جست‌وجوی استاد و پژوهشگر

Josep Altet Sanahujesمشاهده پروفایل

دانشیار

• Integrated Circuit Design
• Thermal Management in Electronics
• RF Circuit Engineering
• +۴ مورد دیگر

Josep Altet Sanahujes is an Associate Professor in the Department of Electronic Engineering at the Polytechnic University of Catalonia (UPC). His research focuses on high-performance integrated circuits, particularly leveraging thermal measurements to characterize and optimize RF analog circuits. He leads the HIPICS (High Performance Integrated Circuits and Systems Design) group, specializing in temperature sensor development for on-chip testing and monitoring of aging effects in CMOS amplifiers. Education: M.S. in Electronic Engineering, La Salle Universitat Ramon Llull (1993) Ph.D., Electronic Engineering Department, Universitat Politècnica de Catalunya (1998) Research Interests: Dr. Altet pioneers thermal-based strategies for analog circuit testing, including the design of compact temperature sensors compatible with RF technologies. He explores thermal coupling characterization and electro-thermal interaction in integrated circuits, with applications in automotive AI, energy-efficient DNN accelerators, and astrophysical instrumentation. His work bridges fundamental physics (e.g., thermal phase lag analysis) with practical device optimization. Key Contributions: His recent articles address cutting-edge topics like energy-efficient mixed-precision DNN inference via binary segmentation, aging compensation in RF amplifiers using DC temperature measurements, and security enhancements through thermal signatures. He has published extensively on topics ranging from heterodyne infrared imaging for current tracking to BPF-based thermal sensors. Lab & Teams: Primary affiliation with the HIPICS research group at UPC, collaborating on interdisciplinary projects involving thermal-aware circuit design and embedded systems.

Diego Mateo Peñaمشاهده پروفایل

دانشیار

• Mixed-Signal Integrated Circuits
• RF Circuit Design
• CMOS Technology
• +۶ مورد دیگر

Diego Mateo Peña is an Associate Professor at the Department of Electronic Engineering at the Universitat Politècnica de Catalunya (UPC), specifically within the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona. He holds an M.S. and Ph.D. in Electronic Engineering from UPC, completed in 1993 and 1998, respectively. Member of the HIPICS (High-Performance Integrated Circuits and Systems Design Group) and EFRICS (Efficient and Robust Integrated Circuits and Systems) research groups. Expertise includes mixed-signal and RF integrated circuits, substrate noise analysis, UWB-IR circuit design, and thermal monitoring techniques. His research focuses on circuit reliability, aging effects in CMOS devices, and thermal-based characterization. He has co-authored over 50 conference papers and 16 journal articles, with an emphasis on RF circuit design and integrated systems. Key projects involve developing low-power IoT receivers, 22nm FDSOI processors, and thermal-aware circuit monitoring systems. His work bridges theoretical analysis and practical applications in nanoscale electronics and robust system design.

Anders Hedegaard Hansenمشاهده پروفایل

دانشیار

• Fluid Power Systems
• Wave Energy Conversion
• Hydraulic Drives
• +۲ مورد دیگر

Anders Hedegaard Hansen is Associate Professor in Energy Technology at Aalborg University's Faculty of Engineering and Science, where he leads research in fluid power systems and wave energy conversion. His work focuses on hydraulic power take-off systems, digital hydraulics, and model predictive control applications. Hansen directs multiple EUDP-funded projects including 'PTO TWIN' for wave energy converter optimization and 'DigitalPTO' for hydraulic power take-off systems. His research has produced innovations in energy-efficient hydraulic transformers and control algorithms, recognized by multiple Best Paper Awards from ASME. With over 39 publications, Hansen's work spans wave energy conversion, hydraulic drive systems, and tribomechatronics. He teaches courses in fluid power systems modeling and received Aalborg University's Teacher of the Year award in 2023 and 2010.

Eric Daltonمشاهده پروفایل

مدرس

• Materials Science
• Thermal Management
• Corrosion Engineering
• +۳ مورد دیگر

Eric Dalton is a Lecturer in the School of Engineering at the University of Limerick. His research spans materials development, system reliability, thermal management of electronics, and biomedical microfluidics. He has collaborated with organizations like NASA, ESA, and MIT on advanced cooling systems and nanomaterials. Dr. Dalton holds a Doctor of Philosophy (2005) and a Bachelor of Science (1999). He teaches modules including Energy Storage, Semiconductor I, and Semiconductor II. His work focuses on nanomaterial characterization (e.g., thermoelectric nanowires), electronic reliability (solder joint analysis), and thermal interface materials for microelectronics. Key research areas include: Ultra-long nanowire production using diamond synchrotron analysis. Phase-changing thermal interface materials for fiber optic lasers. Microfluidic bio-reactors for genetic diagnostics. His recent publications highlight advancements in thermoelectric device measurement, magnetic micropump performance, and carbon nanotube composites. He has contributed to industry standards like the IPC creep corrosion guideline and engaged in innovation partnerships with global companies. Dr. Dalton’s work aligns with UN Sustainable Development Goals, particularly through sustainable materials innovation and advanced thermal management solutions for electronics.

Grover Larkinsمشاهده پروفایل

دانشیار

• Cryoelectronics
• Superconductivity
• Space Technology
• +۴ مورد دیگر

Grover Larkins is an Associate Professor in the Electrical Engineering Department at Florida International University (FIU), and Director of the Future Aerospace Science and Technology (FAST) Center funded by the U.S. Air Force Office of Scientific Research. His research focuses on cryogenic communications systems and superconductivity for space applications. The FAST Center develops advanced cryoelectronics for aerospace environments, leveraging interdisciplinary expertise in materials science and microwave engineering. His academic career spans over three decades with affiliations in both Electrical and Mechanical Engineering at FIU. Research interests include cryogenic sensor design, superconducting microelectromechanical systems (MEMS), and high-frequency superconducting devices. He has led projects involving dielectric material integration, thermal cycling effects on superconductors, and novel fabrication techniques for space-qualified components. Key research themes include: Superconductivity in 2D materials like graphene Cryogenic sensor and actuator development MEMS-based RF circuit design Space-qualified electronic systems Publications span 1982–2021 with recent work emphasizing cryogenic ventilator systems, graphene doping effects, and superconducting-MEMS hybrid devices. His work bridges theoretical condensed matter physics with practical aerospace electronics applications. Labs under the FAST Center include advanced cryogenic testing facilities and cleanroom fabrication environments for MEMS devices. Collaborations involve U.S. Air Force Research Labs and NASA-related space technology initiatives.

Hamed Dalirمشاهده پروفایل

دانشیار

• Mid-IR Photonics
• On-chip Light Sources
• Classical & Quantum Sensors
• +۳ مورد دیگر

Hamed Dalir is an Associate Professor of Electrical and Computer Engineering at the University of Florida, serving as Area Chair for Electronics since 2025. His research focuses on Mid-IR Photonics, On-chip Light Sources, Classical & Quantum Sensors, Ising Machine, AI Accelerators, and Photonics Integrated Circuits. PD, University of California – Berkeley, USA (2016) Ph.D., Institute of Science Tokyo, Japan (2014) M.Sc., Institute of Science Tokyo, Japan (2011) PD, University of Florida (2025) Visiting Scholar, University of Texas at Austin (2025) PD, The George Washington University (2023-2025) Dr. Dalir's research bridges photonic computing, semiconductor lasers, AI hardware, and integrated sensing systems. He has over 300 peer-reviewed publications and 20 patents, with industry collaborations from TSMC, Broadcom, and Fuji Xerox. His work includes photonic neural networks, orbital angular momentum beams, and quantum sensing. The 15 most recent publications highlight trends in AI acceleration, quantum optics, and semiconductor packaging. Key themes include photonic convolutional neural networks, orbital angular momentum beam processing, phase-change materials, and advanced packaging techniques for reliability and security. His lab develops hardware-efficient algorithms and hybrid optical-electronic systems for computing and sensing. ECE Teaching Excellence Award, 2025 Best Project Collaboration Award, TSMC, 2025 ECE Faculty of the Year (Student Choice), 2024–25 Rising Star in Photonics, ACS, 2025 Stanford–Elsevier Top 2% Scientists Ranking (top 0.3% in field), 2024 IAAM Scientist Medal & Fellow, 2024 SPIE Senior Member, 2023 AFWERX Commercialization Solutions, 2022 SPIE Community Champion, 2020 IEEE/Optica Senior Member, 2018 JSPS Fellow, 2015 KAKENHI Early Investigator Award, 2014 JSPS Young Scientist Award, 2014 GCOE Golden Award, Tokyo, 2012 Yoshida Foundation Award, 2009 Dr. Dalir advises current Ph.D. students Sydney Mutchnick, Salem Altaleb, Bel Jahannia, Joseph Crandall, Abdulaziz Bazammul, and Vahid Najmi. Former advisees include Dr. Jiachi Ye (Ph.D. 2025) and Dr. Haoyan Kang (Ph.D. 2023). His lab (CHIP Lab) collaborates with institutions like TSMC and Volkswagen Group, focusing on wafer-scale fabrication, photonic neural networks, and quantum sensing.

• Power Systems
• Clustering Algorithms
• Energy Grid Stability
• +۳ مورد دیگر

Dr. Faycal Znidi is Assistant Professor of Electrical Engineering, specializing in power systems analysis and energy grid technologies. His research develops advanced clustering algorithms for grid stability and partitioning. Research focuses on power system coherency detection, transient stability indices, intelligent islanding schemes, and renewable integration. Methodologies employ hierarchical clustering, spectral analysis, and synchronization coefficients to enhance grid resilience and fault management. Recent publications address emerging challenges in sustainable energy systems, including perovskite solar cells, 3D graphene applications, and pandemic modeling. Educational contributions examine engineering pedagogy through comparative studies of capstone programs.

Susantha Herathمشاهده پروفایل

استاد

• Systems and Security
• Network Security
• Natural Language Processing
• +۴ مورد دیگر

Dr. Susantha Herath is a Professor in the Information Systems Department at St. Cloud State University's Herberger Business School. He joined the department in Fall 2001 and has prior academic experience at the University of Aizu (1993-2001) and South West Texas State University (1990-1993). His primary teaching areas include Management Information Systems, Systems Analysis and Design, Security and Management of Operating Systems, IT Risk Management, and Research Methodology. Ph.D. in Electrical and Computer Engineering, Keio University, Japan, 1991 M. Eng. in Industrial and Computer Engineering, Electro-Communications University, Japan, 1987 B.S. in Business Administration, Sri Jayawardhanapura University, Sri Lanka, 1978 His research focuses on high-performance dependable systems, network security, natural language processing, and artificial intelligence. He explores probabilistic models for anomaly detection, statistical quality control approaches to intrusion detection, and interdisciplinary applications of computer forensics in accounting and healthcare. His work spans cybersecurity, machine learning, and educational innovations in information systems. Dr. Herath has received recognition as a Senior Member of IEEE. His publications demonstrate expertise in spam detection, economic modeling, and enhancing security curricula through active learning methodologies. He has also contributed foundational work in multilingual perception analysis and computer security laboratories.

Rachmadian Wulandanaمشاهده پروفایل

دانشیار

• Renewable Energy
• Hydrokinetic Energy
• Vortex-Induced Bladeless Turbines
• +۶ مورد دیگر

Rachmadian Wulandana is an Associate Professor in the Engineering Programs department at the State University of New York at New Paltz (SUNY New Paltz), within the School of Science & Engineering. He holds a Ph.D. in Mechanical Engineering from the University of Pittsburgh. His research focuses on renewable energy systems, particularly hydrokinetic and vortex-induced bladeless turbines, with applications in sustainable energy generation and thermoelectric cooling technologies. Education: Ph.D. Mechanical Engineering, University of Pittsburgh, PA His expertise spans fluid dynamics, finite element analysis, and computational fluid dynamics (CFD). Key research areas include optimizing turbine designs for energy harvesting, analyzing autorotating systems, and developing innovative thermal systems. He has contributed to advancing hydrokinetic energy applications in regions like Bangladesh and explored novel sensor technologies for turbine RPM measurement using Hall Effect principles. His publications highlight trends in sustainable energy technologies, with a focus on improving turbine efficiency, reducing drag, and leveraging vortex dynamics for energy production. Recent work also emphasizes educational tools like open-water flumes for fluid mechanics laboratories. Scientific Awards: 2016 Outstanding Lecturer Teaching Award from Wright State University As Mechanical Engineering Program Coordinator, he contributes to curriculum development and faculty development initiatives. He actively reviews manuscripts for MDPI and participates in committees for the Annual Indonesia Focus Conference.

Aimee Nelsonمشاهده پروفایل

استاد

• Neurophysiology
• Transcranial magnetic stimulation
• Motor control and plasticity
• +۷ مورد دیگر

Aimee Nelson is a Professor in the Department of Kinesiology at McMaster University, Canada. She obtained her BSc in Psychology from McMaster (1997) and her PhD from the University of Toronto (2003). Her research integrates non-invasive brain stimulation, electromyography and behavioural assays to investigate sensorimotor plasticity, pain syndromes, movement disorders and exercise-induced neuroplasticity. Education: PhD, University of Toronto, 2003 BSc Psychology, McMaster University, 1997 Research interests span neurophysiology of motor control, transcranial magnetic stimulation methodologies, spinal cord injury, Parkinson’s disease, dystonia, sex-hormone influences on plasticity, and development of EMG-based biofeedback technologies for rehabilitation. Across >100 peer-reviewed articles her group has advanced understanding of afferent inhibition circuits, ipsilateral motor pathways, exercise-neuroplasticity interactions, and TMS-based therapeutics for chronic pain conditions including CRPS, fibromyalgia and diabetic neuropathy. She currently teaches undergraduate and graduate neurophysiology courses (KINESIOL 3HN3, 4AN3, 729, 713) and maintains an active laboratory at McMaster University.

Aimin Songمشاهده پروفایل

استاد

• Nanoelectronics
• Graphene
• Thin-film electronics
• +۵ مورد دیگر

Professor Aimin Song is a Professor of Nanoelectronics at the University of Manchester's School of Electrical & Electronic Engineering. His research focuses on novel nanoelectronic devices, printable thin-film electronics, and graphene-based technologies. He holds roles including Chair of the China Thin Film Electronics Symposium and Associate Editor of IEEE Access. His work has led to awards such as the University of Manchester's Researcher of the Year and Royal Society Brian Mercer Award. Education: PhD from Chinese Academy of Sciences (1995), followed by fellowships at University of Glasgow and University of Munich. He joined Manchester as a Lecturer in 2002 and became Professor in 2006. Research interests include ultra-fast graphene detectors, high-frequency nanodiodes, and terahertz metamaterials. His lab develops room-temperature-operating devices like the ballistic rectifier and self-switching diode, achieving up to 1.5 THz speeds. Collaborations span EU Framework Projects and industry partnerships. Key achievements include over 215 publications in journals like Nature Communications and 4 completed EU-funded projects. His students have won prestigious awards such as Rolls-Royce prizes and Best Conference Paper Awards. Current research emphasizes wearable electronics, energy harvesting, and THz imaging applications. Labs/Teams: Leads the Nanoelectronics Research Group, contributing to the Institute for Data Science and AI. Active in developing printed electronics through spin-off company Nano ePrint Ltd.

Ila Fieteمشاهده پروفایل

استاد

• Computational Neuroscience
• Neuroscience
• Cognitive Science
• +۳ مورد دیگر

Ila Fiete is a Professor in the Department of Brain and Cognitive Sciences at MIT, an Associate Investigator at the McGovern Institute for Brain Research, and Director of the K. Lisa Yang Integrative and Computational Neuroscience (ICoN) Center. Her research focuses on understanding neural mechanisms underlying memory, navigation, and learning through computational and theoretical neuroscience approaches. Education: B.S. in Physics and Mathematics from the University of Michigan; Ph.D. in Physics from Harvard University. Research Interests: Grid cell function, spatial cognition, neural network dynamics, cognitive mapping, and the interplay between neuroscience and artificial intelligence. Her work has elucidated theoretical frameworks for grid cell activity stabilization, developmental emergence, and their roles in spatial and episodic memory. She pioneered studies connecting neural error correction codes to fault-tolerant networks and developed computational models of hippocampal-entorhinal circuits. Awards: 2022 Swartz Prize for Theoretical and Computational Neuroscience (Society for Neuroscience). Labs/Teams: Leads the Fiete Lab and directs the ICoN Center, fostering interdisciplinary research at the interface of neuroscience, computation, and AI. Her work bridges biological plausibility with machine learning innovation. Current Projects: Exploring modular neural architectures, fault tolerance in biological systems, and the cognitive foundations of spatial navigation. Active in developing neuro-inspired AI models and studying neural dynamics across scales.

Ken Maiمشاهده پروفایل

استاد پژوهشی

• High-Performance Circuit Design
• Secure IC Design
• Radiation Hardening
• +۴ مورد دیگر

Ken Mai is a Principal Systems Scientist in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. He holds a B.S., M.S., and Ph.D. in Electrical Engineering from Stanford University. His research focuses on high-performance circuit design, secure IC design, radiation hardening, reconfigurable computing, and computer architecture. He has received prestigious awards including the NSF CAREER Award and the George Tallman Ladd Research Award. Education: Ph.D., Electrical Engineering, Stanford University, 2005 M.S., Electrical Engineering, Stanford University, 1997 B.S., Electrical Engineering, Stanford University, 1993 Research Interests: Dr. Mai’s work addresses challenges in nanometer-scale CMOS technology, including interconnect delay, leakage, and soft errors. His projects include secure IC design against hardware attacks, robust memory techniques using digital communications, 100GHz logic via cryogenic cooling, and bio-implantable computing platforms. He collaborates with neurosurgeons at the University of Pittsburgh for medical applications. Key Projects: Secure IC Design: Countermeasures against invasive/non-invasive attacks Robust Memory Design: Error correction codes for resilience 100GHz Logic: Cryogenically cooled CMOS for high-speed applications Bio-Implantable Systems: Low-power, high-density computing for medical devices Awards: NSF CAREER Award George Tallman Ladd Research Award Eta Kappa Nu Excellence in Teaching Award Teaching and Advising: He teaches courses like 18-322 (Digital Circuits), 18-617 (Memory Systems), and advises students including Mudit Bhargava and Mark McCartney. His lab, the VLSI Design Group, develops tools and methodologies linking circuit design to architectural design. Contact: Email: kenmai@ece.cmu.edu Website: Carnegie Mellon VLSI Design Group

Hui Maمشاهده پروفایل

مدرس ارشد

• Electrical Asset Management
• Power Systems
• High Voltage Engineering
• +۵ مورد دیگر

Dr. Hui Ma is a Senior Lecturer in Power & Energy Systems at the University of Queensland, affiliated with the School of Electrical Engineering and Computer Science. His expertise spans Electrical Asset Management, Power Systems, and High Voltage Engineering. He holds a PhD from the University of Adelaide and has been at UQ since 2008. Education: B.Eng and M.Eng from Xi'an Jiaotong University; M.Eng (Research) from Nanyang Technological University; PhD from the University of Adelaide. Research focuses on improving electricity network visibility through modeling, sensing, and signal processing, alongside data mining for renewable-integrated grids. Key areas include transformer condition monitoring, insulation diagnostics, and smart grid technologies. He edits IEEE Transactions on Power Delivery and participates in IEEE Smart Grid initiatives. Recent work addresses PV forecasting, low-frequency grid instability, and transformer aging analysis. His publications emphasize machine learning applications in energy systems and fault diagnosis. Collaborations involve Australian utilities and industry partners.

Apurva Ratan Murtyمشاهده پروفایل

استادیار

• Visual Intelligence
• Neurodevelopmental Disorders
• fMRI
• +۴ مورد دیگر

Apurva Ratan Murty is an Assistant Professor at the School of Psychology, Georgia Institute of Technology. His research focuses on uncovering neural codes and algorithms underlying human vision by integrating biological studies with artificial intelligence models. The Murty Lab employs advanced neuroimaging techniques (fMRI, MEG, EEG) and computational methods (deep learning, generative AI) to explore perceptual development, neurodevelopmental disorders, and invariant object representations. He holds a PhD from the Indian Institute of Science (IISc) and completed postdoctoral research at MIT's McGovern Institute. Education: PhD in Neuroscience, Indian Institute of Science (IISc), Bangalore, India (Adviser: S.P. Arun) Postdoctoral Research: MIT's Center for Brains, Minds, and Machines (Advisers: Nancy Kanwisher, James DiCarlo) Research Interests: The lab investigates how visual information transforms into perception through neural circuits, emphasizing cross-disciplinary approaches to bridge biological and artificial vision systems. Key themes include modeling neurodevelopmental disorders, analyzing fMRI data with AI, and developing biologically constrained computational frameworks. Awards: NIH K99/R00 Pathway to Independence Award Young Systems Scientist Award, National Systems Society of India Institute Medal, Indian Institute of Science Labs & Teams: The Murty Lab actively recruits PhD students, postdocs, and undergraduates. The lab's work is showcased at murtylab.com , emphasizing translational research in vision science and neurotechnology.