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<!DOCTYPE html>
<html lang="zxx">
<head>
<!-- metas -->
<meta charset="utf-8">
<meta name="author" content="themepaa">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<meta name="keywords" content="Daniel Stabile">
<meta name="description" content="Daniel Stabile's Professional Website">
<!-- title -->
<title>Daniel Stabile</title>
<!-- Favicon -->
<link rel="shortcut icon" type="image/x-icon" href="favicon.svg">
<!-- Critical CSS -->
<style>
@font-face{font-family:"Font Awesome 5 Brands";font-style:normal;font-weight:400;font-display:swap;src:url(/static/plugin/font-awesome/webfonts/fa-brands-400.eot);src:url(/static/plugin/font-awesome/webfonts/fa-brands-400.eot) format("embedded-opentype"),url(/static/plugin/font-awesome/webfonts/fa-brands-400.woff2) format("woff2"),url(/static/plugin/font-awesome/webfonts/fa-brands-400.woff) format("woff"),url(/static/plugin/font-awesome/webfonts/fa-brands-400.ttf) format("truetype"),url(/static/plugin/font-awesome/webfonts/fa-brands-400.svg) format("svg")}@font-face{font-family:"Font Awesome 5 Free";font-style:normal;font-weight:400;font-display:swap;src:url(/static/plugin/font-awesome/webfonts/fa-regular-400.eot);src:url(/static/plugin/font-awesome/webfonts/fa-regular-400.eot) format("embedded-opentype"),url(/static/plugin/font-awesome/webfonts/fa-regular-400.woff2) format("woff2"),url(/static/plugin/font-awesome/webfonts/fa-regular-400.woff) format("woff"),url(/static/plugin/font-awesome/webfonts/fa-regular-400.ttf) format("truetype"),url(/static/plugin/font-awesome/webfonts/fa-regular-400.svg) format("svg")}@font-face{font-family:"Font Awesome 5 Free";font-style:normal;font-weight:900;font-display:swap;src:url(/static/plugin/font-awesome/webfonts/fa-solid-900.eot);src:url(/static/plugin/font-awesome/webfonts/fa-solid-900.eot) format("embedded-opentype"),url(/static/plugin/font-awesome/webfonts/fa-solid-900.woff2) format("woff2"),url(/static/plugin/font-awesome/webfonts/fa-solid-900.woff) format("woff"),url(/static/plugin/font-awesome/webfonts/fa-solid-900.ttf) format("truetype"),url(/static/plugin/font-awesome/webfonts/fa-solid-900.svg) format("svg")}:root{--blue:#007bff;--indigo:#6610f2;--purple:#6f42c1;--pink:#e83e8c;--red:#dc3545;--orange:#fd7e14;--yellow:#ffc107;--green:#28a745;--teal:#20c997;--cyan:#17a2b8;--white:#fff;--gray:#6c757d;--gray-dark:#343a40;--primary:#007bff;--secondary:#6c757d;--success:#28a745;--info:#17a2b8;--warning:#ffc107;--danger:#dc3545;--light:#f8f9fa;--dark:#343a40;--breakpoint-xs:0;--breakpoint-sm:576px;--breakpoint-md:768px;--breakpoint-lg:992px;--breakpoint-xl:1200px;--font-family-sans-serif:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";--font-family-monospace:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace}*,::after,::before{box-sizing:border-box}html{font-family:sans-serif;line-height:1.15;-webkit-text-size-adjust:100%}header,main,nav,section{display:block}body{margin:0;font-family:-apple-system,BlinkMacSystemFont,"Segoe 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<h1>Daniel Stabile</h1>
<h2>A Passionate <span id="type-it"></span></h2>
<p> I'm a Master's student studying computer science
at Cornell University. I do research in assistive robotics at the <a href="https://emprise.cs.cornell.edu/">EmPRISE Lab</a>
with the goal of giving autonomy to people with disabilities in activities of daily living (ADLs). My project focuses on
aiding people with mobility limitations with food preparation.
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<h6>Born in <span class="theme-color">Ecuador</span> raised in <span class="theme-color">New
York</span></h6>
<p> I received my BS from Cornell Engineering in CS and am currently pursuing
my MS in CS as well. After graduating in May 2023, I plan to join the workforce.
Click the link below to learn more about what I've worked on!
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<h6 class="dark-color">Python</h6>
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<h6 class="dark-color">oCaml</h6>
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<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="85"
aria-valuemin="0" aria-valuemax="100">
<!-- <span>85%</span> -->
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<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="80"
aria-valuemin="0" aria-valuemax="100">
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<h6 class="dark-color">HTML</h6>
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<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="85"
aria-valuemin="0" aria-valuemax="100">
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<h6 class="dark-color">CSS</h6>
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<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="85"
aria-valuemin="0" aria-valuemax="100">
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<h6 class="dark-color">Matlab</h6>
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<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="75"
aria-valuemin="0" aria-valuemax="100">
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<h6 class="dark-color">Bash Scripting</h6>
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<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="70"
aria-valuemin="0" aria-valuemax="100">
<!-- <span>70%</span> -->
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<h6 class="dark-color">C#</h6>
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<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="85"
aria-valuemin="0" aria-valuemax="100">
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<h6 class="dark-color">Linux</h6>
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<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="90"
aria-valuemin="0" aria-valuemax="100">
<!-- <span>90%</span> -->
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<h6 class="dark-color">Vim</h6>
<div class="skill-bar">
<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="85"
aria-valuemin="0" aria-valuemax="100">
<!-- <span>85%</span> -->
</div>
</div>
</div>
<!-- end skill -->
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<h6 class="dark-color">VS Code</h6>
<div class="skill-bar">
<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="100"
aria-valuemin="0" aria-valuemax="100">
<!-- <span>100%</span> -->
</div>
</div>
</div><!-- /skill -->
<!-- end skill -->
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<h6 class="dark-color">GitHub</h6>
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<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="95"
aria-valuemin="0" aria-valuemax="100">
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<h4 class="theme-color" style="margin-top:18px;">CRAFT</h4>
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<h6 class="dark-color">Illustrator</h6>
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<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="95"
aria-valuemin="0" aria-valuemax="100">
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<h6 class="dark-color">Premiere</h6>
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<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="90"
aria-valuemin="0" aria-valuemax="100">
<!-- <span>90%</span> -->
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<!-- end skill -->
<!-- skill -->
<div class="skill-lt">
<h6 class="dark-color">Photoshop</h6>
<div class="skill-bar">
<div class="skill-bar-in theme-bg" role="progressbar" aria-valuenow="85"
aria-valuemin="0" aria-valuemax="100">
<!-- <span>85%</span> -->
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</div>
<!-- end skill -->
</div>
</div>
</div>
</section>
<!-- End fun -->
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<h5>Project</h5>
<span>2022</span>
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data-target="#modalPancake"></a>
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<h6>January 2022 - May 2022</h6>
For our final project we worked with a WidowX robot from Interbotix and trained it to flip pancakes!
Our setup included 3 realsense cameras, AR tags mounted on pancakes, a custom 3D printed frying pan, and a cage surrounding the robot.
By collecting rosbags of joint states and velocities during demo flips we were able to train a model that took
in the joint/velocity states of the robot and the pose of the pancake (poses were obtained with AR tags) and outputted
velocity commands for each joint. Here's a video of our project: <a href="https://www.youtube.com/watch?v=RDyN1C-MnKs&ab_channel=EmPRISELab"> Pancake Flipping Robot</a>
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<h5>Project</h5>
<span>2021</span>
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data-target="#modalIUU"></a>
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<h6>August 2021 - December 2021</h6>
This was another project done with MIT Lincoln Laboratory and other Cornell students.
The goal was to train a model that would take in SAR satellite data, identify
vessels, and categorize them as IUU or not. IUU fishing is a problem often associated with drug/human
trafficking and other illegal activities not to mention it can wreck ecosystems and often is done in waters
of developing nations who don't have the resources to combat it.
This project was for the xView3 challenge and while our team did not perform well, it was a huge learning
lesson for me. If nothing else it, taught me a lot about working in cross-institutional teams and I like
to think that the experience will be very helpful in my future career.
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<h5>Project</h5>
<span>2021</span>
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<h6>June 2021 - August 2021</h6>
This summer I interned at MIT Lincoln Laboratories and worked on a project focused
detecting anomalous flight activities using machine learning. I can't comment much
on this project for security reasons, but my core contribution was developing a pipeline
generate synthetic data which could be used for training. The problem with detecting
anomalous flight activities is that they're anomalous... and hence rare. Using synthetic
data helps create example data for models to train on!
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<h5>Project</h5>
<span>2020</span>
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<h6>August 2020 - December 2020</h6>
In the fall of 2020, I took ENGRC 3025 Creating and Communicating Your
Digital Professionalism taught by Dr. Allison Hutchison and Dr. Rick
Evans. For the final project, I created a professional website using
HTML, CSS, and the bootstrap framework. The website is built upon a
template but was significantly altered to fit my rhetorical situation.
The most notable changes from the template are the projects and travel
sections. I wanted to keep the site to one page so for the projects
section I created modals with more information for each project listed.
More challenging and time consuming was the addition of a world vector
map listing all the countries I have visited. I used the jQuery module
JQVmap which provided all the core functionality but was difficult to
fit and get at the correct scale. This website will always be a work in
progress, so keep checking in to stay updated!
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alt="SVG element for MRI CNN">
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<h5>Project</h5>
<span>2020</span>
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<h6>August 2020 - December 2020</h6>
In the fall of 2020, I took ECE 5970 Machine Learning with Biomedical
Data taught by Mert Sabuncu. For our final project my team implemented a
convolutional neural network for segmenting low-grade glioma in brain
MRIs. I was the team leader, delegating work and overseeing the overall
design of our implementation in PyTorch. We ended up using a standard
UNet and focused on different preprocessing and data augmentation
techniques to improve accuracy. I also experimented with skull stripping
to try and improve our model’s performance which had mixed results. For
this project we used Google Colab to train our model using their free
GPUs.
<br><br>
To view our Google Colab notebook check the link below on the left. A
link to our final report for the project is also included.
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<h5>Internship</h5>
<span>2020</span>
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</h5>
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<h6>June 2020 - August 2020</h6>
In the summer of 2020, I worked as an intern at Maculogix, a biomedical
startup. The company specializes in building equipment to detect
age-related macular degeneration. The product I worked with was the
AdaptDx Pro, a portable VR headset-looking device that could detect AMD
within 15 minutes. When I arrived at the company there was a production
bottleneck with the final calibration of the device, a process that took
about 2 hours and 15 minutes. I was tasked with cutting down the time
for a 40-minute section of this calibration process, a task that had to
be repeated for each side of the device totaling 80 minutes. I was given
instructions to implement a modified binary search that at best, would
have cut down the time in half. However, after running a series of
experiments, collecting data, and analyzing it, I realized that by using
a modified interpolation search, that I could not only decrease the time
by more than half but also increase the accuracy. In the end, I was able
to cut the section down to 10 minutes for a total reduction of 80
minutes to only 20. After realizing how significant of a change had been
made, my boss tasked me with cutting down the time for the entire
calibration pipeline. Using computer vision algorithms and a FLIR
camera, I was able to automate several steps in the calibration process
that were originally performed manually, such as alignment of internal
cameras and lights. In the end I was able to reduce the time for a
complete calibration from 2 hour 15 minutes to only 35 minutes. This
eliminated the production bottleneck and allowed the company to cut
costs by not having to hire as many calibration techs as they had
pla/beenned on.
<br><br>
While the calibration software was my main contribution, there were a
couple other things I worked on during my internship. I wrote a 40-page
ISO 9000 compliant verification and validation document for my
calibration software. I also discovered during my experiments with the
device, that some of the sensors used for calibration were incapable of
measuring light at the microlux level as accurately as my algorithms
needed. After bringing this up with my boss, I was placed in charge of
collecting data specifically for our sensors and sharing this with
company we purchased them from.
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alt="SVG element for VitalMask project with Vita Innovations">
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<h5>Project</h5>
<span>2020</span>
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<h6>August 2020 - December 2020</h6>
In the spring of 2020, several members of Cornell Biomedical Device
broke off to
create a startup, Vita Innovations. Our product, VitalMask, is a smart
mask that
measures patient vitals and is designed for ED waiting rooms. The idea
was born
during the beginning of the Covid-19 as a response to ED overcrowding. I
worked
on the website (see link below) and the desktop application which was
written in
Python using Kivy. The application allows for users to monitor multiple
masks
simultaneously through Bluetooth connections. The application allows for
custom
thresholds to be given to each mask’s vitals that sound an alert when
they are
exceeded.
<br><br>
The startup is part of several accelerators and won 15k in seed funding
after
winning the
Business Today’s 2020 Impact Challenge. For more information about
VitalMask,
check out the website link tomorrow.
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alt="SVG element for BruxFree project with CUBMD">
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<h5>Project</h5>
<span>2020</span>
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<h6>August 2020 - December 2020</h6>
BruxFree is a bruxism monitoring system that alerts users when they are
having
an episode. The idea is the user can wear an external device with
sensors that
detect when the muscles of the jaw are clenching excessively. I pitched
this
idea in the fall of 2019 to my engineering team (Cornell Biomedical
Device) and
we worked on it in the Spring of 2020 for the Engineering Innovation
competition, for which we placed as finalist. When we
started
building device, our product development team split into hardware and
software.
I led the latter in the creation of an android app that could be hooked
up to
our device and collect data and send notifications to users. You can
find more
information about the device on the engineering team’s website linked
below.
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alt="SVG element for MIPS project with Schaffer-Nishimura Lab">
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<h5>Research</h5>
<span>2020</span>
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Processing
Pipeline</h5>
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<h6>August 2020 - December 2020</h6>
My main project for the Schaffer-Nishimura lab was the development of a
MATLAB
pipeline for pre-processing multiphoton acquisitions. Named the
Multiphoton
Image Processing Stream (MIPS as an homage to the processor
architecture), this
project was a yearlong endeavor and resulted in a program that serves as
a
standard for the lab. Members of the lab use the program to perform
basic
operations such as channel splitting, normalization, and comb correction
among
more. There were also more advanced features, such as unmixing and the
use of
custom TIFF tags to document operations performed on acquisitions. The
code
performs much faster than MATLABs standard operations as reading and
writing
images is performed through the ScanImage library.
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<h5>Project</h5>
<span>2019</span>
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Management
System</h5>
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<h6>August 2019 - December 2019</h6>