PhdFit
Resume-aware faculty matching

Find professors who actually fit you

Review faculty evidence in public, then use the workspace to turn your background into a shortlist, outreach, and meeting prep.

Profile-awarePaper evidenceSix agents
Yun Raymond Fu

Yun Raymond Fu

· Professor, Jointly Appointed with College of Engineering

Northeastern University · Artificial Intelligence and Data Science

Active 2002–2025

h-index79
Citations28.7k
Papers618226 last 5y
Funding$2.4M

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

Faculty page
See your match with Yun Raymond Fu — sign in to PhdFit.Sign in

About

Yun Raymond Fu is a professor in the Khoury College of Computer Sciences and the College of Engineering at Northeastern University, based in Boston. His research interests include machine learning, computational intelligence, big data mining, computer vision, pattern recognition, and cyber-physical systems. He has published extensively in leading journals, books, book chapters, and international conferences and workshops, and serves as an associate editor, chair, program committee member, and reviewer for many of these venues and publications. Fu is also a successful serial entrepreneur for technology commercialization, having founded and served as president of Giaran, a spinoff from Northeastern University that focused on neural network-based augmented reality and facial image processing technologies, which was acquired by Shiseido in 2017. He has received numerous awards, including seven young investigator awards from prestigious organizations such as NAE, ONR, ARO, IEEE, INNS, UIUC, and the Grainger Foundation, as well as nine best paper awards from IEEE, IAPR, SPIE, and SIAM. He holds major industrial research awards from companies like Google, Samsung, Zebra, Adobe, and Mathworks. Fu is a fellow of AAAS, IEEE, IAPR, OSA, SPIE, and AAIA; a Lifetime Distinguished Member of ACM; and a Lifetime Senior Member of AAAI and the Institute of Mathematical Statistics. He is also a member of the ACM Future of Computing Academy, Global Young Academy, AAAS, and INNS.

Research topics

  • Artificial Intelligence
  • Computer Science
  • Computer vision
  • Machine Learning
  • Mathematics
  • Telecommunications
  • Data Mining
  • Theoretical computer science
  • Engineering
  • Speech recognition

Selected publications

  • Residual Dense Network for Image Restoration

    IEEE Transactions on Pattern Analysis and Machine Intelligence · 2020 · 862 citations

    Senior authorCorresponding

    Recently, deep convolutional neural network (CNN) has achieved great success for image restoration (IR) and provided hierarchical features at the same time. However, most deep CNN based IR models do not make full use of the hierarchical features from the original low-quality images; thereby, resulting in relatively-low performance. In this work, we propose a novel and efficient residual dense network (RDN) to address this problem in IR, by making a better tradeoff between efficiency and effectiv…

    DOI

  • Skeleton Aware Multi-modal Sign Language Recognition

    2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · 2021 · 260 citations

    Senior authorCorresponding

    Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master. Sign Language Recognition (SLR) aims to bridge the gap between sign language users and others by recognizing signs from given videos. It is an essential yet challenging task since sign language is performed with the fast and complex movement of hand gestures, body posture, and even facial expressions. Recently, skeleton-based action recognition attracts increasing attention…

    DOI

  • Adaptive Trajectory Prediction via Transferable GNN

    2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022 · 86 citations

    Senior authorCorresponding

    Pedestrian trajectory prediction is an essential component in a wide range of AI applications such as autonomous driving and robotics. Existing methods usually assume the training and testing motions follow the same pattern while ignoring the potential distribution differences (e.g., shopping mall and street). This issue results in inevitable performance decrease. To address this issue, we propose a novel Transferable Graph Neural Network (TGNN) frame-work, which jointly conducts trajectory pred…

    DOI

  • LightAvatar: Efficient Head Avatar as Dynamic Neural Light Field

    Lecture notes in computer science · 2025-01-01 · 3 citations

    book-chapterSenior author
    PublisherDOI

  • A Competency Framework for Electric Vehicle Maintenance Technicians: Addressing the ESG Imperatives of the BEV Industry

    Preprints.org · 2025-04-29 · 2 citations

    preprintOpen accessSenior author

    The rapid evolution of the battery electric vehicle (BEV) industry calls for a robust, specialized competency framework for maintenance technicians. This study develops such a framework through an extensive literature review and analysis of domestic and international standards, identifying a broad set of potential competency items. A three-round Delphi process was then employed with 15 experts—education directors, senior technical supervisors, and veteran maintenance technicians—to refine the fr…

    PublisherOA PDFDOI

Recent grants

Frequent coauthors

Awards & honors

  • Seven young investigator awards from NAE, ONR, ARO, IEEE, IN…
  • Nine best paper awards from IEEE, IAPR, SPIE, and SIAM
  • Fellow of AAAS
  • Fellow of IEEE
  • Fellow of IAPR

Similar researchers at Northeastern University

  • Resume-aware match score
  • Save to shortlist
  • AI-drafted outreach

See your match with Yun Raymond Fu

PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.

Sign in to PhdFitHow it works
  • Free to start
  • No credit card
  • 30-second signup