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<!DOCTYPE html>
<!--[if lt IE 8 ]><html class="no-js ie ie7" lang="en"> <![endif]-->
<!--[if IE 8 ]><html class="no-js ie ie8" lang="en"> <![endif]-->
<!--[if (gte IE 8)|!(IE)]><!--><html class="no-js" lang="en"> <!--<![endif]-->
<head>
<!--- Basic Page Needs
================================================== -->
<meta charset="utf-8">
<title>man adarsh_pal_singh</title>
<meta name="description" content="">
<meta name="author" content="">
<!-- Mobile Specific Metas
================================================== -->
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1">
<!-- CSS
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<link rel="stylesheet" href="css/default.css">
<link rel="stylesheet" href="css/layout.css">
<link rel="stylesheet" href="css/media-queries.css">
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<!-- Script
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<script src="js/modernizr.js"></script>
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<link rel="shortcut icon" href="favicon.ico" >
</head>
<body>
<!-- Header
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<header id="home">
<nav id="nav-wrap">
<a class="mobile-btn" href="#nav-wrap" title="Show navigation">Show navigation</a>
<a class="mobile-btn" href="#" title="Hide navigation">Hide navigation</a>
<ul id="nav" class="nav">
<li class="current"><a class="smoothscroll" href="#home">Home</a></li>
<li><a class="smoothscroll" href="#about">About</a></li>
<li><a class="smoothscroll" href="#resume">Experience</a></li>
<li><a class="smoothscroll" href="#portfolio">Works</a></li>
<li><a class="smoothscroll" href="#testimonials">Testimonials</a></li>
<li><a href="blog.html">Blog</a></li>
<li><a href="Resume.pdf" target="_blank">Resume</a></li>
</ul> <!-- end #nav -->
</nav> <!-- end #nav-wrap -->
<div class="row banner">
<div class="banner-text">
<h1 class="responsive-headline">I'm Adarsh Pal Singh.</h1>
<h3>I'm currently pursuing my <span>MS (by Research)</span> from <span>IIIT Hyderabad</span>, India. I've done some
cool stuff during the 5 years of my engineering so please scroll down
to learn more <a class="smoothscroll" href="#about">about me</a>!</h3>
<hr />
<ul class="social">
<li><a href="https://www.facebook.com/adarsh1001" target="_blank"><i class="fa fa-facebook"></i></a></li>
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<li><a href="https://github.com/adarsh1001" target="_blank"><i class="fa fa-github"></i></a></li>
<li><a href="mailto:[email protected]"><i class="fa fa-envelope"></i></a></li>
</ul>
</div>
</div>
<p class="scrolldown">
<a class="smoothscroll" href="#about"><i class="icon-down-circle"></i></a>
</p>
</header> <!-- Header End -->
<!-- About Section
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<section id="about">
<div class="row">
<div class="three columns">
<img class="profile-pic" src="images/adarsh_pro.jpg" alt="" />
</div>
<div class="nine columns main-col">
<h2>My Journey In A Nutshell</h2>
<p align="left">I'm a Bachelor's + MS by Research Dual Degree ECE student at IIIT Hyderabad, India. My MS is on the
topic of Machine Learning for Smarter and More Efficient IoT Systems.
I've published 2 papers as part of my MS and am currently working on my thesis. I've
been the recepient of the Dean's List Award 6 times in a row for my academic performance.
During the course of my engineering, I served in 2 different companies as an intern. The first one was
the Linux Foundation where I interned for about 6 months in 2018. This was a remote open source internship
wherein I collaborated with a team from Huawei (OPNFV Project) to develop a cloud-native stack for edge devices.
I received the Gambia Community Award for my contributions to OPNFV and was the only undergrad speaker selected by
the Linux Foundation to deliver talks at the Open Networking Summit and the OPNFV Plugfest. In the
summer of 2019, I interned in the AI/ML team of Ernst & Young, Bangalore. I had a dual role
of a DevOps as well as a Machine Learning Engineer.</p>
<p align="left">I'm an avid book reader. Some of my favorites include The Scarlet Letter, Maud's Line, The Girl On The Train,
Surely You're Joking Mr. Feynman and The Martian. I also love reading Japanese manga. One Piece is my absolute
favorite. I own an Ibanez acoustic guitar and can be found strumming to the Beatles or Oasis in the afternoons if
I'm not in the badminton court or hooked to some Netflix series.</p>
<div class="row">
<div class="columns download">
<p>
<a href="Resume.pdf" target="_blank" class="button"><i class="fa fa-download"></i>Download Resume</a>
</p>
</div>
</div> <!-- end row -->
</div> <!-- end .main-col -->
</div>
</section> <!-- About Section End-->
<!-- Resume Section
================================================== -->
<section id="resume">
<!-- Education
----------------------------------------------- -->
<div class="row education">
<div class="three columns header-col">
<h1>Education</h1>
</div>
<div class="nine columns main-col">
<div class="row item">
<div class="twelve columns">
<h3>International Institute of Information Technology (IIIT), Hyderabad</h3>
<h3><img src="images/jobs/iiit.png" alt="Engg@IIIT" width="150"/></h3>
<p class="info">B.Tech. (Hons.) & MS by Research in ECE <span>•</span> <em class="date">Aug 2016 - Jun 2020</em></p>
<p align="left">
Pursuing a dual degree programme in electronics and communication engineering. I have a <b>CGPA of 9.27/10</b> and
have been the recepient of the <b>Dean's List Award</b> 6 times in a row (semesters 3 to 8). Some of my favorite
subjects include: Statistical Methods in AI, Data Warehousing and Data Mining, Computer Vision, Algorithms and
Operating Systems, Communication Networks, Embedded Hardware Design, Linear Algebra and Graph Theory. My MS
is on the topic of Machine Learning for Smarter and More Efficient IoT Systems. I've <b>published 2 international papers</b> as part of my MS.
I received the <b>Research List Award</b> for publishing my first paper during my undergrad.
I also served as the <b>Head TA</b> for the course Embedded Hardware Design in Monsoon 2017. My primary
job was to design lab experiments centered around Arduino and Xilinx Zedboard and take lab sessions every week.
</p>
</div>
</div> <!-- item end -->
</div> <!-- main-col end -->
</div> <!-- End Education -->
<!-- Work
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<div class="row work">
<div class="three columns header-col">
<h1>Work Experience</h1>
</div>
<div class="nine columns main-col">
<div class="row item">
<div class="twelve columns">
<h3>Ernst & Young</h3>
<h3><img src="images/jobs/ey.png" alt="Intern@EY" width="150"/></h3>
<p class="info">Machine Learning & DevOps Intern <span>•</span> <em class="date">Bangalore, India</em> <span>•</span> <em class="date">May 2019 - Aug 2019</em></p>
<p align="left">
I was selected from campus for this internship among a pool of 300 IIITians. I interned for around 3 months
in the AI/ML team of E&Y.
Worked on the problem of clustering in high dimensional sentence embeddings
(BERT, USE, XLNet). Also worked on optimizing the time and space complexity
of some core algorithms like Nearest Neighbor for large databases. In the DevOps role,
I dockerized a deep learning application and implemented an end-to-end CI/CD pipeline from scratch
in Azure DevOps.
</p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h3>The Linux Foundation</h3>
<h3><img src="images/jobs/lf.png" alt="Intern@LF" width="150"/></h3>
<p class="info">Edge Cloud Networking Intern <span>•</span> <em class="date"><b><a href="https://github.com/opnfv/clover/tree/master/edge/sample" target="_blank" style="color: rgb(7, 89, 109)">Remote</a></b></em> <span>•</span> <em class="date">May 2018 - Dec 2018</em></p>
<p align="left">
I was selected as an <b><a href="https://wiki.opnfv.org/display/DEV/Intern+Project%3A+Edge+cloud+native+cluster" target="_blank" style="color: rgb(7, 89, 109)">LFN intern</a></b>
in the OPNFV project for a period of 6 months. The crux of my work was to implement a kubernetes-based
small-footprint edge cluster supporting cloud-native framework and develop exemplar microservice-oriented
applications for the edge as well as the edge-cloud paradigm of the future.
In the first phase, a versatile Ansible script was developed to form a k8s cluster out of 2+ Raspberry Pi 3
devices. Exemplar microservice apps, containerized using Docker, were developed and tested on this edge cluster.
In particular, a low-latency real-time video streaming app employing UV4L was developed and tested.
The next phase focused on an edge-cloud paradigm for ML apps. A demo was created for ONS-18 which involved containerizing
a YOLO-based real-time object detector for this edge as well as GKE (with Nvidia P100 GPU) and highlighting
the pros and cons of a collaborative ML paradigm.</p>
<p align="left">
I received the <b><a href="https://wiki.opnfv.org/display/DEV/Gambia+Award+Winners" target="_blank" style="color: rgb(7, 89, 109)">Gambia Community Award</a></b> in the intern category.
I also got 2 opportunities to present my work-
one at the <b><a href="https://sched.co/FmsC" target="_blank" style="color: rgb(7, 89, 109)">Open Networking Summit</a></b>,
Amsterdam and another at the <b><a href="https://wiki.lfnetworking.org/display/LN/Topic+Proposals+for+Jan+%2719" target="_blank" style="color: rgb(7, 89, 109)">OPNFV Plugfest</a></b>, Paris and was the only
undergrad speaker in both the places.
</p>
</div>
</div> <!-- item end -->
</div> <!-- main-col end -->
</div> <!-- End Work -->
<!-- Research Work
----------------------------------------------- -->
<div class="row research work">
<div class="three columns header-col">
<h1>Research Experience</h1>
</div>
<div class="nine columns main-col">
<div class="row item">
<div class="twelve columns">
<h3>SPCRC Lab, IIIT Hyderabad</h3>
<h3><img src="images/jobs/iiit.png" alt="MS@SPCRC" width="150"/></h3>
<p class="info">Research Student <span>•</span> <em class="date">Hyderabad, India</em> <span>•</span> <em class="date">May 2017 - May 2020</em></p>
<p align="left">
Pursuing research under Dr. Sachin Chaudhari on the application of machine learning towards smarter and more efficient
IoT systems. As of August 2019, I'm a full-time Research Assistant in the SPCRC lab and have been
overseeing various government and industry sponsored projects as well.
Published two papers as part of my MS: one on the problem of offloading trained machine learning models to
constrained sensor nodes for data transmission reduction and another on machine learning-based human occupancy
estimation in rooms using non-intrusive sensor nodes.
</p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h3>Norwegian University of Science and Technology (NTNU), Trondheim</h3>
<h3><img src="images/jobs/ntnu.png" alt="Intern@NTNU" width="150"/></h3>
<p class="info">Research Intern <span>•</span> <em class="date">Trondheim, Norway</em> <span>•</span> <em class="date">May 2018 - Jun 2018</em></p>
<p align="left">
Worked under Prof. Stefan Werner and Dr. Frank Kraemer on robust machine
learning-based IoT systems that work on a collaboration of edge and cloud.
Also explored the problem of transfer learning in machine learning-based IoT applications.
</p>
</div>
</div> <!-- item end -->
<div class="row item">
<div class="twelve columns">
<h3>Publications</h3>
<ol>
<li><a href="https://dl.acm.org/doi/abs/10.1145/3341105.3373967" target="_blank" style="color: rgb(7, 89, 109)">
Embedded Machine Learning-Based Data Reduction In Application-Specific Constrained IoT Networks</a>,
in proceedings of the 35th ACM Symposium on Applied Computing (SAC ’20), Czech Republic, 2020.</li>
<li><a href="https://ieeexplore.ieee.org/document/8644432" target="_blank" style="color: rgb(7, 89, 109)">
Machine Learning-Based Occupancy Estimation Using Multivariate Sensor Nodes</a>, in proceedings of the 2018 IEEE Globecom
Workshops (full paper in CCNCPS), Abu Dhabi, 2018.</li>
</ol>
</div>
</div> <!-- item end -->
</div> <!-- main-col end -->
</div> <!-- End Work -->
<!-- Skills
----------------------------------------------- -->
<div class="row skills">
<div class="three columns header-col">
<h1>Skills</h1>
</div>
<div class="nine columns main-col">
<p>I'm inclined towards Software Development, DevOps and Machine Learning.
</p>
<div class="bars">
<ul class="skills">
<li><span class="bar-expand photoshop"></span><em>Python3</em></li>
<li><span class="bar-expand illustrator"></span><em>C, C++</em></li>
<li><span class="bar-expand wordpress"></span><em>Docker, Kubernetes</em></li>
<li><span class="bar-expand css"></span><em>Ansible, Jenkins, Azure DevOps</em></li>
<li><span class="bar-expand html5"></span><em>ML (Scikit, TF, PyTorch)</em></li>
<li><span class="bar-expand jquery"></span><em>Linux SysAdmin</em></li>
</ul>
</div><!-- end skill-bars -->
</div> <!-- main-col end -->
</div> <!-- End skills -->
</section> <!-- Resume Section End-->
<!-- Portfolio Section
================================================== -->
<section id="portfolio">
<div class="row">
<div class="twelve columns collapsed">
<h1>Some stuff that I've developed and talks that I've delivered. <br>
<small>{Tap the images to expand!}</small>
</h1>
<!-- portfolio-wrapper -->
<div id="portfolio-wrapper" class="bgrid-quarters s-bgrid-thirds cf">
<div class="columns portfolio-item">
<div class="item-wrap">
<a href="#modal-01" title="">
<img alt="" src="images/portfolio/arduino.jpg">
<div class="overlay">
<div class="portfolio-item-meta">
<h5>micro-learn</h5>
<p>A Python Library for Embedded Machine Learning</p>
</div>
</div>
<div class="link-icon"><i class="icon-plus"></i></div>
</a>
</div>
</div> <!-- item end -->
<div class="columns portfolio-item">
<div class="item-wrap">
<a href="#modal-02" title="">
<img alt="" src="images/portfolio/paris.jpg">
<div class="overlay">
<div class="portfolio-item-meta">
<h5>OPNFV Plugfest, Paris 2019</h5>
<p>Lightning Talk: Edge Cloud-Native Cluster</p>
</div>
</div>
<div class="link-icon"><i class="icon-plus"></i></div>
</a>
</div>
</div> <!-- item end -->
<div class="columns portfolio-item">
<div class="item-wrap">
<a href="#modal-03" title="">
<img alt="" src="images/portfolio/amsterdam.jpg">
<div class="overlay">
<div class="portfolio-item-meta">
<h5>Open Networking Summit, Amsterdam 2018</h5>
<p>Lightning Talk: Edge of Tomorrow</p>
</div>
</div>
<div class="link-icon"><i class="icon-plus"></i></div>
</a>
</div>
</div> <!-- item end -->
<div class="columns portfolio-item">
<div class="item-wrap">
<a href="#modal-04" title="">
<img alt="" src="images/portfolio/cluster.jpg">
<div class="overlay">
<div class="portfolio-item-meta">
<h5>Raspberry Pi Kubernetes Cluster</h5>
<p>LFN Project</p>
</div>
</div>
<div class="link-icon"><i class="icon-plus"></i></div>
</a>
</div>
</div> <!-- item end -->
<div class="columns portfolio-item">
<div class="item-wrap">
<a href="#modal-05" title="">
<img alt="" src="images/portfolio/segment.jpg">
<div class="overlay">
<div class="portfolio-item-meta">
<h5>Hand Segmentation Using RefineNet</h5>
<p>Computer Vision Course Project</p>
</div>
</div>
<div class="link-icon"><i class="icon-plus"></i></div>
</a>
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<h5>IEEE Globecom, Abu Dhabi 2018</h5>
<p>Paper Presentation: Machine Learning-Based Occupancy Estimation</p>
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<h5>NASA's CanSat Competition 2018</h5>
<p>World Rank 24</p>
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<h5>Song Popularity Predictor</h5>
<p>Statistical Methods in AI Course Project</p>
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<h5>Faster Matrix Algebra Library For Symmetric Matrices In Eigen</h5>
<p>Personal C++ Project</p>
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<h5>Understanding And Predicting Trends In Cryptocurrency Prices</h5>
<p>Data Mining Course Project</p>
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<a class="button" href="blog.html">Visit My Blog</a></p>
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<h4>micro-learn: A Python Library for Embedded Machine Learning</h4>
<p align="left">
micro-learn is a Python library for converting trained machine learning models into inference code that can
run on microcontrollers in real time.
</p>
<p align="left">
ML algorithms typically require heavy computing and memory resources in the training phase,
far greater than what a typical microcontroller can offer. However, post training, many of these
algorithms boil down to simple parameters that require simple arithmetic & logical operations for inference.
These can easily run on microcontrollers in real time. The purpose of this library is to convert ML models
(trained using scikit-learn) directly into Arduino inference code.
</p>
<p align="left">
All the inference algorithms are optimized for microcontrollers and require least possible arithmetic computations.
Division operations have been converted to multiplications since the latter is much more computationally efficient.
Note that all the algorithms are exact and not approximate. Please refer to my ACM paper for theoretical and
practical foundations regarding this procedure.
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<span class="categories"><i class="fa fa-tag"></i>Embedded Systems, Machine Learning, Scikit-Learn, Python</span>
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<a href="https://dl.acm.org/doi/abs/10.1145/3341105.3373967" target="_blank">Paper</a>
<a href="https://github.com/adarsh1001/micro-learn" target="_blank"> Code</a>
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<h4>Lightning Talk: "Edge Cloud-Native Cluster", OPNFV Plugfest, Paris 2019</h4>
<p align="left">
One of the biggest perks of interning in the Linux Foundation Networking (LFN) program is that you get a fully
sponsored trip to an LF event. Not only that, you also get to present your work as a 10-15 minute lightning talk to a
room full of industry experts! My internship concluded successfully in December 2018 and I began packing my bags
for this trip in the second week of January itself.
</p>
<p align="left">
This time around (2019), the OPNFV plugfest was not a standalone LF event. Rather, a combined OPNFV Plugfest and
ONAP DDF took place for 4 whole days at Nokia Paris-Saclay. I got to attend tons of exciting talks and tutorials
that were undertaken by the developers from various companies like Redhat, China Mobile, Huawei, Intel.
The intern talks were scheduled early morning on the last day of the event. I've
attached my presentation below which is pretty self-explanatory.
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<span class="categories"><i class="fa fa-tag"></i>Edge, Ansible, Docker, K8s, YOLOv3, Python, Bash</span>
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<a href="ppts/opnfv_plugfest.pdf" target="_blank">Presentation</a>
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<h4>Lightning Talk: "Edge of Tomorrow: Deploying Collaborative Machine Intelligence to the Edge", Open Networking Summit, Amsterdam 2018</h4>
<p align="left">
I was only halfway through my LFN internship when one day my mentor, Wenjing Chu, suggested that we should
submit a talk proposal for the Europe edition of the Open Networking Summit (2018). He was convinced that our
work on edge-cloud and collaborative machine learning was something that companies would be interested in. And
sure enough, our proposal was accepted by LF to be presented as a 10 minute lightning talk. I've attached our
presentation below which is pretty much self-explanatory.
</p>
<p align="left">
P.s. This was the first time I'd ever given a talk in a foreign country to a room full of industry experts. A
great experience indeed!
</p>
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</div>
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<a href="https://events19.linuxfoundation.org/wp-content/uploads/2017/12/Edge-of-Tomorrow.pdf" target="_blank">Presentation</a>
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<h4>Raspberry Pi Kubernetes Cluster</h4>
<p align="left">
As the heading suggests, this project revolves around the creation of a kubernetes cluster using 2 or more
Raspberry Pi devices (ARM-based). After manually playing with docker and kubernetes on the RPi for weeks, I
developed a versatile Ansible script that can create and disassemble clusters with ease. You can try it out
yourself if you own at least 2 Raspberry Pi devices. An exemplar containerized app is also included in the repo
which can help you get started with your own edge cluster-based projects.
</p>
<p align="left">
A fully detailed guide and code can be found in the below link.
</p>
<span class="categories"><i class="fa fa-tag"></i>Ansible, Docker, Kubernetes, Bash</span>
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<h4>Hand Segmentation Using RefineNet</h4>
<p align="left">
The aim of this project was to develop an egocentric hand segmentation model using RefineNet in Python3.
For this, we worked with two CVPR papers and replicated the results using our own implementation in PyTorch (the
authors had written the code in Matlab). The PyTorch implementation of RefineNet (which is a multipath
refinement network over ResNet) was done from scratch. After that, the model was trained on the standard
Pascal person-parts dataset with a new binary layer of hand and no-hand. The model was further fine-tuned on
four different ego-centric hands datasets.
</p>
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The details about the implementation and the code can be found below.
</p>
<span class="categories"><i class="fa fa-tag"></i>Computer Vision, Semantic Segmentation, ResNet, RefineNet</span>
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<a href="ppts/hand_segmentation.pdf" target="_blank">Details</a>
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<h4>Paper Presentation: "Machine Learning-Based Occupancy Estimation Using Multivariate Sensor Nodes",
IEEE Globecom, Abu Dhabi 2018
</h4>
<p align="left">
This was my first international paper. As a rule, if an IEEE or ACM paper gets accepted for publication,
at least one of the authors is expected to attend the conference and present the paper.
</p>
<p align="left">
<b>Abstract:</b> In buildings, a large chunk of energy is spent on heating, ventilation and air conditioning systems.
One way to optimize their usage is to make them demand-driven depending on human occupancy.
This paper focuses on accurately estimating the number of occupants in a room by leveraging multiple
heterogeneous sensor nodes and machine learning models. For this purpose, low-cost and non-intrusive
sensors such as CO2, temperature, illumination, sound and motion were used. The sensor nodes were
deployed in a room in a star configuration and measurements were recorded for a period of four days.
A regression based method is proposed for calculating the slope of CO2, a new feature derived from
real-time CO2 values. Supervised learning algorithms such as LDA, QDA, SVM and random forest (RF) were
used on several
different combinations of feature sets. Moreover, multiple performance metrics such as accuracy, F1 score
and confusion matrix were used to evaluate the performance of our models. Experimental results demonstrate
a maximum accuracy of 98.4% and a high F1 score of 0.953 for estimating the number of occupants in the room.
PCA was also applied to evaluate the performance of a dataset with reduced
dimensionality.
</p>
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The presentation and the paper link can be found below.
</p>
<span class="categories"><i class="fa fa-tag"></i>Machine Learning, Internet of Things</span>
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<a href="ppts/globecom.pdf" target="_blank">Presentation</a>
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<h4>World Rank 24 at NASA's CanSat Competition 2018</h4>
<p align="left">
Worked part-time for around 5 months on the CanSat 2018 mission statement along with 8 other IIITians.
</p>
<p align="left">
The mission statement of CanSat 2018 was to construct a small satellite containing various sensors such as
temperature, pressure, accelerometer, voltage and GPS, a microcontroller and a communication radio. A payload
section was also to be designed for protecting a delicate instrument. During the final launch, an egg was put in this
section. The CanSat was also armed with a heat shield and a parachute. As per the problem statement, the
CanSat was to be dropped from a height of around 700m. It was expected to perform a series of manoeuvres such as
letting go of the heat shield and opening the parachute at certain altitudes, sending telemetry data to the
ground control every second and most importantly, remain steady enough to ensure that the egg in the payload
section doesn't break even after impact.
</p>
<p align="left">
All of us in the CanSat team grew up watching science fiction movies. To create something like this from
scratch was an amazing experience. I still can't explain the feeling I got when I learnt that the egg in
our payload section didn't even have a scratch on it! For more details, please visit the following link.
</p>
<span class="categories"><i class="fa fa-tag"></i>Embedded Systems, Communication, Aerodynamics</span>
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<h4>Song Popularity Predictor</h4>
<p align="left">
The aim of this project was to determine the popularity of songs using its musical features as well as other
song metrics. We started with the analysis of the million song dataset (MSD). The dataset required a lot of
preprocessing before it could be used in any way. After that, traditional machine learning models were
trained to classify songs into three categories- highly popular, popular and unpopular. Nearest neighbor
exhibited the highest accuracy. Next, we moved onto time-series based experiments as the songs are, of
course, time-series in nature. An LSTM was trained on the time-series features that we had earlier left out.
Since the MSD dataset was quite old, we collected our own dataset consisting of 889 current hit songs and
tested different models on that too. The features were extracted using the PyAudioAnalysis library.
</p>
<p align="left">
The details about the implementation and the code can be found below.
</p>
<span class="categories"><i class="fa fa-tag"></i>Python3, Machine Learning, Time Series, HDF5</span>
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<a href="ppts/song_popularity.pdf" target="_blank">Details</a>
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<h4>Faster Matrix Algebra Library For Symmetric Matrices In C++ Eigen</h4>
<p align="left">
This project was given as a GSoC project by the ATLAS team (part of the Large Hadron Collider project) in
2018. I was initally trying for this internship but then ended up taking the Linux Foundation internship instead.
However, I did complete most of the tasks laid out for the project on my own.
</p>
<p align="left">
In this project, a standalone C++ class was built which can work with Symmetric Matrices on top of the
Eigen Library. Working only with the upper/lower triangular part of a symmetric matrix considerably reduces
the storage space as well as the complexity for different matrix operations. The code exhibits the use
of templates coupled with operator overloading.
</p>
<span class="categories"><i class="fa fa-tag"></i>C++, OOPS, Templates, Polymorphism</span>
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<h4>Understanding And Predicting Trends In Cryptocurrency Prices Using Data Mining Techniques</h4>
<p align="left">
In a span of 10 years, we’ve witnessed the inception and
gain in the popularity of the blockchain technology and cryptocurriences. Various cryptocurrencies
have come up and evolved in a short span of time. Just like the regular stock market, the trends of different
cryptocurrency coin prices is of great importance to the cryptocurrency traders and investors.
However, unlike the stock market analysis which has been going on for decades, the cryptocurrency market is
still referred to as "highly volatile" thereby giving motivation to researchers to contribute to this theme. <br>
In this project, data mining techniques were employed to understand different factors affecting Bitcoin and
Etherium, and models were built that can predict different future trends of the market. First, a
binary classification problem was posed (rise/fall of price in a day) and supervised learning techniques
were employed on the dataset.
More advanced time-series based techniques were also explored that could predict future values. A comparison
with regular stock market is also presented.
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<span class="categories"><i class="fa fa-tag"></i>Python3, Machine Learning, Time-series</span>
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<p>I had the pleasure to supervise Adarsh as an intern in a Linux Foundation open source project.
I tend to give my interns an open ended problem to solve and see how far they can go.
Adarsh is one of the rare students who not only has the drive to reach far but also the skills and
dedication to pull it off. His work to investigate the benefit of edge computing with an online
object recognition algorithm is both practical and insightful. I highly recommend Adarsh to anyone.
</p>
<cite>Wenjing Chu, Head of Open Source and Research at Futurewei (Huawei) Technologies and Member of the Technical Advisory Council at The Linux Foundation, Santa Clara, CA.</cite>
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