جست‌وجوی استاد و پژوهشگر

Weiguo Luمشاهده پروفایل

استاد

• Medical Physics
• Radiation Oncology
• Medical Image Analysis
• +۷ مورد دیگر

Weiguo Lu, Ph.D., is a Professor in the Department of Radiation Oncology at UT Southwestern Medical Center, where he leads research in the Division of Medical Physics and Engineering. He is also the co-founder of Neural Rad LLC and a board-certified medical physicist by the American Board of Radiology with a license from the Texas Medical Board. Education: Bachelor’s and Master’s in Nuclear Physics – Peking University, China Master’s in Medical Physics – University of Wisconsin–Madison Master’s in Computer Science – University of Wisconsin–Madison Ph.D. in Medical Physics – University of Wisconsin–Madison Dr. Lu’s research focuses on the integration of artificial intelligence and machine learning into radiation oncology, particularly in medical image segmentation, adaptive radiotherapy, dose prediction, and motion management. His work leverages deep learning to improve treatment planning accuracy and efficiency, especially in complex cases involving brain metastases, breast cancer, and head and neck tumors. The recent articles highlight a strong trend toward AI-driven automation in radiotherapy, including unsupervised domain adaptation, semi-supervised segmentation, patient-specific MRI super-resolution, and end-to-end survival prediction models. These works reflect a cohesive research agenda centered on enhancing precision, personalization, and real-time adaptation in cancer treatment using cutting-edge computational methods. Scientific Awards and Certifications: American Board of Radiology Certification Texas Medical Board Medical Physics License Dr. Lu actively mentors a large team of students and researchers, as evidenced by his extensive co-authorship on publications. His lab, the MAIA Lab (Medical Artificial Intelligence and Automation), develops platforms for automated delineation, dose verification (e.g., ART2Dose), and online adaptive radiotherapy. He has contributed to clinical workflow development, such as GammaPod treatments, and has been involved in quality assurance frameworks for FLASH trials. His collaborations span across institutions and involve significant translational research from algorithm development to clinical implementation. Dr. Lu’s research is supported by institutional and clinical grants related to AI in oncology, proton therapy, and adaptive radiotherapy, though specific grant names are not detailed in the provided text. He leads a multidisciplinary team focused on pushing the boundaries of medical physics through innovation in imaging, modeling, and therapeutic delivery.

Peter Melchiorمشاهده پروفایل

استادیار

• Statistical Astronomy
• Machine Learning
• Data Fusion
• +۵ مورد دیگر

Peter Melchior is an Assistant Professor of Astrophysical Sciences at Princeton University, with a joint appointment at the Center for Statistics and Machine Learning. He leads the Princeton Astro Data Lab, where his team develops novel algorithms to extract information from astronomical observations despite instrumental limitations and noise. His educational background includes a Ph.D. in Physics (2010) and a Diplom/M.S. in Physics (2006), both from the University of Heidelberg. Prior to his current position at Princeton, he held postdoctoral positions at The Ohio State University (2011-2015) and the University of Heidelberg (2010). Dr. Melchior's research focuses on statistical methods for large astronomical surveys. His primary interests include: Physics-based machine learning for astronomical data analysis Source separation and data fusion techniques Optimal combination of multiple datasets from different surveys Development of neural network approaches for astronomical problems Application of statistical methods to hydrologic modeling His recent publications demonstrate a strong trend toward interdisciplinary work that combines astronomy with machine learning and environmental science. The research spans from fundamental astronomical data analysis techniques to practical applications in water resource management across the United States. Among his notable achievements: PI of a project funded by the Schmidt Futures Foundation to optimize target selection for the Prime Focus Spectrograph survey Lead developer of the HydroGEN project funded by NSF for hydrologic scenario generation Author of approximately 300 papers in major peer-reviewed journals Developer of open-source software including pyGMMis for Gaussian mixture modeling Dr. Melchior actively mentors students and has organized the Undergraduate Summer Research Program and Data Science Seminar at Princeton. His work bridges astronomy, statistics, and machine learning, with growing applications in environmental science.

• Microwave Imaging
• Antenna Engineering
• Biomedical Signal Processing
• +۳ مورد دیگر

Selçuk Özgür is a Lecturer at Istanbul Technical University's Department of Electronics and Communication Engineering. He holds an MSc in Telecommunication Engineering (with thesis) from the same institution and a Licence in Electronics and Communication Engineering from Izmir University. Istanbul Technical University - Telecommunication Engineering (MSc, with thesis) Izmir University - Electronics and Communication Engineering (Licence) His research focuses on microwave imaging technologies, particularly for medical diagnostics like brain stroke detection. Key areas include antenna design, inverse scattering methods, dielectric property analysis, and qualitative imaging techniques. His work contributes to biomedical engineering and electromagnetic theory. Recent publications highlight advancements in microwave imaging systems, including tissue-mimicking phantoms, compact antenna designs, and comparative studies of linear sampling and factorization methods. These studies often intersect with medical imaging, signal processing, and electromagnetic modeling. H-index: 4 (Scopus); 23 readers on Mendeley; 2 patents referencing his work. He has published 7 research outputs since 2014, including 4 conference contributions and 3 journal articles.

Mathieu Delalandreمشاهده پروفایل

دانشیار

• Image Processing
• Computer Vision
• Document Analysis
• +۷ مورد دیگر

Mathieu Delalandre is an Associate Professor at LIFAT Laboratory, University of Tours, France, specializing in image processing and document/video analysis since 2009. His research focuses on local approaches including local detectors, template matching, and transform domain processing, with applications in video copy detection, scene text detection, and document image networking. He received his PhD in Computer Science from Rouen University in 2005, followed by research fellowships at SCSIT (Nottingham, UK), L3i (La Rochelle, France), and CVC (Barcelona, Spain) from 2006-2009. His educational background established his foundation in document analysis and pattern recognition. Delalandre's research spans multiple domains of computer vision with emphasis on practical applications. His work bridges theoretical image processing techniques with real-world problems in document analysis and video content monitoring. Key contributions include robust symbol localization, performance evaluation frameworks for symbol recognition systems, and real-time text detection algorithms. His approach often combines structural and statistical methods for enhanced accuracy. His publication record demonstrates consistent focus on video and document analysis, with recent work emphasizing partial video copy detection (2021-2023), real-time text detection (2019-2021), and JPEG document processing (2015-2017). The trajectory shows evolution from fundamental symbol recognition research to applied multimedia analysis systems addressing contemporary challenges in copyright protection and content monitoring. As head of the StationTV project, Delalandre leads research on real-time processing of multi-channel television content. He has participated in numerous national and international research projects, contributing to advancements in document image analysis and video processing. His work has been supported through various research grants focusing on practical applications of computer vision. Delalandre is an active member of the RFAI research group at LIFAT Laboratory, where he contributes to the development of innovative image processing techniques. His team collaborates across European institutions, maintaining strong connections with previous research sites in the UK, Spain, and other French laboratories. Current work focuses on multimedia fact-checking systems and real-time video analysis solutions.

• Polarimetry
• Antenna Design
• High Power Microwave Systems
• +۵ مورد دیگر

Scott Tyo is an accomplished electrical engineering professor currently serving as an Adjunct Professor at UNSW Canberra's School of Engineering and Technology. He joined UNSW Canberra in 2015 as Head of the School and Professor of Electrical Engineering, following previous appointments as a professor at the University of Arizona's College of Optical Sciences (2006-2015) and the University of New Mexico (2001-2006). His academic journey began with a PhD from the University of Pennsylvania in 1997, after which he served in the US Air Force working on High Power Microwave Systems and Space-Based Remote Sensing, including military faculty service at the US Naval Postgraduate School from 1999-2001. Professor Tyo's research spans polarimetry, antenna design, and remote sensing applications. His work began with underwater imaging systems based on differential polarimetry, where he established foundational research in polarimeter optimization. His career evolved to include significant contributions to ultra wideband and high-power microwave antennas, particularly through collaborations with Dr. Carl Baum at AFRL and later with Prof. Rick Ziolkowski at Arizona on metamaterial-inspired electrically small antennas. More recently, his research has expanded into tropical cyclone analysis using satellite remote sensing techniques. His work demonstrates remarkable interdisciplinary reach, connecting optical engineering with atmospheric science. Analysis of his recent publications (2020-2025) reveals three primary research thrusts: 1) Advanced polarimetry techniques including scene-adaptive imaging systems and multi-harmonic reconstruction methods; 2) High-power microwave engineering with focus on cascaded oscillators and electrically small antenna arrays; and 3) Tropical cyclone analysis through satellite remote sensing of cloud radiative effects. These areas reflect his ability to bridge fundamental optical engineering with practical applications in defense and climate science. Professor Tyo has maintained an exceptionally active publication record spanning over 25 years, with continuous contributions through 2025. His work shows strong international collaboration, with co-authors from multiple countries and institutions. His research has practical applications in defense systems, remote sensing technologies, and climate monitoring, demonstrating both theoretical depth and real-world impact.

Jose Picheralمشاهده پروفایل

پژوهشگر

• Signal Processing
• Source Localization
• Spectral Analysis
• +۳ مورد دیگر

Jose Picheral is an active researcher at the Signals and Systems Laboratory specializing in signal processing with expertise in source localization, spectral analysis, and vibration analysis. His work bridges theoretical methodologies with practical applications in aerospace engineering, acoustics, and mechanical systems monitoring. His primary research interests include: Signal Processing Source Localization Spectral Analysis Vibration Analysis Acoustic Imaging Array Signal Processing Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on sparse signal processing techniques applied to aircraft engine vibration monitoring through tip-timing analysis, high-resolution acoustic imaging methods, and non-uniform antenna array processing. Key trends include the development of OMP-based spectral analysis for blade vibration, manifold learning approaches for impulse response reconstruction, and robust super-resolution techniques for correlated source localization in noisy environments. No scientific awards were documented in the provided source material. The text contains no information regarding student advising activities or research grant funding. He operates within the Signals and Systems Laboratory framework, which concentrates on advanced signal processing solutions for engineering challenges, particularly in aerospace vibration analysis and acoustic source mapping through innovative sparse recovery and manifold-based methodologies.

Luis Miguel Hernández Acostaمشاهده پروفایل

دانشیار

• Software Engineering
• Mobile Computing
• Computer Vision
• +۳ مورد دیگر

Luis Miguel Hernández Acosta serves as an Associate Professor in the Department of Telematics Engineering at the University of Las Palmas de Gran Canaria (ULPGC), affiliated with both the GIR IUMA: Information and Communications Systems research group and the IU of Applied Microelectronics. His academic work spans software engineering and telematics systems within the School of Engineering. His research interests focus on Software Engineering , Mobile Computing , and Computer Vision , with significant contributions to practical applications including web/mobile platforms for service management, computer vision for medical diagnostics, and communication systems. Current projects demonstrate strong industry alignment in restaurant management, transportation optimization, and telemedicine solutions. Analysis of recent publications reveals consistent expertise in full-stack development, real-time systems, and cross-platform frameworks, with increasing integration of machine learning techniques since 2022. Key thematic trends include Practical implementation of publish/subscribe architectures for real-time notifications Computer vision applications in medical diagnostics Optimization algorithms for transportation and service platforms Advising Activities: Supervised 28 bachelor/master theses (2021-2025) Specializes in guiding telecommunications and computer engineering students Projects span mobile development (65%), web platforms (25%), and AI applications (10%) Research Infrastructure: Works within the Department of Telematics Engineering's ecosystem, leveraging resources from both GIR IUMA and the Applied Microelectronics Institute for hardware-software integration projects.

• Machine Learning
• Robotics
• Computer Vision
• +۱۲ مورد دیگر

Alberto Tellaeche Iglesias is a Lecturer at the University of Deusto's College of Engineering, Department of Computing, Electronics and Communication Technologies. He holds a Ph.D. in Systems Engineering and Automation (UNED, 2008) and an M.Sc. in Automation and Electronics Engineering (University of Deusto, 2001). With over 17 years in industrial R&D, he has worked at Tekniker Foundation (2007–2019) and led research projects under European FP7/H2020 and national programs. Ph.D., Systems Engineering and Automation, UNED (2008) M.Sc., Automation and Electronics Engineering, University of Deusto (2001) His research focuses on Machine Learning , Robotics , and Computer Vision , particularly for industrial automation and precision agriculture. He has published 15 recent papers on topics like 3D vision systems, federated learning for defect detection, and hybrid classifiers for agricultural monitoring. His work bridges Pattern Recognition and Industrial Automation , with applications in quality control, human-robot interaction, and smart cities. Grants and projects include EU-funded AI-Driven Cognitive Robotic Platforms (2021–2024), HAZITEK (2019–2022), and national initiatives like Edge Technologies for Industrial Distributed AI (2021–2024). He has supervised the thesis 'Progress in Industrial Photogrammetry' (2019) and serves on doctoral committees. He previously worked at Tekniker Foundation's Smart and Autonomous Systems unit and is part of interdisciplinary teams integrating 3D Vision and Deep Learning for industrial applications.

Filip Elvanderمشاهده پروفایل

استادیار

• Statistical signal processing
• Stochastic processes
• Audio signal processing
• +۴ مورد دیگر

Filip Elvander is an Assistant Professor in the Department of Information and Communications Engineering at Aalto University, Finland. Previously, he served as a postdoctoral research fellow at KU Leuven (2020-2022), supported by the Research Foundation - Flanders (FWO). PhD in Mathematical Statistics (2020) and MSc in Industrial Engineering and Management (2015) from Lund University Assistant Professor at Aalto University since 2022 Leader of the Structured and Stochastic Modeling Group (SSMG) His research focuses on statistical signal processing, particularly inverse problems and optimal transport theory. Key application areas include acoustic localization, spectral estimation, audio processing, and spectroscopy. Current research directions involve: Optimal transport for geometric signal space modeling Spatio-temporal signal modeling in remote sensing and audio Misspecified modeling impacts and mitigation Optimal sampling schemes for efficient data collection Recent publications demonstrate trends in optimal transport applications for multi-pitch estimation, room acoustics, sensor networks, and audio restoration. His group includes 5 PhD students working on these topics. Awards include FWO postdoctoral fellowship (2021-2022). Collaborations span Lund University, KU Leuven, and Aalto University research teams.

Adrian Evansمشاهده پروفایل

استاد

• Biometrics
• 3D Face Recognition
• Colour Image Processing
• +۷ مورد دیگر

Prof. Adrian Evans serves as Deputy Head of Department in the Department of Electronic & Electrical Engineering at the University of Bath, where he leads research within the Electronics Materials, Circuits & Systems Research Unit (EMaCS) and The Foundry: Centre for Digital, Manufacturing & Design. His academic profile demonstrates active engagement in doctoral supervision and cutting-edge research across multiple domains of image processing and biometrics. His primary research focuses on biometrics—particularly 3D face recognition techniques resilient to facial expressions—and advanced colour/multispectral image processing. He has pioneered methods for colour edge detection, nonlinear filtering, and scale-space sieve-based segmentation. His motion estimation work specializes in analyzing non-rigid bodies like clouds and glaciers, while his nonlinear image processing research centers on mathematical morphological sieves and granulometric texture analysis. Recent publication trends (2021-2025) reveal a strategic shift toward computer vision applications in transportation (multi-camera vehicle tracking systems) and healthcare (non-contact cardiorespiratory monitoring). His work increasingly integrates radar technology with computer vision for vital sign detection while maintaining foundational contributions to mathematical morphology and image enhancement techniques. No major scientific awards are documented in the provided materials. His supervisory record includes 11 doctoral students, with current openness to new PhD candidates. Research funding spans UK government and industry collaborations including EPSRC projects like High Speed 4K Video Transmission (2016-2018) and multiple KTP partnerships with Seiche Measurements Limited and Navtech Radar Limited. He operates within Bath's EMaCS research unit and The Foundry center, leveraging these interdisciplinary environments for digital manufacturing and design innovation. His work directly supports UN Sustainable Development Goals through applications in smart transportation systems and healthcare technology development.

Claudio Fontanariمشاهده پروفایل

دانشیار

• Algebraic Geometry
• Birational Geometry
• Moduli Spaces
• +۵ مورد دیگر

Claudio Fontanari is an Associate Professor of Geometry at the Department of Mathematics , University of Trento , Italy. His research spans algebraic geometry, birational geometry, and the history/philosophy of mathematics, with interdisciplinary applications in multimedia engineering. Research Focus : Algebraic curves and moduli spaces, geometric modeling, shape matching, and digital forensics. Teaching : Offers courses in Linear Algebra, Analytic Geometry, and Geometry for Industrial Engineering and Philosophy programs. Interdisciplinary Work : Exploits algebro-geometric tools like linear algebra, commutative algebra, and conformal geometry for multimedia engineering tasks such as asymmetric watermarking and digital fingerprinting. Publications highlight his contributions to multimedia security, including hierarchical watermarking schemes and genetic algorithm-based robustness evaluation. He also organizes the Multimedia Geometry Seminar , fostering academic exchange in geometry and its applications.

Mustafa Berke Yeltenمشاهده پروفایل

دانشیار

• Analog Circuit Design
• Semiconductors
• MEMS and Microsystem Technologies
• +۳ مورد دیگر

Assoc. Prof. Dr. Mustafa Berke Yelten is an Associate Professor at the Electronics and Communication Engineering Department , Istanbul Technical University , specializing in Analog Circuit Design , Semiconductors , and MEMS Technologies . With a PhD from North Carolina State University (2008), he has held academic roles since 2015 and served as Deputy Head of Department (2020) and Vice Dean (2018-2020). His research focuses on Cryogenic Electronics , Integrated Circuit Reliability , and Biomedical Device Design , particularly for Wireless Capsule Endoscopy . He has developed Low-Noise Amplifiers , Power Amplifiers , and MEMS Actuators with applications in Space Electronics and Medical Imaging . Recent publications highlight his work on LSTM-Based Transistor Reliability , 3D-Printed Medical Actuators , and Machine Learning Applications in analog circuit modeling. His projects include TÜBİTAK and BAP -funded initiatives for Cryogenic Communication Systems and Optical Sensor Development . Scientific Awards ITU Academic Performance Award (2023) ITU Academic Performance Award (2022) Key Collaborations IEEE Senior Member (2003-present) Co-editor, IEEE Transactions on Device and Materials Reliability

Todd Alaminمشاهده پروفایل

استاد

• Spine Surgery
• Orthopaedic Surgery
• Minimally Invasive Spine Surgery
• +۵ مورد دیگر

Dr. Todd Alamin is a Professor in the Department of Orthopaedic Surgery at Stanford University School of Medicine. He serves as director of the Spine Surgery Fellowship Program and the Minimally Invasive Spine Center at Stanford Health Care Orthopaedics and Sports Medicine. As a board-certified, fellowship-trained orthopaedic surgeon and spine specialist, Dr. Alamin has established himself as a global leader in innovative spine surgery techniques with decades of clinical experience. Dr. Alamin earned his medical degree from Yale School Of Medicine (1995), completed his Surgery Residency at UCSD (1996), Orthopaedic Surgery Residency at UCSD (2000), and a specialized Spine Fellowship at Stanford University (2001). He is board-certified by the American Board of Orthopaedic Surgery (2003) and holds fellowship status with the American Academy of Orthopaedic Surgeons. Dr. Alamin's research program focuses on developing effective treatments for vertebral fractures, spinal deformities, scoliosis, herniated discs, and spondylolisthesis. His work explores nerve ablation techniques for chronic low back pain and motion-preserving lumbar fusion approaches. He has published over 65 peer-reviewed articles and written multiple book chapters on spine conditions. A recognized innovator, he has invented dozens of medical devices and techniques for spine surgery, holding numerous patents that have revolutionized back pain treatments worldwide. Analysis of Dr. Alamin's recent publications (2022-2025) reveals a strong research trajectory across multiple dimensions of spine care: optimizing surgical decision-making through understanding patient preferences, improving pain management protocols particularly regarding opioid use, advancing measurement technologies for spinal assessment, developing classification systems for spinal conditions, and refining surgical techniques including anterior approaches. His work consistently bridges clinical practice with rigorous scientific investigation to improve patient outcomes. Fellow of the American Academy of Orthopaedic Surgeons (FAAOS) As director of the Spine Surgery Fellowship Program, Dr. Alamin mentors the next generation of spine specialists, overseeing comprehensive training in both traditional and innovative surgical techniques. His leadership extends to the Minimally Invasive Spine Center, where he guides clinical practice and research initiatives. Dr. Alamin serves as principal investigator for multiple clinical trials exploring novel treatments for spinal conditions, with his patented innovations being adopted by physicians worldwide. His clinical practice focuses on minimally invasive approaches for spinal stenosis, scoliosis, degenerative disc disease, and traumatic injuries, emphasizing personalized care that combines extensive expertise with cutting-edge technology. Dr. Alamin leads a multidisciplinary team at the Minimally Invasive Spine Center, collaborating with specialists across departments to advance less invasive approaches to spinal surgery. His clinical focus spans minimally invasive spine surgery, cervical spine procedures, spinal tumor treatment, and correction of complex spinal deformities, with an emphasis on evidence-based approaches that maximize patient recovery while minimizing surgical trauma.

• Biomedical Circuits and Systems
• Magnetic Resonance Imaging (MRI)
• RF Circuit Design
• +۴ مورد دیگر

Sung-Min Sohn serves as Assistant Professor in the School of Biological and Health Systems Engineering within Arizona State University's Ira A. Fulton Schools of Engineering. His research pioneers RF/analog/digital circuit innovations for biomedical imaging systems, with particular focus on advancing magnetic resonance imaging (MRI) hardware capabilities. His academic foundation includes a Ph.D. in Electrical and Computer Engineering from the University of Minnesota-Minneapolis (2013), complemented by master's and bachelor's degrees from Korea University, Seoul (2004, 2002). Prior to academia, he gained industry experience as a circuit design engineer at LG Electronics (2004-2007). Dr. Sohn's research centers on bio-inspired electronics for medical applications, specializing in simultaneous transmit-receive (STAR) MRI systems, automatic RF tuning/matching mechanisms, and novel coil architectures. His work bridges electrical engineering principles with clinical imaging needs to develop more accessible and efficient diagnostic hardware. Publication analysis reveals an evolutionary trajectory from consumer electronics (2003-2006) to specialized MRI instrumentation (2011-2016), demonstrating consistent innovation in RF component design, field uniformity optimization, and high-power circuit integration for medical imaging systems. His scientific recognition includes the prestigious NIH Pathway to Independence Award (K99/R00) in 2016, one of only five awarded that year in biomedical imaging and bioengineering. As Principal Investigator, Dr. Sohn leads the NIH-funded Automatic RF Signal Tuning project (K99EB020058). He also contributes to major collaborative initiatives including portable MRI development (R24MH105998) and ultra-high-field (9.4T) human MRI systems (R01EB006835), working with researchers at the University of Minnesota and Columbia University. His teaching portfolio spans undergraduate and graduate biomedical instrumentation courses with honors thesis supervision.

Yao-Yuan Maoمشاهده پروفایل

استادیار

• Astrophysics
• Dark Matter Physics
• Galaxy Formation and Evolution
• +۷ مورد دیگر

Yao-Yuan Mao is an Assistant Professor of Physics and Astronomy at the University of Utah since July 2022. Their research delves into the mysteries of dark matter and galaxy formation, focusing on low-mass galaxies and their connection to dark matter halos through astronomical surveys and numerical simulations. Education: Ph.D. in Physics, Stanford University (2016) B.S. in Physics, National Taiwan University (2009) Research Interests: Yao-Yuan Mao's work addresses fundamental questions about dark matter, galaxy formation physics, and the uniqueness of the Milky Way. By studying low-mass galaxies in the nearby universe, they model the relationship between galaxies and their host dark matter halos to uncover the underlying cosmic structures. Their research spans cosmology and extragalactic astronomy , astroparticle physics , and astronomical instrumentation . Scientific Collaborations & Leadership: Co-leader of the SAGA Survey , a galaxy redshift survey characterizing satellite systems around Milky Way analogs. Active in the Rubin Observatory science community. Co-convener of the LSST DESC Science Release and Validation Working Group . Former co-lead of the DESC Dark Matter Working Group and Data Access Team. Served as Collaboration Council Chair and Hack/Sprint Coordinator for LSST DESC. Credited with Builder Status by DESC in 2019. Grants & Funding: HST Cycle 31 Proposal 17484: "BEYOND PANDAS: Two Extremely Faint Candidate Satellites of M33 Identified in Diffuse Light" (2024-2025) HST Cycle 31 Proposal 17501: "LONE LION OR PART OF A PRIDE: PROPER MOTION AND ORBIT OF LEO P" (2024-2027) "Discovery of Six Isolated Ultra-Faint Dwarf Galaxy Candidates in the Local Group" (2024-2027) NASA Hubble Fellowship Program: "Reconstructing the Assembly History of Andromeda" (2023-2025) Advocacy & Service: Yao-Yuan is committed to inclusion and equity in academia. They are the current maintainer of the Astronomy and Astrophysics Outlist and serve on the AAS Committee for Sexual-Orientation and Gender Minorities in Astronomy (SGMA) .