جست‌وجوی استاد و پژوهشگر

• Process Technology
• Device Design
• Silicon Carbide (SiC) Electronics
• +۳ مورد دیگر

Carl-Mikael Zetterling is a Professor and Head of Department at Kungliga Tekniska Högskolan (KTH) in Stockholm, Sweden, affiliated with the School of Electrical Engineering and Computer Science (ICT) and the Electronics and Embedded Systems department. His research focuses on process technology and device design for high-temperature, high-power silicon carbide (SiC) electronics, expanding into SiC-based analog and integrated circuits. He has authored over 300 publications, including books on SiC process technology and plagiarism prevention. Dr. Zetterling has held leadership roles such as Vice Dean of the School of ICT (2013–2017) and teacher representative on KTH's faculty board. He has collaborated internationally at Stanford University, Kyoto University, and Kyoto Institute of Technology. His work addresses applications in extreme environments, including Venus exploration and fusion reactor monitoring, with a focus on radiation tolerance and thermal resilience. The 15 most recent publications highlight trends in wide bandgap semiconductors, gamma irradiation effects on SiC devices, and high-temperature integrated circuits. His articles span structural health monitoring with machine learning, novel SiC diode designs, and radiation-hardened electronics. Key contributions include advancements in self-aligned contacts, trench MOSFETs, and compact modeling for extreme conditions. While no formal awards are listed, his roles in technical program committees (TMS Electronic Materials Conference, IEEE SISC Conference) and editorial work demonstrate significant academic service. He teaches courses ranging from digital design to high-temperature electronics, overseeing degree projects in embedded systems, communication, and nanotechnology.

Mehrdad Moallemمشاهده پروفایل

استاد

• Control Systems
• Power Electronics
• Energy Harvesting
• +۳ مورد دیگر

Dr. Mehrdad Moallem is a Professor and Graduate Student Supervisor in the Department of Mechatronic Systems Engineering at Simon Fraser University (SFU). He holds a Ph.D. in Electrical & Computer Engineering from Concordia University (1997), an M.Sc. from Sharif University (1988), and a B.Sc. from Shiraz University (1986). His research focuses on control systems in sustainable energy, power electronics, energy harvesting, robotics, and embedded systems. He has authored/co-authored four technical books and serves on editorial boards for journals like IEEE/ASME Transactions on Mechatronics. Dr. Moallem has held academic roles at Duke University and the University of Western Ontario. His teaching includes courses on real-time control systems, mechatronics design, and microprocessors. Research interests span embedded control systems, nonlinear dynamics, and applications in renewable energy and robotics. Recent work includes IoT-enabled lighting systems for agriculture, RF cavity control, and smart energy harvesting. He emphasizes hands-on student projects and industry collaboration, such as the Siemens Certification Program and Industry 4.0 bootcamps. Dr. Moallem's lab develops innovative solutions for energy efficiency and automation, with a focus on sustainable systems and smart manufacturing. He actively advises graduate students on advanced topics like grid-connected inverters, motor drives, and vibration control.

Alp Akcayمشاهده پروفایل

دانشیار

• Simulation-based optimization
• Digital twins
• Data analytics
• +۳ مورد دیگر

Alp Akcay is an Associate Professor of Industrial Engineering at Northeastern University's College of Engineering in Boston, USA. He specializes in smart manufacturing systems and supply chain optimization using stochastic operations research, machine learning, and simulation techniques. His research collaborations with semiconductor firms like NXP, Nexperia, and ASML have produced data-driven solutions for production planning, predictive maintenance, and obsolescence management. Education: PhD in Operations Management and Manufacturing from Carnegie Mellon University. Research interests include digital twin technologies, semiconductor manufacturing processes, and Industry 4.0 applications. He currently serves as Associate Editor for the Journal of Simulation and coordinates the Manufacturing & Industry 4.0 track at the Winter Simulation Conference. Professional affiliations: INFORMS, INFORMS QSR Society, I-SIM (INFORMS Simulation Society).

Yuebing Zhengمشاهده پروفایل

استاد

• Nanophotonics
• Optical Nanotechnologies
• Advanced Materials Science
• +۷ مورد دیگر

Yuebing Zheng is a Professor of Mechanical Engineering & Materials Science and Engineering at the University of Texas at Austin, holding the Cullen Trust for Higher Education Endowed Professorship. He leads a research group innovating optical nanotechnologies for applications in health, energy, and manufacturing. His work focuses on light-matter interactions, optically active materials, and interdisciplinary training. Key roles include Graduate Advisor for the Materials Science Program and past leadership as Associate/Assistant Professor since 2013. Education: PhD in Engineering Science and Mechanics (2010), Penn State University Postdoctoral Researcher (2010-2013), UCLA (Chemistry and Biochemistry) MSc in Physics (2003), National University of Singapore BSc in Physics (2001), Nankai University Research Interests: Optical manipulation technologies (e.g., optothermal tweezers) Nanophotonics and metamaterials Machine learning for materials discovery Biomedical applications (e.g., cell analysis, chiral sensing) Clean energy systems Recent Article Trends: Focus on AI-driven materials design, optothermal microrobotics, and advanced optical systems for energy and biomedical applications. Key innovations include photonic batteries, graphene moiré systems, and steerable active particle swarms. Awards: 2025 SPIE Fellow 2024 Optica Fellow 2017 NIH New Innovator Award 2014 Beckman Young Investigator Multiple best paper awards (2019–2023) Advising & Grants: Supervised over 20 PhD students/postdocs. Active grants from NIH, NSF, ONR, NASA, and industry partnerships. Current lab focuses on optical manipulation, metamaterials, and AI-integrated nanotechnology. Labs/Teams: Director of the Zheng Research Group, affiliated with the Texas Materials Institute. Collaborates on projects merging nanoscience with machine learning and biomedical engineering.

Shoufeng Lanمشاهده پروفایل

استادیار

• Nanophotonics
• Quantum Photonics
• Metamaterials
• +۳ مورد دیگر

Dr. Shoufeng Lan is an Assistant Professor in the Department of Mechanical Engineering at Texas A&M University's College of Engineering, with affiliated appointments in Electrical & Computer Engineering and Materials Science & Engineering. His research focuses on advanced nanophotonics, exploring light-matter interactions across disciplines including quantum photonics, metamaterials, and 2D materials. Educational background includes a Ph.D. in Electrical and Computer Engineering with a Physics minor from Georgia Institute of Technology (2017), M.S. in ECE/Physics from University of New Mexico (2012), and dual B.S./B.E. degrees from Nankai University/Tianjin University (2007). Research interests span: Light-assisted control, sensing and manufacturing mechanics Plasmonic/metamaterial development Nonlinear/quantum/topological photonics Photon-induced chemical and biomedical synthesis His publications demonstrate consistent innovation in nanophotonics with recent focus on optical metamaterials, exciton control, and machine learning applications in photonics. Award highlights include the 2022 IAC Undergraduate Teaching Award and 2018 Sigma Xi Best Thesis Award. Current doctoral students include Yixin Chen and Sam Lin. Funded research includes NSF-supported work on Optical Hybrid Materials and DARPA-supported semiconductor manufacturing initiatives. Leads the Lán Laboratory (Lab for Advanced Nanophotonics) focusing on photon-matter interactions for energy and information technology applications.

Chen Xiaodongمشاهده پروفایل

استاد

• Materials Science
• Flexible Electronics
• Nanotechnology
• +۵ مورد دیگر

Professor Chen Xiaodong is a Distinguished University Professor at Nanyang Technological University (NTU), Singapore, holding primary appointment in the School of Materials Science & Engineering with courtesy appointments in the Lee Kong Chian School of Medicine and School of Chemistry, Chemical Engineering and Biotechnology. He serves as Deputy Director of the Institute for Digital Molecular Analytics and Science (IDMxS) and Director of both the Innovative Centre for Flexible Devices (iFlex) and Max Planck-NTU Joint Lab for Artificial Senses. His research spans mechanomaterials science and engineering, flexible electronics, sense digitalization, cyber-human interfaces and systems, and carbon-negative technology. Professor Chen's work focuses on developing methods for controlling materials architecture at 1-100 nm scale to solve fundamental and applied problems in energy, environment, and healthcare. His group integrates expertise from materials science, chemistry, biology, physics, and engineering to create innovative solutions. His scientific contributions have been recognized through numerous prestigious awards including the Singapore President's Science Award, National Research Foundation Investigatorship and Fellowship, Friedrich Wilhelm Bessel Research Award, Dan Maydan Prize in Nanoscience and Nanotechnology, and election to multiple national academies including Singapore National Academy of Science, Academy of Engineering Singapore, and German National Academy of Sciences Leopoldina. Professor Chen serves as Editor-in-Chief of ACS Nano and sits on editorial boards of numerous prestigious journals including Advanced Materials, Chemical Reviews, and Matter. He has mentored numerous PhD students and research fellows who have gone on to faculty positions at institutions worldwide. His laboratory develops cutting-edge technologies in flexible electronics, bio-inspired materials, and nano-bio interfaces, with strong industry collaborations and translational research focus.

Jingrui Heمشاهده پروفایل

استادیار

• Statistical Machine Learning
• Large Scale Data Mining
• Heterogeneous Machine Learning
• +۶ مورد دیگر

Jingrui He is an Assistant Professor in the Computer Science Department at Stevens Institute of Technology, with primary research focuses on statistical machine learning and large-scale data mining. Her work spans theoretical algorithm development and practical applications in diverse domains. University: Stevens Institute of Technology Department: Computer Science Department Academic Rank: Assistant Professor Research interests include: Heterogeneous machine learning techniques Rare category analysis and detection Social network analysis Public safety applications Traffic analytics Multimedia processing Virtual metrology in semiconductor manufacturing Publishing trends show consistent contributions to machine learning, data mining, and graph-based methods across multiple domains including semiconductor manufacturing, social networks, and multimedia. She has collaborated with researchers from Stevens Institute of Technology, IBM, and Carnegie Mellon University on both theoretical and applied problems.

Xuming Heمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۹ مورد دیگر

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

Kai Hobergمشاهده پروفایل

استاد

• Supply Chain Analytics
• Inventory Modeling
• Digital Transformation in Supply Chains
• +۷ مورد دیگر

Prof. Dr. Kai Hoberg is a Professor of Supply Chain and Operations Strategy at Kühne Logistics University (KLU) since 2017, where he also served as Department Head of the Operations and Technology Department from 2017 to 2023. Prior to joining KLU as an Associate Professor in 2012, he was an Assistant Professor at the University of Cologne (2010–2012) and a strategy consultant at Booz & Company (2006–2010). His research interests span supply chain analytics and technology integration inventory modeling for intermittent demand digital transformation in operations management additive manufacturing in after-sales services human-machine interaction in forecasting pharmaceutical supply chain challenges IoT-enabled vendor-managed inventory behavioral aspects of operations . Recent publications highlight empirical studies leveraging machine learning for semiconductor order fulfillment, typologies for additive manufacturing adoption, and process mining applications in SCM. His work frequently combines theoretical modeling with real-world validation, including partnerships with firms in food manufacturing, postal services, and medical devices. He earned a PhD in Supply Chain Management from Münster University (2006) and a Diplom in Industrial Engineering from Paderborn University and Monash University. He has held visiting scholar roles at institutions like Cornell, NUS, Oxford, and Stellenbosch.

Yu Zhangمشاهده پروفایل

مدرس

• Text Mining
• Information Management
• Social Computing
• +۱۲ مورد دیگر

Dr. Yu Zhang is a Lecturer of Data Science at the School of Business, UNSW Canberra. His academic career focuses on text mining, knowledge and information management, social computing, and bibliometric analysis, with interdisciplinary applications in areas such as sustainable logistics, supply chain management, and net-zero energy solutions. Fields of Interest: Text Mining, Information Management, Social Computing, Bibliometric Analysis, Machine Learning for Information Systems, Heterogeneous Network Analysis, Data Mining for Asset Management, Sustainable Logistics, Supply Chain Management, Net-zero Energy in Green Buildings, and Transportation. Grants: Served as CI in projects like "Online health monitoring in Li-ion batteries via trustworthy AI" (ACT Government, $1.22M) and "Delivering net-zero energy buildings" (TRaCE Lab to Market, $1.05M). Awards: Best Paper Award (Runner-up) at ADMA 2024 and Excellent Paper Award at ICEBE 2024. Teaching: Coordinated courses in Data Analytics, Workforce Planning Research, Business Capstone, and Logistics Intelligence with Big Data Analysis. Supervision: Guided research on topics like federated learning for healthcare fraud detection, blockchain-based carbon offset management, and tier-based supply chain visibility. His publications span materials science and photovoltaic technologies, with a focus on thin-film solar cells and defect passivation methods. For collaboration or supervision inquiries, contact him at m.yuzhang@unsw.edu.au .

Patrick Kluthمشاهده پروفایل

استاد

• Ion Beam Modification of Materials
• Solid-State Nanopores
• Synchrotron Radiation Techniques
• +۴ مورد دیگر

Patrick Kluth is a Professor at the Research School of Physics, Australian National University, leading a research group focused on swift heavy ion-modified materials and nanopore technology. His work bridges materials science, physics, and biomedical applications. Education : Dipl. Phys. from Düsseldorf, Germany; PhD in Physics from RWTH Aachen, Germany (2002, summa cum laude). Research Interests center on: Ion track technology for solid-state nanopore fabrication Advanced materials characterization (SAXS, X-ray absorption spectroscopy) Defect engineering in semiconductors and superconductors Nano-fabrication and semiconductor processing methods Bio-sensor development and ion separation technologies Recent Research Trends show a focus on: Developing affordable microcontroller-assisted nanopore fabrication platforms Enhancing flux pinning in superconductors via ion irradiation Engineering nanomaterials for space applications (carbon-fibre composites) Exploring radiation effects on perovskite solar cells and graphene-enhanced composites Combining machine learning with nanopore sensing for biomarker detection Scientific Awards : Feodor-Lynen Fellowship Borcherts-Medal for PhD excellence Three ARC Fellowships (Postdoctoral, Research, Future) Leadership Roles : Head of Department (2018-2020), Associate Director HDR (2020-2023). His projects include collaborations on Alzheimer's detection sensors and carbon-fibre additive manufacturing for space applications.

• Electronic Design Automation (EDA)
• VLSI Circuit Design
• Algorithmic Aspects of Chip Design
• +۵ مورد دیگر

Martin D. F. Wong is the Edward C. Jordan Professor of Electrical and Computer Engineering and Executive Associate Dean of the College of Engineering at the University of Illinois. A pioneer in Electronic Design Automation (EDA) and VLSI circuit design, his work has significantly advanced chip design methodologies through algorithmic innovations. He holds over 450 publications and has been recognized with prestigious awards, including the ASP-DAC Most Frequent Author Award and the inaugural EDA Research Award from Synopsys. Wong’s research focuses on EDA, computational lithography, and 3D integrated circuits. He has mentored 48 PhD students, many of whom have excelled in academia and industry. His contributions include foundational frameworks like OpenILT (Inverse Lithography Technique) and Xplace (global placement). He is an IEEE Fellow and has served as a Distinguished Lecturer for the IEEE Circuits and Systems Society. Key Achievements: Recipient of six best-paper awards in chip design and routing optimization Developed GPU-accelerated tools for static timing analysis and global routing Advances in machine learning applications for EDA, including congestion prediction and hotspot detection Wong’s legacy combines technical innovation with mentorship, shaping the future of semiconductor design and manufacturing.

Gerd Grauمشاهده پروفایل

دانشیار

• Semiconductor devices
• Microfabrication
• Printed electronics
• +۴ مورد دیگر

Gerd Grau is an Associate Professor of Electrical Engineering at York University, affiliated with the Lassonde School of Engineering and the Department of Electrical Engineering & Computer Science. He holds a BA and MEng from the University of Cambridge and a PhD from UC Berkeley (2016). His research focuses on semiconductor devices, microfabrication, printed electronics, and additive manufacturing, funded by NSERC, CIHR, Mitacs, and industry partners. He leads the Electronics Additive Manufacturing (E-AM) Lab, which integrates 3D printing and printed electronics for applications in biomedical devices, aerospace, and smart materials. Education: BA and MEng (University of Cambridge), PhD (UC Berkeley, 2016). Research interests include printed transistor devices, carbon fiber composites with integrated sensors, and machine learning optimization for printing processes. His lab houses advanced equipment like 3D printers, micro-inkjet systems, and characterization tools. Grau supervises a dynamic group of graduate and undergraduate students, with former advisees now leading roles in industry and academia. Teaching includes courses on semiconductor physics, nanoelectronics, and printed electronics. The E-AM Lab collaborates with Prof. Garrett Melenka on carbon fiber structural health monitoring and explores novel applications of laser-induced graphene for energy storage and environmental sensors.

David Horsleyمشاهده پروفایل

استاد

• Micro-electromechanical Systems (MEMS)
• Microfabricated Sensors and Actuators
• Mechatronics
• +۳ مورد دیگر

David Horsley is a Professor in the Department of Electrical and Computer Engineering at Northeastern University, based at the Oakland, CA campus. He serves as Deputy Director of the Institute for NanoSystems Innovation, a bicoastal research institute focused on semiconductor and nanotechnology advancements. Horsley earned his PhD in Mechanical Engineering from the University of California, Berkeley in 1998. His research focuses on Micro-Electromechanical Systems (MEMS), including the design and manufacturing of microfabricated sensors/actuators, mechatronics, and control systems. Notable projects include DARPA-funded work on gyroscopes and ultrasonic transducers, as well as NSF and industry collaborations in deep tech innovation. He has received prestigious awards such as the IEEE Fellowship and NSF CAREER Award. His work spans academic-industry partnerships, with contributions to ultrasonic imaging, MEMS-based sensors, and inertial systems. Education: PhD, Mechanical Engineering (UC Berkeley, 1998) Research interests emphasize MEMS applications in biomedical imaging, navigation systems, and consumer electronics. Recent publications highlight advancements in piezoelectric micromachined ultrasonic transducers (PMUTs), including designs for long-range detection, fingerprint sensing, and array-based imaging systems. Horsley leads interdisciplinary teams in developing miniaturized sensors and resonators with enhanced performance metrics. His lab actively explores synchronization phenomena in micromechanical oscillators to improve stability in gyroscopes and other precision instruments. Key Awards: Global Network Accelerator Award (2024), NSF CAREER Award (2009) Grants: $2M DARPA FLASH project, NSF EAGER grant for international tech partnerships As Deputy Director, Horsley oversees the Institute for NanoSystems Innovation’s initiatives in nanoscale semiconductor design, fostering collaboration between Northeastern’s Boston and Oakland campuses. His lab develops MEMS technologies with applications in healthcare, robotics, and smart devices, emphasizing scalable manufacturing processes and cross-disciplinary innovation.

Andy Shihمشاهده پروفایل

استاد

• Organic semiconductor devices
• Microfabrication & nanofabrication
• Printed and flexible electronics
• +۵ مورد دیگر

Andy Shih is a Professor in the Department of Electrical Engineering at École de technologie supérieure (ÉTS) in Montreal, Canada. He holds a B.Eng. and M.Eng. in Electrical Engineering from McGill University and a Ph.D. in Electrical Engineering from Massachusetts Institute of Technology. His research is conducted at the LaCIME (Communications and Microelectronic Integration Laboratory), where he focuses on innovative materials and advanced manufacturing. Dr. Shih's research interests span organic semiconductor devices, microfabrication & nanofabrication, printed and flexible electronics, sustainable electronic materials, organic transistors and sensors, soft MEMS, AI-enhanced sensing, and biomedical monitoring technologies. His work bridges materials science, electrical engineering, and biomedical applications, with particular emphasis on developing smart bandages, printed sensors, and flexible electronics for healthcare monitoring. His publications reveal a strong focus on organic electronics, sensor development, and biomedical applications, with increasing integration of AI techniques for sensor enhancement and data analysis. Dr. Shih teaches courses including Electromagnetism (ELE312), Microsystem Fabrication Processes (ELE676), and Photovoltaic Solar Energy Systems (ENR889). His supervision portfolio includes numerous doctoral and master's students working on diverse projects spanning printed electronics, MEMS, sensor development, AI applications in sensing, and photovoltaic systems. His research has resulted in multiple patents related to thin-film transistors, acoustic resonators, and sensor technologies.