جستجوی استاد راهنمای دانشگاهی

Noah J. Cowanمشاهده پروفایل

استاد

• Neuromechanics
• Robotics
• Neuroscience
• +۵ مورد دیگر

Noah J. Cowan is a Professor of Mechanical Engineering at Johns Hopkins University's Whiting School of Engineering, with secondary appointments in Computer Science, Electrical & Computer Engineering, and Neuroscience. He is the founder and director of the Locomotion in Mechanical and Biological Systems (LIMBS) Laboratory, part of the Laboratory for Computational Sensing and Robotics. His research focuses on neuromechanics, robotics, and control theory, bridging neuroscience, biomechanics, and engineering. Cowan's work investigates how organisms achieve precise locomotion and applies these insights to advance robotics, neuroprosthetics, and rehabilitation technologies. Education: B.S. Electrical Engineering (Ohio State, 1995), M.S. and Ph.D. Electrical Engineering & Computer Science (University of Michigan, 1997/2001). Postdoctoral fellowship at UC Berkeley (2001–2003) before joining Johns Hopkins. Research Interests: Neuromechanics of motion, bio-inspired robotics, multisensory integration in animals (e.g., electric fish, Drosophila), and sensorimotor control in clinical contexts like cerebellar ataxia. His lab studies how neural circuits interact with biomechanics to produce movement, with applications to robotic design and neurological disorder treatments. Awards & Recognition: Presidential Early Career Award for Scientists and Engineers (2010), IEEE Fellow, NSF CAREER Award (2009), and multiple teaching and research excellence awards at Johns Hopkins. His work has been published in top journals like Nature , Proceedings of the National Academy of Sciences , and IEEE Transactions on Robotics . Outreach & Mentorship: Longtime mentor for high school and undergraduate students in STEM, leading programs like the Baltimore Ingenuity Project and WISE. Served as team leader for the STEM Achievement in Baltimore Elementary Schools (SABES) initiative. Key Projects: Development of the LIMBS Lab’s VR systems for animal studies, bioelectric navigation technologies for medical devices, and collaborations with clinicians on upper limb movement disorders. His team’s research on electric fish and fruit flies has revealed principles of adaptive control applicable to robotics and AI.

Hassan Rivazمشاهده پروفایل

استاد

• Medical image analysis
• Deep learning
• Machine learning
• +۵ مورد دیگر

Professor Hassan Rivaz is a Full Professor and Concordia University Research Chair in Medical Imaging with Deep Learning at Concordia University's Gina Cody School of Engineering and Computer Science. He holds appointments in the Department of Electrical and Computer Engineering and is cross-appointed to the Department of Computer Science & Software Engineering. Dr. Rivaz serves as the Founding Director of the IMPACT Lab and actively supervises PhD students in Electrical and Computer Engineering and Computer Science programs. Dr. Rivaz received his PhD from Johns Hopkins University in 2011, Master's degree from the University of British Columbia, and Bachelor's degree from Sharif University, followed by postdoctoral training at McGill University. His academic journey includes prestigious awards such as the NSERC Post-Doctoral Fellowship and Jeanne Timmins Costello Post-Doctoral Award. His research focuses on advancing medical image analysis through deep learning techniques, particularly in ultrasound imaging applications. Dr. Rivaz has made significant contributions to quantitative ultrasound, cancer detection, lymphedema assessment, and ultrasound elastography. His work bridges theoretical algorithm development with practical clinical applications, addressing challenges in medical image denoising, segmentation, registration, and tissue characterization. The IMPACT Lab under his direction develops innovative solutions for medical imaging problems with direct clinical relevance. Analysis of his recent publications reveals a strong emphasis on deep learning applications for ultrasound image processing, with particular focus on denoising techniques, elastography improvements, and segmentation algorithms. His work consistently addresses the challenge of working with real clinical data rather than simulated environments, contributing to more practical medical imaging solutions. Dr. Rivaz has received numerous prestigious awards including: Concordia University Research Chair in Medical Imaging with Deep Learning (2023–2028) QBIN/RBIQ Rising Star in Bio-Imaging in Quebec (2022) Concordia University Research Chair in Medical Image Analysis (2018-2023) Petro-Canada Young Innovator Award (2016–2018) He actively mentors graduate students, with many recipients of competitive scholarships including NSERC CGS, FRQNT, and FRQS awards. Dr. Rivaz serves on editorial boards for top journals including IEEE Transactions on Medical Imaging (since 2017), Medical Image Analysis (since 2025), and IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (since 2018). He has organized major conferences including IEEE EMBC 2020, ISBI 2021, and IEEE IUS 2023, and served as Area Chair for MICCAI from 2017 to 2024. As Founding Director of the IMPACT Lab, Dr. Rivaz leads a multidisciplinary research team focused on innovative medical imaging solutions. The lab maintains strong collaborations with hospitals and research institutions to translate imaging technologies into clinical practice. Current projects include developing AI-powered ultrasound analysis tools, quantitative imaging biomarkers for cancer diagnosis, and advanced techniques for ultrasound elastography with applications in tissue characterization and disease detection.

• Computer Science
• Networking
• Telecommunications
• +۴ مورد دیگر

Prof. Liam Murphy is a Full Professor of Computer Science & Informatics at University College Dublin (UCD) and Director of the Performance Engineering Laboratory. He holds a B.E. from UCD, M.Sc. and Ph.D. from UC Berkeley. His research focuses on performance engineering of networks, software systems, and multimedia transmissions. He has published over 150 peer-reviewed papers and is an IEEE member and Fellow of the Irish Computer Society. Education: B.E. in Electrical Engineering, UCD (1985) M.Sc. & Ph.D. in Electrical Engineering & Computer Sciences, UC Berkeley (1988, 1992) Research Interests: Dynamic resource allocation in networks Cloud computing efficiency Software performance engineering Wireless multimedia systems Quality of Service (QoS) optimization Recent work emphasizes energy-efficient cloud workflows, multi-objective data center optimization, and decentralized traffic simulation. Grants & Awards: Fellow of the Irish Computer Society (2007) Conference Paper Awards (2004, 2002, 2001) Principal Investigator in multiple funded projects (e.g., EU-funded traffic simulation, cloud resource allocation) Advising & Labs: Directed 24 Ph.D. and 8 M.Sc. students. Leads the Performance Engineering Laboratory (PEL), focusing on distributed systems, cloud efficiency, and network performance. Collaborates on industry-relevant projects like crovan (UCD/DCU campus company). Teaching: Coordinates courses on computer science fundamentals, distributed systems performance, and software engineering at UCD.

Jeremy Dahlمشاهده پروفایل

استاد

• Medical Ultrasound Imaging
• Ultrasonic Beamforming
• Image Reconstruction
• +۷ مورد دیگر

Jeremy Dahl is a Professor of Radiology (Pediatric Radiology) at Stanford University School of Medicine. He directs the Ultrasound Imaging & Instrumentation Lab and serves as Director of Research Academic Affairs in the Department of Radiology since 2020. He holds multiple affiliations across Stanford including Bio-X, the Cardiovascular Institute, Wu Tsai Human Performance Alliance, Maternal & Child Health Research Institute, Stanford Cancer Institute, and Wu Tsai Neurosciences Institute. Dr. Dahl received his B.S. in Electrical Engineering from the University of Cincinnati (1999) and Ph.D. in Biomedical Engineering from Duke University (2004). His research focuses on developing ultrasonic beamforming and image reconstruction methods for diagnostic imaging applications, particularly techniques that generate high-quality images in difficult-to-image patients. His laboratory specializes in B-mode and Doppler imaging techniques that utilize additional information from ultrasonic wavefields to improve image quality and develop real-time imaging systems for clinical applications including cardiac, liver, and fetal imaging. Dr. Dahl's research has led to significant advancements in ultrasound molecular imaging platforms, sound speed estimation, aberration correction, and reverberation noise suppression. His work often bridges engineering innovation with clinical applications for cancer detection and other diseases. His recent publications demonstrate strong focus on machine learning applications in ultrasound, distributed aberration correction, and molecular imaging techniques. Fellow, American Institute of Ultrasound in Medicine (2021) Senior Member, Institute of Electrical and Electronics Engineers (2020) Distinguished Investigator Award, The Academy for Radiology & Biomedical Imaging Research (2018) Outstanding Paper Award, IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society (2011) Dr. Dahl serves in editorial roles for major journals including IEEE Transactions on Medical Imaging (2017-2024) and IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (2013-Present). His laboratory has successfully translated numerous innovations into clinical applications, with multiple patents including recent developments in pulsed focused ultrasound therapy and speed of sound quantification.

Ron Drorمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Molecular Dynamics Simulation
• +۶ مورد دیگر

Ron Dror is the Cheriton Family Professor of Computer Science at the Stanford Artificial Intelligence Lab , with courtesy appointments in Structural Biology and Molecular & Cellular Physiology . He also holds affiliations with Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), the Institute for Computational and Mathematical Engineering (ICME), Sarafan ChEM-H, and the Wu Tsai Neurosciences Institute. Education: PhD in Electrical Engineering and Computer Science, MIT MPhil in Biological Sciences, University of Cambridge (Churchill Scholar) BS in Mathematics and Electrical & Computer Engineering, Rice University (summa cum laude) Ron leads a multidisciplinary research group that combines molecular simulation and machine learning to study biomolecular structure, dynamics, and function. His work focuses on developing computational methods to accelerate drug discovery by predicting molecular interactions and designing more effective therapeutics. Current projects include the PENSA software library for analyzing biomolecular ensembles and FRAME framework for structure-based ligand design. His research has produced groundbreaking work on G-protein-coupled receptors (GPCRs) , RNA structure prediction , and mitochondrial transport mechanisms . Key publications highlight applications of geometric deep learning and molecular dynamics simulations in structural biology. Scientific Awards: Cheriton Family Professorship (2023) Two Gordon Bell Prizes (2014, 2009) Best Paper Awards at NeurIPS (2021), IPDPS (2013), SC11 (2011), SC09 (2009), SC06 (2006) Science Magazine Top 10 Breakthrough (2010) Fulbright Scholarship , NSF Fellowship , DoD Fellowship , Whitaker Foundation Fellowship Ron has advised numerous doctoral and master’s students including EJ Fine , Masha Karelina , and Briana Sobecks . His lab collaborates with experimentalists across academia and industry, applying computational methods to diverse biomedical problems such as RNA structure prediction , GPCR signaling , and mitochondrial metabolism .

Shubhra Pasayatمشاهده پروفایل

استاد

• Semiconductor Materials
• Optoelectronics
• Power Electronics
• +۳ مورد دیگر

Professor Shubhra Pasayat is affiliated with the Department of Electrical and Computer Engineering and Materials Science & Engineering at the University of Wisconsin-Madison. She holds a PhD (2021) and MS (2017) from University of California, Santa Barbara, and a B.Tech (2013) from Indian Institute of Technology Kharagpur. Research focus: MOCVD growth of III-nitrides/oxides for optoelectronics, power electronics, quantum materials, and bio-photonics Key applications: LEDs, LASERs, HEMTs, bio-photonics, and sanitization technologies Her research interests center on wide bandgap semiconductor materials, particularly group-III nitrides and oxides. She works on strain relaxation techniques using porous GaN substrates, quantum dot engineering for visible light emission, and high-voltage GaN HEMT development. Current projects include lattice engineering for improved crystal quality and thermal management in RF devices. Publications highlight advancements in ultraviolet lasers, high-electron-mobility transistors, and micro-LEDs. Her work spans fundamental material studies and device optimization for industrial applications like electric vehicles, 5G/6G communications, and horticultural lighting. 2024 Awards : NSF CAREER, ONR DEPSCoR, WARF Early Career Innovator 2022 Awards : UCSB ECE Outstanding Dissertation, JAP Best Paper She teaches ECE 235 Introduction to Solid State Electronics and mentors research at both graduate and undergraduate levels.

Qingguo Liمشاهده پروفایل

استاد

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

• Robotics
• Micro/Nanorobotics
• Physical Intelligence
• +۳ مورد دیگر

Metin Sitti is a Professor and currently serves as a part-time adjunct professor at Koç University's Faculty of Medicine and Engineering. He is also the President of Koç University and Director of the Max Planck Institute for Intelligent Systems since 2014. His research spans robotics , micro/nanorobotics , physical intelligence , bio-inspired robotics , and materials science . Undergraduate: Boğaziçi University (Electrical and Electronics Engineering, 1992) Master's: Boğaziçi University (1994) PhD: University of Tokyo (Electrical Engineering, 1999) His research focuses on small-scale robotics for biomedical applications, including magnetic microrobot swarms , biohybrid systems , and functional materials . Recent publications emphasize 3D control , wireless actuation , and clinical translation of robotic systems. Scientific Awards : 2020 Scientific Breakthrough of the Year 2018 Koç University Rahmi M. Koç Science Medal 2014 IEEE/ASME Best Mechatronics Paper Award 2013/2012 World RoboCup Micro-Robotics First Prize He has advised numerous students and collaborates with institutions like Carnegie Mellon University and University of Stuttgart. His work integrates physical intelligence into miniature robots for medical imaging , drug delivery , and autonomous systems .

Tobi Delbruckمشاهده پروفایل

استاد

• Neuromorphic Engineering
• Event-Based Vision Systems
• Silicon Retinas
• +۶ مورد دیگر

Tobi Delbruck is a titular professor of physics and electrical engineering at ETH Zurich, where he leads the Sensors Group at the Institute for Neuroinformatics (INI) in Zurich, Switzerland. He collaborates closely with Shih-Chii Liu and Giacomo Indiveri as part of the 'hardware groups' at INI. Delbruck has also served as visiting faculty at Caltech and is a Fellow of the IEEE. His work focuses on bio-inspired and neuromorphic event-based sensory processing systems. Professor Delbruck's research spans multiple areas of neuromorphic engineering, with particular emphasis on event-based vision systems and low-power analog VLSI circuits. His work has significantly advanced the field of Dynamic Vision Sensors (DVS), which mimic the human retina's response to changes in brightness rather than capturing full frames. This approach enables extremely low-latency vision processing with minimal power consumption, making it ideal for high-speed applications and robotics. His research has applications in robotics, autonomous systems, and low-power embedded vision. Delbruck is an active contributor to the neuromorphic engineering community, co-organizing the annual Telluride Workshop on Neuromorphic Engineering and serving in leadership roles with IEEE. He has authored numerous influential publications and co-authored books including "Event-Based Neuromorphic Systems" and "Analog VLSI: Circuits and Principles." His jAER (Java Address-Event Representation) project provides open-source tools for real-time event-based sensory processing. Analysis of his recent publications shows a clear trend toward integrating event-based vision with deep learning techniques and applying these systems to practical robotics problems. His scientific achievements have been recognized with multiple awards including: IEEE Fellow Winner of Best Live Demonstration award at ISCAS 2012 Honorable Mention Award from Sensory Systems Technical Committee at ISCAS 2012 Overall Best Student Paper Award and Best Paper Award from Sensory Systems Technical Committee at ISCAS 2010 Winner of the 2006 ISSCC Jan Van Vessem Outstanding European Paper Award Professor Delbruck actively mentors students and has supervised numerous PhD and Master's theses in the areas of neuromorphic engineering and event-based vision systems. His group has secured significant research funding from various sources to support their innovative work in bio-inspired sensory processing. He teaches courses on "Electronics for Physicists II (Digital)" and "Neuromorphic Engineering," helping to train the next generation of researchers in this field. The Sensors Group at INI, which Delbruck leads, operates state-of-the-art facilities for designing and testing neuromorphic vision systems. The group maintains close collaborations with researchers worldwide and has developed several important open-source resources including the jAER project and bias generator design kits. Their work continues to push the boundaries of what's possible with event-based sensory processing, with applications ranging from high-speed robotics to low-power embedded vision systems.

Jiajun Wuمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Robotics
• +۵ مورد دیگر

Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University. He holds multiple affiliations including membership in Bio-X, Faculty Affiliate status at the Institute for Human-Centered Artificial Intelligence (HAI), and membership in both the Wu Tsai Human Performance Alliance and Wu Tsai Neurosciences Institute. Dr. Wu earned his Ph.D. and S.M. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology before joining Stanford. Dr. Wu's research program focuses on creating AI systems that understand and interact with the physical world through the integration of computer vision, machine learning, robotics, and cognitive science. His work emphasizes physics-based modeling combined with deep learning to develop systems capable of perceiving, reasoning about, and predicting physical interactions. Key research areas include 3D scene understanding, neurosymbolic AI approaches, multimodal perception (combining vision, sound, and language), and embodied intelligence for robotics applications. His lab develops novel frameworks that bridge the gap between neural networks and symbolic reasoning to create more interpretable and robust AI systems. Analysis of Dr. Wu's recent publications reveals a strong trajectory toward integrated multimodal understanding for embodied AI. His work increasingly combines vision, sound, and language processing with physical reasoning to create systems that can interact meaningfully with the physical world. There's a clear progression from foundational computer vision research toward practical robotics applications, with significant emphasis on foundation models for robotics, sim2real transfer techniques, and creating comprehensive datasets for embodied AI research. Dr. Wu's exceptional contributions have been recognized with numerous prestigious awards including the NSF CAREER award (2024), Young Investigator Programs from ONR (2024) and AFOSR (2023), the Okawa research grant (2024), and being named to IEEE Intelligent Systems' 'AI's 10 to Watch' (2024). He has received multiple best paper awards at leading conferences including ICRA (2024), SIGGRAPH Asia (2023), and CoRL (2023). Dr. Wu actively mentors a large cohort of students across multiple levels, serving as primary advisor for doctoral candidates, master's students, and numerous independent researchers. His research is supported by substantial funding from major technology companies including Google, Meta, Amazon, Samsung, and J.P. Morgan, as well as government agencies like NSF, ONR, and AFOSR, reflecting the significance and impact of his work in physical AI and multimodal perception systems. Dr. Wu leads a dynamic research group at Stanford that collaborates extensively with the Wu Tsai Neurosciences Institute and Institute for Human-Centered AI. Current projects include developing neurosymbolic models for computer graphics, creating multisensory datasets like OBJECTFOLDER 2.0 for sim2real transfer in robotics, and building foundation models for embodied intelligence that can understand and manipulate objects with human-like physical intuition.

Tosiron Adegbijaمشاهده پروفایل

دانشیار

• Computer Architecture
• Embedded Systems Design
• Low-Power Embedded Systems
• +۱۴ مورد دیگر

Tosiron Adegbija is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Arizona, where he serves as Director of Graduate Studies and Thomas R. Brown Endowed Fellow. He is a member of the Graduate Faculty and actively contributes to research and teaching in computer architecture and embedded systems. Education: PhD in Electrical and Computer Engineering, University of Florida, 2015 MS in Electrical and Computer Engineering, University of Florida, 2011 BS in Electrical Engineering, University of Ilorin, Nigeria, 2005 His research centers on energy-efficient computing with a focus on bio-inspired computer architecture , including spiking neural network (SNN) accelerators and in-memory computing. He also explores domain-specific architectures , adaptable memory systems , and microprocessor optimizations for IoT . His work leverages novel memory technologies like STT-RAM to enhance performance and reduce energy consumption in embedded and resource-constrained systems. Recent publications highlight trends in hybrid SNN acceleration, domain-specific accelerator generation, and system-level design space exploration. His research is increasingly focused on neuromorphic computing, automated hardware design, and ultra-efficient architectures using emerging materials like antiferromagnetic tunnel junctions. Scientific Awards: National Science Foundation (NSF) CAREER Award (2019) Elected IEEE Senior Member (2020) Best Paper Award at IEEE ISVLSI (2014) Teaching Award, University of Arizona (2018) ACM GLSVLSI Travel Award (2015) He advises numerous graduate and undergraduate students, many of whom have pursued careers at institutions like Pacific Northwest National Labs, Micron Technology, and Amazon. He has secured significant funding, including a $1.9M NSF FuSE2 grant for energy-efficient computing. His lab collaborates with UA Physics, CMU, and UNL. He has also developed educational tools for Chipyard and RISC-V, supporting hands-on learning in computer architecture. Labs and Research Teams: Leads a research group focused on bio-inspired and domain-specific computing, fostering innovation in energy-efficient hardware. The lab emphasizes hardware/software co-design, neuromorphic engineering, and real-world deployment in IoT and biomedical applications.

Surya Ganguliمشاهده پروفایل

دانشیار

• Theoretical Neuroscience
• Computational Neuroscience
• Machine Learning
• +۴ مورد دیگر

Surya Ganguli is an Associate Professor in the Department of Applied Physics at Stanford University, with courtesy appointments in Neurobiology and Electrical Engineering. He serves as Senior Fellow at the Stanford Institute for Human-Centered AI and is affiliated with the Stanford Neuroscience Institute , Bio-X , and Wu Tsai Neurosciences Institute . His research spans theoretical neuroscience, machine learning, and statistical mechanics. Ph.D. , UC Berkeley, Theoretical Physics (2004) M.A. , UC Berkeley, Physics (2000) M.A. , UC Berkeley, Mathematics (2004) M.Eng. , MIT, Electrical Engineering and Computer Science (1998) B.S. , MIT, Physics (1998) B.S. , MIT, Mathematics (1998) B.S. , MIT, Electrical Engineering and Computer Science (1998) His lab explores how higher-level cognitive phenomena emerge from neural network dynamics, focusing on perception, memory, attention, and decision-making . Research themes include statistical mechanics of learning , neural representational geometry , and biologically plausible learning rules . Current work examines nonlinear interactions in neural networks through the Schmidt Science Polymath Award (2023). Key article trends reveal expertise in neural coding limits (2022 Nature), synaptic plasticity models (2022 Neural Computation), and deep learning theory (2022 NeurIPS publications). His 2019 Annual Review chapter on statistical mechanics of deep learning established foundational insights into network criticality. Scientific Awards : NSF Career Award (2019) Simons Foundation Investigator (2016) McKnight Scholar Award (2015) James S. McDonnell Foundation Scholar (2014) Sloan Research Fellow (2013) As advisor, he mentors 10+ doctoral students across Applied Physics, Neurosciences, and Computer Science, including Vamshi Balanaga and Mason Kamb. His lab collaborates with experimental teams at Stanford and beyond, supported by grants from NSF , Simons Foundation , and Swartz Foundation . The Neural Dynamics & Computation Lab unites physicists, mathematicians, and neuroscientists to decode cognition through interdisciplinary methods.

Peng Xiongمشاهده پروفایل

استاد

• Mesoscale Physics
• Spintronics
• Organic/Solid-State Hybrid Structures
• +۱ مورد دیگر

Peng Xiong is a Professor in the Department of Physics at Florida State University, with a research focus on electron and spin transport in low-dimensional quantum materials. He is affiliated with the Integrative NanoScience Institute (INSI) and has made significant contributions to mesoscale physics, spintronics, and organic/solid-state hybrid systems. B.S. in Physics (1987, University of Science and Technology of China) Ph.D. in Physics (1993, Brown University) Postdoctoral Fellowship (1993-1997, University of California at San Diego) Research Interests: Mesoscale Physics: Quantum phase transitions and fluctuation effects in 2D and 1D systems, semiconductor nanowires, carbon nanotubes, and nano-magnetism. Spintronics: Spin-polarized transport in hybrid structures (ferromagnet/normal metal, ferromagnet/superconductor, ferromagnet/semiconductor), magnetic semiconductors, and spin injection/detection. Organic/Solid-State Hybrids: Nanoscale biosensors utilizing magnetic and electrical principles, bio-mechanical devices, organic/solid interfaces, and template-directed nanostructure self-assembly. Publication Trends: His recent work spans superconducting fluctuations in ultrathin films, chirality-induced spin transport in semiconductors, interplay between structural chirality and spin-orbital effects, ion migration dynamics in 1D hybrids, quantum interference in nanowire loops, and modulation of nanomaterial properties through surface defect engineering. These studies often combine material synthesis, nanofabrication, cryogenic transport, and tunneling measurements. Scientific Recognition: Alfred P. Sloan Research Fellowship (1998) University Teaching Award (2003) PAI Award for Excellence (2004) Developing Scholar Award (2007) Fellow of the American Physical Society (2012) Advising Legacy: He has mentored numerous graduate students including Jeffrey Parker (Ph.D. 2003) Yongqing Li (Ph.D. 2003) Tianhan Liu (Ph.D. 2021) Jacob Hudis (Ph.D. 2021) and continues to guide current candidates like Yuwaraj Adhikari and Zhenqi Hua. Experimental Facilities: The lab at FSU features advanced equipment for nano-fabrication, including clean rooms, mask aligners, and thin film deposition systems. Cryogenic capabilities extend to dilution refrigerators (15 mK) and He3/He4 cryostats for ultralow-temperature studies of magnetic and superconducting systems.

Thomas E. Doyleمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Human-Computer Interfacing (HCI)
• Machine Learning
• +۶ مورد دیگر

Dr. Thomas E. Doyle is an Associate Professor at the McMaster School of Biomedical Engineering and the Department of Electrical & Computer Engineering at McMaster University. His research focuses on biomedical signal processing, human-computer interfacing (HCI), and machine learning applications for healthcare augmentation, rehabilitation, and enhancement. He holds a Ph.D. from Western Ontario, Canada, and teaches courses like COMPENG 2DI4 (Logic Design). His work bridges cybernetics and clinical applications, emphasizing AI-driven solutions for medical diagnostics, patient monitoring, and space exploration. Education: B.E.Sc, B.Sc, M.E.Sc, Ph.D. from Western Ontario, Canada Recent Projects: Developed AI systems for remote healthcare diagnostics (2023) Collaborated with NASA on medical emergency simulators for deep space missions (2017–2023) Led ventilator development efforts for local hospitals during the pandemic (2020) His research interests span machine learning for mental health diagnostics, trust quantification in medical AI, and extended reality (XR) for medical training. He emphasizes interdisciplinary approaches, integrating computational methods with healthcare challenges. Recent publications highlight applications in pediatric emergency care, chronic pain management, and reliable medical device design. Dr. Doyle actively engages in educational initiatives, including first-year engineering pedagogy and experiential learning programs. He has received funding for projects such as the Educating the Engineer of 2025 (EtE-25) awards and contributes to initiatives like the Digital & Smart Systems and Health & Bio-innovation research clusters at McMaster.

Brian Hieمشاهده پروفایل

استادیار

• Machine Learning
• Computational Biology
• Protein Engineering
• +۶ مورد دیگر

Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University , a Dieter Schwarz Foundation Stanford Data Science Faculty Fellow , and an Innovation Investigator at Arc Institute . He leads the Laboratory of Evolutionary Design , focusing on the intersection of biology and machine learning . His prior roles include a Stanford Science Fellow in the Stanford University School of Medicine and a Visiting Researcher at Meta AI . Education: Ph.D. , Electrical Engineering and Computer Science , Massachusetts Institute of Technology (2021) Bachelor’s Degree , Stanford University Research Interests: Brian’s work bridges machine learning and computational biology , with a focus on protein engineering , single-cell RNA sequencing , and viral evolution . His Evolutionary velocity framework predicts protein evolutionary dynamics across timescales, while his Scanorama algorithm enables efficient integration of heterogeneous single-cell datasets. He also develops structure-informed language models for antibody optimization and uncertainty-aware ML for biological discovery. Publication Trends: His recent work (2023) emphasizes structure-based inverse folding for antibody evolution, evolutionary scale modeling , and unsupervised optimization . Earlier studies (2022-2021) cover evolutionary velocity , multi-modal single-cell analysis , and viral escape prediction using natural language analogies. Scientific Awards: Stanford Science Fellow (2021) National Defense Science and Engineering Graduate Fellowship (2019) Advising: He mentors doctoral students including Brandon Ameglio , Garyk Brixi , and Chang M. Yun , with a focus on biological design and computational methods . Labs & Collaborations: His lab collaborates with Bio-X and the Institute for Human-Centered Artificial Intelligence (HAI) , and he maintains affiliations with Sarafan ChEM-H and Stanford Data Science .