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Penghui Du

Undergraduate

Southern University of Science and Technology

🥳Aloha! My name is Penghui Du, I am a senior at the Southern University of Science and Technology, majoring in Intelligent Medical Engineering. I’m currently an undergraduate research assistant at the Martinos Center for Biomedical Imaging in Harvard Medical School.

I love coding, and I believe our brains also function according to an intricate, yet elegant, ‘code’ that enables our incredible cognitive abilities. Propelled by this notion, I am eager to delve into the field of neuroscience, aiming to uncover the underpinning ‘code’ that explains our mind and behaviour, and provide new insights into the treatment of neurological disorders.

Outside of my academic interests, I enjoy cooking, jogging, and travelling.

Experience

Undergraduate

Southern University of Science and Technology

August 2020 – June 2024 Shenzhen, China

Major: Intelligent Medical Engineering
Academic Supervisor: Dr. Quanying Liu
GPA: 3.84 / 4 (92.79), Ranking 2 / 22
2022 BME “Fortunatt” Scholarship
2022 Outstanding Student Scholarships (First Prize)

Visiting Student

University of Zurich

February 2023 – June 2023 Zurich, Switzerland

Main Subject: Neuroinformatics
Courses: Clinical Neuroscience; Comparative Behavioural Neuroscience; Computational Vision; Models of Computation; Primates Origins of Human Sociality, Cognition, and Mind
GPA: 5.3 / 6

Undergraduate Research Assistant

Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School

July 2023 – December 2023 Charlestown, Unites States

Supervisor: Dr. Jingyuan Chen (https://jechenlab.com/)
Research Focus: Human Cerebral Cortex Organization Estimated by Functional PET-FDG “Metabolic Connectivity”

Accomplishments

2022 Neuromatch Computational Neuroscience Summer School

Neuromatch Academy Jul 2022

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

Tsinghua University and Peking University Jul 2022

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

Guangdong Biomedical Engineering Association Mar 2022 – Jul 2022

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.

Department of Education of Guangdong Province Nov 2021 – May 2022

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

Projects

Human Cerebral Cortex Organization Estimated by Functional PET-FDG Metabolic Connectivity

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.

In Prep | Exploring the effect of psychotropic drugs on zebrafish brain dynamics by high-throughput calcium imaging

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.

Assessing Generalization of Cognitive Tasks Using Multi-regional Modular Recurrent Neural Networks with Transfer Learning

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.

CT image segmentation of key organs for nasopharyngeal cancer radiation therapy

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.

Publications

Youzhi Qu, Chen Wei, Penghui Du, Wenxin Che, Chi Zhang, Wanli Ouyang, Yatao Bian, Feiyang Xu, Bin Hu, Kai Du, Haiyan Wu, Jia Liu, Quanying Liu (2024). Integration of cognitive tasks into artificial general intelligence test for large models. iScience.

DOI

Youzhi Qu, Penghui Du, Wenxin Che, Chen Wei, Chi Zhang, Wanli Ouyang, Yatao Bian, Feiyang Xu, Bin Hu, Kai Du, Haiyan Wu, Jia Liu, Quanying Liu (2023). Promoting interactions between cognitive science and large language models. The Innovation.

DOI

Youzhi Qu, Xinyao Jian, Wenxin Che, Penghui Du, Kai Fu, Quanying Liu (2022). Transfer learning to decode brain states reflecting the relationship between cognitive tasks. In International Workshop on Human Brain and Artificial Intelligence.

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