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Yu ZhongView profile

Professor

• Advanced Sustainable Materials & Manufacturing
• Solid Oxide Fuel Cells
• Light Alloys (Magnesium, Aluminum)
• +6 more

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.

Nathalie RenevierView profile

Senior Lecturer

• Surface Engineering
• Plasma Physics
• Tribology
• +3 more

Nathalie Renevier is a Senior Lecturer in Mechanical and Aerospace Engineering at the University of Central Lancashire (UCLan), serving as the academic lead for Mechanical and Aerospace Engineering. She holds a physics and materials sciences background, with over 10 years' industry experience across Europe. Her expertise focuses on plasma-assisted processes, tribological testing, nano-tribology, and Lean Six-Sigma methodologies. She is also the course leader for undergraduate maintenance engineering programs. Her research interests include plasma nitriding, surface engineering, tribological coatings, and material characterization. Key projects involve developing plasma-enhanced nitriding reactors, studying MoS₂-based composite coatings, and analyzing wear mechanisms in high-performance materials. She leads initiatives in low-temperature plasma-assisted treatments for metals and alloys, with applications in automotive and industrial machining. Renevier’s work bridges theoretical and applied research, emphasizing industrial collaboration. Notable contributions include advancements in surface coatings for dry machining, novel surface profiling systems for 3D components, and plasma diagnostics via optical emission spectroscopy. Her publications span journals like Surface & Coatings Technology and Vacuum , focusing on plasma processes, tribology, and material science. Her academic leadership includes managing undergraduate programs and fostering innovation in maintenance engineering. Collaborations with the European Commission and industry partners highlight her commitment to translating research into practical solutions. Current projects explore advanced coatings for tribological performance and surface analysis methodologies.

Beiwen LiView profile

Associate Professor

• High-speed 3D Optical Sensing
• Additive Manufacturing
• Remanufacturing Processes
• +3 more

Dr. Beiwen Li is an Associate Professor of Mechanical Engineering at the University of Georgia. Previously, he held positions at Iowa State University as an Assistant and Associate Professor, including the William and Virginia Binger Endowed Professorship. His research focuses on high-speed 3D optical sensing, with applications in manufacturing and remanufacturing processes. His work has been featured in top journals like Optics Express and Applied Optics. Dr. Li’s expertise spans optical metrology, additive manufacturing, and robotics. He has developed advanced techniques in structured light scanning, fringe projection profilometry, and hyperspectral imaging. His lab has secured funding from NSF, REMADE Institute, DOE, and industrial partners. Key Research Themes: 3D surface topography monitoring, corrosion analysis, robotic assembly, and AI-driven manufacturing. Awards: 2020 SPIE Rising Researcher Award, 2024 SME Outstanding Young Engineer Award, and multiple early-career recognitions. His recent publications emphasize real-time monitoring of additive manufacturing processes, unifying optical sensing with machine learning for quality assurance. He pioneered systems like VR Co-Lab for human-robot collaboration and autonomous robotic scanning for smart factories. Dr. Li leads the Riverbend Research Lab North, integrating experimental and computational methods to solve industrial challenges in precision engineering and manufacturing sustainability.

Yasaman GhasempourView profile

Assistant Professor

• Wireless Communication
• Terahertz Networks
• Millimeter-Wave Systems
• +7 more

Yasaman Ghasempour is an Assistant Professor in the Department of Electrical and Computer Engineering at Princeton University, affiliated with the School of Engineering and Applied Science. She joined Princeton in Spring 2021 and leads the Smart Wireless Agile Networks Lab. Her research focuses on next-generation wireless communication and sensing systems, particularly in millimeter-wave (mmWave) and terahertz (THz) networks, IoT, robotic wireless networks, and security. She holds a Ph.D. and M.Sc. in Electrical and Computer Engineering from Rice University (2020 and 2016, respectively). Education: Ph.D., Rice University (2020); M.Sc., Rice University (2016) Labs: Smart Wireless Agile Networks Lab Her research spans adaptive mmWave wireless networks , THz communication/sensing systems , intelligent surfaces , and cross-layer security . She develops theoretical frameworks and experimental systems to enable ultra-fast, secure, and robust wireless networks. Notable contributions include metamaterial-based sensors for fruit ripeness, non-invasive wireless sensing, and security protocols against adversarial attacks. Awards include the NSF CAREER Award (2022), AFOSR Young Investigator Award (2024), and multiple best paper awards at ACM SenSys, IEEE WCNC, and ACM MobiCom. She advises over 10 graduate students, fostering innovation in wireless systems and security. Grants: NSF CAREER, AFOSR Young Investigator, Princeton SEAS Faculty Award Key Projects: Sub-THz metamaterial stickers, robotic perception via wireless sensing, secure THz networks

• Optical Communications
• Optical Networks
• Optimization Algorithms
• +2 more

João José de Oliveira Pires is an Assistant Professor at the Department of Electrical and Computer Engineering, University of Lisbon. His research focuses on optical communications, optical networks, optimization algorithms, and broadband access networks. He teaches subjects like High-Speed Networks and Optical Communications Systems. His work emphasizes improving network performance through advanced signal processing, amplifier design, and cost-effective deployment strategies. Key contributions include studies on Raman amplification, fault-induced power transients, and genetic algorithms for link power control. His research often intersects machine learning applications in optical network optimization. University Affiliation: University of Lisbon Department: Electrical and Computer Engineering Key Research Themes: Optical Network Design, Signal Integrity, and Cost-Efficient Solutions Publications span optimization of wideband systems, crosstalk mitigation, and multi-layer network frameworks. Collaborations include work with institutions like the Telecommunications Institute (Tecnico Lisboa) and international researchers in optical communications.

Ali BazziView profile

Professor

• Fault-tolerant power electronics
• Electric motor drives
• Distributed generation (solar photovoltaics, wind)
• +3 more

Dr. Ali Bazzi is the Madonna Professor in Power Engineering at the University of Connecticut (UConn), within the Department of Electrical and Computer Engineering. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (UIUC) and B.E./M.E. degrees from the American University of Beirut (AUB). He co-founded Valcon Labs Inc., a deep-tech startup focused on aircraft electrification, and previously worked at Delphi Corp. and Bitrode Corporation. His research focuses on fault-tolerant power electronics, electric motor drives, renewable energy integration (solar/wind), and WBG semiconductor applications. Education: Ph.D., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign M.E., Electrical and Computer Engineering, American University of Beirut B.E., Electrical and Computer Engineering, American University of Beirut Research Interests: Dr. Bazzi's work emphasizes reliability, efficiency, and fault mitigation in power systems, including extraterrestrial microgrids, electric vehicle propulsion, and high-voltage insulation technologies. His contributions span real-time control algorithms, fault diagnosis, and advanced materials for medium-voltage drives. Publications & Patents: With over 120 peer-reviewed publications and 10 U.S. patents, his work includes innovations in active EMI filters, insulation breakdown prediction, and cyber-physical testbeds for space habitats. Recent trends highlight space microgrid reliability, WBG semiconductor applications, and fault-tolerant drive systems. Awards & Grants: Awarded the Madonna Professorship, he has led grants from UTC and industry partners. His patents include methods for fault diagnosis, data-driven anomaly detection, and insulation material optimization. Labs & Teams: Director of the Power Electronics and Drives Advanced Research Laboratory at UConn, he collaborates with industry partners like Valcon Labs on aircraft electrification and propulsion systems.

Chih-Jen SungView profile

Professor

• Combustion
• Propulsion
• Sustainable Energy
• +4 more

Chih-Jen (Jackie) Sung is the Connecticut Clean Energy Fund Professor in Sustainable Energy at the University of Connecticut's Mechanical Engineering Department since 2009. Previously, he served at Case Western Reserve University (1999–2009). He holds a B.S. from National Chiao Tung University (1986), M.S.E. from National Taiwan University (1988), and M.A./Ph.D. from Princeton University (1991/1994). His research focuses on combustion fundamentals, including chemically reacting flows, catalytic combustion, laser diagnostics, and sustainable energy technologies. Key areas include alternative fuel utilization, soot/NOx formation, and combustion instabilities. His work is funded by federal and industrial sponsors. Dr. Sung has authored/co-authored over 200 publications and seven book chapters. He received awards such as the NSF CAREER Award (2002), multiple AIAA Best Paper Awards, and ASME/Combustion Institute Fellowships. He serves on the Editorial Board of Combustion Theory and Modelling . His research emphasizes bridging experimental and computational approaches, with applications in propulsion systems, pollution reduction, and clean energy. Recent work includes studies on aviation fuel surrogates, cryogenic spray cooling, and ammonia combustion.

Abbas KaziempourView profile

Lecturer

• Deep Learning
• Machine Learning
• Neuroscience
• +6 more

Abbas Kaziempour is a Lecturer at the School of Data Science, University of Virginia. He holds a Ph.D. in Electrical Engineering from the University of Maryland and a B.S. in Electrical Engineering from the University of Tehran. His research bridges deep learning, signal processing, and neuroscience, focusing on optimization, nonlinear dimensionality reduction, and biomedical imaging technologies. Ph.D., Electrical Engineering, University of Maryland B.S., Electrical Engineering, University of Tehran Kaziempour's work spans three key areas: (1) advancing deep learning theory through analysis of loss landscapes and optimization algorithms, (2) developing compressed sensing and polynomial PCA techniques for neuroscience and biomedical signal processing, and (3) engineering high-speed imaging systems like kilohertz frame-rate two-photon tomography. His recent publications highlight methodological innovations in machine learning and their applications to neural data analysis. As an applied scientist at Amazon's search relevance team, he translates theoretical insights into practical feature design and modeling solutions for industrial-scale problems. His academic contributions include (1) theoretical guarantees for sparse model recovery, (2) stability analysis of autoregressive estimation, and (3) novel approaches to signal deconvolution using state-space models.

Shamin SadrafshariView profile

Researcher

• Biomedical Engineering
• Mechanical Engineering
• Neural Engineering
• +3 more

Shamin Sadrafshari is a Researcher at the University of Bath, affiliated with the Department of Mechanical Engineering. He is part of the Foundry: Centre for Digital, Manufacturing & Design and the Centre for Bioengineering & Biomedical Technologies (CBio), as well as the Bath Institute for the Augmented Human. His research focuses on biomedical engineering, neural interfaces, and energy harvesting technologies. Current projects include improving tactile display systems through the IAA-funded 'Improving User Experience on Sensory Substitution Tactile Display Pad' (2023-2023). Research interests span urinary bladder physiology, neural signal processing, piezoelectric energy harvesting, and MEMS-based actuators. His work bridges mechanical engineering with bioelectronics, focusing on applications like spinal cord injury rehabilitation and industrial tool monitoring. Notable contributions include advancements in tool condition monitoring using machine learning, velocity-selective neural processing systems, and vibration-based energy harvesting designs. Collaborations span veterinary medicine, microelectronics, and human-computer interaction. Ongoing work aims to develop neural interfaces for bladder control systems and optimize manufacturing processes through real-time tool wear analysis. His research portfolio includes 15 peer-reviewed publications since 2011, with recent focus on biomedical device design and smart manufacturing solutions. Current affiliations emphasize interdisciplinary innovation in augmented human technologies and bioengineering.

• Nuclear Instrumentation
• Radiation-Hard Sensors
• Monolithic Active Pixel Sensors
• +7 more

Vicente González Millán is a full Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia. His work bridges electronics technology and nuclear physics, with strong involvement in international collaborations such as CERN, AGATA, and NEDA. His research focuses on the development of advanced radiation-hard semiconductor sensors, particularly the MALTA series of monolithic active pixel sensors, for applications in high-energy and nuclear physics experiments. He is actively involved in detector design, signal processing, FPGA-based data acquisition systems, and real-time data analysis techniques. His work supports cutting-edge experiments in nuclear structure, particle detection, and medical physics instrumentation. The recent publications show a consistent focus on detector development, sensor characterization, and data acquisition systems. Key themes include radiation hardness, timing performance, charge calibration, and the use of machine learning for event reconstruction in neutron detection arrays. The work is highly experimental and closely tied to large-scale facilities like CERN and GANIL. He leads or is a core member of two research groups: i2N (Electronic Instrumentation in Medical and Nuclear Physics) and IRIMED (Research in Radiophysics and Nuclear Instrumentation in Medicine), highlighting the dual application of his work in both fundamental physics and medical technology. While no specific awards are listed, his sustained publication record and leadership in major detector projects indicate significant recognition in the field. He also contributes to the development of real-time trigger systems, digital front-end electronics, and FPGA-based solutions for gamma and neutron spectroscopy.

• Power System Protection
• Power Systems
• Renewable Energy Integration
• +9 more

Professor Campbell Booth is a senior academic and administrator at the University of Strathclyde, Faculty of Engineering, where he serves as Vice Dean – Research. He is based in the Department of Electronic and Electrical Engineering and was previously Head of Department from 2017 to 2021. A long-standing member of the Strathclyde community since 1987, he completed his BEng and PhD at the university and has progressed from research roles to full professorship. Doctor of Philosophy, University of Strathclyde (1997) Bachelor of Engineering, Electronic and Electrical Engineering, University of Strathclyde (1991) His research centers on the protection and control of modern and future power systems, particularly those with high penetrations of renewable energy, energy storage, power converters, and DC links. He focuses on practical implementation through prototyping, real-time simulation, and laboratory testing, often in collaboration with the Power Networks Demonstration Centre (PNDC). Key areas include adaptive protection, superconducting fault current limiters, distributed optical sensing, and multi-terminal HVDC systems. His teaching includes second-year Electronic and Electrical Principles II and advanced power system protection courses at the fourth and fifth year levels, as well as industry short courses and STEM outreach as a STEM Ambassador. His recent publications reflect a strong trend in applying machine learning and advanced control strategies to power system stability, protection in inverter-dominated grids, and real-time dispatch. These works are highly relevant to the transition toward low-inertia, renewable-rich energy systems. His scientific recognition includes: IET Best Paper Award 2025 Spectator Magazine Economic Innovator of the Year 2022 Best Paper Award 2019 Sustainable Energy Award 2017 Best Offshore Renewables Innovation 2017 Best University Technology 2014 He actively supervises research students and is involved in numerous funded projects, including collaborations with Shell and Scottish and Southern Energy. He leads PNDC activities in protection and automation and is engaged in international research initiatives, particularly with China. His professional service includes organizing major symposia and serving as a peer reviewer for IEEE Transactions on Power Electronics. He is also a key contributor to the UN Sustainable Development Goals, particularly through education and low-carbon energy research. His work is supported by extensive external funding and collaboration networks, positioning him as a leading figure in modern power systems research.

• Information Systems
• Computer System Architecture
• Coding and Information Theory
• +3 more

Dr. Omer Sella is a Research Associate in the Department of Computing at Imperial College London's Faculty of Engineering, specializing in DNA-based archival data storage. He explores molecular biology processes to optimize cost, throughput, and error rates in DNA storage systems. Education : PhD (University of Cambridge), MSc & BSc (Hebrew University of Jerusalem) His research bridges computing and biological systems, focusing on: DNA data encoding/decoding mechanisms Error correction in biological storage Cost-effective archival storage solutions Applications of bioinformatics to computing Key article trends include DNA storage reliability, motif-based encoding tools, and comparative studies of glass vs. DNA as archival media. He also investigates high-speed networking and coding theory applications.

• Machine Learning
• Cloud Computing
• Fault Diagnosis
• +3 more

Chao Yin is a researcher at Shanghai University , Department of Computer Engineering and Science. His work spans multiple domains including Machine Learning, Cloud Computing, Fault Diagnosis, and Supply Chain Optimization. Key research areas: Machine Learning , Cloud Computing , Quantum Computing , Supply Chain Systems , Network Security Scientific Contributions (2024-2025): Developed heterogeneous graph neural networks for automotive supply chain analysis Created MSDF-VAE cloud-edge fault diagnosis framework using transfer learning Proposed quantum metrology methods with Heisenberg-limited precision Designed LARP pseudonym protocol for V2X communication Optimized fog computing resource scheduling with hybrid metaheuristics Prior Work (2012-2023): Contributed to fluid animation feature preservation from single images Developed label distribution learning for facial age estimation Designed erasure coding storage systems for big data Created multi-agent manufacturing networks in cloud environments

Cătălin HrițcuView profile

Adjunct Professor

• Formal Methods for Security
• Secure Compilation
• Compartmentalization
• +12 more

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, and an Adjunct Professor at Ruhr University Bochum's Faculty of Computer Science. He is involved in the CASA Cluster of Excellence and HGI at RUB. PhD in Computer Science from Saarland University Habilitation in Computer Science from École Normale Supérieure (ENS) Paris Former Tenured Researcher at Inria Paris Postdoctoral Research Associate at University of Pennsylvania Visiting Researcher at Microsoft Research Redmond His research spans formal methods for security , focusing on secure compilation, memory safety, compartmentalization, information flow control, and security protocols. In programming languages , he explores program verification, proof assistants like F*, dependent types, formal semantics, and mechanized metatheory. He also works on verifying security-critical systems such as reference monitors, compilation chains, and tagged architectures. His recent publications (2015–2025) emphasize secure compilation techniques, relational program logics, memory safety, and proof automation in F*. Key trends include formal verification of IO programs, high-speed cryptography, and cross-component security analysis. Scientific Awards : ERC Starting Grant on Formally Secure Compilation Distinguished Paper Awards at CSF Symposium (2019, 2021, 2025) Hrițcu has advised numerous PhD students and collaborated on F* verification system development. He has served on program committees for CPP, POPL, PriSC, and ICFP, and organized artifact evaluation committees. He leads the Formally Verified Security group at MPI-SP and contributes to the CASA Cluster of Excellence, focusing on formal verification of security-critical systems and advancing secure compilation frameworks.

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +7 more

Xinchao Wang is an Assistant Professor and Presidential Young Professor in the Department of Electrical and Computer Engineering at the National University of Singapore (NUS), College of Design and Engineering. He leads the xML Lab, focusing on machine learning and artificial intelligence. Prior to joining NUS, he was an Assistant Professor at Stevens Institute of Technology in the Department of Computer Science and a Postdoctoral Researcher at the Beckman Institute, University of Illinois Urbana-Champaign, under the late Professor Thomas Huang. He earned his Ph.D. in Computer Vision from École polytechnique fédérale de Lausanne (EPFL) under Professor Pascal Fua and a B.Sc. in Computing from the Hong Kong Polytechnic University with first-class honors and a perfect GPA. His research spans machine learning, computer vision, and artificial intelligence, with a focus on diffusion models, vision-language models, 3D generation, and dataset distillation. His recent publications demonstrate strong trends in efficient generative modeling, including acceleration techniques for diffusion models, 3D scene reconstruction, and novel architectures for vision and language tasks. His work frequently appears in top-tier conferences such as CVPR, NeurIPS, ICCV, ECCV, and ICLR. AI's 10 to Watch, IEEE (2025) Young Research Award, NUS (2025) Young Research Award, NUS CDE (2024) Best Paper Award, IEEE VCIP 2022 Outstanding Early Career Award, NUS CDE (2023) College Teaching Excellence Award, NUS CDE (2023) He actively advises Ph.D. students, many of whom have received prestigious awards including the Google Fellowship and ByteDance Scholarship. He has served as an Associate Editor for IEEE Transactions on Image Processing since 2023. His lab is actively recruiting Ph.D. students, postdocs, visiting scholars, and research assistants in machine learning and AI. He teaches core courses at NUS including Introduction to Machine Learning, Deep Learning, and Pattern Recognition.