جستجوی استاد راهنمای دانشگاهی

• Integrated Circuits
• Quantum Computing
• mm-Wave Transmission
• +۳ مورد دیگر

Harald Pretl is a Professor at the Department of Integrated Circuits within the Institute for Integrated Circuits and Quantum Computing at Johannes Kepler University Linz (JKU). Holding the title Univ.-Prof., he maintains active research leadership with current projects extending through 2029 and 107 documented research outputs including patents, articles, and conference proceedings. His research spans integrated circuits, quantum computing, mm-wave transmission, biomedical sensing, and open-source EDA tools. He specializes in high-frequency circuit design, ultra-low-power biomedical sensors, and educational applications of open-source design methodologies for analog/mixed-signal IC development. Recent work demonstrates cross-disciplinary innovation bridging engineering, neuroscience, and artistic expression. Recent publications reveal strong emphasis on practical implementation challenges: THz transmitter efficiency, brain-computer interface applications, ADC performance metrics, and open-source layout automation. These works collectively advance wireless communication systems, biomedical instrumentation, and accessible semiconductor design education through open-toolchain development. Prof. Pretl has supervised 3 students and actively promotes open-source EDA adoption through educational initiatives. His funded projects include Sub-blocks generators for NSSAR ADC (2025-2029), Open Parasitic Extraction for KLayout, and United Micro Technology collaborations, demonstrating sustained industry engagement. He co-leads the JKU LIT - SAL Intelligent Wireless Systems Lab (IWS Lab) and contributes to the High-Performance Integrated Quantum Computing project. Current activities include 42 documented presentations such as 'Using Open-Source EDA Tools in Hands-On IC Design Education' (2025) and 'Recent Developments in Ultra-Low-Power Biomedical Sensing' (2025), reflecting his dual focus on research innovation and pedagogical advancement.

Alan W. Chiuمشاهده پروفایل

استاد

• Neural Engineering
• Brain-Computer Interfaces
• Neuroprosthetics
• +۱ مورد دیگر

Dr. Alan W. Chiu serves as Professor and Samuel F. Hulbert Endowed Chair in the Department of Biology & Biomedical Engineering at Rose-Hulman Institute of Technology, where he specializes in neural engineering applications for neurological rehabilitation and assistive technologies. His academic credentials include: PhD in Electrical and Computer Engineering from University of Toronto (2006) MASc in Electrical and Computer Engineering from University of Toronto (2002) BASc in Engineering Science from University of Toronto (2000) Dr. Chiu's research centers on developing neural models and brain-computer interfaces (BCI) to restore functionality for individuals with neurological disabilities. His work integrates oscillator-based neural modeling with real-time BCI systems, focusing on neuroprosthetics and error-correction mechanisms in cognitive tasks. Current projects explore mixed reality environments for prosthetic control and strategies to mitigate sensory distractors in BCI applications. Analysis of his 2021-2024 publications reveals a dual trajectory: advancing BCI robustness through machine learning-enhanced signal processing (particularly in SSVEP and ERP paradigms), while simultaneously innovating biomedical engineering education through interdisciplinary curriculum design. His research consistently bridges theoretical neural modeling with practical assistive device implementation. His primary honor is the Samuel F. Hulbert Endowed Chair. Additional distinctions may exist but are not documented in the source material. Dr. Chiu actively mentors undergraduate researchers as supervisor of year-round and summer projects, building on his prior role as co-director of Rose-Hulman's Independent Project/Research Opportunities Program (IPROP). He contributes to accreditation standards as BMES Commissioner for ABET's Engineering Accreditation Commission, though specific grant funding details remain undisclosed. His research group focuses on translating neural engineering concepts into functional prototypes, with particular emphasis on EEG-based control systems and cognitive error detection frameworks for communication assistance.

• Neuromorphic Electronics
• Neuroscience
• Biointerfaces
• +۳ مورد دیگر

Dr. Hongwei Tan serves as Group Leader at the Department of Molecular Electronics, Max Planck Institute for Polymer Research (MPIP) in Mainz since November 2024. Previously, he was Research Fellow (2022-2024) and Postdoctoral Fellow (2017-2022) at Aalto University's Department of Applied Physics, following postdoctoral work at the University of Massachusetts Amherst on neurointerface devices. His academic credentials include: Bachelor of Physics, Nankai University (2011) Doctorate in Materials Physics and Chemistry, University of Chinese Academy of Sciences (2016), focusing on neuromorphic photomemristors Tan's research pioneers neuromorphic biointerfaces—integrating brain-inspired electronics with biological systems to enable energy-efficient computation and seamless biointeraction. His work targets transformative applications in brain-machine interfaces, prosthetics, and diagnostics through interdisciplinary convergence of neuroscience, materials science, and engineering. His publication portfolio (2015-2024) reveals a clear evolution from foundational optoelectronic memristor research toward complex neuromorphic systems for sensory processing. Key thematic trajectories include photomemristor-based vision systems, tactile coding with spiking nerves, and nanoionic human-machine interfaces, demonstrating consistent innovation in bioinspired hardware. As MPIP Group Leader, Tan directs research strategy and team mentorship within his neuromorphic electronics program, though specific advisees and grant details remain unlisted in available materials. His laboratory focuses on developing integrated neuromorphic systems—from novel materials and devices to algorithms—that facilitate bidirectional communication between electronic and biological neural networks for next-generation neurotechnologies.

• Neuroergonomics
• Human-Machine Interaction
• Human Factors
• +۳ مورد دیگر

Dr. Julian Elias Reiser is a Research Fellow in the Experimental Ergonomics and Stress & Work Design group at the Leibniz Research Centre for Working Environment and Human Factors (IfADo), affiliated with TU Dortmund University. His work focuses on applying mobile neuroimaging techniques, particularly EEG, to assess cognitive workload and human performance in real-world settings such as driving and walking. His educational background includes: PhD in Psychology (2020) from TU Dortmund University, with a thesis titled "Neuroergonomics in the wild - Workload assessment using mobile EEG" M.Sc. Psychology from the University of Ulm (2016) B.Sc. Business Psychology from Leuphana University Lüneburg (2014) Dr. Reiser's research interests lie at the intersection of neuroscience and human factors. He investigates how cognitive processes unfold during complex tasks in natural environments, using mobile EEG and eye-tracking to measure workload, attention, and operator state. His work aims to improve human-machine interaction and workplace design by providing objective neurophysiological measures of mental effort. His recent publications (2023-2025) highlight a strong emphasis on real-world applications of neuroergonomics. Key themes include the use of eye blink-related potentials as indicators of cognitive load, the impact of automation on driver attention, and the development of signal processing methods for decoding brain activity in dynamic settings. His research spans transportation, occupational health, and human-computer interaction. No scientific awards are listed in the available information. There is no information provided regarding students advised or research grants obtained. Dr. Reiser is part of the Experimental Ergonomics team led by Prof. Dr. Edmund Wascher within the Ergonomics department at IfADo. The team conducts cutting-edge research on human factors in various work contexts, with a focus on developing and applying neuroergonomic methods to assess and enhance human performance in real-world environments.

Monty Escabiمشاهده پروفایل

استاد

• Auditory Neuroscience
• Computational Neuroscience
• Speech Recognition
• +۳ مورد دیگر

Monty Escabi is a Professor in the Department of Biomedical Engineering at the University of Connecticut (Storrs), where he leads the Physiological Acoustics Laboratory. His research bridges neurophysiology and computational modeling to understand auditory processing, focusing on neural coding of complex sounds in the inferior colliculus, thalamus, and cortex. He develops biologically inspired algorithms for noise-robust speech recognition systems. Education: B.S., Electrical Engineering, Florida International University (1993) M.S., Electrical Engineering: Signal Processing and Stochastic Modeling, Columbia University (1995) Ph.D., Bioengineering, University of California at Berkeley and San Francisco (2000) Research Interests: Dr. Escabi investigates how the central nervous system processes speech and natural sounds in noisy environments. His work combines neurophysiological recordings, computational modeling, and psychoacoustics to study: (1) spectrotemporal encoding in auditory pathways, (2) neural mechanisms for sound-source segregation, and (3) biologically inspired hearing technologies. Key areas include neural ensemble coding, auditory hierarchy optimization, and translating neurobiological principles into machine learning models for speech recognition. Publication Trends: Recent articles focus on neural mechanisms of auditory perception, computational models of speech recognition, and sound-statistics-based approaches to hearing science. Predominant themes include noise-robust auditory processing, hierarchical neural coding, and applications in neuroprosthetics, reflecting interdisciplinary integration of neuroscience, engineering, and machine learning. Laboratory: The Physiological Acoustics Laboratory employs large-scale neural recordings and computational techniques to study natural hearing principles and develop advanced auditory technologies.

Pan Wangمشاهده پروفایل

استادیار

• Generative AI
• Human-machine Co-creativity
• Brain Computer Interface
• +۳ مورد دیگر

Pan Wang is an Assistant Professor at the Faculty of Industrial Design Engineering (IDE) at TU Delft. Her research focuses on AI for design, particularly Human-machine (AI) co-creativity, integrating human-computer interaction, brain-computer interfaces, and AI-driven creative processes. She developed the 'symbiotic creativity' framework, an interactive system enabling human-AI collaboration throughout creative workflows. Education: PhD from the Dyson School of Design Engineering and Data Science Institute at Imperial College London. Research emphases include generative AI, brain-computer interfaces, and AI-generated artworks. She explores innovative methods for human-AI interaction in design contexts. Publications (2024–2025) highlight advancements in AI-co-creation tools, generative models for typography, and evaluation frameworks for AI art. Current work addresses interdisciplinary applications of AI in creative industries. No secondary employment declared (2023–2026). Teaches research modules (3 & 5 ECTS) at IDE, contributing to both academic and applied design education.

Joseph Makinمشاهده پروفایل

استادیار

• Mathematical Neuroscience
• Neural Computation
• Brain-Machine Interfaces
• +۷ مورد دیگر

Joseph Makin is an Assistant Professor of Electrical and Computer Engineering at Purdue University, affiliated with the Elmore Family School of Electrical and Computer Engineering. His research focuses on mathematical neuroscience, neural computation, and brain-machine interfaces (BMIs), with a strong emphasis on machine learning applications in neuroscience. He leads the Makin Lab, which develops algorithms for BMI/BCI systems, investigates theoretical aspects of neural computation, and explores pure machine learning principles inspired by biological systems. Education: Not explicitly listed in provided texts. His work bridges computational neuroscience and engineering, particularly in decoding speech and movement from neural activity using ECoG arrays. Notable projects include real-time speech decoding in paralyzed individuals and developing encoder-decoder frameworks for cortical activity translation. The lab also focuses on advancing recurrent neural network architectures and probabilistic graphical models, as evidenced by their textbook An Introduction to Modern Statistical Learning . Recent research trends emphasize self-supervised learning for speech decoding, hybrid system modeling of biological processes, and translational applications in neuroprosthetics. His team collaborates with neurosurgeons and biomedical engineers to translate theoretical insights into clinical tools. Scientific Awards: No explicit awards listed, though his BCI project reached finalist status in a major competition. Makin advises graduate students in machine learning and BMI projects, prioritizing strong backgrounds in neural networks and statistical methods. The Makin Lab is part of Purdue's broader efforts in computational neuroscience and biomedical engineering.

• Materials Science
• Photovoltaics
• Semiconductor Physics
• +۳ مورد دیگر

Bruno Ehrler is a Professor at the University of Groningen (honorary) and Group Leader of the Hybrid Solar Cells Group at AMOLF in Amsterdam since 2014. His research focuses on perovskite materials science, including fundamental studies and device applications like solar cells. He holds significant grants (ERC Starting, NWO Vidi) and is a WIN Rising Star award recipient. He previously worked at the University of Cambridge, where he earned his PhD in Physics under Prof. Neil Greenham. His expertise spans optoelectronics, quantum dots, and singlet fission photovoltaics. Ehrler contributes to national energy initiatives and serves on advisory boards for the Dutch Chemistry Council and nanoGe conferences. Education: PhD in Physics, University of Cambridge (2009–2012) MSci in Physics, University of London (Queen Mary College, 2005–2009) Studies at RWTH Aachen and University of London Research Interests: Perovskite materials, solar cell efficiency, ion migration dynamics, photonic materials, and sustainable energy technologies. His work bridges fundamental material science with applied device engineering, addressing challenges in scalability, stability, and energy conversion efficiency. Grants & Awards: ERC Starting Grant (2020): Artificial synapses from halide perovskites NWO Vidi Grant (2017): Metal halide perovskites WIN Rising Star Award (2018) Advising & Labs: Leads the Hybrid Solar Cells Group at AMOLF, focusing on perovskite innovation. Collaborates on national energy projects like the Netherlands Energy Research Alliance (NERA). Active in mentoring early-career researchers through advisory roles and grant programs.

Quentin Gallotمشاهده پروفایل

پژوهشگر

• Neuroinformatics
• Computational Neuroscience
• Neural Signal Processing
• +۳ مورد دیگر

Quentin Gallot is affiliated with the Neuroinformatics Professorship at ETH Zürich (Eidgenössische Technische Hochschule Zürich), located at Y55 G72 Winterthurerstrasse 190 in Zurich, Switzerland. His role involves supporting research activities within the Neuroinformatics domain. While specific research interests are not explicitly detailed in the provided text, his department's focus suggests engagement with computational models of neural systems, neuroimaging analysis, and interdisciplinary approaches to understanding brain function. Professional activities likely include laboratory support, data management, and technical collaboration within the neuroscientific community. No formal academic awards or student advisement records are mentioned in the text. His institutional contact information reflects active participation in the institute's operational structure.

Julia M. Stephenمشاهده پروفایل

استاد مدعو

• Neuroscience
• Developmental Neuroscience
• Cognitive Neuroscience
• +۴ مورد دیگر

Julia M. Stephen serves as an Adjunct Professor in the Department of Physics and Astronomy at the University of New Mexico and holds dual appointments as Director of the MEG/EEG Core and Professor of Translational Neuroscience at the Mind Research Network (MRN). Her primary research基地 focuses on advanced neuroimaging techniques to investigate brain development across the human lifespan, with particular emphasis on translational applications for neurological and psychiatric disorders. Dr. Stephen earned her PhD from the University of Minnesota, establishing the foundation for her specialized work in biophysics and neuroimaging. Her educational background directly supports her current methodological expertise in magnetoencephalography and multimodal brain imaging approaches. Her research program centers on developmental neuroscience, specifically examining typical and atypical brain maturation from infancy through aging. Key investigation areas include fetal alcohol spectrum disorders (FASD), autism spectrum disorders, schizophrenia, and Alzheimer's disease, with a focus on identifying neural biomarkers through MEG, EEG, and fMRI. She employs rigorous multimodal approaches to study sensory processing, cognitive development, and neural network dynamics, particularly investigating how prenatal exposures and aging processes alter brain function. Analysis of Dr. Stephen's publication record reveals consistent focus on developmental trajectories of brain function, with strong emphasis on FASD (7/15 articles), schizophrenia (4/15), and neuroimaging methodology development (3/15). Her work demonstrates increasing specialization in infant and child neurodevelopment since 2018, with recent publications highlighting MEG applications for early biomarker identification in prenatal exposure conditions and multisensory integration deficits. Dr. Stephen leads multiple NIH-funded research initiatives including the DevMind Study (tracking childhood brain development), investigations into prenatal alcohol exposure markers, and schizophrenia-related multisensory processing projects. Her laboratory infrastructure includes the MEG/EEG Core facility at MRN, which provides advanced neuroimaging capabilities for developmental and clinical neuroscience research. As a Principal Investigator, she directs interdisciplinary teams comprising neuroscientists, physicists, and clinicians to advance understanding of brain-behavior relationships across the lifespan.

• Atrial Fibrillation Management
• Frailty in Aging
• Brain-Computer Interfaces
• +۳ مورد دیگر

Dr. Asangaedem Akpan is an Adjunct Associate Professor at the University of Western Australia (UWA) within the UWA Medical School's Internal Medicine department. His research focuses on aging-related health challenges including frailty mechanisms, atrial fibrillation management in elderly populations, and neurotechnology applications for cognitive enhancement. He has contributed to systematic reviews on brain-computer interfaces and led studies on healthcare pathway adherence in elderly care homes. His work intersects cardiology, gerontology, and biomedical engineering, with notable contributions to understanding inflammageing through metabolomics. Dr. Akpan collaborates internationally on aging-related research, addressing both clinical and public health dimensions of aging populations. His recent studies emphasize translational applications of metabolomics data and the development of frailty intervention strategies aligned with UN Sustainable Development Goals. No scientific awards have been explicitly listed in the provided profile. His research activities involve multidisciplinary teams focusing on global health challenges, with publications spanning peer-reviewed journals like BMC Geriatrics and Frontiers in Aging. His current work continues to explore innovative solutions for aging-related healthcare through cutting-edge methodologies in systems biology and medical technology.

• Neural Engineering
• Biomedical Engineering
• Neurotechnology
• +۳ مورد دیگر

Lan Luan is an Associate Professor in the Department of Electrical and Computer Engineering at Rice University, affiliated with the Neuroengineering Initiative. She transitioned from Physics to Biomedical Engineering, joining Rice in 2019 after roles at Stanford, Harvard, and the University of Texas at Austin. Her research focuses on multimodal neural interfaces integrating electrical, optical, and other technologies to study brain and spinal cord activity for treating neurological disorders. Education: B.S. in Physics (with a minor in Electrical Engineering) from the University of Science and Technology of China (2004), Ph.D. in Physics from Stanford University (2011). Postdoctoral training at Harvard University. Honors include the 2017 NHLBI Mentored Research Award and 2010 Harvard Golub Fellowship. Research emphasizes neurotechnology for chronic neural recording and stimulation, with applications in stroke recovery, neurovascular dynamics, and neuroprosthetics. The Luan Lab collaborates with industry partners like SpikeGadgets Inc. on SBIR grants. Her work spans ultraflexible electrodes, microfluidic devices for glaucoma, and neural spike analysis. Advising includes mentoring Ph.D., Master’s, and undergraduate students in applied physics and biomedical engineering. The lab hosts interdisciplinary teams from ECE, Bioengineering, and other disciplines. Ongoing projects explore long-term neural-vascular interactions and next-generation biocompatible interfaces.

Tian Zhongمشاهده پروفایل

استادیار

• Quantum Photonics
• Quantum Information
• Quantum Networking
• +۳ مورد دیگر

Tian Zhong is an Assistant Professor of Molecular Engineering at the University of Chicago's Pritzker School of Molecular Engineering. His research focuses on quantum photonics and solid-state quantum technologies, particularly leveraging rare-earth doped materials for quantum networks and optical quantum memory. He completed his PhD in Electrical Engineering at MIT (2013) and a postdoc at Caltech’s Institute of Quantum Information and Matter. Key roles include pioneering rare-earth quantum nanophotonics and developing scalable quantum optical networks. Research interests include quantum entanglement purification, epitaxial thin films for quantum devices, and hardware-software co-design for quantum computing. Notable achievements include a DoD ARO Young Investigator Award (2020) and an NSF CAREER Award (2020). His lab, Zhong Quantum Lab, explores quantum interconnects and hybrid systems, with recent grants from the Betty Moore Foundation for novel quantum matter projects. Publications span quantum networking protocols, coherence properties of rare-earth ions, and interdisciplinary studies in microbiome-gut-brain interactions. Advising focuses on training students in quantum device fabrication and theoretical modeling, with active projects on dilution fridge setups and quantum repeater networks.

Myungin Leeمشاهده پروفایل

مدرس

• Multimodal Instrument Design
• Human-Computer Interaction (HCI)
• Machine Learning
• +۷ مورد دیگر

Myungin Lee is a Lecturer at the Department of Immersive Media Design, University of Maryland, College Park. His research focuses on designing multimodal instruments and interactive systems that integrate principles from HCI, machine learning, signal processing, and neuroscience. Key projects include NeuResonance (inter-brain synchronization feedback) and FractalBrain (EEG-based VR mindfulness tools). He has contributed to venues like ACM CHI, IEEE VIS, and NIME, and holds patents on reverberation time estimation using neural networks. Research interests span crossmodal correspondence in XR, neuro-interactive systems, and real-time audio-visual simulations. Notable collaborations include the NASA Ocean Project and AlloSphere Research Group . His work has been showcased at Ars Electronica and Getty PST 2024. Education: PhD in Media Arts & Technology (implied by dissertation, 2023) Patents: Multichannel microphone-based reverberation time estimation methods (WO2018111038A1, US10854218B2, etc.).

Jun Nishidaمشاهده پروفایل

استادیار

• Human Computer Interaction
• IoT and Wearables Technology
• Haptics
• +۴ مورد دیگر

Jun Nishida is an Assistant Professor at the University of Maryland, College Park's Department of Computer Science, leading the Embodied Dynamics Laboratory. He holds a Ph.D. in Human Informatics from the University of Tsukuba (2019) and previously served as a postdoctoral fellow at the University of Chicago's Human Computer Integration Lab. His research focuses on wearable interfaces that share embodied experiences through technologies like electrical muscle stimulation and exoskeletons, aiming to enhance rehabilitation, education, and design collaboration. Education : Ph.D., Human Informatics, University of Tsukuba, Japan (2019) Postdoctoral Research, University of Chicago (2019–2022) Research Interests : Nishida explores human augmentation via wearable devices, emphasizing haptics, motor learning, and cognitive enhancement. His work bridges psychological theory with hardware innovation, such as exoskeletons that morph physical capabilities and systems enabling interpersonal kinesthetic synchronization. Recent projects include DexteriSync (hand dexterity sharing) and Preemptive Action (EMS-driven reaction acceleration). Awards : ACM UIST Best Paper Award (2020) Microsoft Research Asia Fellowship (2016) Forbes 30 Under 30 (2018) Guinness World Record (2022) Lab & Grants : His Embodied Dynamics Lab develops devices for body-to-body communication and cognitive enhancement. He advises four PhD students and has received grants from NSF, JSPS, and Microsoft Research. Labs/Teams : Leads the Embodied Dynamics Lab, collaborating with institutions like Sony Computer Science Laboratories and the University of Chicago.