DataRes F22 Research Coding Assessment

Overall, just head over to ResearchApps.ipynb, click on the "Open in Colab" button, save a copy of the notebook, and get to coding! After you're done, send me the file at irsyadadam@g.ucla.edu with the subject "DataRes Research Applications," and you should get a confirmation email back if I'm free.

Remarks from Research Head

hihihihi my name is Irsyad, and I'm the Research Head this year!

I think for this quarter, we're either going to do object detection or NLP using BERT from the 2018 paper. If you don't know what any of these words mean, or are nervous regarding the work being done, don't worry!

I will teach you everything from the ground up. You just have to put in the effort and show that you want to learn.

I just want to push this message across extremely heavily: we are not approaching these deep learning projects from the perspective of a data analyst or programmer; we are approaching these projects with the mindset of a researcher. Just like any biologist or scientist, we are going to dive deeply into novel literature regarding our field, and take existing technologies and extend them into applications of our liking. Just like a biologist looks at past studies, we will also look at recent studies regarding deep learning, and learn from them.

That is what makes the DataRes Research team different from other data science organizations. We are scientists first, and then programmers.

If you don't like this format, feel free to join when I am gone :) Other than that, hope to see you on the team!

Thanks, Irsyad Adam

DataRes Research Team Meeting Overviews:

We will meet for at most 2 hours each week, where our meetings will consist of me teaching for the first half, and everyone applying this knowledge to their projects the second half. Working outside of these meetings is expected for you to present a good presentation at the end of the quarter.

COMPUTER REQUIREMENTS:

• Python!
• a command line interface like Anaconda Prompt/Ubuntu/OSX terminal
• a github account that you can push code to
• Jupyter Notebook (we will be testing on .ipynb, but our pipelines wil all be python files .py)
• a computer you can bring to the meetings (no dedicated gpu required)

DataRes Projects Overview:

S22: Graph Deep Learning: Integration of GCN Pipeline with Neo4j DBMS for Social Analysis

F23: Representation Learning with Transformer-based Language Models with Temporal Data

W23: Increasing the Effectiveness of Real-time Object Detection with Synthetic Dataset Generation