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Yuxin ChenView profile

Professor

• statistics
• optimization
• reinforcement learning
• +5 more

Yuxin Chen is a Professor at the University of Pennsylvania , holding joint appointments in the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering . Prior to UPenn, he was an Assistant Professor at Princeton University (2017-2021) and a Postdoctoral Researcher at Stanford University (2015-2017). His research spans statistics, optimization, reinforcement learning theory, diffusion models, and information theory , with a focus on theoretical foundations and practical algorithms for machine learning. Education : Ph.D. in Electrical Engineering (Stanford, 2015), M.S. in Statistics (Stanford, 2013), M.S. in Electrical and Computer Engineering (UT Austin, 2010), B.E. in Electrical/Microelectronics (Tsinghua, 2008). Research Interests encompass theoretical and applied aspects of machine learning, including nonconvex optimization , sample complexity analysis , low-dimensional adaptation , and generative modeling . His work bridges mathematical rigor with real-world applications, particularly in scientific imaging and high-dimensional data analysis. Scientific Awards include the SIAM Activity Group on Imaging Science Best Paper Prize (2024) Alfred P. Sloan Fellowship (2022) NSF Career Award (2022) Google Research Scholar Award (2022) IEEE Transactions on Power Electronics Prize Paper Award (2024) Advising and Grants : He has mentored numerous students who have transitioned to academic roles at institutions like UIUC and UW-Madison. His research is supported by grants from the NSF , Amazon , and Google , with recent projects focusing on controllable diffusion models and efficient reinforcement learning algorithms .

• Microelectronics
• Semiconductor Packaging
• Materials Science
• +3 more

Shenyi Liu is a Doctoral Researcher at Aalto University's Department of Electrical Engineering and Automation , focusing on advanced semiconductor packaging and reliability engineering. Active in the Electronics Integration and Reliability research group, Liu contributes to developing innovative interconnect solutions for MEMS and power electronics. Email: shenyi.liu@aalto.fi Research Interests center on low-temperature bonding technologies , 3D packaging architectures , and failure mechanism analysis in electronic components. Key methodologies include solid-liquid interdiffusion (SLID) bonding and thermal-mechanical stress characterization. Publication Trends show expertise in MEMS integration , interconnect reliability , and power component packaging , with recent work addressing TSV interconnects, die-attach fatigue cracks, and Cu-Sn-In bonding systems. Collaborations span materials engineering and applied physics domains.

Joseph SifakisView profile

Researcher

• Model Checking
• Concurrent Systems Specification and Verification
• Component-Based Design
• +3 more

Joseph Sifakis is a CNRS Research Director and founder of Verimag Laboratory in Grenoble, France. He holds the INRIA-Schneider endowed industrial chair since 2008 and has been instrumental in advancing concurrent systems specification and verification. Education: Electrical Engineering (Technical University of Athens), Computer Science (University of Grenoble) Research interests focus on component-based design , real-time systems , and correct-by-construction techniques . He pioneered the development of the BIP framework and contributed to model checking, a cornerstone of industrial system verification. Recent publications emphasize component-based modeling, formal verification, and distributed system design, reflecting his work's impact on embedded systems and critical applications like aerospace and telecommunications. Scientific awards include: Turing Award (2007) CNRS Silver Medal (2001) Test-of-Time Award (2012) Multiple honorary doctorates (2008-2011) Member of prestigious academies Industry collaborations span Airbus, ST Microelectronics, and the European Space Agency, with applications in aeronautics, telecommunications, and industrial software standards. He leads the ARTIST2 Network of Excellence and directs the CARNOT Institute 'Intelligent Software and Systems'.

Longji CuiView profile

Assistant Professor

• Thermal Energy Sciences
• Ultrahigh Resolution Sensors
• Scanning Probe Microscopy
• +4 more

Dr. Longji Cui is an Assistant Professor in the Thermo Fluid Sciences, Materials, and Micro/Nanoscale disciplines at the University of Colorado Boulder, affiliated with the Department of Mechanical Engineering within the College of Engineering and Applied Science. His laboratory focuses on high-precision instrumentation and computational techniques to explore energy transport, conversion, and dissipation at extreme scales, including scanning thermal microscopy, picowatt-resolution sensors, and nanophotonics. Lab Location: ECME 1B66F / ECME 108 Office Location: ECME 267B Research Interests: Dr. Cui's work spans thermal energy sciences, ultrahigh-resolution sensing, scanning probe microscopy, nano-optics, and quantum engineering. His interdisciplinary projects address critical challenges in sustainable energy systems, next-generation microelectronics, and advanced sensor technologies for high-performance applications. Notable contributions include innovations in thermophotovoltaic systems, molecular-scale thermal transport, and plasmonic light emission mechanisms. Recent publications emphasize near-field thermal radiation, quantized thermal transport in single-atom junctions, and enhanced energy conversion through nanoscale engineering. These studies bridge fundamental physics with practical applications in renewable energy and nanotechnology. Awards: 2025 CEAS Innovation & Entrepreneurship Fellow 2024 ASME Rising Star Award 2023 NSF CAREER Award 2023 CU Boulder Lab Venture Challenge Award His research group collaborates across disciplines to advance instrumentation for atomic-scale thermal measurements and develop novel materials for energy applications. Ongoing efforts include optimizing thermophotovoltaic devices and exploring hot-carrier dynamics in plasmonic systems.

• Big Data
• Complex Networks
• Graph Neural Networks
• +1 more

Dr. Yanqing Hu is an Associate Professor at the Department of Statistics and Data Science, School of Science, Southern University of Science and Technology (SUSTech). With a Ph.D. in Systems Theory from Beijing Normal University (2011) and postdoctoral experience at the Levich Institute, City University of New York (2011-2013), his work focuses on big data analysis of complex systems, particularly in social media dynamics, network resilience, and graph neural network applications. Ph.D.: Beijing Normal University (Systems Theory, 2011) Postdoctoral: Levich Institute, CUNY (2011-2013) Research spans complex network analysis, information spreading mechanisms, and predictability of network structures. His work combines theoretical frameworks with real-world applications in social networks, infrastructure systems, and brain connectivity. Recent publications explore information percolation in social media, resilience quantification in interdependent networks, and intrinsic structure predictability. These studies appear in high-impact journals like Nature Human Behaviour (IF: 24.3), Nature Communications (IF: 17.7), and PNAS (IF: 10). World AI Conference Youth Outstanding Paper Nomination Beijing Outstanding Doctoral Dissertation Award Guangdong Special Support for Young Talents Guangdong Outstanding Youth Fund Collaborations include leading researchers from Boston University, King's College London, and Shenzhen-Hong Kong Institute of Microelectronics. His work informs network defense strategies and efficient navigation mechanisms in complex systems.

• Quantum Computing
• MEMS Sensors
• Analog Circuit Design
• +5 more

Hua Chen is an Associate Researcher and PhD Supervisor at the School of Quantum Science and Engineering at Southern University of Science and Technology (SUSTech), with extensive experience in quantum computing circuit design and MEMS sensor interfaces. Previously, he served as an Associate Researcher at the Institute of Microelectronics, Chinese Academy of Sciences (2021-2022) and as an Assistant Researcher there from 2017-2021, building on his industry experience as an RF/Analog IC Design Engineer at Southwest Integrated Circuit Design Co., Ltd (2007-2010). Education: Ph.D. in Microelectronics and Solid-State Electronics, University of Chinese Academy of Sciences (2014-2017) M.Eng. in Electronic and Communication Engineering, University of Chinese Academy of Sciences (2011-2014) B.Eng. in Microelectronics, Chongqing University of Posts and Telecommunications (2003-2007) Dr. Chen's research focuses on interface circuit design for quantum computing and MEMS sensors, with particular expertise in high performance analog/RF/mixed-signal IC design. His recent work has shifted toward cryogenic circuit design for quantum bit control and readout systems, representing a strategic pivot toward China's national priority in quantum information technology. His publications reveal a consistent trajectory from MEMS gyroscopes and oscillators toward quantum computing hardware support circuits, demonstrating his ability to adapt expertise to emerging technological frontiers. Dr. Chen's research output shows a clear evolution from traditional MEMS sensor interfaces toward quantum cryogenic electronics, with his 15 most recent publications heavily concentrated in cryogenic circuit design for quantum applications. This represents a strategic shift aligning with China's substantial investment in quantum computing research, particularly in the development of control and readout electronics for scalable quantum systems. His significant honors include: Special talent of Shenzhen's Pengcheng Peacock Plan (January 2024) IEEE Senior Member (February 2022) Intellectual Property Specialist of Chinese Academy of Sciences (January 2020) Outstanding Employee (Top 10%) at Institute of Microelectronics CAS (2020) Multiple Graduate Student Scholarships from Institute of Microelectronics CAS (2015-2017) As a PhD Supervisor at SUSTech, Dr. Chen mentors graduate students in quantum circuit design while leading research funded through multiple sources including a Beijing Natural Science Foundation General Project (RMB 200,000) for RF MEMS disk oscillator drive circuits and a Youth Project (RMB 100,000) for MEMS gyroscope phase alignment. He has also participated in major national projects totaling over RMB 27 million, demonstrating his integration into China's strategic research initiatives. Dr. Chen leads a research group at the International Quantum Academy in Shenzhen focused on cryogenic CMOS integrated circuit design for quantum computing applications. His team develops specialized low-temperature measurement and control chips that address critical challenges in scaling quantum computing systems, with particular emphasis on MEMS-based quantum frequency references and cryogenic amplifiers that operate at temperatures near absolute zero.

Michal LipsonView profile

Professor

• Nanophotonics
• Silicon Photonics
• Electro-optic properties of silicon
• +2 more

Michal Lipson serves as the Eugene Higgins Professor of Electrical Engineering and Professor of Applied Physics at Columbia University's Fu Foundation School of Engineering and Applied Science. Elected to both the National Academy of Engineering and National Academy of Sciences, she pioneered critical building blocks in silicon photonics that have transformed the field, with over 50,000 related publications annually. Her research has generated more than 250 scientific publications and 45 issued patents. Lipson's research focuses on nanophotonics and silicon photonics, where she demonstrated the ability to tailor electro-optic properties of silicon in landmark 2004 and 2005 Nature papers. Her work has enabled the development of photonic devices and circuits that now form the foundation of over 1,000 papers published yearly. She investigates novel optical phenomena while developing practical applications that address major bottlenecks in microelectronics. Her research spans fundamental physics to practical device implementation, with particular emphasis on integrated photonic systems. Analysis of her recent publications reveals a strategic expansion from foundational silicon photonics into emerging applications including quantum information processing, machine learning acceleration, biomedical sensing, and topological photonics. While maintaining core expertise in silicon-based devices, her work increasingly incorporates 2D materials, heterogeneous integration, and novel optical phenomena to push performance boundaries. The research demonstrates consistent progression from fundamental device physics to system-level implementations with practical applications. National Academy of Engineering (2025) National Academy of Sciences MacArthur Fellowship Blavatnik Award Optica's R.W. Wood Prize IEEE Photonics Award John Tyndall Award NAS Comstock Prize in Physics Thomson Reuters Top 1% Highly Cited Researcher (annually since 2014) Professor Lipson has mentored an exceptional research group, graduating 40 PhD students and 2 MS students, with numerous postdocs and visiting researchers. Her alumni occupy prominent positions including professorships at major universities (Rochester, Ottawa, UNICAMP, Johns Hopkins), leadership roles at Intel, Bell Labs, and startups she co-founded (HyperLight, Voyant Photonics). Her laboratory has received substantial research funding supporting cutting-edge work in nanofabrication, optical characterization, and device development. Current research directions include quantum photonics, AI-accelerated optical systems, and novel materials integration. The Lipson Research Group operates state-of-the-art facilities for nanophotonic device design, fabrication, and characterization. The team comprises principal investigators, postdoctoral researchers, PhD students, and administrative staff working collaboratively across disciplines including electrical engineering, materials science, physics, and applied physics. The group maintains strong industry partnerships while pursuing fundamental scientific advances in light-matter interactions at the nanoscale.

Roland A. FischerView profile

Professor

• Catalysis
• Energy Conversion
• Gas Storage and Separation
• +5 more

Prof. Roland A. Fischer is a Full Professor at the Chair of Inorganic and Metal-Organic Chemistry at Technical University of Munich (TUM). Previously, he held a Full Professorship at Ruhr University Bochum (1997–2015). His research focuses on multifunctional metal-organic frameworks (MOFs), clusters, and composites for energy conversion, catalysis, gas storage, and environmental applications. He leads the Catalysis Research Center and has pioneered advancements in MOF-based catalytic systems and stimuli-responsive materials. Education: 1981–1986: Diplom in Chemistry (TUM) 1989: PhD, Dr. rer. nat. (TUM) 1995: Habilitation (TUM) Research Interests: His work integrates molecular and extended catalytic systems, including: - Design of MOFs for photocatalytic fuel production - Nanoparticle encapsulation in robust frameworks - Redox-switchable materials and photochromic systems - Cluster chemistry and superatom complexes - Applications in energy storage, environmental remediation, and biomedical technologies. Major Achievements: Over 680+ publications, h-index 101 (Scopus 2025) Coordinator of EU projects (SURMOF, ENHANCE, DEFNET) Recipient of Heinz-Maier-Leibnitz Award (1993) and Alfried Krupp Award (1996) Editorial roles: Angewandte Chemie , Chemical Vapour Deposition Grants & Teams: He has secured major grants including DFG Priority Programs (CVD-Materials, COORNETs) and led interdisciplinary teams in EU initiatives. His lab collaborates globally, including visiting professorships at Kyoto University and IIT Bombay. Labs & Facilities: His research uses advanced facilities like the Catalysis Research Center and contributes to platforms such as the Munich Catalysis Alliance. Key tools include atomic layer deposition, in situ characterization, and MOF-based device fabrication.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +9 more

Jim Torresen is a Professor at the Norwegian University of Science and Technology (NTNU), specializing in Computer Science, Artificial Intelligence, and Robotics. He earned his M.Sc. and Dr.ing. (Ph.D.) in computer architecture and design from NTNU in 1991 and 1996 respectively, followed by industry experience in hardware design before transitioning to academia in 1999. Research Interests: His work spans Machine Learning, Evolvable Hardware, and Ethical AI, with notable contributions to music technology, facial expression recognition, and healthcare monitoring systems. He actively explores interdisciplinary applications of AI in creative domains and clinical environments. Publications & Editorial Roles: Torresen has published extensively in journals like Frontiers in Artificial Intelligence and Genetic Programming and Evolvable Machines . He serves as a Topic Editor for Frontiers in Explainable AI and has editorial roles in robotics and biomedical AI domains.

• Wireless Communications
• Signal Processing
• Physical Layer Security
• +3 more

Georges Kaddoum is a Professor at École de technologie supérieure (ÉTS) , specializing in Electrical Engineering. He holds the Canada Research Chair in Unlocking the Power of IoT 6G-Networks and the LACIME – Communications and Microelectronic Integration Laboratory affiliation. His work bridges wireless communications, IoT, and machine learning. Research Interests include wireless communication systems, physical layer security, machine learning for networking, and 6G technologies. He focuses on optimizing network performance in challenging environments (impulsive noise, underwater, non-terrestrial networks) and developing AI-driven solutions for jamming mitigation, resource allocation, and secure IoT frameworks. Recent Publications highlight 6G-enabled vehicular networks, quantum-safe blockchain integration, federated learning for transportation systems, and deep learning-based receivers for chaotic communication systems. His work emphasizes semantic communication, interference management, and digital twin applications. Awards IEEE TCSC Award for Excellence in Scalable Computing (2022) Prix d’excellence de la relève (Université du Québec, 2018) Prix d’excellence en recherche (ÉTS, 2018) Multiple IEEE Exemplary Reviewer and Best Paper Awards (2014–2022) Supervision includes 15+ PhD/Master’s students working on topics like index modulation, physical layer security, UAV communications, and intelligent resource management. Research Units Ultra Research Chair on Intelligent Tactical Wireless Networks LACIME Laboratory Canada Research Chair in IoT 6G-Networks

Hui WuView profile

Professor

• High-speed electronics
• RF and microwave integrated circuits
• Wireless and wireline communication circuits and systems
• +7 more

Hui Wu is a Professor of Electrical and Computer Engineering at the University of Rochester, affiliated with the Hajim School of Engineering & Applied Sciences. He leads the Laboratory for Advanced Integrated Circuits and Systems. Educations: BSc (Electrical Engineering), Tsinghua University (1996) MSc (Microelectronics), Tsinghua University (1998) PhD (Electrical Engineering), California Institute of Technology (2003) Research Interests: Focuses on high-speed electronics, RF/microwave circuits, silicon photonics, electronic-photonic integration, ultra-wideband radios, and nanoelectronics. Specializes in optical/electrical interconnects, high-performance clocking, and spintronics. Labs & Teams: Directs the Laboratory for Advanced Integrated Circuits and Systems, advancing research in integrated circuits and systems.

• Machine Learning in Electronics
• Electrostatic Discharge (ESD) Protection
• Circuit Reliability
• +3 more

Elyse Rosenbaum is the Melvin and Anne Louise Hassebrock Professor in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. She also serves as the Acting Associate Dean for Research at the Grainger College of Engineering. She is the director of the NSF-supported Center for Advanced Electronics through Machine Learning (CAEML), a collaboration between the University of Illinois, North Carolina State University, and Penn State University. Education: Ph.D. in Electrical Engineering, University of California, Berkeley, 1992 M.S. in Electrical Engineering, Stanford University B.S. in Electrical Engineering, Cornell University (with distinction) Research Interests: Her research focuses on machine learning applications in electronics, ESD-robust high-speed I/O circuit design, compact modeling, behavioral modeling of circuits, and CDM-ESD protection for advanced packaging technologies. Scientific Awards: IEEE Fellow for contributions to electrostatic discharge reliability of integrated circuits Best Student Paper Award, IEDM Outstanding and Best Paper Awards, EOS/ESD Symposium Technical Excellence Award, SRC NSF CAREER Award IBM Faculty Award ESD Association’s Industry Pioneer Recognition Award Advising and Grants: She supervises graduate and undergraduate researchers, primarily focusing on those with strong academic records and relevant experience. Her work is supported by NSF and other prominent organizations. Labs and Teams: She leads the CAEML center, which aims to apply machine learning to optimize microelectronic circuits and systems, enhancing design automation and reliability.

• Global Navigation Satellite Systems (GNSS)
• Avionics Systems
• Wireless Communication
• +7 more

René Jr Landry is a full Professor in the Department of Electrical Engineering at École de technologie supérieure (ETS), Université du Québec, specializing in Global Navigation Satellite Systems (GNSS), avionics, and wireless communication technologies. His academic journey includes a B.Ing. from Polytechnique Montréal, M.Sc. from University of Surrey (UK), and Ph.D. from SupAréo in Toulouse. He maintains active research leadership through two key laboratories: LASSENA (Laboratory of Space Technologies, Embedded Systems, Navigation and Avionics) and LACIME (Communications and Microelectronic Integration Laboratory). His research spans critical aerospace navigation domains including GNSS signal processing, inertial navigation systems, software-defined radio for avionics, radio frequency interference mitigation, and indoor positioning technologies. Landry's work addresses real-world challenges in satellite navigation robustness, precision positioning in urban/denied environments, and next-generation avionic system security. His current projects focus on blockchain-enhanced IoT security, AI-driven GNSS disruption analysis, and adaptive RF front-ends for multi-band avionics applications. Analysis of his recent publications reveals strong emphasis on resilient positioning systems through multi-constellation integration (particularly Iridium-NEXT), blockchain applications for navigation security, and explainable AI techniques for GNSS signal quality assessment. His work increasingly bridges traditional navigation engineering with cutting-edge security and machine learning paradigms. 2014 Prix d'excellence du c.a. pour les services à la collectivité Landry has supervised over 100 graduate students across doctoral, master's, and research projects since 2005, with current supervision extending through Summer 2025. His research funding supports multiple industry partnerships focused on avionics certification, software-defined radio implementations, and next-generation navigation systems. The LASSENA laboratory under his leadership develops certified avionic products from open-source SDR platforms and advances multi-sensor fusion techniques for challenging navigation environments. His research infrastructure includes specialized facilities for GNSS signal simulation, avionics hardware testing, and multi-sensor integration. Current work emphasizes flight-tested validation of RF front-end technologies, blockchain-secured navigation data, and real-time interference mitigation systems for aviation applications.

David BlaauwView profile

Professor

• Ultra-Low Power Circuits
• IoT and Sensor Networks
• RF Communication
• +7 more

David Blaauw is the Kensall D. Wise Collegiate Professor of Electrical Engineering and Computer Science (EECS) at the University of Michigan. His research focuses on ultra-low-power analog/mixed-signal circuits, mm-scale sensors, neural networks, and biomedical applications. He leads the Blaauw Lab, which has pioneered innovations like the Michigan Micro Mote (M^3) and neural recording probes. His work emphasizes real-world deployability, with applications in environmental monitoring (e.g., monarch butterflies), medical devices, and robotics. Education: B.S. in Physics and Computer Science, Duke University (1986) Ph.D. in Computer Science, University of Illinois Urbana-Champaign (1991) Research Interests: Blaauw’s lab explores ultra-low-power computing, mm-scale systems, RF communication, in-memory computing, and genomics acceleration. Key projects include: Millimeter-scale computers (e.g., 0.04mm³ temperature sensors) Wireless neural interfaces for brain-machine communication Energy-efficient accelerators for edge AI and genomics Micro-robotics with sensing/actuation/computation Awards: IEEE Fellow 2016 SIA-SRC Faculty Award Motorola Innovation Award Best Paper Awards at ISSCC, ISCA, and RFIC Advising & Impact: Over 600 publications, 65 patents, and 4 startup companies spun from his lab. Current research includes genome sequencing accelerators (GenAx) and neural recording dust for brain mapping. He directs the Michigan Integrated Circuits Lab and chairs major conferences like ISSCC and DAC. Labs/Teams: Blaauw Lab (University of Michigan) Michigan Integrated Circuits Lab (MICAL)

Ye (Sarah) SunView profile

Associate Professor

• Wearable sensors
• Wearable robotics
• Smart and connected health
• +3 more

Ye (Sarah) Sun is an Associate Professor in the Department of Mechanical Engineering at the University of Virginia (UVA), part of the School of Engineering and Applied Science. She joined UVA in 2021 after serving as an Associate Professor at Michigan Technological University. Her work focuses on wearable sensors, robotics, smart health systems, and cyber-physical systems. She leads the WEARLab research group. Education: Ph.D. in Electrical Engineering from Case Western Reserve University (2021), B.S. in Instrumentation Engineering from Tianjin University (not specified). Research interests include wearable electronics, health monitoring, and human-technology interaction. Her interdisciplinary approach integrates engineering innovations with healthcare applications. Notable projects involve self-powered triboelectric sensors and optical fiber-based health monitoring systems. Recent publications highlight advancements in photodiode technologies for high-frequency applications, including millimeter-wave generation and photonic integrated circuits. Awards include the NSF CAREER Award (2018) and NSF BRITE Award (2022). She has organized major conferences and holds editorial roles in health technology journals. Grants include NSF funding for cyber-physical systems and smart health initiatives. Her lab collaborates on projects involving wearable robotics and connected health solutions, with a focus on real-world applications in healthcare and IoT.