جست‌وجوی استاد و پژوهشگر

Gianni Antichiمشاهده پروفایل

مدرس ارشد

• Networks
• Computer Architecture
• Programmable Hardware
• +۳ مورد دیگر

Dr. Gianni Antichi is a Senior Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science, and an Associate Professor at Politecnico di Milano. His research focuses on network architecture, programmable hardware, and data plane optimization. He holds grants from EPSRC and Facebook Research, and has received multiple awards including Best Paper at SIGCOMM 2017. Antichi teaches modules on distributed systems, computer networks, and Internet protocols. Research Interests: End-host networking stacks, programmable hardware (SmartNICs/FPGA), network telemetry, data center optimization, and traffic analysis. His work spans from low-level data plane design to high-level network monitoring solutions. Key Awards: EPSRC New Investigator Award (2020), Facebook Networking Research Award (2020), Best Student Paper at EuroSys 2025, and ACM SIGCOMM Best Paper (2017). Grants & Projects: Leads NEAT (EPSRC-funded network telemetry project), and collaborates with industry on software data plane optimization. Active in mentoring PhD students and researchers in distributed systems and network hardware. Labs/Teams: Part of Queen Mary’s Centre for Networks, Communications and Systems, focusing on programmable networking and high-performance systems.

Unnikrishna Pillaiمشاهده پروفایل

استاد

• Signal Processing
• Radar Systems
• Machine Learning
• +۳ مورد دیگر

Unnikrishna Pillai is a Professor of Electrical Engineering at the Tandon School of Engineering, New York University since 1995. He previously held roles at Polytechnic Institute of New York from 1985. His research focuses on radar signal processing, synthetic aperture radar (SAR), autonomous systems, machine learning, and portfolio risk management. He has co-authored five textbooks and developed over 250 educational YouTube videos. Education: Ph.D. in Systems Engineering (University of Pennsylvania, 1985), M.S. in Electrical Engineering (IIT Kanpur, 1982), B.Tech. in Electronics Engineering (Banaras Hindu University, 1980). Research emphasizes signal decomposition for clutter mitigation, waveform design for radar systems, and dynamic swarming for vehicle coordination. His work bridges theoretical contributions with practical applications in defense and transportation. Teaching contributions include supplementary video lectures on electrical engineering topics. Non-academic interests include cultural projects like translating the Bhagavad Gita and producing classical Indian music recordings.

Stefania Espositoمشاهده پروفایل

مدرس

• Combustion Engineering
• Sustainable Fuels
• Pollutant Emissions Modeling
• +۳ مورد دیگر

Dr. Stefania Esposito is a Lecturer in the Department of Mechanical Engineering at the University of Bath. Her research focuses on future combustion systems, including internal combustion engines, alternative sustainable fuels (hydrogen, methanol), and pollutant emission modeling. She holds a PhD from RWTH Aachen University (2020), an MSc from the University of Bologna (2015), and a BSc from Università di Cassino e del Lazio Meridionale (2013). Her work aligns with UN Sustainable Development Goals, emphasizing clean energy and environmental sustainability. Key projects include the Royal Society-funded 'Characterization of Ammonia Injection Process for Carbon-Free Propulsion' and the JLR Prosperity Partnership exploring low-emission automotive technologies. Dr. Esposito’s research outputs span combustion modeling, hydrogen and methanol fuel systems, and emission control. She collaborates on advanced propulsion systems and has published extensively in journals like International Journal of Hydrogen Energy and Journal of Engineering for Gas Turbines and Power . Her expertise includes computational fluid dynamics (CFD), experimental validation, and data-driven approaches for engine optimization.

Min Panمشاهده پروفایل

استاد

• Intelligent Machine Systems
• Control Systems
• Robotics
• +۵ مورد دیگر

Min Pan is a Professor of Intelligent Machine Systems at the University of Bath, affiliated with the Department of Mechanical Engineering and institutes like the Institute for Mathematical Innovation (IMI). She holds a UKRI Future Leaders Fellowship and has received prestigious awards including the Royal Academy of Engineering Fellowship and the Leverhulme Trust Fellowship. Her research focuses on intelligent machine systems, dynamic systems modeling, control, and robotics, with a strong emphasis on fluid power control and soft robotics. She earned her PhD in Mechanical Engineering from the University of Bath in 2012 and has since contributed to over 80 publications, many featured on journal covers like Advanced Science and Advanced Materials Technologies . Her work is funded by UKRI, EPSRC, and Innovate UK, among others. She collaborates with academic and industrial partners to advance applications in energy-efficient hydraulic systems, soft actuators, and self-healing materials. Min actively supervises doctoral students and leads projects such as the A New Analytical Framework for Dexterous Soft Robotic Manipulators and Hydraulic Pressure Ripple Energy Harvesting . Education: PhD in Mechanical Engineering (University of Bath, 2012) Awards: Donald Julius Groen Prize (2014), Royal Society funding, and multiple fellowships. Research Interests: Bioinspired robotics, energy-efficient fluid systems, soft actuators, and material science for advanced applications. Her work bridges fundamental analysis with practical innovations, addressing challenges in sustainability and industrial efficiency.

Dror Baronمشاهده پروفایل

دانشیار

• Quantum Information Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dror Baron is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and has held positions including Postdoctoral Researcher at Rice University, Visiting Scientist at the Technion, and Quantitative Research Analyst at Menta Capital. His research focuses on the intersection of fast algorithms, signal processing, and information theory, with particular interests in quantum information science, machine learning, and communication systems. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (2003) M.Sc. in Electrical Engineering, Israel Institute of Technology (1999) B.Sc. in Electrical Engineering, Israel Institute of Technology (1997) Research Interests: Quantum error mitigation and algorithms Machine learning applications in signal processing Group testing and compressed sensing Generative adversarial networks (GANs) for system modeling Federated learning and privacy-preserving techniques Channel estimation and mmWave communication Awards & Honors: IEEE Senior Member (2010) M. E. Van Valkenburg Graduate Research Award (2002) Program for Outstanding Students, Technion (1994–1997) President's Roll every semester, Technion (1994–1997) Recent Contributions: His work includes developing algorithms to reduce testing quantities for pandemic management, advancing GAN-based receiver modeling, and exploring quantum advantage in finance. Collaborations span academia and industry, with notable contributions to federated learning security and compressed sensing theory.

• Manufacturing Engineering
• Aerospace Materials
• Tool Wear Monitoring
• +۵ مورد دیگر

Jose Luis Cantero Guisandez is a Full Professor at the Department of Mechanical Engineering, University Carlos III of Madrid. He is affiliated with the Álvaro Alonso Barba Institute of Chemistry and Materials Technology and leads the 'Mechanical and Biomechanical Component Manufacturing and Design Technology' research group. His work focuses on advanced machining processes, tool wear monitoring, composite materials (especially CFRP), and aerospace component fabrication. Key research interests include drilling process optimization for hybrid material stacks, tool failure detection using signal analysis (wavelet transforms), and machine learning applications in manufacturing systems. He has published extensively on topics like Inconel 718 machining, Haynes 282 finishing, and MQL drilling techniques. Current projects involve collaboration with Airbus on drilling process improvement (funded by Airbus Operations S.L.) and digitalization of industrial drilling systems (DIGITDRILL project with AEI). He has supervised multiple theses on tool health monitoring and multi-material drilling optimization. His work integrates experimental analysis with numerical modeling, addressing challenges like delamination in CFRP drilling and thermal effects in dry machining. Recent efforts emphasize data-driven approaches for process control and predictive maintenance in aerospace manufacturing.

Paul Patrasمشاهده پروفایل

استاد

• Artificial Intelligence
• Network Traffic Analytics
• Network Security & Privacy
• +۴ مورد دیگر

Paul Patras is a Professor of Mobile Intelligence at the University of Edinburgh's School of Informatics, leading the Mobile Intelligence Lab and affiliated with the Institute for Computing Systems Architecture (ICSA) and Security & Privacy group. He co-founded Net AI, a university spin-off advancing AI-driven network management. His expertise spans AI for network traffic analytics, security, and biomedical innovation. He holds a Ph.D. from University Carlos III of Madrid and has held visiting roles at institutions like Northeastern University and Rice University. Research focuses on bridging mathematical models with real-world network applications, including high-speed data stream processing and adversarial robustness. Recent work includes Stable-Sketch for web-scale data streams and Sabre for adversarial noise filtering. Awards include the SICSA Best Dissertation Award (2024, student Alec Diallo) and a best paper award at WWW '24 (with Weihe Li). Advising includes PhD students like Weihe Li and postdocs like Alec Diallo. He has supervised numerous students in areas like network security and machine learning. His labs and collaborations drive innovation in 6G, edge computing, and AI ethics. He frequently speaks at global forums, including the UN's AI for Good Summit and 6G Evolution Summit.

• Microgrids
• Photovoltaic Systems
• Power Electronics
• +۸ مورد دیگر

Abderezak Lashab is an Assistant Professor at Aalborg University's Department of Electric Power Systems and Microgrids within the Faculty of Engineering and Science. He specializes in microgrid technologies, photovoltaic systems, and power electronics. His research focuses on enhancing the stability, efficiency, and resilience of energy systems, particularly in renewable energy integration, smart grids, and electric vehicle infrastructure. Key projects include the HECATE initiative exploring hybrid electric regional aircraft distribution technologies. Affiliations: AAU Energy, Microgrids Research Group Research Interests: Microgrid control strategies, photovoltaic system optimization, battery storage solutions, and cybersecurity in energy networks. His work emphasizes practical applications such as disaster-resilient mobile microgrids and EV charging infrastructure sustainability. Publications (82+): Focus on advanced control algorithms, power electronics, and renewable energy systems. Recent trends highlight grid stability under high PV penetration and smart grid resilience. Grants: HECATE project (2023-2025) funded by Horizon JU Innovation Action Labs/Teams: Active contributor to AAU's energy research groups, collaborating on projects involving hybrid electric systems and IoT-enabled cybersecurity.

Dorrin Jarrahbashiمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• Multiphase Flows
• Supercritical Fluids
• +۲ مورد دیگر

Dr. Dorrin Jarrahbashi is an Associate Professor in Mechanical Engineering at Texas A&M University. Her Computational Thermo-Fluids Laboratory investigates multiphase flows at extreme conditions, with applications in energy systems and advanced manufacturing. Education includes: Postdoctoral Fellowship, Georgia Institute of Technology (2015-2017) PhD in Mechanical & Aerospace Engineering, UC Irvine (2014) M.S. from Sharif University of Technology (2007) B.S. from Iran University of Science & Technology (2005) Research focuses on: High-speed multiphase flows Transcritical fluid behavior Combustion instability Nanoparticle spray deposition Supercritical CO2 applications Awards recognize contributions to fluid dynamics research and teaching excellence. Recent publications focus on computational methods for complex flows, experimental characterization of atomization processes, and optimization techniques for energy systems.

Joseph R. Cavallaroمشاهده پروفایل

استاد

• VLSI Signal Processing
• Wireless Communication Systems Architectures
• VLSI Systems Design and Prototyping
• +۳ مورد دیگر

Joseph R. Cavallaro is a Professor in the Department of Electrical and Computer Engineering and holds a courtesy appointment in the Department of Computer Science at Rice University. He serves as Director of the Center for Multimedia Communications. His research focuses on VLSI processor architectures, wireless communication systems, and high-speed computer arithmetic. Cavallaro joined Rice in 1988 and has contributed extensively to advancements in special-purpose VLSI systems for signal processing, robotics, and computer graphics. Education: B.S. Electrical Engineering, University of Pennsylvania (1981) M.S. Electrical Engineering, Princeton University (1982) Ph.D. Electrical Engineering, Cornell University (1988) Research Interests: Special-purpose VLSI architectures for signal processing Wireless communication systems design Low-power and high-throughput LDPC decoders Hardware acceleration for neural networks RF fingerprint identification for IoT security Key Achievements: IEEE Fellow (2015) for contributions to VLSI architectures and wireless communications Recipient of the NSF Research Initiation Award (1989-1992) IEEE Circuits and Systems Society Distinguished Lecturer (2012-2013) Advising & Grants: Principal Investigator for NeTS: BRICK grant (2017) Collaborative Research: BAMM grant (2014) Guided research in FPGA-based accelerators, massive MIMO systems, and biomedical signal processing Labs & Teams: Leads the Center for Multimedia Communications and collaborates on projects involving VLSI design, edge computing, and medical signal processing.

Haoyang Caoمشاهده پروفایل

استادیار

• Stochastic controls
• Stochastic differential games
• Mean-field games
• +۳ مورد دیگر

Haoyang Cao is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University, affiliated with the Data Science and AI Institute. Her research focuses on stochastic controls, stochastic differential games, mean-field games, and theoretical machine learning paradigms such as generative models and transfer learning. She bridges stochastic analysis with financial mathematics applications, including portfolio optimization and inventory control. Cao holds a PhD from UC Berkeley (2020) and a BS from the University of Hong Kong (2015). Prior to joining JHU, she was a postdoc at École Polytechnique (2022–2023), a research associate at the Alan Turing Institute (2020–2021), and a visiting scholar at the University of Oxford's Mathematical Institute. Her education includes: PhD in Industrial Engineering and Operations Research, UC Berkeley (2020) Bachelor of Science in Mathematics, University of Hong Kong (2015) Research interests emphasize high-dimensional stochastic games and machine learning applications, particularly in finance and healthcare. She develops computational tools for solving complex stochastic systems and explores theoretical foundations of generative models and transfer learning. Recent work includes analyzing risk in transfer learning, approximation techniques in mean-field games, and anomaly detection via meta-learning. Her work on stochastic controls and games has been applied to financial mathematics, inventory control, and healthcare systems. Key contributions include frameworks for transfer learning feasibility and GAN training dynamics analysis. Labs/Teams: Active member of the Data Science and AI Institute at Johns Hopkins University, collaborating on interdisciplinary projects combining stochastic analysis and machine learning.

Cătălin Hrițcuمشاهده پروفایل

استاد

• Formal Methods for Security
• Secure Compilation
• Compartmentalization
• +۱۵ مورد دیگر

Cătălin Hrițcu is a tenured faculty member and head of the Formally Verified Security group at the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. His work bridges programming languages, formal verification, and systems security, with a strong focus on developing foundational methods for secure compilation and verified systems. His research interests include formal methods for security, programming languages, and the design and verification of security-critical systems. Specifically, he works on secure compilation, compartmentalization, memory safety, information flow control, security protocols, program verification, proof assistants (especially F*), dependent types, formal semantics, mechanized metatheory, and property-based testing. His research group develops techniques to ensure that high-assurance verified code remains secure when interacting with untrusted or weakly-typed components. The recent publications show a consistent focus on foundational verification of security properties, particularly in the context of the F* language and secure compilation. Key themes include secure interoperability between verified and unverified code, end-to-end verification of cryptographic implementations, and formal proofs of compiler correctness. His work often combines deep theoretical insight with practical implementation, targeting real-world systems like CHERI and high-speed cryptography. Scientific awards include: ERC Starting Grant on formally secure compilation Hrițcu has advised numerous students and researchers, though specific names are not listed in the source. He has led significant research grants, notably the ERC Starting Grant, which supports foundational work in secure compilation. His leadership extends to major conferences where he has served as program chair, steering committee chair, and organizing committee member across POPL, ICFP, CPP, and PriSC. He leads the Formally Verified Security group at MPI-SP, which focuses on building and verifying secure systems using formal methods. The group actively contributes to the F* verification system and works on projects involving secure compilation chains, reference monitors, and tagged architectures.

• Mechanical Engineering
• Electrical Engineering
• Biomedical Engineering
• +۵ مورد دیگر

Miguel Fernández Muñoz is an Assistant Professor at the Department of Signal and Communication Theory within the College of Engineering at Universidad de Alcalá. His research focuses on interdisciplinary engineering challenges at the intersection of mechanical, electrical, and biomedical systems. He holds a PhD from Universidad de Alcalá (2023), supervised by Dr. Efrén Díez Jiménez and Dr. Rocío Sánchez Montero, titled "Development of miniaturized wireless power transmission systems for electromechanical actuators in medical and industrial applications". Research Interests: Wireless power transfer and energy harvesting MEMS and microelectromechanical systems Antenna design for biomedical applications Geothermal cooling systems for renewable energy Advanced mechanical design and manufacturing Electromagnetic compatibility and interference mitigation Recent Work Trends: His publications (2022-2025) emphasize practical applications of miniaturized systems, with significant contributions to wireless power solutions for medical devices, MEMS-based micromotors, and geothermal-enhanced solar panel efficiency. He has also explored innovative mechanical designs like high-speed postal canceling machines and rapid transport systems compatible with existing rail infrastructure. Labs/Groups: Member of the TM Tecnologías Mecánicas, Eléctricas y Térmicas research group, focusing on mechanical, thermal, and electrical engineering technologies. Active in both fundamental research and applied industrial solutions.

• Passive Radar Systems
• Signal Processing
• Sensor Networks
• +۵ مورد دیگر

Nerea del Rey Maestre is an Assistant Professor in the Department of Signal Theory and Communications at the Universidad de Alcalá (UAH), Spain. She holds a PhD in Engineering from UAH (2018), focusing on Design and optimization of robust detection techniques in passive radar applications , supervised by Dr. María del Pilar Jarabo Amores and Dr. Manuel Rosa Zurera. Her research centers on intelligent passive radar systems, emphasizing clutter modeling, target detection, and surveillance applications. She leads the AES3 research group (Acoustic and Electromagnetic Smart Sensor Networks and Signal Processing), developing technologies for drone detection, ground traffic monitoring, and maritime surveillance using DVB-T/S, GPS, and SAR-based passive radar architectures. Her work spans signal processing innovations such as neural network-based CFAR detectors, planar antenna arrays, and decentralized radar processing frameworks. Key focus areas include non-coherent spatial integration, motion compensation for high-speed targets, and robust detection in non-homogeneous environments. Experimental validations include real-world scenarios like urban traffic imaging and coastal monitoring with TerraSAR-X and Sentinel-1 data. Her contributions address challenges in radar clutter characterization, Doppler shift compensation, and passive array optimization using commercial off-the-shelf (COTS) antennas. Ongoing projects involve the IDEPAR demonstrator for terrestrial radar surveillance and machine learning techniques for small boat detection in SAR clutter. Research funding has supported projects like the IDEPAR technological demonstrator and studies on PAZ satellite-based passive radar applications. Collaborations include multi-disciplinary teams tackling sensor network design, signal processing algorithms, and real-world deployment challenges in surveillance systems.

Yu Zhongمشاهده پروفایل

استاد

• Advanced Sustainable Materials & Manufacturing
• Solid Oxide Fuel Cells
• Light Alloys (Magnesium, Aluminum)
• +۶ مورد دیگر

Dr. Yu Zhong is a Professor in the Department of Mechanical & Materials Engineering at Worcester Polytechnic Institute (WPI). He holds an affiliated appointment in the Department of Materials Science & Engineering. He earned his Ph.D. in Materials Science from Pennsylvania State University (2005) and an M.S. in Materials Science and Engineering from Sichuan University (2000). Before joining WPI, he worked at Saint-Gobain's High Performance Research Center (2005–2013) and served as an Assistant Professor at Florida International University (2013–2016). Affiliations: Worcester Polytechnic Institute (Current), Florida International University (2013–2016), Saint-Gobain Research Center (2005–2013) Research Interests: Advanced Sustainable Materials & Manufacturing, Solid Oxide Fuel Cells, Light Alloys (Magnesium/Aluminum), Lithium-Ion Batteries, High-Performance Ceramics, Computational Thermodynamics, and Machine Learning in Materials Science. His research focuses on integrating computational methods (first-principles calculations, CALPHAD modeling) with experimental validation to design novel materials. Notable contributions include high-entropy alloys, oxygen transport membranes, and lithium-ion battery materials. Dr. Zhong has authored over 70 peer-reviewed articles, 2 book chapters, and 2 patents. His work is funded by agencies such as DOE, NSF, and the Nuclear Energy University Program. He has received awards including the James Nichols Heald Research Award (2021) and TMS FMD Young Leaders Professional Development Award (2016). Labs/Teams: Integrated Materials and Processes Design (IMPD) Group at WPI, focusing on computational thermodynamics, machine learning, and experimental validation for advanced materials.