Data and Proposal Questions for A Framework for Unsupervised Deep Clustering

[YuruJing]

Hello everyone,

Nice to meet you all!

I’m Yuru Jing (Bertie) from China, and I recently completed my postgraduate studies in Data Science and Machine Learning at University College London. Prior to this, I obtained a Bachelor's degree in Mathematics with a minor in Finance from institutions in Wisconsin and Illinois, graduating in 2020.

During my last two years of undergraduate, I delved into machine learning and mathematical modelling, particularly focusing on its applications in biomedicine and healthcare. I spearheaded projects such as machine learning clustering for Iris Species in Intercollegiate Biomathematics Alliance and conducted research and dissertation on Statistical Modeling of SARS-Cov-2 Mutation which I presented at the 2021 International Symposium on Biomathematics and Ecology Education and Research.

In my postgraduate studies, I refined my expertise in AI for healthcare, contributing to publications such as "Machine Learning Performance Analysis to Predict Stroke" and "Auto-outlier Fusion Technique for Chest X-ray Classification." Moreover, I collaborated with Moorfields Eye Hospital NHS Foundation Trust for my final dissertation, delving into "New Designs to Predict Refractive Error From Retinal Fundus Images using Deep Learning." I found great passion in addressing complex healthcare challenges, including multimodal features extraction and fusion, semi-supervised clustering, and imbalanced classification.

I am particularly drawn to the "A Framework for Unsupervised Deep Clustering" project, and I believe my related skills and experiences as follows would be highly beneficial to make a contribution fot this project.

Highlights:

Deconvolution-based image restoration and Neural Spike Simulation
Autoencoder networks, transfer Learning, Multi-task Learning
Imbalanced classification, semi-supervised clustering in healthcare (structured and unstructured data)
Multimodal feature extraction, segmentation, augmentation and fusion (IMUs motion capture, EMGs, etc.)
I am enthusiastic about engaging with this community and eager to explore potential collaborations. If anyone shares an interest in this project, I am keen to further discuss, learn from each other, and collaborate effectively.

Github: https://github.com/YuruJing
LinkedIn: https://www.linkedin.com/in/yurujing/
Email: bertiejing@gmail.com

[YuruJing]

Hi @zeydabadi ,

Hope you are all well.

After reviewing the introduction of the project, "A Framework for Unsupervised Deep Clustering," I have some questions regarding the dataset and application requirements. I believe clarifying these points would benefit not only myself but also other interested contributors.

About Dataset

In my general search of available open EEG datasets, I've noticed that they typically consist of hybrid spectral, temporal, spatial, or demographic features alongside raw sensor data. Could you please clarify if we will be using raw sensor data exclusively, extracting features such as spectral and temporal features from it? Or will we be working with directly mixed features (spectral features + raw sensor data for example) or multimodal data? Additionally, could you provide a good example EEG dataset if possible?

About CV/Proposal

I've reviewed the application template and have questions regarding certain sections:

• For the Abstract/Project Summary, should we simply describe our ideas and methodologies similar to a general publication abstract? Or should we include references to support our ideas and methodologies?

• Regarding the Personal Background (Brief CV) section, should we submit a one-page brief CV, or should we provide a paragraph similar to our self-introduction in the discussion forum? Is there a specific length requirement for this section?

• The overall application template consists of 10 parts. Are there any page limitations for each section? What range of pages is recommended for the entire application?

Your clarification on these matters would be greatly appreciated. :)

Thank you for your assistance!