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resume/rsi-application/main.tex

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\documentclass[10pt, letterpaper]{article}
% Packages:
\usepackage[
ignoreheadfoot, % set margins without considering header and footer
top=2 cm, % seperation between body and page edge from the top
bottom=2 cm, % seperation between body and page edge from the bottom
left=2 cm, % seperation between body and page edge from the left
right=2 cm, % seperation between body and page edge from the right
footskip=1.0 cm, % seperation between body and footer
% showframe % for debugging
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\usepackage{titlesec} % for customizing section titles
\usepackage{tabularx} % for making tables with fixed width columns
\usepackage{array} % tabularx requires this
\usepackage[dvipsnames]{xcolor} % for coloring text
\definecolor{primaryColor}{RGB}{0, 79, 144} % define primary color
\usepackage{enumitem} % for customizing lists
\usepackage{fontawesome5} % for using icons
\usepackage{amsmath} % for math
\usepackage[
pdftitle={Keshav Anand's RSI Application},
pdfauthor={Keshav Anand},
pdfcreator={LaTeX with RenderCV},
colorlinks=true,
urlcolor=primaryColor
]{hyperref} % for links, metadata and bookmarks
\usepackage[pscoord]{eso-pic} % for floating text on the page
\usepackage{calc} % for calculating lengths
\usepackage{bookmark} % for bookmarks
\usepackage{lastpage} % for getting the total number of pages
\usepackage{changepage} % for one column entries (adjustwidth environment)
\usepackage{paracol} % for two and three column entries
\usepackage{ifthen} % for conditional statements
\usepackage{needspace} % for avoiding page brake right after the section title
\usepackage{iftex} % check if engine is pdflatex, xetex or luatex
\usepackage{xstring}
% Ensure that generate pdf is machine readable/ATS parsable:
\ifPDFTeX
\input{glyphtounicode}
\pdfgentounicode=1
% \usepackage[T1]{fontenc} % this breaks sb2nov
\usepackage[utf8]{inputenc}
\usepackage{lmodern}
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% Some settings:
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\patchcmd{\ps@customFooterStyle}{\thepage}{
\color{gray}\textit{\small Keshav Anand - Page \thepage}
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% left space:
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% top space:
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% bottom space:
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\newcommand{\placelastupdatedtext}{% \placetextbox{<horizontal pos>}{<vertical pos>}{<stuff>}
\AddToShipoutPictureFG*{% Add <stuff> to current page foreground
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\small\color{gray}\textit{Last updated in December 2025}\hspace{\widthof{Last updated in December 2025}}
}}}%
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% save the original href command in a new command:
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% new command for external links:
\renewcommand{\href}[2]{\hrefWithoutArrow{#1}{\ifthenelse{\equal{#2}{}}{ }{#2 }\raisebox{.15ex}{\footnotesize \faExternalLink*}}}
\begin{document}
\newcommand{\AND}{\unskip
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\begin{header}
\textbf{\fontsize{24 pt}{24 pt}\selectfont Keshav Anand — RSI Application}
% \vspace{0.3 cm}
% \normalsize
% \mbox{{\color{black}\footnotesize\faMapMarker*}\hspace*{0.13cm}Your Location}%
% \kern 0.25 cm%
% \AND%
% \kern 0.25 cm%
% \mbox{\hrefWithoutArrow{mailto:youremail@yourdomain.com}{\color{black}{\footnotesize\faEnvelope[regular]}\hspace*{0.13cm}youremail@yourdomain.com}}%
% \kern 0.25 cm%
% \AND%
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% \mbox{\hrefWithoutArrow{tel:+90-541-999-99-99}{\color{black}{\footnotesize\faPhone*}\hspace*{0.13cm}0541 999 99 99}}%
% \kern 0.25 cm%
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% \kern 0.25 cm%
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% \mbox{\hrefWithoutArrow{https://github.com/yourusername}{\color{black}{\footnotesize\faGithub}\hspace*{0.13cm}yourusername}}%
\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{\promptA}{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{\promptA}[\n]
\promptA{ }(\n)
\end{onecolentry}
\section{2. What are your long term goals?}
\begin{onecolentry}
\textbf{Limit: 5000 Characters}
\end{onecolentry}
\vspace{0.3 cm}
\newcommand{\promptB}{Every word amazed me. ChatGPT did not feel like yet another website so much
as a magical portal to infinite knowledge. I still remember the thrill when my computer science teacher
unveiled the mystical tool that could ace our rock-paper-scissors coding exam in under a minute. Little did I
realize that in less than three years, AI would touch everything I do —
from school projects to software debugging, even revising this very essay. But it was in the Summer of 2023 that
my best friend asked me a life-changing question: ``How does it work?'' I was completely clueless, and I told him
that a stenographer was typing at the other end. As months passed and my AI usage expontially increased,
this question haunted me. I was determined not to be a mere user, but to understand and develop the magic
behind the curtains.
\\\\
That Summer was when I embarked on my first long-term goal: to fully understand the
inner workings of the devices and programs I use. My journey in achieving this started when I joined my
robotics team, Technical Turbulence. As the sole software member, I learned everything from scratch.
I started by dissecting each wire, realizing that understanding hardware was crucial to mastering software.
From PWM control to I2C communication, I slowly worked up the layers of abstraction, eventually
coming to the software that I was responsible to write. Today, I continue my pursuit of deep technical knowledge
through my independent research projects. In my ninth grade, I decided to grasp the fascinating concept of a
thermoelectric generator. My research eventually culminated in a novel prototype that utilized the fascinating
Seebeck effect to automatically stir a cooking pot. The following year, I dove into high-level signal processing
and machine learning concepts to develop GaitGuardian, my ISEF-winning Parkinson's research project.
Through these experiences, I have learned that true mastery comes from understanding the layers beneath the surface,
which is why I am committed to this lifelong goal of deep technical knowledge. As I continue to uncover more
information, I continue pursuing this goal through passion projects. My latest endeavor of hosting a full
server on an old chromebook is pushing me to learn Linux system administration, networking, and cybersecurity. The final
website code is the same, but the knowledge I gain from understanding the server-side is invaluable. It is with this goal
in mind that I approach RSI, eager to learn from experts and deepen my understanding of computer science.}
\begin{onecolentry}
\StrLen{\promptB}[\n]
\promptB{ }(\n)
\end{onecolentry}
\end{document}