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Eva Aimable NaumannView profile

Assistant Professor

• Neuroscience
• Neurobiology
• Neural Circuits
• +7 more

Eva Aimable Naumann is an Assistant Professor at Duke University with joint appointments in Neurobiology, Psychology and Neuroscience, Biomedical Engineering, and Cell Biology. She is affiliated with the Duke Regeneration Center and the Duke Institute for Brain Sciences . Education : Ph.D. from Ludwig Maximilian University of Munich (2010), MSc from University of Konstanz in Biology, and postdoctoral training at University College London and Harvard University Center for Brain Sciences. Dr. Naumann's research focuses on neural circuit dynamics , particularly how whole-brain circuits guide behavior in translucent zebrafish . Her lab combines whole-brain imaging , behavioral analysis , and functional perturbations to create brain-scale circuit models of individual behaviors. Key themes include visuomotor transformations, neural connectivity, and comparative behavioral neuroscience. Recent publications emphasize gigapixel microscopy , comparative neural circuits across teleost species, and gut-brain axis dynamics . Her lab also develops improv , an open-source platform for adaptive neuroscience experiments. Scientific Awards : Sloan Research Fellowship (2020) . Dr. Naumann's grants focus on neural mechanisms of behavior, brain-scale imaging technology, and real-time neural hypothesis testing. She advises students through courses like NEUROSCI 493-496 and NEUROBIO 793, integrating cutting-edge research into training programs.

• Intelligent Sensing
• Machine Learning
• Unmanned Autonomous Systems
• +3 more

Dr. Qi Hao is an Associate Professor and Deputy Director of the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He also serves as Executive Director of the SUSTech & Haylion Intelligent Transportation Center and Head of the SUSTech & FengXiangBiao ICV Education Joint Laboratory. Prior to joining SUSTech in 2014, he was an Assistant Professor at the University of Alabama (2007-2014) following postdoctoral training at the University of Kentucky. Dr. Hao received his PhD in Electrical and Computer Engineering from Duke University in 2006, with Master's and Bachelor's degrees from Shanghai Jiaotong University (1997 and 1994). His academic journey includes research positions at Singapore's Data Storage Institute (1998-2001) and engineering experience at Shanghai Electric Tool Research Institute (1997-1998). As an expert in intelligent sensing and unmanned autonomous systems, Dr. Hao's research spans bionic intelligent sensor design, machine learning applications, and autonomous vehicle technologies. His work demonstrates a strong integration of theoretical frameworks with practical implementations, particularly in pyroelectric infrared sensing, UAV systems, and distributed sensor networks. His research has evolved to address increasingly complex challenges in autonomous navigation, multi-robot coordination, and edge computing for intelligent transportation systems. Dr. Hao's publication trend reveals a strategic shift toward integrated autonomous systems, with recent work emphasizing robotics, machine learning, and wireless communication. His research bridges computer vision, sensor fusion, and deep learning to solve practical challenges in autonomous vehicle perception, wireless energy transfer, and human activity recognition. The interdisciplinary nature of his work connects electrical engineering principles with cutting-edge AI techniques. US National Science Foundation Research Awards (2009, 2011) Multiple IEEE conference best paper awards and finalists (2012-2021) Shenzhen Overseas High-level Talents Peacock Plan Category C (2017) Advanced Educators in Shenzhen (2017) Outstanding Teacher of Guangdong Province (2021) SUSTech Outstanding Research and Service Awards (2017-2021) Dr. Hao has secured significant research funding as Principal Investigator for projects from NSFC, Shenzhen Nanshan District, and industry partners including Huawei, Intel, and Haylion Technology. His current projects focus on intelligent binary sensing, next-generation camera systems, autonomous driving safety frameworks, and federated learning for vehicle perception. He has also contributed to educational initiatives through Guangdong Province and Shenzhen City education reform projects. Leading the SUSTech & Haylion Intelligent Transportation Center and the SUSTech & FengXiangBiao ICV Education Joint Laboratory, Dr. Hao oversees research teams developing solutions for intelligent transportation challenges. His labs integrate expertise across computer science, electrical engineering, and mechanical engineering to advance autonomous vehicle technologies, sensor networks, and robotics applications for real-world implementation.

• Robotics
• Artificial Intelligence
• Autonomous Vehicles
• +4 more

Kuo-Yi Chao is a Researcher at the Chair for Robotics, Artificial Intelligence, and Real-Time Systems at the Technical University of Munich (TUM), focusing on multimodal sensor fusion for Vehicle-to-Everything (V2X) applications. He received his B.Sc. and M.Sc. in Electrical Engineering and Computer Technology from TUM. His expertise spans multi-agent systems, visual language models, real-time communication, and digital twin technologies. His 2022 publication in the Journal of NeuroEngineering and Rehabilitation highlights his work on intuitive control systems for robotic prostheses, emphasizing sensor fusion and human-robot interaction. He contributes to teaching through courses like 'Einführung in die digitale Signalverarbeitung (IN2061)' and offers thesis topics in collaborative camera perception, real-time V2X data transmission, and object list generation for autonomous systems.

• Additive Manufacturing
• Laser Metal Deposition
• Material Extrusion Processes
• +5 more

Maria Grazia Guerra is an Assistant Professor at the Department of Mechanics, Mathematics & Management at the Polytechnic University of Bari, Italy . Her work focuses on additive manufacturing, material extrusion processes, and optical monitoring systems for advanced manufacturing technologies. Research Interests: Maria's research spans multiple facets of additive manufacturing, including laser metal deposition, material extrusion, and sintering technologies. She investigates dimensional accuracy, defect detection, and sustainability assessment through exergetic analysis, while also exploring photogrammetry applications for micro-scale measurements and surface characterization. Publications: Maria has published extensively on topics such as layerwise monitoring in laser-powder bed fusion, surface grinding of thermoplastics, and chemical vapor treatment for polymer parts. Her work frequently addresses process optimization, geometric accuracy, and the development of novel methodologies for industrial measurement systems. Contact: Email: mariagrazia.guerra@poliba.it

John See Su YangView profile

Associate Professor

• Computer vision
• Artificial intelligence
• Affective computing
• +7 more

Dr. John See Su Yang is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences, Malaysia Campus. He serves as Programme Director for BSc Computing Science and leads the Multimedia Data Analysis (MuDA) Lab. Previously, he was a Senior Lecturer at Multimedia University and founded the Visual Processing (ViPr) Lab, and served as a Visiting Research Fellow at Shanghai Jiao Tong University from 2017-2019. Received Bachelor, Masters, and PhD degrees from Multimedia University Published over 140 articles in top journals and conferences including IEEE T-PAMI, CVPR, and NeurIPS Secured over MYR 3 million in research funding as PI/Co-PI Dr. See's research spans multimedia signal processing and computer vision with particular focus on affect and emotion understanding from images and videos. His work includes facial micro-expression analysis, image aesthetics, visual surveillance tasks, activity recognition, and classical image/video processing algorithms. His research has practical applications in micro-expression detection, human-robot interaction, and multimodal content generation. His recent publications (2023-2025) demonstrate strong activity in wind turbine blade inspection using UAVs, micro-expression analysis networks, drowsiness detection, and human-robot interaction. His work bridges theoretical computer vision advances with practical applications in renewable energy, transportation safety, and human-computer interaction. Best Reviewer Award, IEEE ICIP 2020 Outstanding Reviewer Award, CVPR 2021 Dr. See actively mentors PhD students and serves on editorial boards for Signal Processing (as Subject Editor), IEEE Transactions on Multimedia, and several other prestigious journals. He is a Senior Member of IEEE and has served on technical committees for IEEE Multimedia Systems and Applications and Multimedia Signal Processing. His MuDA Lab collaborates with the Visual Processing Lab at Multimedia University and Shanghai Jiao Tong University, focusing on emotional analysis and multimodal systems for solving real-world problems. Dr. See accepts PhD students for projects on aesthetics and emotion processing in media and new paradigms for understanding human emotions in real-world scenarios.

Hovannes KulhandjianView profile

Associate Professor

• Wireless Communications
• Applied Machine Learning
• Intelligent Transportation Systems
• +8 more

Hovannes Kulhandjian is an Associate Professor in the Department of Electrical and Computer Engineering at California State University, Fresno (Fresno State), within the Lyles College of Engineering. He teaches undergraduate and graduate courses in electrical and computer engineering and conducts research in wireless communications, applied machine learning, and their applications in transportation and agriculture. His educational background includes: Ph.D. in Electrical Engineering from the State University of New York at Buffalo (2014) M.S. in Electrical Engineering from the State University of New York at Buffalo (2010) B.S. in Electronics Engineering with high honors (magna cum laude) from the American University in Cairo (2008) Dr. Kulhandjian's research spans wireless communications, applied machine learning, and their applications. His work in intelligent transportation systems includes AI-based road inspection and pedestrian detection, while in precision agriculture, he develops drone-based systems for weed detection and tree health monitoring. He also explores underwater acoustic communications, visible light communications, and physical layer security. His recent publications demonstrate a strong trend in applying artificial intelligence to solve real-world problems in transportation and agriculture, often using drones and multi-sensor fusion. In communications, he advances techniques for next-generation wireless systems, including OTFS and NOMA for 6G, and optical wireless for IoT. Scientific awards and honors: IEEE Senior Member Outstanding Reviewer Award from ELSEVIER Ad Hoc Networks Outstanding Reviewer Award from ELSEVIER Computer Networks Claude C. Laval Award for Innovative Technology and Research Dr. Kulhandjian advises Master's students through thesis (ECE 299) and project (ECE 298) courses. His research is supported by multiple grants, including the Department of Defense Research and Education Program, NSF-ADVANCE Research Alliance Seed Grant, CSU-WATER Faculty Research Incentive, and the Fresno State Transportation Institute SB1 Research Grant for six consecutive years. During his doctoral studies, he worked in the Wireless Networks and Embedded Systems (WiNES) Laboratory at SUNY Buffalo. At Fresno State, he leads a research group focused on the development of innovative solutions in wireless communications and AI applications, collaborating with various institutions and industry partners.

Riadh MunjyView profile

Professor

• Photogrammetry
• Unmanned Aircraft Systems (UAS) Mapping
• Remote Sensing
• +5 more

Riadh Munjy serves as a Professor in Geomatics Engineering at California State University, Fresno's Lyles College of Engineering, where he teaches core courses including CE 205 (Computing in Engineering Analysis), GME 123 (Stereo-Photogrammetry), and GME 125 (Analytical Photogrammetry). His academic foundation includes a Ph.D. in Civil Engineering (1982), M.S. in Applied Mathematics (1981), and M.S.C.E. (1979) from the University of Washington, complemented by a B.S. in Civil Engineering from the University of Baghdad (1976). Ph.D., Civil Engineering, University of Washington (1979-1982) M.S., Applied Mathematics, University of Washington (1980-1981) M.S.C.E., Civil Engineering, University of Washington (1978-1979) B.S., Civil Engineering, University of Baghdad (1972-1976) Professor Munjy's research centers on advancing photogrammetric methodologies with particular emphasis on Unmanned Aircraft Systems (UAS) mapping accuracy, sensor calibration, and novel processing techniques for LIDAR and IFSAR data. His work bridges theoretical mathematics with practical applications in transportation infrastructure, disaster response, and terrain modeling, consistently addressing industry pain points like rolling shutter effects in UAV imagery and LIDAR strip adjustment challenges. He has pioneered standards for GPS-controlled photogrammetry adopted by Caltrans and contributed foundational chapters to the ASPRS Manual of Photogrammetry. Analysis of his 15 most recent publications (2010-2020) reveals a strategic evolution from traditional photogrammetric techniques toward UAS-centric workflows, with increasing focus on automated processing, accuracy validation, and integration of multi-sensor data. His research consistently targets real-world implementation barriers in transportation mapping and corridor projects, demonstrating strong industry relevance through Caltrans collaborations and ASPRS leadership. American Society of Photogrammetry and Remote Sensing Fellow Award (2020) ASPRS Fairchild Award (2014) Caltrans Research Innovation Award (2004) Five School of Engineering Research Excellence Awards (1992, 1996, 1998, 2002, 2003) ASPRS Meritorious Service Awards (1992, 1997) Halliburton Research Award (1992) Professor Munjy's research program has been consistently supported through Caltrans-funded projects including GPS Photogrammetry (2005), UAS Research (2018), and corridor mapping standards development. His advisory impact extends through co-authoring the ASPRS Manual of Photogrammetry and mentoring numerous conference presentations, though formal student lists aren't documented. Current initiatives focus on sUAS data accuracy validation and rolling shutter effect mitigation, positioning his work at the forefront of evolving UAV mapping regulations.

Slobodan IlicView profile

Adjunct Professor

• 3D Computer Vision
• Computer Vision
• Medical Imaging
• +3 more

PD Dr. Slobodan Ilic is a Senior Key Expert Research Scientist at Siemens AG (since 2014) and an Adjunct Professor at the Chair of Computer Science Applications in Medicine , Technical University of Munich (TUM). His work bridges 3D computer vision and medical imaging , with a focus on real-time object detection, deformable surface modeling, and depth data analysis. Current Roles: Adjunct Professor at TUM, Senior Key Expert at Siemens Research Themes: 6D pose estimation, non-rigid 3D reconstruction, RGB-D data processing Labs: CAMP Chair (TUM), Siemens AG Research Division His recent work explores LLM-driven control systems , semantic-aware 3D generation , and cross-modal medical imaging . Articles highlight advancements in point cloud registration , rotation-invariant descriptors , and hyperspectral calibration . While no specific awards are documented in the provided text, his team at Siemens/TUM advises PhD candidates in 3D vision for robotics and medical applications .

• Metamaterials & metasurfaces
• Optical MEMS/NEMS
• Laser spectroscopy (LIBS, SFG, LII)
• +5 more

IKEZAWA Satoshi is an Assistant Professor at the Faculty of Science and Engineering, Waseda University, Japan, specialising in metamaterial/metasurface optics, laser spectroscopy and MEMS/NEMS sensors. He received his Doctor of Engineering from Waseda in 2009 and subsequently held assistant and associate positions at Waseda, Kyushu University and Tokyo University of Agriculture & Technology before returning to Waseda in 2023. Education: 2007-2009: Doctoral course, Graduate School of Information, Production and Systems, Waseda University (Ph.D. in Engineering, 2009) 2004-2006: Master course, Information Production and Systems, Waseda University Research interests cover design and nanofabrication of dielectric metasurfaces for holographic movies, varifocal metalenses, polarisation-selective devices, micro-LIBS systems, fibre-optic and environmental sensors, and high-frequency quartz MEMS resonators. His recent publications (2020-2024) demonstrate a clear trend toward ultrathin multifunctional metasurface optics—integrating holography, beam-shaping, polarisation control and varifocal imaging—using high-index dielectrics (Si, SiN) for visible to LWIR wavelengths, with applications in AR/VR, 3-D displays, optical communications and miniature atomic clocks. Selected awards: Best Presentation – Runner-up, ICST 2024 Optics Prize, Metaphotonics Research Group, Japan, 2024 APS Best Applied Paper Award, 2023 IEEJ Sensor & Micromachine Division Excellent Paper Award, 2023 Group Technical Activity Awards (IEEJ) 2023 & 2024 for Society 5.0 sensing white-papers He serves on numerous scientific committees (IEEE, IEEJ, JSAP, LSJ, IMEKO) and has co-authored >80 refereed papers (Scopus h-index 11; Google Scholar h-index 13, 620 citations). His work has been highlighted by IEEE Sensors Council, Optica Publishing Group and AAAS EurekAlert!, underlining technological impacts in miniaturised optical devices and environmental sensing.

• Intelligent robotics
• Mechanics and mechatronics
• Robotics and intelligent system
• +3 more

Shigeki Sugano is a Professor at Waseda University's Faculty of Science and Engineering, School of Creative Science and Engineering. He has served as Senior Dean of the Faculty of Science and Engineering (2020-2024) and Dean of the School of Creative Science and Engineering (2014-2020). His academic journey at Waseda spans from Research Assistant (1986-1990) to Assistant Professor (1990-1992), Associate Professor (1992-1998), and Professor (1998-present), with a visiting scholar position at Stanford University (1993-1994). Dr. Sugano's research focuses on intelligent robotics, mechanics and mechatronics, and robotics and intelligent systems. His work emphasizes tactile sensing, human-robot collaboration, robotic manipulation, and assistive technologies. His extensive publication record demonstrates expertise in developing practical robotic systems for applications in disaster response, healthcare, infrastructure inspection, and human augmentation. He has made significant contributions to the development of tactile sensors, human-robot interaction frameworks, and robotic systems for gait analysis and assistance. His recent publications (2024-2025) reveal a strong focus on tactile sensing technologies (H-PME sensors, uSkin systems), human-robot collaboration frameworks, and advanced manipulation techniques using deep learning. His research integrates mechanical engineering principles with AI techniques to create practical robotic solutions. The consistent publication output across multiple high-impact venues demonstrates sustained research productivity and relevance in the robotics field. SII2025 Best Paper Award (2025) SI2024 優秀講演表彰 (2024) 文部科学大臣表彰（科学技術賞・研究部門）(2017) IEEE Fellow Award (2007) 日本ロボット学会功労賞 (2012) Dr. Sugano currently serves as President of The Robotics Society of Japan (2023-present) and previously as President of The Society of Instrument and Control Engineers (2017-2018). His leadership extends to editorial roles, including Editor-in-Chief of the International Journal of Advanced Robotics (2007-2012). His research group has secured numerous grants supporting their work in robotics and intelligent systems, with applications spanning healthcare, manufacturing, and infrastructure maintenance. He maintains active collaborations with researchers worldwide, as evidenced by his extensive co-authorship network.

Kyle E. FitchView profile

Assistant Professor

• Arctic Cloud Microphysics
• Riming Processes
• Space Weather
• +3 more

Kyle E. Fitch is an Assistant Professor of Atmospheric Science at the Air Force Institute of Technology (AFIT) within the Department of Engineering Physics. He serves as the Atmospheric Science M.S. Curriculum Chair, directing coursework for 10 students and teaching 3-4 courses annually, including programming and physics refresher modules for new students. PhD in Atmospheric Sciences, University of Utah (2020) MS in Applied Physics, AFIT (2016) BS in Atmospheric Sciences, University of Washington (2006) His research focuses on Arctic cloud microphysics , including riming processes, graupel formation in thin clouds, and aerosol impacts on precipitation. He integrates space weather studies (e.g., ionospheric electron density, solar flares) and machine learning for predictive modeling. His work addresses Department of Defense weather priorities through funded research. Recent publications highlight collaborations on attention-based neural networks for lightning prediction, GNSS radio occultation for ionospheric modeling, and WRF-LES simulations of Arctic clouds. He presented at the 105th AMS Annual Meeting and QuIESCENT Arctic Workshop . Graduate School Team of the Year (2024) Dean’s Distinguished Teaching Professor (2022/2023) Advanced Technical Intelligence Award (2016) Distinguished Graduate, AFIT (2016) As a Weather and Environmental Sciences Officer in the Air Force, he has deployed to Afghanistan and worked with the National Air and Space Intelligence Center. His technical expertise includes VIIRS cloud base height validation , Multi-Angle Snowflake Camera analysis, and atmospheric model development .

Dinesh RajanView profile

Professor

• Wireless Networking
• Information Theory
• 3D Imaging
• +3 more

Dinesh Rajan is the Cecil H. Green Professor in the Department of Electrical and Computer Engineering at the Lyle School of Engineering , Southern Methodist University (SMU) . He currently serves as the Associate Dean for Faculty Affairs and Curriculum Innovation and previously chaired the ECE department for over 10 years. Education: Ph.D. in Electrical and Computer Engineering, Rice University M.S. in Electrical and Computer Engineering, Rice University B.Tech in Electrical Engineering, Indian Institute of Technology, Madras Research Interests: Dinesh Rajan's research spans wireless networking , information theory , and 3D computational imaging . His work includes probabilistic modeling of motion blur in depth maps, location-specific analysis for vehicular systems, mobile beamforming, and smartphone-based structural health monitoring. His recent work integrates advanced signal processing with real-world applications in transportation and infrastructure. Scientific Awards: NSF CAREER Award Fulbright Specialist Golden Mustang Teaching Award (SMU Internal) Senior Ford Research Award (SMU Internal) Advising & Curriculum Leadership: As Associate Dean, Rajan leads initiatives in curriculum innovation and faculty development. His leadership in the ECE department for over a decade reflects his commitment to academic excellence and student success. His research group has contributed to cutting-edge projects in wireless systems and imaging technologies.

Kevin BurdgeView profile

Assistant Professor

• Compact Binary Systems
• Gravitational-Wave Astrophysics
• Time Domain Astronomy
• +12 more

Kevin Burdge is an Assistant Professor of Physics at the Massachusetts Institute of Technology (MIT), affiliated with the MIT Binary Star Astrophysics Group and the Kavli Institute for Astrophysics and Space Research (MKI). He conducts observational astrophysics research focused on compact binary systems, including white dwarfs, neutron stars, and black holes, using time-domain and multi-messenger techniques. His research spans: Discovery and characterization of compact binaries Gravitational-wave and electromagnetic multi-messenger astrophysics Development of high-speed astronomical instrumentation Stellar evolution and accretion processes Observations with LISA, Rubin Observatory, JWST, and AXIS Burdge's recent work emphasizes identifying rare and exotic binary systems, such as the first known black hole in a triple-star system, and expanding the population of LISA-detectable sources. His group develops GPU-based algorithms for rapid data analysis and operates the 'Lightspeed' camera on the Magellan telescopes for high-speed photometry. The publication trends reflect a strong focus on ultra-compact binaries, orbital dynamics, gravitational-wave emission, and multi-wavelength follow-up. His research combines theoretical modeling with cutting-edge observational tools across optical, X-ray, and gravitational-wave domains. Scientific honors include: Pappalardo Fellowship, MIT (2021) Burdge advises students through his research group and has secured support through major collaborations like LISA and AXIS. He has been instrumental in designing the proposed Galactic-plane survey for AXIS, expected to uncover over a million new X-ray sources. His grants and research are supported by MIT, NASA, and NSF-level initiatives. He leads the MIT Binary Star Astrophysics Group, which focuses on discovery-driven astrophysics using next-generation observatories. The team integrates instrumentation development, data science, and theoretical astrophysics to explore binary evolution and compact object physics.

• Computer Vision
• Biometrics
• Face Recognition
• +3 more

Xiaoming Liu is a Professor at Michigan State University with extensive contributions to computer vision and biometrics. His research spans face recognition, 3D reconstruction, image forgery detection, and adversarial machine learning, with over 300 publications from 1999 to 2025. His primary research interests include Computer Vision , Biometrics , and Adversarial Machine Learning . Liu's work focuses on developing robust systems for face recognition at scale, detecting image manipulations, and creating 3D reconstruction techniques. Recent projects include SapiensID for human recognition, FRCSyn for synthetic face recognition, and proactive watermarking schemes. Liu's publication trends show increasing focus on multi-modal biometrics (combining face, body, and gait), image forgery detection using hierarchical approaches, and adversarial defense mechanisms . His 2023-2025 work emphasizes synthetic data applications and physics-driven recognition systems. Liu has mentored numerous students including Feng Liu, Minchul Kim, and Xiao Guo, who frequently co-author his papers. His research has been supported by grants enabling projects like FarSight (long-range biometrics) and ProMark (proactive watermarking). He leads research in biometrics security and computer vision, with recent work focusing on ethical AI applications and robust recognition systems. His team develops tools for detecting deepfakes and improving recognition in challenging conditions.

• Magnetic Resonance Imaging
• Control Systems
• Digital Electronics
• +3 more

Paul Chang is a Control/MR Engineer and PhD student at the Max Planck Institute of Biological Cybernetics, working in the High-field Magnetic-Resonance Group since 2013. His doctoral research focuses on real-time feedback B0 shim systems for ultra-high field MRI to improve magnetic field homogeneity and image quality. His educational background includes: MSc in Control Systems from Imperial College London (2011-2012) with thesis on chemical sensing software development BSc (Eng) in Mechatronics from the University of Cape Town (2007-2010) with additional majors in Mathematics and Economics Chang's research integrates control theory, digital electronics, and biomedical engineering to solve MRI challenges. He specializes in fractional/integer PID controllers, real-time field monitoring systems, and embedded controller implementation using FPGA technology. His work addresses critical limitations in ultra-high field MRI related to B0 field inhomogeneity caused by physiological artifacts and hardware limitations. His publication record demonstrates consistent interdisciplinary innovation across biomedical sensing and control systems. Key themes include the development of Parylene C-based pH sensors for neural applications, MRI-guided neurosurgical planning tools, and advanced control algorithms for hydraulic and MRI systems. This reflects a strong pattern of translating theoretical control concepts into practical biomedical solutions with emphasis on real-time system implementation. Chang contributes to the High-field Magnetic-Resonance Group's core mission through hardware development (field cameras, shim amplifiers), software implementation (asymmetric multiprocessor systems on Zynq 7020 boards), and algorithm design for dynamic shimming. His technical expertise spans FPGA programming, Siemens gradient system interfacing, and spherical harmonic function computation for magnetic field correction.