☕️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: Dr. Randy Buckner (https://bucknerlab.fas.harvard.edu/).
- Summer intern at Cognitive Neurogenetics Lab (https://cng-lab.github.io/).
- Supervised by Dr. Bin Wan and Dr. Sofie Valk.
- Research Project: A deep learning pipeline for predicting individual brain glucose metabolism from geometry, microstructure, and hemodynamics.
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 Dr. Dimitri Van De Ville. We explored how to characterize structure-informed functional connectivity via statistical signal analysis on graphs.
- 2025/02 - 2025/06: Semester project at MetMRS Group, supervised by Dr. Zhiwei Huang and Dr. Lijing Xin. We explored statistical analysis of functional glutamate mapping under visual tasks, and compared to brain activity patterns derived from BOLD-fMRI.
- 2025/02 - 2025/06: Lab immersion at Laboratory of Sensory Processing, supervised by Dr. Sylvain Crochet and Dr. Carl Petersen. We developed a computational model on mouse licking behaviour, incoporating various biological priors includuing motivation, expectation, exploration and behaviour cost.
Undergraduate Research Assistant
- Supervisor: Dr. Jingyuan Chen (https://jechenlab.com/).
- Research Project: Human Cerebral Cortex Organization Estimated by Functional PET-FDG “Metabolic Connectivity”.
Regular 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 certificateProjects
In this study, we applied a local-global analytical framework to a resting-state simultaneous fPET-FDG and BOLD-fMRI dataset to delineate the cortical organization of resting-state metabolic connectivity.
![[OHBM 2024] Human Cerebral Cortex Organization Estimated by Functional PET-FDG Metabolic Connectivity](/project/human-cerebral-cortex-organization-estimated-by-functional-pet-fdg-metabolic-connectivity/featured_hu6e5488439624d870e067fc8241241570_2974244_540x0_resize_q75_h2_lanczos_3.webp)
In this study, we collected calcium imaging data from zebrafish under various drug conditions using epifluorescence microscope, and explored the effects of distinct drugs on the activity and connectivity of brain networks.
In this study, we proposed a multi-regional modular recurrent neural network to simulate the cognitive processes. The model is structured into three different modules: perception, information integration, and decision. Here
![[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).
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