"Sleek, hands-free electric longboard that accelerates, brakes as riders shift weight"

###### Creating an aesthetic, fully functional, pressure controlled, electric longboard was a highlight of my senior year.

###### It winning the top prize out of all 60+ teams in that year's *Engineering in George R. Brown Engineering Design Showcase* was the icing on the cake.

[Click here to watch our board in action!](https://www.youtube.com/watch?v=pEpF\_1QBUQ0\&ab_channel=RiceUniversity)

[Click here for the official news article on our project](https://news.rice.edu/news/2022/rice-u-students-develop-sleek-hands-free-electric-longboard-accelerates-brakes-riders)



Using personalization and Large Language Models to generate tweets conditioned on user personality, politeness, and topics

Article In Progress.

Please reference the completed github Project: <https://github.com/Qulxis/StyleTransfer_SpeechGen>


Conditioning Tweet generation

Using deep-learning to diagnose Covid-19 cases from patient audio

<div style="display: flex; justify-content: center;">
    <img src="/images/Example%20of%20Mel%20Spectro%20Covid%20posititve.png" alt="Mobile Example 1" width="500px">
</div>

### Paper and  Repository:

**Please refer to the paper for in depth information about this project**

*   [Link to Paper](https://github.com/aem713/COVIDNet/raw/main/Reports/DSCI%20400%20-%20COVIDNet%20-%20Final%20Report.pdf)

*   [Link to Repository](https://github.com/aem713/COVIDNet)

### Description

At the start of 2021, the covid-19 pandemic was in full swing. In response, our junior data science team endeavored to diagnose covid-19 from patient cough audio using deep learning. We used a  labeled set of 10,000+ recordings of patients coughing and breathing. Our fundamental approach was to transform the audio recordings in Mel-Spectrograms: 2-dimensional frequency graphs in the Mel scale as shown above. We then used those as inputs to train a convolutional neural network to run in parallel with a Single-Layer Perceptron that used handcrafted features as inputs.

<div style="text-align: center">Figure 1: CNN Structure Used</div>

<div style="display: flex; justify-content: center;">
    <img src="/images/cnn_diagram_horiz3.png" alt="Mobile Example 1" width="500px">
</div>

<div style="text-align: left"></div>

<div style="text-align: center">Figure 2: Aggregate Model</div>

<div style="display: flex; justify-content: center;">
    <img src="/images/ending_diagram.png" alt="Mobile Example 1" width="400px">
</div>

Our project went through the full data science pipeline. By wrangling the data, we converted the data into usable training, testing, and validation sets.

<div style="text-align: center">Figure 3: Reorganization of Data</div>

<div style="display: flex; justify-content: center;">
    <img src="/images/fileorg.png" alt="Mobile Example 1">
</div>
Through data exploration, we carefully analyzed the demographic and symptom association of the patients to better understand patterns and better understand feature importance. By looking at previous work, we found promise in using ImageNet for transfer learning to accommodate for our dataset's relatively small size. We documented our work using [Github](https\://github.com/aem713/COVIDNet) and wrote a final research paper on our project. This project proved to be a valuable experience personally as we had guidance from Dr. Arko Barman to ensure proper procedure and direction.

COVIDNet was my first start-to-finish data science project.


Breadboard

Creating an aesthetic, fully functional, pressure controlled, electric longboard was a highlight of my senior year.

It winning the top prize out of all 60+ teams in that year's Engineering in George R. Brown Engineering Design Showcase was the icing on the cake.