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\usepackage[
    pdftitle={Keshav Anand's RSI Application},
    pdfauthor={Keshav Anand},
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% Ensure that generate pdf is machine readable/ATS parsable:
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% new command for external links:
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\begin{document}
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    \begin{header}
        \textbf{\fontsize{24 pt}{24 pt}\selectfont Keshav Anand — RSI Application}

    %     \vspace{0.3 cm}

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    \end{header}

    \vspace{0.2 cm}


    \section{1. Why did you choose these research fields?}

        \vspace{0.2 cm}


        
        \begin{onecolentry}
            \textbf{Prompt: }Articulate why the research fields chosen on the previous page are intriguing and exciting to you. For each sub-field, state what you perceive as the one or two most interesting questions or problems in this area.  Explain why these sorts of questions interest you. Your responses are shared with mentors. Please respond with clarity and specificity, including what specific prior research/coursework/etc experiences have prepared you to ``hit the ground running'' in these fields at RSI.

        \end{onecolentry}

        \vspace{0.2 cm}

        \begin{onecolentry}
            \begin{highlights}
                \item Field 1: Computer Science — Machine Learning for Signal Processing
                \item Field 2: Robotics/Mechatronics — Autonomous Motion Planning
            \end{highlights}
        \end{onecolentry}

        \vspace{0.2 cm}


        \begin{onecolentry}
            \textbf{Limit: 5000 Characters}

        \end{onecolentry}


        \vspace{0.2 cm}

         \newcommand{\mytext}{Firstly, I find Computer Science deeply intriguing as it is the perfect crossover between my two passions of
            mathematics and problem-solving. The ability to optimize a computer chip to solve a pratical problem has 
            always fascinated me, and the rising popularity of Machine Learning and Artificial Intelligence further
            piqued my interest during the COVID-19 pandemic. My research project from 2024 - 2025 started my journey
            with signal processing, as I had worked on a project to predict Freezing of Gait (FoG) episodes in Parkinson's
            Disease patients using a belt-mounted IMU sensor and machine learning algorithms. In addition to building
            an end-to-end hardware embedding with a custom PCB, I also developed a strong understanding of signal processing
            techniques. My novel pipeline involved using fourier transforms, z-score normalization, and wavelet denoising
            to filter out noise from the raw IMU data. Unlike existing approachs that used time-domain features, I fed the
            cleaned time-series data into a 1D CNN, acting as an automatic feature extractor (with no flattening). 
            This was passed into a hybrid biLISTM with temporal and spatial attention mechanisms,
            allowing for segmented windows to be read both forwards and backwards, and a final dense layer would
            output the boolean state of whether a FoG episode was occuring.}



        \begin{onecolentry}


        \StrLen{\mytext}[\n]
        \mytext{ }(\n)

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\end{document}
