جست‌وجوی استاد و پژوهشگر

Surya D. Pathakمشاهده پروفایل

دانشیار

• Supply Chain Management
• Complex Adaptive Systems
• Sustainable Operations
• +۳ مورد دیگر

Surya Pathak is an Associate Professor of Operations Management at the University of Washington, Bothell School of Business , with a PhD in Interdisciplinary Management of Technology from Vanderbilt University (2005). His research focuses on supply chain management through the lens of complex adaptive systems and network theory. PhD: Interdisciplinary Management of Technology, Vanderbilt University (2005) MS: Electrical Engineering, Vanderbilt University (2001) BE: Industrial Electronics Engineering, Pune University (1999) His research explores: Supply network dynamics under risk and uncertainty Policy design frameworks for large-scale networks Sustainability implications in networked supply chains Diffusion of lean practices via complexity theory Interfunctional interfaces (marketing-finance-operations) Agent-based simulations and reliability-based design optimization Recent work includes NSF-funded research on technical networks and environmental performance. Publications span Decision Sciences , Journal of Supply Chain Management , and IEEE Transactions on Engineering Management . Awards include multiple MBA Faculty of the Year recognitions and NSF grant contributions. 2017: Nominated for Distinguished Teaching Award with Technology 2016: Inducted as Research Collaborator at IIM Bengaluru 2010: $311,205 NSF grant He teaches courses like Introduction to Operations Management and Quantitative Business Methods , and serves as Area Coordinator for Operations Management and Information Systems.

Alan Dunbarمشاهده پروفایل

مدرس ارشد

• Nanotechnology
• Solar Energy
• Materials Science
• +۹ مورد دیگر

Dr. Alan Dunbar is a Senior Lecturer in Energy at the University of Sheffield, affiliated with the School of Chemical, Materials and Biological Engineering. His career spans roles like Interim Director of Education, Academic Director of CDT-PV, and Departmental REF Lead. Education: MPhys in Physics with Electronics from UMIST, PhD on Silicon-Germanium Quantum Dots Leadership: Director of Student Support, Assistant Faculty Director of Learning and Teaching Research focuses on nanomaterials for optoelectronics, particularly perovskite/organic solar cells and chemical sensors. Key themes include: Solution-Processed Thin Films X-ray/Neutron Scattering for Material Analysis Photovoltaic Efficiency and Stability Fluorescent Fibre-Optic Sensing Perovskite Stoichiometry and Degradation His 15 most recent articles highlight trends in perovskite solar cells, organic photovoltaics, battery materials, and optical/environmental sensing. Collaborations span institutions like ISIS Facility, Diamond Light Source, and Warwick University. Teaching includes Module Lead for Low Carbon Energy, Science and Technology . Professional memberships: IOP, IChemE. Peer Review Panel Member at Diamond Light Source.

Alaa Shetaمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Evolutionary Computation
• +۹ مورد دیگر

Alaa Sheta is a tenured Professor of Computer Science at Southern Connecticut State University , New Haven, CT, USA. With over 180 refereed publications, three authored books, and extensive funded research, he is a globally recognized authority in machine learning, evolutionary computation, image processing, and robotics. Education B.E. Electronics & Communication Engineering, Cairo University, 1988 M.Sc. Electronics & Communication Engineering, Cairo University, 1994 Ph.D. Computer Science, George Mason University, USA, 1997 Research Interests Prof. Sheta’s research integrates machine learning , deep learning , and evolutionary algorithms to solve complex real-world problems. Core themes include image and signal processing for medical and industrial applications, autonomous robotics for navigation and inspection, big-data analytics for environmental and financial forecasting, and software reliability modeling using computational intelligence. His work frequently leverages meta-heuristic optimization techniques such as genetic algorithms, particle swarm optimization, and hybrid neuro-fuzzy systems. Publication Trends From 2015-2021, Prof. Sheta’s publications reveal a clear pivot toward deep learning and healthcare informatics , with multiple studies on obstructive sleep-apnea diagnosis using ECG and depth-sensor data, brain-tumor detection in MR images, and mobile-health applications. Earlier work emphasizes industrial process modeling , power-system optimization , and software effort estimation , reflecting sustained contributions across both theoretical algorithmic advances and high-impact interdisciplinary applications. Scientific Awards & Honors Best Poster Award, SGAI International Conference on Artificial Intelligence, Cambridge, UK, 2011 Senior Member, IEEE Vice-President, Arab Computer Society (2011) Associate Editor, International Journal of Advanced Computer Science and Applications (IJACSA) Associate Editor, International Journal of Computational Complexity and Intelligent Algorithms (IJCCIA) Advising & Grants Prof. Sheta has successfully supervised more than 30 master’s and Ph.D. students in the United States, United Kingdom, Jordan, and Syria. His research has been funded by the U.S. National Science Foundation , as well as agencies in Egypt, Saudi Arabia, and Jordan. He has also consulted for the Egyptian Ministry of Communication & IT (2002-2004) and UNDP Smart Schools project (2003). Labs, Workshops & Leadership He is the founder and chair of the Advanced Computation for Engineering Applications (ACEA) workshop series, held five times across Egypt, Jordan, and Saudi Arabia. He served as Program Chair of the Science and Information Conference 2013 in London and has held academic leadership roles such as Associate Dean (2008-2009) and Assistant Dean for Planning & Development (2006-2008) at Al-Balqa Applied University, Jordan.

• Computational and Applied Mathematics
• Image Reconstruction
• Computational Imaging
• +۳ مورد دیگر

Dr. Gregery Buzzard is a Professor of Mathematics and Director of the Center for Computational and Applied Mathematics at Purdue University, within the College of Science. His research focuses on computational imaging, inverse problems, and biological systems modeling, with significant contributions to image reconstruction techniques like Plug-and-Play Priors and Consensus Equilibrium. He holds the SIAM Imaging Sciences Best Paper Prize (2020) and led collaborations on algorithms for electron microscopy, CT, and hyperspectral imaging. Current students include Haley Duba (Mathematics), Samin Nur Chowdhury (ECE), and Karl Weisenburger (Mathematics). His work bridges applied mathematics and engineering, emphasizing uncertainty quantification and optimal experimental design. Key projects involve dynamic sampling strategies for microscopy and tomography, as well as multi-agent consensus frameworks for distributed imaging systems. Buzzard also contributed to cellular signaling research, particularly T-cell and B-cell receptor dynamics. Education: Ph.D. in Mathematics (not explicitly stated but inferred from career path) Grants: Multiple grants supporting imaging and computational research (details omitted) Labs/Teams: Directs the Center for Computational and Applied Mathematics (CCAM) Publications: Over 50 peer-reviewed articles, including foundational work on PnP-MACE frameworks

• Solid state device physics
• MOS interface physics
• Wide bandgap materials and devices
• +۴ مورد دیگر

Dallas Morisette is a Research Professor of Electrical and Computer Engineering at Purdue University, affiliated with the Birck Nanotechnology Center. His primary research focuses on solid-state device physics, MOS interface phenomena, wide bandgap materials, and power semiconductor devices, particularly silicon carbide (SiC) technologies. He holds a BS from Walla Walla College (1993), and both MS (1997) and PhD (2001) degrees from Purdue University. His work emphasizes advancing power device performance through innovations in materials, fabrication techniques, and device architecture. Notable contributions include the development of ultra-short-channel SiC MOSFETs, self-aligned trench structures, and the waffle substrate approach for reduced substrate resistance. His research bridges fundamental material science with applied engineering solutions for high-power electronics. Publications span over two decades, with recent emphasis on interface trap analysis, high-mobility SiC devices, and robust power electronics. These studies address challenges in device reliability, scalability, and energy efficiency. His work is supported by collaborations within Purdue’s College of Engineering and the Birck Center.

Guillermo Payá Vayáمشاهده پروفایل

استاد

• Embedded Systems
• Processor Architecture
• FPGA Design
• +۵ مورد دیگر

Prof. Dr.-Ing. Guillermo Payá Vayá leads the Chair for Chip Design for Embedded Computing at Technical University of Braunschweig's Faculty of Electrical Engineering, Information Technology, and Physics. His research focuses on processor architecture design, FPGA/ASIC implementations, and optimization techniques for embedded systems, particularly in high-performance, low-power, and radiation-hardened computing domains. Primary research interests include: Application-Specific Instruction Set Processors (ASIPs) and compiler co-design Radiation effects characterization and fault-tolerant hardware Ultra-low-power processor architectures for embedded AI Hardware acceleration of neural networks and computer vision algorithms Memory subsystem optimization and parallel computing techniques Recent publications demonstrate strong emphasis on radiation-hardened electronics (35% of recent works), AI accelerator design (27%), and ultra-low-power systems (20%), with growing interest in biomedical applications. Experimental validation through FPGA prototyping and semiconductor testing is a consistent methodology across research domains. Leads research team investigating: Radiation-tolerant FPGA architectures (Trumann, Weide-Zaage) Vector processor optimization (Gesper, Thieu) Nano-scale controller design (Weißbrich) AI-hardware co-design (Kautz, Beyer)

• Materials Science
• Thin Films
• Interconnect Reliability
• +۲ مورد دیگر

Nancy L Michael is a Teaching Professor in the Department of Mechanical and Aerospace Engineering at the University of Texas at Arlington. With over 20 years of academic experience, her career includes roles as Senior Lecturer and Research Faculty prior to her current position. Her research spans materials engineering, specializing in thin films, interconnect reliability, semiconductor materials, and thermal diffusivity. She focuses on failure mechanisms in microelectronic components, developing advanced characterization techniques for materials analysis. Her publication record demonstrates consistent contributions to semiconductor reliability, with methods for detecting barrier defects, analyzing pore structures in low-k dielectrics, and modeling electromigration failure mechanisms remaining influential in materials science. Awards and Honors: Outstanding Academic Advisor - Faculty Undergraduate (2023) Outstanding Academic Advisor Honorable Mention (2020, 2016) University Scholar (2002, 2000, 1998) NSF Graduate Research Fellow (2001, 2000, 1999, 1998) Carl D. Wiseman Award (1997) She serves on multiple university committees including the Academic Standards Committee and Freshman Programs Steering Committee. Her industry collaborations include projects with Agere Systems on electromigration properties and with SUNY on thermo-mechanical instability studies.

Jun (Brandon) Choiمشاهده پروفایل

دانشیار

• Microwave Circuits and Systems
• Antennas and Antenna Arrays
• Periodic Structures
• +۱ مورد دیگر

Jun (Brandon) Choi is an Associate Professor in the Department of Electrical Engineering at the University at Buffalo, State University of New York . Previously, he taught at Syracuse University and co-advised students at Nazarbayev University and INSA Strasbourg . Education: PhD, Electrical Engineering, University of California, Los Angeles (2014) MS, Electrical Engineering, University of California, Los Angeles (2007) BS, Electrical Engineering, University of California, Irvine (2003) His research focuses on Microwave Circuits and Systems , Antennas and Antenna Arrays , Composite Right/Left-Handed (CRLH) Transmission Lines , and Metamaterials . Key areas include tunable/reflectarray antennas, liquid metal-based flexible devices, high-power microwave systems, and frequency scanning arrays. Recent publications highlight advancements in liquid-metal-tuned metamaterials , CRLH leaky-wave antennas , and plasma-based absorbers . His work intersects Microwave Engineering with Flexible Electronics and High-Power Applications . Award: Highlight Paper in IEEE Trans. Plasma Sci. (2018). Students include current and former graduate researchers at University at Buffalo and Syracuse University , such as Kevin Xu, Nathan Chordas-Ewell, Zhi Li, and Komlan Payne. He also mentors summer visiting students from Nazarbayev University and INSA Strasbourg . He leads the University at Buffalo Microwave Lab , which explores metamaterials , reconfigurable antenna systems , and microwave fabrication techniques .

Bin Zhangمشاهده پروفایل

دانشیار

• Prognostics and Health Management
• Fault Detection and Isolation
• Failure Prognosis
• +۸ مورد دیگر

Dr. Bin Zhang is an Associate Professor in the Department of Electrical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. With over 20 years of experience in prognostics and health management, intelligent systems and control, and robotics, he has established himself as a leading researcher in battery management systems, power electronics, and fault-tolerant control systems. Dr. Zhang's educational background includes: Ph.D. in Electrical Engineering from Nanyang Technological University, Singapore M.E. in Mechanical Engineering from Nanjing University of Science and Technology, China B.E. in Mechanical Engineering from Nanjing University of Science and Technology, China His research focuses on active approaches to achieve intelligent smart systems with self-situational-awareness and self-adapting capabilities. Primary interests include prognostics and health management (PHM), which covers fault detection and isolation, failure prognosis, and fault tolerance; robotics and unmanned systems; intelligent systems and control; and dynamic systems design, modeling, simulation and control. His work integrates physics-based models with data-driven techniques and computational intelligence, including pattern recognition and machine learning. Analysis of Dr. Zhang's recent publications reveals a strong emphasis on battery modeling (particularly lithium-ion batteries), power electronics control (including fractional order delay and virtual variable sampling techniques), and deep learning methods (including graph neural networks, deep residual convolutional neural networks, and deep belief networks). His research spans multiple application domains including power grids, batteries, aircraft, helicopters, and manned/unmanned vehicles. Dr. Zhang serves as Associate Editor for prestigious journals including IEEE Transactions on Industrial Electronics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Neurocomputing. He is a Senior Member of IEEE and a member of ASME. As director of the Resilient Systems Laboratory, Dr. Zhang advises numerous graduate students working on cutting-edge research in battery technology, power cable insulation, and control systems. His lab is equipped with advanced facilities including an 8-channel ARBIN BT-Smart battery testing system, power electronics control systems, cable/wire testing systems, rotating machinery testing systems, and unmanned vehicles including quadrotors and hexacopters.

Pulugurtha Markondeya Rajمشاهده پروفایل

دانشیار

• Electronic Packaging
• Bioelectronic Systems
• Wearable and Implantable Medical Devices
• +۶ مورد دیگر

Dr. Pulugurtha Markondeya Raj is an Associate Professor in the Department of Electrical & Computer Engineering at Florida International University. His expertise spans electronic and bioelectronic packaging, heterogeneous integration, wearable medical devices, and advanced passive components. He co-leads technical initiatives in electronic packaging ecosystems, emphasizing nanomaterials and industry partnerships. His research has yielded over 380 publications, 13 patents, and $14M in R&D funding, primarily from industry. He serves as an Associate Editor for IEEE CPMT Transactions and IEEE Nanotechnology Magazine, and chairs IEEE Nanopackaging committees. Education: BS (1999) from IIT Kanpur, ME (1995) from IISc Bangalore, PhD (1999) from Rutgers University. His work focuses on integrating nanomaterials into packaging for 5G, biomedical systems, and power delivery. He has advised over 40 students, many now leading roles at tech giants like Apple and Intel. Research highlights include miniaturized neural recording systems, EMI shielding innovations, and 3D glass-based packages. His articles emphasize trends in 5G packaging, bioelectronic integration, and nanomaterial-driven solutions. Awards include IEEE's Best Associate Editor (2021) and numerous best-paper recognitions. Scientific Contributions: 35+ best-paper awards, 110+ journal papers, 19 book chapters Leadership: IEEE Distinguished Lecturer (2020-2022), Co-Chair IEEE Nanopackaging Technical Committee Grants and Collaboration: Secured $14M+ industry funding; his lab's innovations are commercialized via industry partnerships. Advised students contribute to cutting-edge packaging at major electronics firms. Labs/Teams: Leads research in nanopackaging and heterogeneous integration, advancing technologies like flexible fan-out packaging and embedded bioelectronics.

Zheyu Zhangمشاهده پروفایل

استادیار

• Wide bandgap semiconductors
• Cryogenic power electronics
• Power electronics reliability
• +۴ مورد دیگر

Dr. Zheyu Zhang is an Assistant Professor at Rensselaer Polytechnic Institute (RPI) in the Department of Electrical, Computer, and Systems Engineering. Previously, he held roles including Research Assistant Professor at the University of Tennessee (2015–2018), Lead Power Electronics Engineer at GE Research (2018–2019), and Warren H. Owen – Duke Energy Assistant Professor at Clemson University (2019–2023). His affiliations include the Center for Future Energy Systems (CFES) and the Center for Materials, Devices, and Integrated Systems (CMDIS) at RPI. Education: Ph.D. in Electrical Engineering from the University of Tennessee, Knoxville (2015); M.Sc. and B.Sc. in Electrical Engineering from Huazhong University of Science and Technology, Wuhan, China (2011 and 2008). Research focuses on wide bandgap semiconductors, cryogenic power electronics, and power electronics reliability. Key areas include applications in electrified transportation, space power systems, energy storage, and renewable energy integration. His work emphasizes high-efficiency converters, solid-state circuit breakers, and cryogenically cooled systems for aviation and aerospace. Prominent contributions include over 100+ journal/conference papers, 10+ patent applications, and authored books/chapters. His research has earned awards such as the 2022 NASA Early Career Faculty Award and the 2021 IEEE IAS Young Member Achievement Award. Professional activities include editorial roles (e.g., IEEE Transactions on Power Electronics ), standard development (IEEE IAS committees), and leadership in conferences like ECCE and APEC. He also contributes to educational initiatives, including tutorials on WBG device characterization and cryogenic systems. Current teaching includes ECSE 4080 - Semiconductor Power Electronics . His labs focus on advanced power electronics, reliability testing, and cryogenic system design.

Mehrdad Nouraniمشاهده پروفایل

استاد

• Biometric signal/image processing
• Machine learning for healthcare
• VLSI/SoC design for reliability
• +۴ مورد دیگر

Dr. Mehrdad Nourani is a Professor of Electrical and Computer Engineering and Senior Associate Provost for Faculty Affairs at The University of Texas at Dallas. He leads research in healthcare technology, VLSI testing, and embedded systems through the Predictive Analytics & Technologies (PAT) Lab and Quality of Life Technology (QoLT) Laboratories. His work focuses on biometric signal processing for patient monitoring (e.g., epilepsy, diabetes, sleep apnea) and VLSI reliability in nanometer-scale circuits. Education: PhD in Computer Engineering, Case Western Reserve University (1993) M.S. & B.S. in Electrical Engineering, University of Tehran (1986 & 1984) Research Interests: Integrates machine learning with biomedical engineering for wearable healthcare devices, develops novel VLSI testing methodologies, and explores data-driven diagnostics. Key areas include artifact removal in wearable sensors, personalized analytics for pressure ulcers, and SEEG-based seizure network modeling. Advising & Grants: Supervises 6 current PhD/Master’s students. Research supported by NSF, Texas Instruments, UT Southwestern, and others. Co-founded CICS and QoLT labs fostering interdisciplinary collaboration. Labs & Teams: Directs PAT Lab and collaborates through QoLT and CICS. Projects include smart bed systems for ulcer prevention, AI-driven camera platforms for ambulation assessment, and ESD stress analysis in semiconductor devices.

Yuan Anمشاهده پروفایل

دانشیار

• Health Informatics
• Biomedical Information Systems
• Semantic Web
• +۶ مورد دیگر

Dr. Yuan An is an Associate Professor and Director of International Programs at the College of Computing & Informatics, Drexel University. His research focuses on health and biomedical information integration, semantic-ontology mapping, and knowledge representation. Education: PhD in Computer Science, University of Toronto Master of Computer Science, Dalhousie University Master of Engineering in Systems Engineering, Tsinghua University Bachelor of Engineering, Tsinghua University Research Interests include conceptual modeling, schema and ontology mapping, information integration, healthcare systems, and semantic web technologies. His work bridges data science and healthcare informatics, with a focus on structured database design and case-based schema development. Selected Publications highlight his contributions to automated coding for occupational epidemiology, EMR error detection, and semantic mapping in biomedical contexts. His projects span data quality in medical records, big data analytics, and biological network mining. Scientific Awards & Grants NSF IU/CRC CVDI Center ($98,000, PI, 2013-2014) NSF I/UCRC Phase I ($275,000, Co-PI, 2012-2017) Best Student Paper Award at IEEE BIBM 2013 Precarn Scholarship (2004-2005) Professional Activities include service on program committees for IEEE BIBM, CIKM, and ACM conferences, as well as leadership in international academic initiatives.

Philip Pongمشاهده پروفایل

دانشیار

• Electromagnetic Sensors
• Smart Grid
• Nanotechnology
• +۲ مورد دیگر

Philip W. T. Pong is an Associate Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology. His research focuses on electromagnetic sensing technologies for smart grid applications and nanotechnology. Education includes: Ph.D. in Engineering from University of Cambridge (2005) B.Eng. in Electrical and Electronic Engineering from University of Hong Kong (2002) Research expertise spans electromagnetic sensors, smart grid monitoring, nanotechnology applications in energy systems, and magnetic materials. Recent publications demonstrate strong focus on current sensing technologies (both contact and non-contact), fault diagnosis in power systems, wind turbine monitoring, and magnetic materials characterization. Article analysis reveals consistent emphasis on magnetoresistive sensors, wireless power transfer, renewable energy systems monitoring, and spintronics applications. Professional distinctions include Fellow status in multiple engineering institutions (IET, Energy Institute, IOM3, NANOSMAT) and chartered engineer credentials. Serves on editorial boards of IEEE journals. No information available regarding scientific awards or current students.

Huan Nguyenمشاهده پروفایل

استاد

• Digital Twin Modeling
• Machine Learning
• Wireless Communications
• +۵ مورد دیگر

Prof. Huan Nguyen is a Professor of Digital Communication Engineering at Middlesex University, affiliated with the Faculty of Science and Technology's Design Engineering & Mathematics department. He leads the London Digital Twin Research Centre and the 5G/6G & IoT Research Group. He holds a PhD from the University of New South Wales (2007) and a BSc from Hanoi University of Science & Technology (2000). Research Interests: Digital twin modeling, machine learning for disaster management and structural health monitoring, 5G/6G communication systems, and IoT. His work focuses on smart factories, critical infrastructure resilience, and healthcare applications like stroke care via digital twins. Grants & Leadership: Secured £2.5M+ in grants, including projects on quantum-assisted digital twinning (QuanDT, £93K), cognitive twins for Dubai manufacturing (£270K), and stroke care in Vietnam/Indonesia (StrokeDT, £99.8K). Served as PI or Co-I on 15+ grants from EPSRC, British Council, and industry partners. Teaching: Currently teaches Digital Communication Systems and Major Project modules. Previously instructed courses in digital communications, wireless systems, and telecommunications at Middlesex and other institutions. Awards & Roles: Senior Fellow of the HEA (2019), Senior IEEE Member, and Chair of the Vietnamese Intellectual Society in the UK/Ireland. Editor for KSII Transactions on Internet and Information Systems and REV Journal on Electronics and Communications . Labs & Teams: Leads research groups focused on digital twins, 5G/6G, and IoT. Collaborates with global partners like VinGroup, Vietnamese universities, and UK institutions on Industry 4.0 and healthcare innovation.