جست‌وجوی استاد و پژوهشگر

Baoxia Miمشاهده پروفایل

دانشیار

• Membrane Separation
• Desalination
• Water Purification
• +۴ مورد دیگر

Baoxia Mi is an Associate Professor and Wood-Calvert Chair in Engineering in the Department of Civil and Environmental Engineering at UC Berkeley's College of Engineering. Her research focuses on physicochemical processes for sustainable water supply, renewable energy production, and public health protection using advanced membrane technology and nanomaterials. Ph.D., Environmental Engineering, University of Illinois at Urbana-Champaign (2006) M.S., Environmental Engineering, Tianjin University (2001) B.S., Civil Engineering, Tianjin University (1998) Her key research areas include: Membrane separation technology Water purification and desalination Nanomaterials for environmental applications Solar evaporation systems Wastewater reuse and brine management Major research projects involve: Graphene-oxide membranes for energy-efficient water treatment Fouling-resistant membrane development through molecular simulations Integrated membrane systems for water-energy-environment sustainability Her laboratory receives funding from: National Science Foundation (NSF) U.S. Department of Energy (DOE) Environmental Protection Agency (EPA) USAID, NWRI, and AMTA Recent research applications include lead removal from drinking water and solar-powered water treatment solutions.

• Digital Circuits and Systems
• Energy Efficiency
• Smart Cities
• +۱۳ مورد دیگر

Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.

• Aircraft Systems Engineering
• Avionics
• Integrated Modular Avionics (IMA)
• +۱۲ مورد دیگر

Prof. Dr.-Ing. Frank Thielecke is a full Professor and the head of the Institute of Aircraft Systems Engineering (Flugzeug-Systemtechnik) at Technische Universität Hamburg (TUHH), Germany. His research is centered on advanced aircraft systems, avionics, flight control, and the integration of emerging technologies such as hydrogen and hybrid-electric propulsion. Institution: Technische Universität Hamburg Department: Institute of Aircraft Systems Engineering (Flugzeug-Systemtechnik) Email: frank.thielecke@tuhh.de Office: Neßpriel 5, Room 1.012, 21129 Hamburg His research interests include integrated modular avionics (IMA), model-based systems engineering (MBSE), aircraft load estimation, health monitoring, fault diagnosis, and sustainable aviation technologies. He leads a research group actively contributing to next-generation aircraft design, with a strong focus on digitalization, virtual testing, and system safety. The recent publications highlight a consistent trend in developing model-based tools and architectures for avionics and aircraft systems. Key themes include the design of IMA platforms, virtual integration, system validation, hydrogen aircraft systems, and control algorithms for UAVs and flexible aircraft. His work frequently appears in AIAA, DASC, DLRK, and CEAS conferences and journals. Prof. Thielecke has been involved in numerous collaborative research projects focusing on more-electric aircraft, fuel cell systems, and advanced actuation. He has contributed to the development of frameworks such as ASHLEY and SArA for avionics platform design and systems architecting. His team also works on noise reduction in hydraulic systems and condition monitoring for aircraft subsystems. He supervises a group of researchers and PhD students, many of whom co-author his publications. While specific student names are not listed, long-term collaborators like Oliver Luderer, Thimo Bielsky, Nils Külper, and Philipp Chrysalidis are likely doctoral candidates or postdoctoral researchers in his group. He has secured funding for projects related to hydrogen aircraft, hybrid propulsion, and digital avionics engineering. His lab, the Institute of Aircraft Systems Engineering, operates test benches for avionics, hydraulic systems, and flight control validation. The team uses advanced simulation, co-simulation (e.g., FMI), and hardware-in-the-loop techniques for virtual integration and testing. Ongoing work includes the development of tools for early validation of flight control platforms and automated requirement-based testing.

• Wireless Networks
• Time-Sensitive Networking (TSN)
• WiFi and UWB Integration
• +۷ مورد دیگر

Jetmir Haxhibeqiri is a Postdoctoral Researcher at Ghent University 's Faculty of Engineering and Architecture , Department of Information Technology. His work focuses on Time-Sensitive Networking (TSN) over wireless systems, WiFi optimization, and Industrial IoT solutions. Key projects: IMEC Postdoctoral Fellowship Collaborations: Jeroen Hoebeke (UGent), Ingrid Moerman (UGent), Xianjun Jiao (UGent) Research Interests : Wireless network coordination, SDN integration for heterogeneous networks, low-latency communication, and machine learning applications in network optimization. Specialized in WiFi-LPWAN coexistence , In-Band Network Telemetry , and Cross-Technology Synchronization . Recent Publications : 2025 work on Wi-Fi-UWB synchronization, 2024 studies on coordinated spatial reuse in WiFi 7, and 2022 research on hardware-efficient PTP clock synchronization. Contributions span from theoretical models to practical implementations in industrial environments. Technical Expertise : Network densification strategies, interference management, and performance evaluation of large-scale wireless deployments. Developed simulation frameworks for LoRaWAN and WiFi TSN, with a focus on ns-3 validation. Education : PhD in Industrial Wireless Communication (2019, Ghent University).

• Computer Architecture
• Emerging Non-Volatile Memory Management
• Energy and Performance optimizations in Network-on-Chip
• +۴ مورد دیگر

Dr. Sukarn Agarwal is an Assistant Professor in the EECS Department at IISER Bhopal. His primary research focuses on modern computer architecture, including emerging non-volatile memory management, energy-efficient optimizations in network-on-chip, thermal-aware chip management, and heterogeneous system design. He utilizes tools such as GEM-5, CACTI, NVSIM, and McPAT for research. Dr. Agarwal actively seeks motivated PhD, M.Tech, and long-term internship candidates aligned with his research areas. Research Interests : Non-volatile memory hierarchies, energy-performance trade-offs, thermal-aware computing, and hybrid memory systems. Key Tools : GEM-5, CACTI, NVSIM, McPAT, Hotspot. His recent work includes publications on approximate computing in MLC-MRAM-based systems, thermal-aware frameworks for ReRAM, and energy-efficient cache architectures using STT-RAM. He emphasizes interdisciplinary approaches to address challenges in modern computing systems.

• Electrical Engineering
• Smart Grids
• Electric Power Systems Regulation
• +۷ مورد دیگر

Dr. Miguel Ángel Sánchez Fornié is a Collaborating Researcher at the Instituto de Investigación Tecnológica (IIT), affiliated with the School of Engineering at Universidad Pontificia Comillas (ICAI). He has been actively involved with IIT since 1980, contributing to European research projects and teaching Smart Grids in Master's programs since 2002. His work bridges industry leadership and academic research, particularly in the digitalization and modernization of electricity systems. Electromechanical Engineering, ICAI (1974) Diploma in Nuclear Safety, MIT, Boston (1977) Postgraduate coursework in Electricity Sector Regulation, 2008 His research focuses on smart grids, electricity system regulation, power system planning and operation, asset management, telecommunications for power systems, cybersecurity, and the application of big data and artificial intelligence in energy systems . He has led and contributed to major European projects such as H2020 and Horizon Europe, focusing on grid resilience, digitalization, and workforce skills development. The recent publications reflect a strong trend toward the digital transformation of electricity distribution networks , with emphasis on metrics, investment analysis, and the integration of distributed energy resources. His work spans from foundational reliability modeling in the 1980s to contemporary policy and technical frameworks for smart grids, showing a lifelong commitment to evolving energy infrastructure. Dr. Sánchez Fornié has been involved in several funded research projects and educational initiatives, including EDDIE, CoordiNet, and Digital Twin for Europe. He has also contributed to workforce development through sectoral skills alliances in the energy domain. He holds leadership roles in external organizations such as FUTURED and the European Technology and Innovation Platform on Smart Energy Networks (ETIP SNET), where he serves as Honorary Co-Chair of WG4. He has organized and participated in numerous scientific conferences and seminars, particularly on digitalization and education in the energy sector. Member, Education for the Digitalisation of the Energy System (EDDIE ASBL), Brussels (2024–Present) Board Member, FUTURED, Zaragoza (2017–Present) Co-Chair (Honorary), WG4, ETIP SNET, Brussels (2017–Present)

Mikel Sanzمشاهده پروفایل

پژوهشگر

• Quantum Computing
• Quantum Algorithms
• Quantum Technologies
• +۵ مورد دیگر

Mikel Sanz is a Ramón y Cajal Researcher and Ikerbasque Fellow at the University of the Basque Country (UPV/EHU) in Bilbao, Spain. His research focuses on quantum computing, quantum algorithms, quantum technologies, and quantum metrology. His research interests include: Quantum Computing and Quantum Algorithms Quantum Metrology and Quantum Sensing Digital-Analog Quantum Computing Quantum Machine Learning Quantum Simulation Quantum Error Correction and Mitigation Dr. Sanz's recent publications demonstrate a strong focus on practical applications of quantum computing across various domains. His work spans quantum hardware design, quantum algorithm development, quantum machine learning applications, and quantum metrology techniques. He has made significant contributions to digital-analog quantum computing approaches, quantum kernel methods, and quantum-enhanced sensing technologies. His scientific awards include being selected as a Ramón y Cajal Researcher, a prestigious research position in Spain for experienced researchers, and an Ikerbasque Fellow, which is awarded by the Basque Foundation for Science to attract top researchers to the Basque Country. Dr. Sanz has collaborated extensively with researchers across multiple institutions, contributing to a wide range of quantum information science projects. His work often bridges theoretical quantum information concepts with practical implementations, particularly in superconducting quantum computing platforms. He is actively involved in advancing quantum technologies through his research group at UPV/EHU, focusing on developing novel quantum algorithms and exploring applications of quantum computing in various scientific and industrial domains.

Stefan Thor Smithمشاهده پروفایل

استاد

• Energy Systems
• Urban Energy Modeling
• Climate Resilience
• +۳ مورد دیگر

Professor Stefan Thor Smith is a distinguished academic at the University of Reading , serving as a Professor in the Department of Energy and Environmental Engineering . His work bridges energy systems with urban sustainability , focusing on the integration of social and technical aspects of energy demand , urban energy system modeling , and climate change resilience . Academic Qualifications Postgraduate Certificate in Academic Practice (University of Reading, 2016) PhD in Built Environment (University of Nottingham, 2009) MSc in Computer Science (University of Glasgow, 2002) BSc in Physics (University of Nottingham, 2001) His research interests span the dynamics of energy demand in socio-technical systems, urban heat fluxes, pollution exposure modeling, and climate adaptation strategies. He has developed novel models for energy demand-side management , building environmental control , and urban climate interactions . Recent publications highlight his expertise in areas such as EV charging infrastructure , urban tree radiative performance , phase change material storage , and anthropogenic heat emissions . His work often involves interdisciplinary collaborations with institutions like the Centre for Research into Energy Demand Solutions and the Institute of Physics . Smith supervises a diverse group of postgraduate students and contributes extensively to teaching modules including Numerical Modelling and Programming and Urban Sustainability . His professional affiliations include the Institute of Physics , International Association of Urban Climatology , and the Higher Education Association .

• Electrical Engineering
• Modeling and Simulation
• Machine Learning
• +۵ مورد دیگر

Igor Simone Stievano is a Full Professor at the Polytechnic University of Turin , affiliated with the Department of Electronics and Telecommunications (DET) and the Interdepartmental Center Ec-L - Energy Center Lab . He holds a PhD in Electrical Engineering and has supervised numerous students in disciplines spanning electromagnetic compatibility, machine learning, and multi-energy networks. His research interests include: Modeling and simulation of integrated circuits Machine learning for signal integrity Multi-energy network resilience Stochastic analysis of electrical systems Electromagnetic compatibility Key projects include the EU-funded SHIMMER initiative on hydrogen injection in gas networks and commercial contracts for high-speed I/O macromodeling. He serves as a chair and committee member at major conferences like the IEEE Workshop on Signal and Power Integrity. Scientific recognitions : IEEE Senior Member Recipient of the 2013 Futuro in Ricerca grant Editorial Board member of ENERGIES (2020-) Stievano actively participates in PhD college evaluations for Mathematical Sciences and Metrology programs at Politecnico di Torino, while teaching courses in Electrical Engineering and Digital Technologies across biomedical, computer, and media engineering curricula.

• Climate Change
• Atmospheric Dynamics
• Renewable Energy
• +۶ مورد دیگر

Juan Antonio Añel Cabanelas is a Professor of Earth Physics at the University of Vigo , affiliated with the EPhysLab research group and the Specialized Group on Atmospheric and Ocean Physics of the Royal Spanish Society of Physics . He serves as an Executive Editor for Geoscientific Model Development and an Associate Editor for PLoS Climate . PhD in Physics (2007) from the University of Vigo, thesis: Climatic analysis of the tropopause using radiosonde data Taught courses in Meteorology, Atmospheric Physics, Computational Science, and Renewable Energy at the University of Vigo and international institutions His research focuses on climate change impacts , upper troposphere-lower stratosphere dynamics , renewable energy modeling , and computational reproducibility in climate research . He emphasizes instrumental data recovery and open science , with recent work addressing stratospheric contraction and mercury cycling . Key publications span extreme weather-energy sector interactions , Fortran code quality , and ozone data analysis . He mentors PhD students in Physics and Computer Science, and has collaborated with institutions in Mexico, Portugal, and the private sector. He advocates for free software and has organized workshops on climate intervention and citizen science . His work is funded by public grants from Spain's Government, Xunta de Galicia, and private entities like Naturgy and Acciona, with computing support from Google and Microsoft.

Sonia Lopez Alarconمشاهده پروفایل

دانشیار

• Quantum Computing
• Computer Architecture
• Heterogeneous Computing
• +۲ مورد دیگر

Sonia Lopez Alarcon is an Associate Professor in the Department of Computer Engineering at the Kate Gleason College of Engineering, Rochester Institute of Technology (RIT). She has been a faculty member since 2009, teaching core courses like Computer Organization and developing quantum computing curricula including the new CMPE-257 undergraduate course and CMPE-757 graduate course. Her research bridges computer architecture and quantum computing with emphasis on practical quantum circuit implementation. Her educational background includes a Bachelor of Physics and Master's in Device Physics from the University Complutense of Madrid (2002), followed by a PhD in Computer Engineering (2009) where she researched cache hierarchy in simultaneous multithreaded architectures. During her studies, she gained industry experience at Lucent Technologies and Fundetel working on integrated circuit design. Dr. Lopez Alarcon's primary research focuses on Quantum Computing and heterogeneous hardware solutions, specifically quantum circuit compilation processes, scalability challenges, and error resilience techniques. She investigates how to translate theoretical quantum algorithms into executable circuits while managing noise and resource constraints, with applications in optimization problems and physics simulations. Her work connects computer engineering principles to emerging quantum technologies. Analysis of her publication timeline shows a strategic shift from traditional computer architecture (2015-2018 cache/HLS research for GPU/heterogeneous systems) to quantum computing (2019-2021). Recent work explores quantum algorithms for combinatorial optimization (Grover's), quantum simulation of physical systems, and machine learning applications, reflecting her adaptation to the rapidly evolving quantum landscape while maintaining her architectural expertise. Her teaching excellence has been recognized through multiple awards: Kate Gleason College of Engineering Exemplary Performance in Teaching Award (2016, 2017, 2020) Computer Engineering Most Effective Teacher Award (2016) She actively mentors graduate students including Mark Danza (MS Computer Engineering candidate 2025), with whom she collaborated on quantum machine learning research featured in Quantum Zeitgeist (May 2025). She contributes to RIT's quantum information science minor launched in 2022, developing curriculum and supervising student research in this emerging field. Dr. Lopez Alarcon leads quantum computing research efforts within RIT's Department of Computer Engineering, collaborating with colleagues like Cory Merkel on quantum algorithm applications. Her work is supported through her personal research website and integration into university-wide quantum initiatives, positioning her at the forefront of academic quantum computing education and research.

Zhi Liمشاهده پروفایل

استادیار

• Hydrological Modeling
• Flood Prediction
• Drought Monitoring
• +۹ مورد دیگر

Zhi Li is an Assistant Professor at the University of Colorado Boulder's College of Engineering and Applied Science, Department of Civil, Environmental and Architectural Engineering. He leads the newly established Flood Lab, focusing on flood prediction and monitoring through remote sensing and coupled hydrologic-hydraulic models. Joined CU Boulder in Fall 2025 Former Dean's Postdoc Fellow at Stanford University PhD in Civil Engineering & Environmental Science from University of Oklahoma (2022) His research spans hydrological modeling , extreme events , and AI4Science applications, particularly in deep learning and intelligent agents for flood risk assessment. Li’s work also connects floods with public health and economic systems , aiming to develop the Flood-Agriculture-Climate-Economics-Disease (FACED) framework. Key Themes: High-resolution flood modeling Climate change impacts on hydro-meteorology Remote sensing integration Flood-agriculture interdependencies Global health implications Scientific Awards: Dean's Postdoc Fellow, Stanford Doerr School of Sustainability (2023) Hoving Fellowship, University of Oklahoma (2019) Li’s recent publications emphasize improved precipitation estimation (IMERG V07), Brown Ocean Effect studies, and Fourier neural operators for rapid flood forecasting. His collaborative work with NOAA and NASA focuses on comparing ground-based and spaceborne radar systems for extreme event analysis.

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

Dr. Aris Dimeas is a Researcher at the National Technical University of Athens in the Department of Electric Power and Industrial Applications . He holds a diploma and PhD in Electrical and Computer Engineering from NTUA and has extensive experience in power systems operations, renewable energy integration, and smart grid technologies. Specialized in AI applications for power systems Developed control software for demand side management Consultant for PPC (2007-2012) Research Focus : Smart grids and digital twin implementations Renewable energy market dynamics Microgrid optimization and control algorithms Collaborations : Active participant in EU research projects, collaborating with HEDNO and other energy grid operators on electronic meters and intelligent network deployments. Teaching : Instructs courses on electric energy systems, power system analysis, and energy management.

Francesco Fornettiمشاهده پروفایل

دانشیار

• Radio Frequency Engineering
• Wide Bandgap Semiconductor Devices
• Gallium Nitride (GaN) Technology
• +۴ مورد دیگر

Dr. Francesco Fornetti is an Associate Professor of Radio Frequency Engineering at the School of Electrical, Electronic and Mechanical Engineering, University of Bristol. His research focuses on wide bandgap semiconductor devices, particularly Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs), and their applications in pulsed power amplifiers for radar systems. He has developed novel test methodologies for GaN amplifiers at microwave frequencies and authored influential publications in RF engineering education. MEng and PhD in Engineering from the University of Bristol His work spans RF semiconductor device characterization, microwave measurement rigs, and pedagogical innovation. His ORCID highlights 8 research outputs, including books and articles that emphasize simulation-based learning tools and authentic assessment in engineering education. His recent 2023 IEEE Microwave Magazine article explores online exam design for RF engineering. Key trends in his publications include interdisciplinary applications of GaN technology, educational software integration, and practical training frameworks. His research has been recognized through multiple teaching awards. Inspiring and Innovative Teaching Award (Engineering) (2022) Most Innovative Teaching of the Year 2022 - Highly Commended (2022) National Teaching Fellowship (NTF) (2023)

Bo Huمشاهده پروفایل

استاد

• Biostatistics
• Bioinformatics
• Neurosurgery
• +۷ مورد دیگر

Bo Hu is Professor of Biostatistics & Bioinformatics and Professor of Neurosurgery at Duke University, where he leads methodological and collaborative research at the intersection of biostatistics, bioinformatics, and clinical neurosciences. His dual appointments situate him within the Division of Biostatistics in the Department of Biostatistics & Bioinformatics and within the neurosurgical faculty. Education: Ph.D. in Biostatistics, University of Wisconsin–Madison, 2006 Research Interests: Professor Hu’s methodological work centers on advanced biostatistical and machine-learning techniques for high-dimensional biomedical data, including generative AI, synthetic data generation, and predictive analytics in medicine. Clinically, he collaborates on precision-medicine trials in oncology, neurodegeneration (Alzheimer’s disease), metabolic disease (type 2 diabetes and bariatric surgery), and treatment-resistant depression. His neuroimaging genetics portfolio explores structural brain endophenotypes in bipolar disorder and epilepsy using single-cell transcriptomic integration. Complementing his medical research, he maintains a vigorous program in remote-sensing informatics, developing deep-learning solutions for object detection, domain adaptation, and energy-infrastructure mapping from overhead imagery. Recent Grant Portfolio: Empagliflozin to Improve Right Ventricular Function in Pulmonary Arterial Hypertension – Cleveland Clinic Lerner College of Medicine (2025-2030) Gender and Asthma – Mayo Clinic Hospital-Arizona (2025-2027) Engaging Patients in Prenatal Genetic Testing Decisions – Cleveland Clinic Lerner College of Medicine (2025-2027) Laboratory & Collaborative Networks: Professor Hu leads interdisciplinary teams that bridge Duke’s Department of Biostatistics & Bioinformatics with clinical departments (Neurosurgery, Psychiatry, Medicine) and external partners such as Cleveland Clinic, Mayo Clinic, and multiple NIH consortia. These collaborations support large-scale clinical trials, multi-omics neuroimaging studies, and AI-driven remote-sensing analytics.