About Me

I am currently a research assistant at the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University.

My research interest lies in Human-Computer Interaction, Extended Reality and Learning Sciences. I am specifically keen on understanding and creating beyond-real interaction techniques & experiences with emergent technologies (e.g., XR, AI) that empower people. Towards this goal, I am working with Prof. David Lindlbauer on spatial computing and Prof. Hong Shen on responsible AI. I was also fortunate to be advised by Prof. Anhong Guo and Prof. Xu Wang in my undergrad, working on the AR intelligent tutor system.

Education

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  Carnegie Mellon University

  Aug 2023 – Aug 2024

  Master of Educational Technology and Applied Learning Sciences

  Pittsburgh, PA

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  University of Michigan

  Aug 2021 – Apr 2023

  BSc in Computer Science

  Ann Arbor, MI

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  Shanghai Jiao Tong University

  Sep 2019 – Aug 2023

  BSc in Electrical and Computer Engineering

  Shanghai, China

Research Projects

New Ears: An Exploratory Study of Audio Interaction Techniques for Performing Search in a Virtual Reality Environment

Muzhe Wu*, Yi-Fei Cheng*, David Lindlbauer

To Appear at IEEE ISMAR 2024

ActiveAI: The Effectiveness of an Interactive Tutoring System in Developing K-12 AI Literacy

Ying-Jui Tseng, Gautam Yadav, Xinying Hou, Muzhe Wu, Yun-Shuo Chou, Claire Che Chen, Chia-Chia Wu, Shi-Gang Chen, Yi-Jo Lin, Guanze Liao, and Kenneth R. Koedinger

To Appear at ECTEL 2024

Rubikon: Intelligent Tutoring for Rubik’s Cube Learning Through AR-enabled Physical Task Reconfiguration

Muzhe Wu*, Haocheng Ren*, Gregory Croisdale, Anhong Guo, Xu Wang

Michigan AI Symposium 2022 (Best Demo )

In Submission

Abstract Poster

Learning to solve a Rubik's Cube requires the learners to repeatedly practice a skill component, e.g., identifying a misplaced square and putting it back in place. However, for 3D physical tasks such as the Rubik's Cube, generating sufficient repeated practice opportunities for learners can be challenging, in part because repeated configuration of physical objects is hard and strenuous.We propose Rubikon, an intelligent tutoring system for learning to solve the Rubik's Cube. Rubikon reduces the necessity for repeated manual configurations of the Rubik's Cube without compromising the tactile experience of handling a physical cube. The foundational design of Rubikon is an AR setup, where learners manipulate a physical cube while seeing an AR-rendered cube on the screen. This allows Rubikon to automatically generate configurations of the Rubik's Cube to target learners' weaknesses and help them exercise diverse knowledge components. A between-subjects experiment showed that learners who used Rubikon scored 25% higher on a post-test to solve the Rubik's Cube compared to baselines.

Prototypes

How Do You See The World

Simulate visual impairments in VR environments to educate people about these conditions and increase awareness.

Video Code

Mini-World

Replicate the sense of co-presence in web browsing with Chrome Extension.

XR Goal Tracker

Prototype immersive XR goal tracking experience exploring concepts of personification and fading.

Code

Wiki-Learn

Enhance informal learning on Wikipedia with LLM-based instruction and assessment generation with Chrome Extension.

Code

Other Projects

Why Antiwork: A RoBERTa-Based System for Work-Related Stress Identification and Leading Factor Analysis

Leverage a subreddit as a data source, create and train a model to detect antiwork sentiments, reveal that lack of authority, frustrating recruitment experiences, and unfair compensation are major contributors to employee dissatisfaction and antiwork sentiments.

Arxiv

Towards Understanding the Relationship between Misinformation and Engagement for Online Medical Videos

Examine the feasibility of computationally understanding the relationship between misinformation and engagement from different modalities in online medical videos.

Slides

Auxiliary Variables Improve Group Accuracy without Group Information

Validate the effectiveness of auxiliary variables in the first stage of the JTT/BAM algorithm resolving the spurious correlation problem with fine-grained datasets.

Paper Code

FAD: Feature Alignment Discriminator for Text Summarization

Building block useful in the fine-tuning process for text generators like BART, which addresses problems of discreteness in adversarial learning for NLP, better captures the word distribution, and achieves SOTA ROUGE score of abstractive text summarization in DailyMail/CNN dataset.

Paper

Retro Game API for Reinforcement Learning

Jim-Team

Reinforcement learning simulation API for retro games (e.g., Pokemon Gold), built on OpenAI Gym-Retro package, featuring utility classes (recorder, interactor, and dataset), vision transform classes (random cropping, random convolution, and gaussian noise) and a GUI visualizing observation space and state information.

Mask Distribution Simulator

C++ program that simulates the distribution of masks among the cities with a certain mask production capacity during the COVID-19 pandemic.

Code