☕️Hi! I’m Penghui, currently pursuing a Master’s degree in the Neuro-X program at EPFL. My research interests focus on neuroimaging data analysis and neuromodulation techniques. I plan to pursue a PhD and am passionate about translating insights from neuroimaging to benefit clinical practice and disease research.
Experience
Visiting Graduate Student
- Supported by EPFL/HMS Bertarelli Fellowship.
- Supervisor: Prof. Randy Buckner (https://bucknerlab.fas.harvard.edu/).
- Research Project: Investigating precision brain mapping for personalized transcranial magnetic stimulation by comparing empirical, group-level, and individualized targeting strategies and assessing their relative benefits and practical trade-offs.
- Summer Intern at Cognitive Neurogenetics Lab (https://cng-lab.github.io/), supervised by Dr. Bin Wan and Prof. Sofie Valk.
- Research Project: Developed a deep learning framework to predict individual brain glucose metabolism from structural and functional MRI features, demonstrating that glucose metabolism can be potentially inferred from MRI-based representations.
Master Student in Neuro-X
- GPA: 5.40 / 6
- EPFL/HMS Bertarelli Fellowship.
- 2025/09 - 2026/01: Semester Project at MIP Lab, supervised by Michael Chan and Prof. Dimitri Van De Ville. Characterized structure-informed functional connectivity using statistical signal analysis on graphs.
- 2025/02 - 2025/06: Semester Project at MetMRS Group, supervised by Dr. Zhiwei Huang and Prof. Lijing Xin. Analyzed functional glutamate mapping under visual tasks and compared with activity patterns derived from BOLD-fMRI.
- 2025/02 - 2025/06: Lab Immersion at Laboratory of Sensory Processing, supervised by Dr. Sylvain Crochet and Prof. Carl Petersen. Developed a computational model of mouse licking behavior incorporating motivation, expectation, exploration, and cost-related priors.
Undergraduate Research Assistant
- Supervisor: Prof. Jingyuan Chen (https://jechenlab.com/).
- Research Project: Investigated the cortical organization of resting-state metabolic connectivity (fPET-FDG), identifying a robust superior–inferior gradient driven by low-frequency dynamics and aligned with known functional and anatomical organization.
Visiting Student in Neuroinformatics
BSc in Intelligent Medical Engineering
- Academic Supervisor: Dr. Quanying Liu.
- GPA: 3.84 / 4 (92.79), Ranking 2 / 22.
- 2024 Distinguished Graduate Award.
- 2022 BME “Fortunatt” Scholarship.
- 2022 SUSTech Outstanding Student Scholarship.
Accomplishments
2022 Neuromatch Computational Neuroscience Summer School
I studied computational neuroscience fundamentals such as reinforcement learning, leaky Integrate-and-Fire models, Hodgkin-Huxley models with my teammates. We then conducted an project on RNN and working memory, and presented our results to other teams.
See certificate
Merit Student of CLS-CIBR-IDG Summer School in Neuroscience
I attended various neuroscience lectures in the summer school, followed by our teams presentation on a chosen paper. I was recognized with a Merit Student Award.
See certificate
First Prize in 2022 Guangdong Undergraduate Biomedical Engineering Innovation Design Competition
We designed a deep learning model, combining Transformer and UNet, for labeling the key organs involved in radiotherapy in CT images. Our unique pre-training approach ensured high segmentation accuracy and reduced computational cost, earning us first prize in the competition.
See certificate
First prize in 13th “Challenge Cup” Entrepreneurship Competition.
We designed a business plan for manufactoring seizure monitor devices for severely ill newborns, and won first prize in the competition. I am team captain in this competition, and I am responsible for proposing technical ideas and designing business plan.
See certificate![[MCOS] Human Cerebral Cortex Organization Characterized by Functional PET-FDG Metabolic Connectivity](/talk/mcos-human-cerebral-cortex-organization-characterized-by-functional-pet-fdg-metabolic-connectivity/featured_hu6d441abe02b23e55aee238c0615c4fd5_123533_150x0_resize_q75_h2_lanczos.webp)
![[Science on Tap] The Organization of Human Cerebral Cortex Estimated by Functional PET-FDG: The Promise and Controversy of Metabolic Connectivity](/talk/science-on-tap-the-organization-of-human-cerebral-cortex-estimated-by-functional-pet-fdg-the-promise-and-controversy-of-metabolic-connectivity/featured_hu87f6e7cf3846a1ffd1f9405ec4c70b41_120771_150x0_resize_q75_h2_lanczos.webp)
Publications
(2026).
Data-Driven Methods in Simultaneous EEG-fPET-fMRI Opportunities and Challenges.
2025 59th Asilomar Conference on Signals, Systems, and Computers.
(2026).
Human Cerebral Cortex Organization Characterized by Functional PET-FDG “Metabolic Connectivity”.
BioRxiv.
(2024).
Integration of cognitive tasks into artificial general intelligence test for large models.
iScience.
(2023).
Promoting interactions between cognitive science and large language models.
The Innovation.
(2022).
Transfer learning to decode brain states reflecting the relationship between cognitive tasks.
In International Workshop on Human Brain and Artificial Intelligence.
Gallery
Contact
- penghui-du@outlook.com / penghui.du@epfl.ch
- +41 77 211 89 07 / +86 158 8937 2606
- 4 Rue Favre-Louis, Ecublens, Vaud 1024