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Biography
Biography
Ding Zhou
Fellow, Harvard University | Academic Visitor, University of Oxford | Msc, Technical University of Munich
Ding Zhou
Fellow, Harvard University | Academic Visitor, University of Oxford | Msc, Technical University of Munich
I am a Fellow at Harvard University USA, specializing in computational neuroscience. I was an Academic visitor at Oxford University UK in 2024, and obtained my master's degree from the Technical University of Munich and Max-Planck Institute, I have studied computational neuroscience since my undergraduate studies at the University of Göttingen Germany.
I am a Fellow at Harvard University USA, specializing in computational neuroscience. I was an Academic visitor at Oxford University UK in 2024, and obtained my master's degree from the Technical University of Munich and Max-Planck Institute, I have studied computational neuroscience since my undergraduate studies at the University of Göttingen Germany.
Research
Research
My work focuses on reverse-engineering the brain to develop brain-inspired machine learning models, translating them into clinical applications. And linking multi-modal structure, functional, molecular and genetic information to discover latent cognitive states of brain.
My work focuses on reverse-engineering the brain to develop brain-inspired machine learning models, translating them into clinical applications. And linking multi-modal structure, functional, molecular and genetic information to discover latent cognitive states of brain.
Selected Projects
Selected Projects
1. I have attended an academic project at Tsinghua University focusing using bio-inspired algorithm hebbian learning to enhance the classical machine learning framework, and using hyperbolic topology to discover the latent geometric shape under the biological brain.
1. I have attended an academic project at Tsinghua University focusing using bio-inspired algorithm hebbian learning to enhance the classical machine learning framework, and using hyperbolic topology to discover the latent geometric shape under the biological brain.
2. I was working as a fellow at Harvard, using reinforcement learning and recurrent networks to simulate zebrafish collective behavior, mapped the artificial neural network activation onto the biological neural network focusing on the whole brain dynamic.
2. I was working as a fellow at Harvard, using reinforcement learning and recurrent networks to simulate zebrafish collective behavior, mapped the artificial neural network activation onto the biological neural network focusing on the whole brain dynamic.
3. I completed my academic visitor work "Simulation based inference for MRI diffusion models" at Oxford. I implemented different simulation-based inference methods for MRI diffusion models, then used the model output to track the connectome of the human brain.
3. I completed my academic visitor work "Simulation based inference for MRI diffusion models" at Oxford. I implemented different simulation-based inference methods for MRI diffusion models, then used the model output to track the connectome of the human brain.
4. I have attended some seminars at the Max Planck Institute of Biological Intelligence and wrote an overview of electron microscopy connectomes, including the entire pipeline from data acquisition and storage to segmentation details.
4. I have attended some seminars at the Max Planck Institute of Biological Intelligence and wrote an overview of electron microscopy connectomes, including the entire pipeline from data acquisition and storage to segmentation details.
5. I did an internship at TUM that analyzed PET brain connectivity using the FSL tool package, including white matter fiber tractography based on PET images. Then registrated PET connectome to MRI connectome.
5. I did an internship at TUM that analyzed PET brain connectivity using the FSL tool package, including white matter fiber tractography based on PET images. Then registrated PET connectome to MRI connectome.
6. I attended a practical project at TUM, during which I developed a deep learning model based on EEG data to classify neural pain.
6. I attended a practical project at TUM, during which I developed a deep learning model based on EEG data to classify neural pain.
7. I have attended a practical course at TUM, in which we have trained a model using self-supervised multimodal learning based on different MRI data, which is potentially useful for Alzheimer's classification and connectomes analysis.
7. I have attended a practical course at TUM, in which we have trained a model using self-supervised multimodal learning based on different MRI data, which is potentially useful for Alzheimer's classification and connectomes analysis.
8. I have cooperated with Oxford, Harvard and TUM, wrote an research proposal at Max Planck Institute about using the intepretable machine learning and statistics inference to research the multi modal brain connectome, integrating the molecule and cellular information.
etc.
8. I have cooperated with Oxford, Harvard and TUM, wrote an research proposal at Max Planck Institute about using the intepretable machine learning and statistics inference to research the multi modal brain connectome, integrating the molecule and cellular information.
etc.
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