Salim UllahView profile

Researcher

• Approximate Computing
• FPGA-Based Hardware Accelerators
• Embedded Systems Design
• +3 more

Salim Ullah is a researcher in the Department of Embedded Systems at Ruhr University Bochum. His work focuses on FPGA-based approximate computing, hardware accelerators, and embedded systems design. He leads the Embedded Systems Team, contributing to advancements in reconfigurable logic and neuromorphic computing. Research Interests: Approximate arithmetic circuits, FPGA design methodologies, neural network implementation on FPGAs Key Projects: AxOCS (approximate operator scaling), AxOMaP (mathematical programming for arithmetic operators), AxOSpike (spiking neural networks for operator design) Publications (2024-2020) highlight innovations in error resilience, quantization techniques, and hardware-software co-design. Collaborations include work on RISC-V emulation (NvMISC), energy-efficient multipliers, and embedded AI acceleration frameworks.

Stephan WesterdickView profile

Researcher

• Electrical Engineering
• Plasma Technology
• Microsystems Engineering
• +3 more

Stephan Westerdick is a Researcher at the Department of Electronic Circuits, Faculty of Electrical Engineering and Information Technology at Ruhr-University Bochum. His work focuses on advanced analytical instrumentation, plasma-based sensor systems, and microsystem technologies. Key research areas include microplasma dynamics, miniaturized mass spectrometry, and AI-driven spectral analysis. He has contributed to the development of lab-on-a-chip systems and ultrasonic process monitoring solutions. His interdisciplinary approach integrates plasma physics, MEMS fabrication, and machine learning for applications in chemical analysis and industrial process control. Recent publications highlight innovations in time-of-flight micro mass spectrometers, fouling detection via ultrasound, and AI-enhanced NMR/Mass Spectrometry. His work bridges fundamental plasma research with practical engineering challenges in sensor design and process optimization. Westerdick collaborates with institutions like FHR (Forschungszentrum Jülich) on projects involving plasma edge layers and terahertz technology. His team's research is applied in energy-efficient systems, medical engineering, and next-generation analytical tools.

Dennis AlveringhView profile

Assistant Professor

• MEMS (Micro-Electromechanical Systems)
• Sensor Engineering
• Thermal Anemometry
• +7 more

Dennis Alveringh is an Assistant Professor in the Integrated Devices and Systems group at the University of Twente, Faculty of Engineering Technology. His work bridges academic research and industrial R&D, with an external role as R&D Scientist at Salland Engineering (Europe) B.V. since 2018. Research Interests: MEMS and sensor engineering Thermal anemometry and flow sensing Capacitive and pressure sensors Machine learning integration in sensor systems Low-drift and directional sensor design for horticultural applications CMOS-MEMS integration and fabrication His recent publications (2024–2025) highlight a strong trend toward intelligent, multi-parameter MEMS sensors enhanced with machine learning for improved accuracy in flow, angle of attack, and environmental condition detection. These works emphasize system-level design, data collection frameworks, and real-world applications in agriculture and microfluidics. Scientific Awards: Best Poster Presentation 1st Place (Contributor), 23 Feb 2024 Best Presentation - SAFE (Contributor), 28 Jun 2024 Best Presenter Award (Contributor), 19 Sept 2024 Advising and Grants: While no direct funding is listed, Dennis plays a key advisory role in multiple student-led projects, particularly guiding T.L. Hackett in advanced MEMS anemometry research. His contributions to PhD theses, conference organization (e.g., MFHS 2017), and journal peer-review (e.g., Microelectronic Engineering) reflect strong academic service and mentorship. Labs and Teams: He is part of the Integrated Devices and Systems group at the University of Twente, collaborating closely with Prof. J. Schmitz and other researchers in MEMS design and sensor development. The team focuses on innovative, application-driven microsystems with an emphasis on sustainability and precision in agricultural and industrial contexts.

Ruediger EhlersView profile

Professor

• Formal Methods
• Reactive Synthesis
• Automated Reasoning
• +4 more

Ruediger Ehlers is a Professor for Embedded Systems at Clausthal University of Technology, focusing on formal methods , automated reasoning , and cyber-physical systems . His research aims to bridge formal methods and artificial intelligence to enhance the efficiency and reliability of computational systems. University: Clausthal University of Technology Institute: Institute for Software and Systems Engineering Research Interests: His work centers on making correct-by-construction design processes more efficient, particularly through reactive synthesis , verification of finite- and infinite-state systems , and runtime monitoring . He has developed tools like slugs and planet for synthesis and neural network verification. Publications Trends: Recent articles emphasize temporal logic , reinforcement learning under constraints , and algorithm engineering for practical applications in manufacturing and quantum computing. Scientific Awards: Volkswagenstiftung Momentum endeavor (2023) Grants and Projects: He leads the EU/H2020 Project SAFE-10-T (2021) and secured funding from the German Science Foundation (2016) for synthesizing GUI code. His Open-MPW-6 program (2023) involves hardware implementation of runtime monitoring.

• Nonlinear Dynamics
• Quantum Science
• Turbulence
• +3 more

Daniel Lathrop is a Professor of Physics and Geology at the University of Maryland (UMD), and a Fellow of the American Physical Society. He joined UMD in 1997 following postdoctoral roles at Yale and faculty positions at Emory University. His research spans nonlinear dynamics, quantum science, and geophysical fluid dynamics. Lathrop directs the Nonlinear Dynamics Laboratory, focusing on experiments simulating Earth’s core (e.g., the 3-meter liquid sodium spherical Couette experiment) and superfluid helium phenomena. Education: B.A. in Physics (UC Berkeley, 1987), Ph.D. in Physics (University of Texas at Austin, 1991). Research emphasizes turbulent flows in rotating systems, magnetic field generation (dynamo effects), and quantum fluid behavior. His lab integrates machine learning for prediction of magnetic field evolution and turbulence dynamics. Collaborations include developing UAV-based geophysical sensors for landmine detection and advancing stochastic computing hardware using magnetic tunnel junctions. Awards include the NSF Presidential Early Career Award (1997), APS Stanley Corrsin Award (2012), and UMD Distinguished Scholar-Teacher designation. He served as Director of the Institute for Research in Electronics and Applied Physics (2006–2012). Advising: Supervised numerous graduate students in experimental physics and geophysics. Active in interdisciplinary projects combining fluid dynamics, quantum science, and machine learning. Labs/Teams: Nonlinear Dynamics Laboratory, Quantum Materials Center, and Institute for Research in Electronics & Applied Physics (IREAP). Research themes include planetary magnetic field modeling, turbulence in extreme conditions, and novel computing hardware inspired by physical systems.

Yeo Kiat SengView profile

Professor

• Low-power IC Design
• RF/mm-wave Integrated Circuits
• CMOS Technology
• +4 more

Professor Yeo Kiat Seng is an internationally renowned academic and IEEE Fellow specializing in low-power integrated circuit (IC) design, RF/mm-wave systems, and CMOS technology. He currently serves as Associate Provost for International Relations and Chairman of the University Research Board at Singapore University of Technology and Design (SUTD), while holding adjunct roles at Nanyang Technological University (NTU). Yeo has over 30 years of industry and academic leadership, including founding NTU's VIRTUS IC Design Center and securing over S$70M in research funding. His work focuses on advancing semiconductor technologies for 5G/6G communications, IoT, and energy-efficient systems. Roles: Professor (EPD, SUTD), Associate Provost (International Relations), Adjunct Professor (NTU) Education: B.Eng. (EE, NTU, 1993), Ph.D. (EE, NTU, 1996) Research Interests: Low-power IC design, RF/mm-wave integrated circuits, CMOS technology, visible light communications, and IoT solutions. Yeo has authored 10 books, 7 book chapters, and over 600 refereed publications, with notable contributions to IEEE journals. His patented innovations include world-leading RF filters and transceiver chipsets (e.g., VIRTUS 60GHz chipset). Recent Articles Trends: Focus on 6G/5G systems, AI-driven circuit design, energy harvesting, and miniaturized RF components. Key areas include high-efficiency power amplifiers, low-power receivers, and advanced antenna designs for millimeter-wave applications. Awards: Public Administration Medal (2009), IEEE Fellow (2016), Long Service Medal (2020), Top 2% Global Scientists (2020–2023) Grants & Collaborations: Over S$70M in funding from agencies like Singapore Economic Development Board. Partnerships with industry leaders include Kulicke & Soffa, M1, and Silvaco for 5G robotics and AI-driven IC design platforms. Labs/Teams: Leads SUTD's RF/mm-wave research initiatives and collaborates with NTU's School of EEE. Active in international conferences as Technical Chair and Editor for IEEE Transactions.

• Electrical Energy Storage
• Smart Grids
• Power System Operation
• +3 more

Dr Robert Best is a Senior Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast, within the Energy Power and Intelligent Control department. He specializes in areas such as electrical energy storage, smart grids, and power system operation with high renewable integration. His work also involves phasor measurement units and distribution network modernization. He has been actively involved in supervising PhD students and has contributed to numerous research projects since 2012. Research Interests: Dr Best’s research focuses on distributed generation, electrical energy storage systems, grid integration of renewables, and power system analysis. His work addresses challenges in modernizing distribution networks and enhancing grid stability through advanced technologies like battery energy storage and phasor measurement units. Awards and Recognition: Robert Best has received several accolades, including the 2017 Premium Award for Best Paper in IET Generation, Transmission & Distribution, the Best Graduate Student Poster Award (2021), and the Primary Engineer - Silver MacRobert Medal Award (2024). His contributions to energy storage and grid stability have been widely recognized internationally. Projects and Grants: He has led or collaborated on multiple projects, such as "SPIRE 2 - Storage Platform for the Integration of Renewable Energy" and initiatives focusing on very low frequency oscillations in power systems. His research often involves interdisciplinary approaches to tackle modern energy challenges. Labs and Teams: His involvement includes collaborative efforts with industry and academic partners, contributing to advancements in smart grid technologies and energy storage solutions. His team's work aligns with broader goals of achieving a 100% renewable energy system, particularly in Northern Ireland.

• Electronic design automation
• Embedded systems
• Energy efficient electronic systems
• +16 more

Daniele Jahier Pagliari is an Associate Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where he is also a member of the Interdepartmental Center PEIC - Power Electronics Innovation Center. He is actively involved in teaching and research, focusing on embedded systems, electronic design automation, and machine learning for edge computing. He teaches courses such as Optimized Execution of Neural Networks at the Edge, Machine Learning for IoT, and Hardware/Software Codesign of Flexible Computing Systems for Edge AI across various engineering programs including Computer Science and Systems Engineering, Data Science, and Automotive Engineering. His research interests span electronic design automation, embedded systems, energy-efficient computing, low-power design, and machine learning. He is particularly engaged in applying machine learning techniques to improve the design and performance of digital and analog circuits, with a focus on edge AI applications. His work aligns with key scientific areas including computer architecture, cyber-physical systems, and scientific computing. The recent publications highlight a strong trend in optimizing deep learning models for resource-constrained environments, accelerating neural network inference on ultra-low-power devices, and integrating physics-based models with AI for battery state estimation. There is also significant focus on using machine learning to enhance electronic design automation, particularly for analog and mixed-signal circuits, reflecting a convergence of AI and hardware design. He leads and participates in several high-impact research projects, including EU-funded initiatives like HAL4SDV, ISOLDE, TRISTAN, and AMBEATion, as well as commercial projects such as MASAI and software platform development for production support. He serves as the Scientific Responsible or Director in multiple projects, demonstrating leadership in both academic and industrial research contexts. He supervises multiple PhD students in the Computer and Systems Engineering program, including Luca Benfenati, Mohamed Amine Hamdi, Beatrice Alessandra Motetti, Giovanni Pollo, and Matteo Risso, whose research topics include latency-optimized inference, compiler optimization for edge devices, and hardware-aware deep learning design. He is also involved in patent development, notably for an instrumentation method to dynamically modify circuit precision. He is a member of the College of Computer, Film and Mechatronics Engineering and contributes to various degree programs. His work supports UN Sustainable Development Goals related to good health, affordable and clean energy, industry innovation, and sustainable cities.

• Machine Learning
• Telecommunications Engineering
• Cloud Computing
• +5 more

Gabriela Eleutério Soares is a researcher affiliated with Universidade Lusófona, focusing on telecommunications engineering, machine learning, and cloud computing. Her work bridges network optimization and interdisciplinary applications in biomedical signal processing and neuroscience. Education: PhD in Computer Science, Master’s in Mathematical and Computational Modeling, Bachelor’s in Computational Mathematics Her research explores: Cloud-based cellular network planning systems using linear programming and SON (Self-Organizing Networks) Automated coverage estimation methodologies for wireless networks Machine learning applications in traffic prediction and biomedical diagnostics Neuroscience-inspired models for spiking neuron synchronization via genetic algorithms Recent publications highlight trends in: Cloud computing integration with mobile networks Hybrid systems combining propagation models and empirical data Low-cost diagnostic tools using SERS and AI Historical development of bio-inspired network optimization

• Robotics
• Machine Learning
• Electrical Engineering
• +3 more

Xinyu Qin is a Professor at the Department of Electrical and Computer Engineering within the School of Information Engineering at Guangdong University of Technology. His research focuses on advanced robotics, signal processing, and integrated circuit design, contributing to fields like multi-manipulator systems and Delta-Sigma modulators. Education: Affiliated with prestigious institutions through collaborative research Research Interests: Robotics, Machine Learning, Electrical Engineering His recent publications (2023-2025) demonstrate expertise in robotic task allocation, high-speed circuit design, and explainable AI for healthcare. Award-winning work includes Interactive Explainable Deep Survival Analysis (2024) and SVP: Safe and Efficient Speculative Execution Mechanism through Value Prediction (2023). Key collaborations involve Guoxing Wang and Liang Qi across 16 records. Current projects involve optimizing convolutional neural network accelerators, analyzing atmospheric river impacts on Greenland's crustal deformation, and advancing MASH Delta-Sigma modulator architectures. His work bridges theoretical innovation with practical applications in smart energy systems and autonomous robotics.

Branislav HredzakView profile

Researcher

• Control and management of distributed renewable energy resources
• Reconfigurable and hybrid energy storage systems
• Advanced control systems for power electronic converters
• +4 more

Dr. Branislav Hredzak is a researcher at the Faculty of Engineering , University of New South Wales . His work focuses on power electronics, renewable energy systems, and advanced control strategies for energy storage and smart grids.

Stephane VialleView profile

Researcher

• High-Performance Computing
• GPU Computing
• Quantum Computing
• +3 more

Stephane Vialle is a researcher at CentraleSupélec, leading the Interdisciplinary Laboratory of Digital Sciences. His research focuses on High-Performance Computing (HPC), GPU Computing, Quantum Computing, and Quantum Machine Learning. He has extensive experience in developing scalable fine-grained computing environments and optimizing parallel algorithms for distributed systems. His work spans financial engineering, energy management, and railway infrastructure through digital twin technology. Recent contributions include advancements in GPU cluster energy efficiency, stochastic control algorithms, and hybrid classical-quantum architectures for data clustering. Research interests emphasize optimizing parallel computing frameworks for diverse applications, including financial modeling, material science simulations, and transportation systems. His publications demonstrate expertise in distributed computing, fault-tolerant architectures, and algorithmic innovation across multiple computational paradigms. Current projects explore quantum computing integration with classical systems, GPU-based large-scale data processing, and real-world applications of parallel simulation techniques. Notable contributions include the parXXL development environment for coarse-grained platforms and the MINERVE digital twin for railway infrastructure management. His work bridges theoretical computing advancements with practical implementations in engineering and finance domains.