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experience.html
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<!DOCTYPE HTML>
<!--
Spectral by HTML5 UP
html5up.net | @ajlkn
Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
-->
<html>
<head>
<title>Ashwat Chidambaram</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
<link rel="stylesheet" href="assets/css/main.css" />
<noscript><link rel="stylesheet" href="assets/css/noscript.css" /></noscript>
</head>
<body class="is-preload">
<!-- Page Wrapper -->
<div id="page-wrapper">
<!-- Header -->
<header id="header">
<h1><a href="index.html">Ashwat Chidambaram</a></h1>
<nav id="nav">
<ul>
<li class="special">
<a href="#menu" class="menuToggle"><span>Menu</span></a>
<div id="menu">
<ul>
<li><a href="index.html">Home</a></li>
<li><a href="projects.html">Projects</a></li>
<li><a href="experience.html">Experience</a></li>
<li><a href="about.html">About Me</a></li>
</ul>
</div>
</li>
</ul>
</nav>
</header>
<!-- Main -->
<article id="main">
<header>
<h2>Work Experience</h2>
<p>“Choose a job you love, and you will <br/>
never have to work a day in your life." <br/>
~ Confucius</p>
</header>
<section class="wrapper style5">
<div class="inner">
<section class="spotlight" style='background-color: #ffffff;'></section>
<section class="spotlight" style='background-color: #ffffff;'>
<div class="image"><img src="images/organizations/nvidia.jpeg" alt="" /></div><div class="content">
<h2 style="font-size:23px !important;">NVIDIA Self-Driving (Autonomous Vehicles)</h2>
<h4><strong><i>Machine Learning Perception Intern<br/> (May 2022 - August 2022)</i></strong></h4>
<p>
<!-- Worked on improving NVIDIA's next-generation self-driving perception algorithms by bridging the gap
between driving simulation and the real world. Implemented sim2real transfer using a zero-shot learning
approach that was adopted, modified, and implemented based on a SOTA research paper. Improved classification
accuracy on select image classes by an average of ~10 to 15%. -->
Worked on improving classification performance for NVIDIA’s traffic sign models throughout the European Union,
by converting simulation data to real-world data using zero reference images. Developed a custom zero-shot sim2real
computer vision model using TensorFlow, OpenCV, and Python, by modifying and implementing various research papers.
Improved classification accuracy on low-data image classes by average of ~10-15%, using my model to do sim2real
image conversion which was used to boost performance on NVIDIA's next-generation core perception models.
</p>
</div>
</section>
<hr/>
<section class="spotlight" style='background-color: #ffffff;'>
<div class="image"><img src="images/organizations/alexa.png" alt="" /></div><div class="content">
<h2 style="font-size:23px !important;">Amazon Devices (Alexa)</h2>
<h4><strong><i>Software Development Intern<br/> (May 2021 - August 2021)</i></strong></h4>
<p>
Developed backend software for a confidential and unreleased feature of Amazon Alexa.
Worked primarily with Java, interfacing with various services such as AWS Lambda and Amazon DynamoDB.
</p>
</div>
</section>
<hr/>
<section class="spotlight" style='background-color: #ffffff;'>
<div class="image"><img src="images/organizations/fakenet.jpg" alt="" /></div><div class="content">
<h2 style="font-size:23px !important;">FakeNetAI (startup)</h2>
<h4><strong><i>Machine Learning Consultant (ML@B) <br/> (January 2021 - May 2021)</i></strong></h4>
<p>
<!-- Industry project partnered between Machine Learning @ Berkeley and Berkeley SkyDeck startup FakeNetAI.
Worked with a team of four students to develop and tune various ML models to distinguish between genuine vs. synthesized audio data found in deepfakes.
Assisted in deploying the model onto a website to run inference on user audio input. The codebase was created with PyTorch in Python and trained with AWS. -->
FakeNetAI is a Berkeley SkyDeck startup that aims to detect synthetic media and deepfakes, in order to protect users from the dangers of these attacks through state-of-the-art fake video detection products.
My role was the following: Developed and tuned various ML model architectures to distinguish between genuine vs. synthesized audio data found in deepfakes. Worked in a team of four students (through Machine Learning @ Berkeley),
using PyTorch and Python to train models through Amazon EC2. Deployed the model onto a custom website to run inference on user audio input files.
</p>
</div>
</section>
<hr/>
<section class="spotlight" style='background-color: #ffffff;'>
<div class="image"><img src="images/organizations/dewaste.png" alt="" /></div><div class="content">
<h2 style="font-size:23px !important;">DeWaste (startup)</h2>
<h4><strong><i>Computer Vision Intern <br/> (August 2020 - December 2020)</i></strong></h4>
<p>
DeWaste utilizes cutting edge technology that collects real time data of food leftovers using computer vision.
This data is then used to generate daily reports and actionable insights which can be used to reduce food waste by engineering the menu to adapt to customer preferences.
I researched into deep learning models to classify food waste across various categories, performed preprocessing and analysis of data within public food waste datasets,
and created documentation for future interns at DeWaste to continue diving into.
</p>
</div>
</section>
<hr/>
<section class="spotlight" style='background-color: #ffffff;'>
<div class="image"><img src="images/organizations/lmspace.jpg" height="420" alt="" /></div><div class="content">
<h2 style="font-size:23px !important;">Lockheed Martin: <br/> Space Systems</h2>
<h4><strong><i>Machine Learning Intern <br/> (June - August 2020)</i></strong></h4>
<p>
Created a vision system using deep learning and computer vision to identify and label defects found within essential space-based hardware.
Helped set up a new machine to automatically retrain/evaluate the model using GPU-accelerated ML with CUDA on an Nvidia DGX server.
Trained the model up to 98% accuracy on collected data, and significantly increased model inference speed which reduced the company's defect detection time by 95%.
</p>
</div>
</section>
<hr/>
<section class="spotlight" style='background-color: #ffffff;'>
<div class="image"><img src="images/organizations/lm.jpg" height="440" alt="" /></div><div class="content">
<h2 style="font-size:23px !important;">Lockheed Martin: <br/> Enterprise Business Services</h2>
<h4><strong><i>Software Engineering Intern <br/> (June - August 2019)</i></strong></h4>
<p>
Developed an application that allows authenticated Lockheed Martin employees to keep track of LM specific projects,
replacing antiquated software no longer being supported by Oracle. Learned and applied various tools such as Angular, Node, Express, Bootstrap, HTML, CSS, and Oracle DB.
</p>
</div>
</section>
<br/>
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</section>
</article>
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