Erzhen Hu

Erzhen Hu

Ph.D student at University of Virginia
Email: eh2qs@virginia.edu
About

I am Erzhen Hu, a Ph.D candidate in Computer Science at the University of Virginia, advised by Prof. Seongkook Heo. Before that, I obtained M.S. in Statistics from University of Virginia, and B.A. in Sociology from Shanghai University. During my PhD, I have had the fortunate opportunity to intern and work with amazing researchers at Microsoft Research and Google.

As we approach the everyday use of virtual world, my research explores how to enable more flexible and frictionless way of connecting with people in virtual and hybrid spaces. My research involves developing and evaluating new interactive systems for distributed and hybrid meetings, such as theory-driven methods to improve ad-hoc and focused conversations, enabling AI-mediated augmented communication, and conversations between human and AI.

Updates

  • June 2024: I have passed the Qualifying Exam and have become a Ph.D. candidate!
  • June 2024: Serving as CSCW 2024 Poster and Demo Program Committee member.
  • May 2024: Attended CHI 2024 at Hawaiʻi, USA.
  • Jan 2024: Excited to start a research internship as Student Researcher at Google.
  • Dec 2023: Serving as CHI 2024 Late-Breaking Work (LBW) Program Committee member.
  • May 2023: Excited to start an internship as Research Intern at Microsoft Research.
  • Mar 2023: Presented OpenMic at the Video Sharing session and ThingShare at the Human-AI collaboration session on Apr 25th (Tue). Check out the CHI 2023 program!
  • Jan 2023: Two first-author papers accepted at CHI 2023.
  • Nov 2022: Serving as CHI 2023 Late-Breaking Work (LBW) Program Committee member.
  • Oct 2022: Reviewed CHI 2023 papers (1*special recognition) and SVed at UIST 2022.
  • May 2022: Presented FluidMeet paper at CHI 2022 in New Orleans.
  • Mar 2022: Presented FluidMeet at the Virtual Social Presence Seminar. You can watch the recorded 40-min talk here.

Selected Publications
Leverage multi-view diffusion models and 3D Gaussian Splatting to enhance XR communication and collaboration around objects. A user can identify 2D content from the web browser or video seethrough, and the system will automatically transform the 2D content into 3D objects and multi-view images for XR communication.
[Arxiv - pdf] [Demo] [30s Preview] [Video Figure] [Project Page]

Leverage deep learning (instance segmentation) to enrich communication centered around objects and elevate real-time collaboration in remote settings.
[Paper] [30s Preview] [Video Figure] [Talk] [Project Page]

Erzhen Hu, Jens Emil Grønbæk, Austin Houck, Seongkook Heo
Employ proxemic metaphors within the virtual conversational space to enhance turn-taking dynamics in groups of varying sizes (small to medium-sized groups).
[Paper] [30s Preview] [Video Figure] [Talk] [Project Page]




Service
  • Program Committee Member: CHI 2023-2024 LBW, CSCW 2024 Demo
  • Reviewer for CHI 2023(*)-2024, CSCW 2022 & 2024 (*), UIST 2023-2024, C&C 2024 (*), MobileHCI 2022(*), Auto UI 2022, NordiCHI 2022(*)
  • (*) Special Recognitions for Outstanding Reviews
  • Student Volunteer for ACM CSCW 2022, ACM UIST 2022, ACM IUI 2022, IEEE VR 2022, ACM ISS 2021



  • Design and source code from Link