Hi, this is Cecelia!!

I’m an enthusiastic learner, while my core motivation is

**How do human adapt to tools?**

By tools, I mean everything created by human to describe or control the world, from symbolics to AI.
By adapt, I mean more than learning to use, but also how tools and human reshape each other in such process.

This has many sub-questions and also requires building up apparatus, and each leads to new sub-questions, e.g.

Outside the lab, I'm always enjoying literatures/mangas/films (How creative could human be?!),
discussing with friends, and creating artworks
for my favorite characters (you might’ve come across me somewhere on the Internet :D)

Education

2023-2027(Expected)

B.E. candidate in Computer Science, University of Science and Technology of China

Research experience

I struggled a long way to find my research interests, and I am grateful to all the mentors who guided me through the process. Here are some of my research experiences:

Exploring the Task Space of Embodied Assistance

Human Computer Integration Lab · University of Chicago

Advisor: Prof. Pedro Lopes Ongoing

  • Developing visual closed-loop control for parallel electrical muscle stimulation.
  • Conducting behavioral and user studies with blind and low-vision participants.
Longitudinal Brain MRI Prediction via Diffeomorphic Deep Learning

National Engineering Lab for Brain-inspired Intelligence Technology

Advisor: Prof. Aiping Liu

  • Designed DRIFT, a two-stage framework for predicting follow-up brain MRIs from baseline scans using a conditional 3D U-Net to generate diffeomorphic velocity fields.
  • Achieved a 78–83% MSE reduction over state-of-the-art methods on 3,926 UK Biobank longitudinal pairs, with interpretable Jacobian maps and velocity fields localizing regional atrophy.
  • Independently led problem formulation, model design, all experiments, and manuscript writing.
mmWave Radar-Based Passive Sensing and Communication System

Lab for Intelligent Networking and Knowledge Engineering

Advisor: Prof. Yubo Yan

  • Conducted electromagnetic simulations and parameter optimization for radar sensing; designed and validated hardware prototypes.
LLM-Empowered Memory Management Optimization Framework

Operating Systems (H) · Course project

Advisor: Prof. Kai Xing · Code ↗

  • Led a five-person team to build a predictive framework using locally deployed Llama 3-8B-Instruct to anticipate user behavior for system-level memory pre-allocation, achieving approximately 70% accuracy on web tasks.
VEGFR-like Surface Display on E. coli for Tumor Inhibition

iGEM · Presented at the Grand Jamboree, Paris

Advisor: Prof. Jiong Hong

  • Contributed to experimental design and data analysis; led scientific communication and the team presentation.