About

My research interest is in developing machine learning algorithms with strong theoretical and empirical performances, with a recent focus on Online and Reinforcement Learning, Algorithmic Robustness against Biased and Noisy Data, Representation Learning, Human-in-the-loop Learning, and Learning for Science.

Research topics

• Computer Science
• Physics
• Acoustics
• Materials science
• Optoelectronics
• Optics
• Telecommunications
• Artificial Intelligence
• Electrical engineering
• Engineering
• Computational physics
• Radiology
• Electronic engineering
• Medicine
• Embedded system
• Composite material

Selected publications

• Circularly Polarized Metamaterial Cage for Homogeneous Signal‐to‐Noise Ratio Enhancement in Magnetic Resonance Imaging

  Advanced Materials · 2026-04-18

  articleSenior authorCorresponding

  ABSTRACT The signal‐to‐noise ratio (SNR) in magnetic resonance imaging (MRI) governs the quality of signal detection and directly impacts the clarity and reliability of the acquired images. Recent advances in metamaterials have enabled lightweight solutions with selective magnetic responses, offering a route to locally boost SNR in targeted anatomical regions but often with compromised field homogeneity. Here, a wireless metamaterial cage constructed from coaxial cables is engineered for homogeneous SNR enhancement at 3.0 T. With its cylindrical geometry and electromagnetic architecture, the device supports circularly polarized resonance through engineered phase‐shifted currents, enabling selective and omnidirectional interaction with the rotating field to achieve a uniform magnetic field distribution. Integrated with the Birdcage coil (BC), the device yields a 31.45‐fold SNR enhancement while maintaining comparable homogeneity to the BC alone, exhibiting only 12.07% variation within the region of interest (ROI). Benchmarking against a state‐of‐the‐art 16‐channel extremity coil further shows that the metacage achieves at least 1.94‐fold and 2.24‐fold higher SNR in axial and coronal planes, respectively, and exhibits substantially lower SNR variation (12.07% compared to 54.83% for the extremity coil). The results establish the metacage as a compelling platform for next‐generation wireless MRI technologies.

  PublisherDOI

• Increased VOC reactivity forcing ozone pollution in the Yangtze River Delta region, China: Evidence from an eight-year observation at an urban site and implications for future control strategies

  Journal of Hazardous Materials · 2025-04-02 · 3 citations

  article1st authorCorresponding
  PublisherDOI

• Synergistic enhancement in Ag quantum dot modified TiO2 via interfacial electron transfer channels and LSPR

  iScience · 2025-12-04 · 2 citations

  articleOpen access

  photocatalyst exhibited outstanding degradation performance and great potential for environmental purification applications.

  PublisherDOI

• Designed VO2/ANF/PVA aerogel composite material for adaptive infrared stealth and dynamic thermal regulation

  Composites Communications · 2025-09-17 · 3 citations

  article
  PublisherDOI

• Optical System Design Method Based on Rotating Risley Prism

  IEEE photonics journal · 2025-05-15 · 2 citations

  articleOpen access

  As a type of beam steering element, the Risley prism has found extensive applications in the field of optics. A single Risley prism can deflect incident light beams with a constant deviation angle, while a rotating multi-Risley prism system enables arbitrary beam deflection through relative rotation of multiple prisms. This study proposed the design methodology of optical systems incorporating Risley prisms for imaging applications. To realize a scanning optical system based on rotating Risley prisms, we start with the theoretical model of optical axis deflection induced by Risley prisms, explore the corresponding optical system design approaches, and develop the associated optical configurations. Through the proposed design methodology, we successfully implement a large field-of-view (60°) and broadband scanning optical system operating across visible (400nm-1000nm) and mid-infrared (3μm-5μm) spectral ranges. This design demonstrates significant potential in applications such as space target detection and infrared countermeasures, substantially expanding the application scope of Risley prisms in optical engineering.

  PublisherDOI

• Few-Shot Deployment of Pretrained MRI Transformers in Brain Imaging Tasks

  Research Square · 2025-09-11 · 1 citations

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Regularization by neural style transfer for MRI field-transfer reconstruction with limited data

  Frontiers in Artificial Intelligence · 2025-06-18 · 1 citations

  articleOpen accessSenior authorCorresponding

  Recent advances in MRI reconstruction have demonstrated remarkable success through deep learning-based models. However, most existing methods rely heavily on large-scale, task-specific datasets, making reconstruction in data-limited settings a critical yet underexplored challenge. While regularization by denoising (RED) leverages denoisers as priors for reconstruction, we propose Regularization by Neural Style Transfer (RNST), a novel framework that integrates a neural style transfer (NST) engine with a denoiser to enable magnetic field-transfer reconstruction. RNST generates high-field-quality images from low-field inputs without requiring paired training data, leveraging style priors to address limited-data settings. Our experiment results demonstrate RNST's ability to reconstruct high-quality images across diverse anatomical planes (axial, coronal, sagittal) and noise levels, achieving superior clarity, contrast, and structural fidelity compared to lower-field references. Crucially, RNST maintains robustness even when style and content images lack exact alignment, broadening its applicability in clinical environments where precise reference matches are unavailable. By combining the strengths of NST and denoising, RNST offers a scalable, data-efficient solution for MRI field-transfer reconstruction, demonstrating significant potential for resource-limited settings.

  PublisherOA PDFDOI

• The potential effect of the deformation of strong-coupling polaron in perovskite materials

  Chinese Physics B · 2025-11-06

  articleCorresponding

  Abstract The electronic structure of perovskite nanomaterial systems can change when exposed to mechanical, a fact that has been extensively applied in the investigation of polaron properties. A deformation potential is formed when interactions among charge carriers and the lattice is mediated by stress. Hence, deformation potential can be considered as an important tool to explore acoustic polaron properties. In this research, we adopted a theoretical method by combining unitary transformation and second quantization, incorporating deformation potential effect into polaron effect. Finally, we derived the effective mass and ground-state energy of strongly-coupled bound polarons in perovskite lattices. Through in-depth analysis and numerical calculations, we found that under the combined influence of sound velocity and deformation potential, polaron coupling strength no longer remained constant; instead, it exhibited a variation with perovskite lattice deformation potential. In addition, the ground-state energy, vibrational frequency, and effective mass of the polaron displayed anomalous behaviors. This research provided an important reference to further understand the physical characteristics of acoustic polarons formed by lattice distortions.

  PublisherDOI

• ESTABLISHMENT AND VALIDATION OF A THEORETICAL MODEL FOR SINGLE LONGITUDINAL AXIAL FLOW THRESHING AND SEPARATION OF MILLET

  INMATEH Agricultural Engineering · 2025-08-20 · 3 citations

  articleOpen accessSenior author

  The influence of the device's structure and operating parameters, along with the material properties of millet, on threshing and separation performance forms the theoretical basis for designing and researching a single longitudinal axial flow threshing and separation device specifically adapted to millet. Therefore, a theoretical model for grain threshing and separation in a single longitudinal axial flow threshing device was established based on variable mass theory. To validate the theoretical model, single-factor tests were conducted on the feeding rate, rotational speed, and water content of Longgu 31 millet. The error analysis between the experimental and calculated values indicates that within a moisture content range of 17.14% to 32.93%, feeding rates varying from 1 to 3 kg/s, and rotational speeds ranging from 700 to 1000 r/min, the R-squared values consistently exceed 0.97. This indicates an excellent fit of the theoretical model. The theoretical model will serve as a valuable reference for the design and investigation of the single longitudinal axial flow separation device.

  PublisherDOI

• Target-driven deep learning for optimization design of electromagnetically induced transparency metasurfaces based on lithium tantalate

  Optics Communications · 2025-03-01 · 5 citations

  article
  PublisherDOI

Recent grants

• Metamaterial-Enabled magnetic Resonance Imaging Enhancement

  NIH · $660k · 2018–2023

• Mechanical Behavior of Amorphous Plasma-Enhanced Chemical Vapor Deposited Silicon Oxide Films for MEMS Applications

  NSF · $162k · 2007–2011

• Collaborative Research: Elastic and Viscoelastic Characterization and Modeling of Polymer based Structures for Biological Applications

  NSF · $225k · 2008–2013

• Diatom-Enabled Scalable Nanomanufacturing for Photonic Devices

  NSF · $428k · 2018–2024

• NER: A Digital Bio/Nanoelectronics Interface for Single Cell Study

  NSF · $106k · 2006–2008

Frequent coauthors

• Yi Fang

  123 shared

• Richard D. Averitt

  University of California, San Diego

  103 shared

• Jingwen Guo

  Inner Mongolia University

  90 shared

• Xiaoguang Zhao

  76 shared

• Kebin Fan

  58 shared

• Stephan W. Anderson

  54 shared

• Renhao Qu

  41 shared

• Jie Zhou

  37 shared

Labs

• Machine LearningPI

Education

• B.S., Applied Mathematics

  University of California, Los Angeles (UCLA)

  2016

• Ph.D., Computer Sciences

  University of Wisconsin-Madison

  2021