☕️Hi! I’m Penghui, a first-year master’s student in Neuro-X at EPFL, with a strong passion for computational neuroimaging and neuromodulation techniques. My long-term goal is to pursue a PhD and contribute to advancements in this fascinating research field.
Experience
Master Student in Neuro-X
Undergraduate Research Assistant
- Supervisor: Dr. Jingyuan Chen (https://jechenlab.com/)
- Research Focus: 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 Outstanding Graduate
- 2022 BME “Fortunatt” Scholarship
- 2022 Outstanding Student Scholarships (First Prize)
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.
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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.
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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.
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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 certificateRecent Posts
Projects
The recent development of high-temporal resolution functional PET (fPET) introduces an emerging focus on “metabolic connectivity (MC)” , providing a complementary perspective to the hemodynamic-based “functional connectivity (FC)” assessed by fMRI. In this study, we applied a connectivity gradient-based analytical scheme on a resting-state simultaneous fPET-fMRI dataset, aiming to characterize the detailed cortical organization of fPET-derived MC and understand how it differs from the fMRI-derived functional network structures.
![[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 research project, we employed a novel high-throughput screening system to collect a vast amount of neural activity data (via calcium imaging) from zebrafish exposed to psychotropic drugs. Subsequently, we analyzed the data to explore the influence of psychotropic drugs on the activity across various brain regions, as well as the functional connectivity within the zebrafish’s brain.
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 a transfer learning approach is adopted to investigate generalizability across tasks. After training models on source tasks, we fixed the information integration layers, transferred the models to target tasks, and tested their performance. By comparing the performance of different source-target task pairs, we assessed the similarity between different cognitive tasks.
Nasopharyngeal cancer is a serious tumor in the upper pharynx, requiring careful outlining of key organs for radiotherapy planning. Traditionally, this is done manually on CT images, a slow, labor-intensive process reliant on the physician’s experience. To improve this, we use a pre-trained 3D model and an enhanced TransUNet network to automate segmentation of four key organs with impressive results.
![[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
(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.
Popular Topics
Artificial General Intelligence
boundary mapping
Cognitive Science
Cognitive tasks
Computational Neuroscience
Computed Tomography
Data Analysis
Data Processing
Deep Learning
Electrophysiology
fMRI
Image Segmentation
Large Language Model
Machine Learning
Medical Image Processing
metabolism
Monkey
Neuroimaging
Neuropharmacology
Transfer Learning
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