|Full Name ||Zhijie Dong |
|Address ||4031 Beckman Institute, 405 N. Mathews Ave., Urbana, IL 61801 |
|Email ||zhijied3 AT illinois DOT edu |
2019 – 2023
Ph.D. in Electrical and Computer Engineering
University of Illinois Urbana-Champaign, Urbana, IL
- Advisor: Prof. Pengfei Song
- Research Interests: 3D ultrafast ultrasound imaging, deep learning in medical imaging, signal & imaging processing
- GPA: 4.0/4.0
2017 - 2018
M.S. in Electrical and Computer Engineering
University of Michigan, Ann Arbor, MI
- Sub Field: Signal & Image Processing and Machine Learning
- GPA: 4.0/4.0
2013 - 2017
B.Eng. in Information Engineering
Southeast University, Nanjing, China
- Honor Student in Chien-Shiung Wu College
- GPA: 3.7/4.0
Research & Projects
2019 - Present
Ultrafast 3D Ultrasound Imaging Using Fast-tilting and Redirecting Re- flectors
Advisor: Prof. Pengfei Song, University of Illinois Urbana-Champaign
- Proposed a new 3D ultrasound imaging technique called Fast Acoustic Steering via Tilting Electromechanical Reflectors (FASTER).
- Achieved high volume-rate 3D ultrasound imaging using FASTER with conven- tional 1D array transducers.
- Apply FASTER 3D imaging in different imaging modalities such as shear wave imaging and ultrasound microvessel imaging
Nonparametric Preference Completion with Pairwise Preference
Advisor: Prof. Clayton Scott, University of Michigan
- Used a k-nearest neighbors-like algorithm to implement preference completion with pairwise preference in a nonparametric setting.
- Established a probability bound of ranking mistakes, which tends to zero in the limiting situation.
Histotripsy System Implementation
Advisor: Prof. Zhen Xu and Dr. Tim Hall, University of Michigan
- Implemented the receiving part of the next generation of Histotripsy system that includes both transmit and receive capability for Non-invasive Ultrasonic Tissue Surgery.
- Used FPGA and HPS to implement ultrasound signal conversion, processing, and transmission with high speed and resolution.
Machine Learning Based Link Adaptation for MIMO System
Advisor: Prof. Xiqi Gao and Prof. Wenjin Wang, Southeast University
- Proposed a link adaptation scheme in MIMO-OFDM systems through machine learning algorithms to maximize spectral efficiency while maintaining transmis- sion reliability.
- Used Autoencoder architecture to extract features from channel state informa- tion (CSI) and exploited intrinsic connection between measurement data and adaptation scheme.
Honors and Awards
- Knight Fellowship, UIUC
- Spring 2022 conference travel award, UIUC
- President Scholarship, Southeast University
- Zhiwei Zhang Scholarship, Southeast University