Chenyu Wang

Chenyu Wang (王晨瑜)

Email

M.S. Student at SIMIT, UCAS

Email: wangchenyu241@mails.ucas.ac.cn

Research Interests: Domain Adaptation, Intelligent Sensing, Medical AI, Transfer Learning

About Me

I am currently an M.S. student in Electronic Information at the Shanghai Institute of Microsystem and Information Technology (SIMIT), University of Chinese Academy of Sciences (UCAS). My research focuses on domain adaptation and intelligent sensing, including gas-sensor drift compensation, cross-dataset transfer learning, and LLM-based clinical triage agents. I received my B.Eng. degree in Electrical Engineering and Automation from Xi'an University of Technology in 2023.

Publications

[1]
UnifiedGas: End-to-End Unsupervised Domain Adaptation via Hierarchical Multi-Level Alignment for Drift-Robust Gas Classification, IEEE Sensors Journal, under review (First author)
[2]
CDDA-Gas: Cross-Dataset Domain Adaptation for Metal-Oxide Gas-Sensor Drift Compensation, ACS Sensors, manuscript in progress (First author)

Research Experience

LLM-based Intelligent Triage for Ophthalmology

2026.03 — 2026.05

Collaboration project with Shanghai AI Laboratory

  • Developed a nurse-style triage model for online ophthalmology consultation and pre-clinical screening.
  • Designed symptom-aware training data and multi-turn decision workflows, followed by SFT and Online RL experiments.
  • Improved triage accuracy to 69.15% while strengthening safe routing for high-risk complaints.

Domain Adaptation for Gas-Sensor Drift Compensation

2024.09 — 2025.09

Graduate research · SIMIT, UCAS

  • Studied cross-batch drift compensation and cross-dataset transfer for long-term gas-sensor deployment.
  • Proposed UnifiedGas and CDDA-Gas to improve distribution alignment and class discriminability.
  • Achieved 81.05% average accuracy on a public gas-sensor drift dataset.

Gas-Sensor Design for Eco-friendly Insulating Gases

2022.03 — 2023.03

Undergraduate research · Xi'an University of Technology

  • Designed a micro gas sensor for fault diagnosis in high-voltage insulation systems.
  • Used a fluorine-sensitive chlorophyll probe array as the sensing layer, with submicron probe structures fabricated through plasma-enhanced chemical vapor deposition.
  • Validated the material and device design for online monitoring scenarios.

Education

University of Chinese Academy of Sciences (UCAS)

M.S. in Electronic Information

Shanghai Institute of Microsystem and Information Technology (SIMIT)

2024.09 — 2027.06

Xi'an University of Technology

B.Eng. in Electrical Engineering and Automation

School of Electrical Engineering

2019.09 — 2023.07

Skills & Honors

Technical Skills

  • Python, PyTorch, data processing and model training
  • Domain adaptation, transfer learning and experiment evaluation
  • Scientific writing, reproducible experiments and ablation studies

Selected Honors

  • Merit Student, University of Chinese Academy of Sciences, 2026
  • First-class Scholarship, Xi'an University of Technology
  • Outstanding Student Leader, Xi'an University of Technology