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    pdftitle={Keshav Anand's RSI Application},
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\begin{document}
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    \begin{header}
        \textbf{\fontsize{24 pt}{24 pt}\selectfont Keshav Anand — RSI Application}

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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}{It was during a COVID-19 YouTube binge where I was first introduced to the art of modern 
            computer. After stumbling into a rabbit hole of videos explaining the inner-workings of a machine,
             I immediately fell in love with Computer Science.
            Over the years, I have also developed a specialized interest in signal processing and its applications.
            My curiousity in this topic stems from my first Math Club meeting in 9th grade, where a senior officer
            had explained the fascinating science of how radio signals are transmitted and received using Fourier transforms.
            These concepts overlapped with my learning of fascinating Calculus concepts, and I was amazed at how signals can 
            be analyzed and processed. Today, the major question that excites me within the field of Signal Processing is how to effectively
            adapt digital signal processing and machine learning for real-time resource-constrained embedded systems. 
            As a hardcore robotics enthusiast, I have always been not just interested in the theoretical software,
            but also the practical hardware embedding of these algorithms. This really sparked my interest in the field 
            of signal processing for embedded systems, prompting me to start a 2 year research project that would become the 
            focus of my life. 
            My ISEF-winning research project from 2024 - 2025, GaitGuardian, was my first major experience 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. 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 in real time. The learning I gained from this research led 
            me to pursue other related signal processing tasks to boost the final product for Parkinson's patients. 
            Researching other Parkinsonian symptoms led me to explore tremor detection (uncontrolled shaking of the hands), 
            and I implemented a real-time tremor detection model that involved a bandpass butterworth filter to isolate tremor frequencies
            between 4-6 Hz, followed by an FFT to extract frequency-domain features. These features were then fed into a lightweight
            1D CNN, resulting in state-of-the-art 99\% accuracy while limiting false positives. I also looked into signal processing 
            within my FTC robotics team, realizing that IMU data could be used to improve odometry and localization. I implemented a 
            custom Kalman filter to fuse IMU data with wheel encoder reading, significantly reducing drift during autonomous navigation 
            and reducing error buildup over time. 
            My second major interest has been in the field of Robotics, springing from a lucky acceptance into my 
            Middle School's robotics team in 6th grade. Due to COVID-19, out team had to start from scratch, and as a 
            completely inexperienced 7th grader, it took me 7 months to simply learn to spin a motor. The same fascination 
            I had with computers was now being applied to physical hardware, and I have been a loyal participant in 
            the First Tech Challenge (FTC) robotics competition. As the software lead of my globally ranked team,
            Technical Turbulence FTC, I have learned a lot about the algorithms that empower robots during the 
            30-second autonomous period of the competition. Today, I am intrigued by two major research questions within 
            the field of autonomous motion planning. First, I wonder how multiple autonomous agents can effectively
            coordinate in real-time to achieve a common goal while avoiding collisions. This question fascinates me
            because it combines elements of path planning, communication protocols, and decision-making under uncertainty.
            Secondly, I am fascinated by the question of whether autonomous robots and vehicles can learn optimal paths from 
            experience rather than relying on pre-programmed maps. This idea of reinforcement learning for motion planning 
            excites me because it provides a pathway for devices to improve performance over time in dynamic environments.
            My experience with robotics has provided me with a strong foundation to tackle these questions, as I have 
            designed and implemented a custom pathing algorithm for my FTC robot. The motion profiling algorithm I developed 
            uses cubic and quintic splines to generate smooth trajectories between points, using inverse kinematics and a PID 
            controller to accurate follow the path. By prioritizing endpoint accuracy over time and path accuracy, our robot's 
            pathing is extermely precise, resulting in a top-30 autonomous ranking globally. Outside FTC, I worked on a passion 
            project to allow for pathing of two vaccuum robots in a shared environment. Using A* for initial pathfinding and
            a custom potential fields algorithm for real-time obstacle avoidance, I made a software system that allowed 
            for efficient cleaning of a dynamic space. Together, my experience in robotics software, signal processing, and 
            machine learning have prrepared me to hit the ground running at RSI, and I am excited to further explore these research questions
            with the help of expert mentors and resources.}


            

        \begin{onecolentry}


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

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