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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
<html>
<head>
<title>Chandrahas</title>
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<div id="doc2" class="yui-t7">
<div id="inner">
<div id="hd">
<div class="yui-gc">
<div class="yui-u first">
<h1>Chandrahas</h1>
<!-- <h1>Chandrahas Dewangan</h1>
-->
<!-- <hr> -->
<div style="margin-top:30px;margin-bottom:20px">
<h2>Applied Scientist</h2>
<h2>Amazon, India</h2>
</div>
<!-- <hr> -->
<!-- <h3><a href="mailto:dewangan.chandrahas@gmail.com">dewangan.chandrahas@gmail.com</a></h3> -->
<!-- <hr> -->
<!-- <a href="Chandrahas.pdf"> -->
<!-- <h2 style="font-weight: bold; color: #333;">CV</h2> -->
<!-- </a> -->
<div style="margin-top:40px;margin-bottom:0px">
<p>
<a class="fa-icon" href="mailto:dewangan.chandrahas@gmail.com" target="_blank">
<i class="fas fa-envelope fa-3x"></i>
</a>
<span style="margin-left:40px;"></span>
<a class="fa-icon" href="https://www.linkedin.com/in/dewanganchandrahas/" target="_blank">
<i class="fab fa-linkedin fa-3x"></i>
</a>
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</a>
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<i class="ai ai-google-scholar-square ai-3x"></i>
</a>
<span style="margin-left:40px;"></span>
<a class="fa-icon" href="Chandrahas.pdf" target="_blank">
<i class="ai ai-cv-square ai-3x"></i>
</a>
</p>
</div>
</div>
<div class="yui-u">
<div class="contact-info">
<center><img src=images/Chandrahas3.jpg height=200></img>
<br>
</center>
</div>
<!--// .contact-info -->
</div>
</div>
<!--// .yui-gc -->
</div>
<!--// hd -->
<div id="bd">
<div id="yui-main">
<div class="yui-b">
<div class="yui-gf">
<div class="yui-u first">
<h2>Profile</h2>
</div>
<div class="yui-u">
<p class="enlarge">
<!--Progressively evolve cross-platform ideas before impactful infomediaries. Energistically visualize tactical initiatives before cross-media catalysts for change. -->
<!-- I am currently working as a Visiting Researcher at Google Research, India. -->
I am currently working as an Applied Scientist at Amazon India.
Prior to that, I was a Postdoctoral Researcher at Google Research India.
I completed my PhD from the Department of <a
href=https://www.csa.iisc.ac.in />Computer Science and Automation</a>, <a
href=https://www.iisc.ac.in />Indian Institute of Science Bangalore</a>
under the guidance of <a href=https://research.google/people/ParthaTalukdar />Dr.
Partha Pratim Talukdar</a>. My thesis topic was on Knowledge Graph Embedding methods.
Prior to that, I have been a master's student in the same department and worked under
the guidance of <a href=https://shivani-agarwal.net />Dr. Shivani
Agarwal</a> in the area of Machine Learning at the <a
href=https://www.shivani-agarwal.net/MLLTGroup/index.html />Machine Learning and
Learning Theory
Lab</a>.
</p>
</div>
</div>
<!--// .yui-gf -->
<div class="yui-gf">
<div class="yui-u first">
<h2>Interests</h2>
</div>
<div class="yui-u">
<div class="talent">
<h2>Knowledge Graphs</h2>
</div>
<div class="talent">
<h2>Natural Language Understanding</h2>
</div>
<div class="talent">
<h2>Machine Learning</h2>
</div>
</div>
<div class="yui-u">
<div class="job">
</div>
<div class="job last">
<p>
I am broadly interested in methods for Knowledge Graph (KG) creation and
expansion and application of such background KGs for end tasks such as Question
Answering and Document Classification. My current research focuses on improving
KG completion methods and their application on densifying Open KGs extracted
from a text corpus.
<!-- I am broadly interested in methods for Knowledge Graph(KG) creation and expansion and application of such background KGs for end tasks such as Question Answering, Document Classification etc. Currently, I am working on techniques for learning interpretable representations for KGs and Text.-->
</p>
</div>
</div>
</div>
<div class="yui-gf">
<div class="yui-u first">
<h2>Experience</h2>
</div>
<!--// .yui-u -->
<div class="yui-u">
<div class="job">
<h2>Applied Scientist</h2>
<h3>Amazon, India</h3>
<h4>November 2022 - Present</h4>
<p>Working on Product Information Extraction.</p>
</div>
<div class="job">
<h2>Visiting Researcher</h2>
<h3>Google Research, India</h3>
<h4>September 2021 - October 2022</h4>
<p>Working on Language Models.</p>
</div>
<div class="job">
<h2>Intern</h2>
<h3>Facebook, London</h3>
<h4>September 2018 - November 2018 (3 months)</h4>
<p>Worked on Search Query Recommendation.</p>
</div>
<div class="job">
<h2>Research Intern</h2>
<h3>IBM Research Lab, Bangalore</h3>
<h4>June 2016 – August 2016 (3 months)</h4>
<p>Worked on Task Specific Knowledge Graph Construction.</p>
</div>
<div class="job last">
<h2>Member of Technical Staff</h2>
<h3>Veveo India Private Limited, Bangalore</h3>
<h4>August 2013 – July 2015 (2 years)</h4>
<p>Worked on conversation based searches on entertainment domain. It requires
solving multiple sub-problems like named-entity recognition, user-intent
detection etc. My work is focused on finding user intents and context management
during conversation. I am also working on a template-based method which can be
used for named-entity recognition and user-intent detection. It can also be used
for generating suggestions as user types the query.</p>
</div>
</div>
<!--// .yui-u -->
</div>
<!--// .yui-gf -->
<div class="yui-gf">
<div class="yui-u first">
<h2>Publication</h2>
</div>
<!--// .yui-u -->
<div class="yui-u">
<div class="job">
<h2>OKGIT: Open Knowledge Graph Link Prediction with Implicit Types</h2>
<h3>Chandrahas, Partha Pratim Talukdar</h3>
<h3>Findings of the ACL: ACL-IJCNLP 2021</h3>
<h3><a href="https://arxiv.org/abs/2106.12806">Paper</a></h3>
<!--p> In this work, we developed a learning algorithm based on orthogonal matching pursuit that automatically
learns a score system type model, which enjoys the benefits of both worlds: like other ML methods,
it is adaptive; like standard score systems, it is easily interpretable by the clinicians. Our experiments
confirm the efficacy of our approach in comparison to standard score systems and ML methods.</p-->
</div>
</div>
<div class="yui-u">
<div class="job">
<h2>Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph
Embeddings</h2>
<h3>Chandrahas, Nilesh Agrawal, Partha Pratim Talukdar</h3>
<h3>International Conference on Natural Language Processing (ICON) - 2020</h3>
<h3>
<a
href="http://www.iitp.ac.in/~ai-nlp-ml/icon2020/resources/ICON2020-Proceedings.pdf#page=84">Paper</a>
</h3>
<!--p> In this work, we developed a learning algorithm based on orthogonal matching pursuit that automatically
learns a score system type model, which enjoys the benefits of both worlds: like other ML methods,
it is adaptive; like standard score systems, it is easily interpretable by the clinicians. Our experiments
confirm the efficacy of our approach in comparison to standard score systems and ML methods.</p-->
</div>
</div>
<div class="yui-u">
<div class="job">
<h2>Inducing Interpretability in Knowledge Graph Embeddings</h2>
<h3>Chandrahas, Tathagata Sengupta, Cibi Pragadeesh, Partha Pratim Talukdar</h3>
<h3>International Conference on Natural Language Processing (ICON) - 2020</h3>
<h3><a
href="http://www.iitp.ac.in/~ai-nlp-ml/icon2020/resources/ICON2020-Proceedings.pdf#page=94">Paper</a>
</h3>
<!--p> In this work, we developed a learning algorithm based on orthogonal matching pursuit that automatically
learns a score system type model, which enjoys the benefits of both worlds: like other ML methods,
it is adaptive; like standard score systems, it is easily interpretable by the clinicians. Our experiments
confirm the efficacy of our approach in comparison to standard score systems and ML methods.</p-->
</div>
</div>
<div class="yui-u">
<div class="job">
<h2>Towards Understanding the Geometry of Knowledge Graph Embeddings</h2>
<h3>Chandrahas, Aditya Sharma, Partha Pratim Talukdar</h3>
<h3>Association for Computational Linguistics (ACL) - 2018</h3>
<h3><a href="https://www.aclweb.org/anthology/papers/P/P18/P18-1012/">Paper</a></h3>
<!--p> In this work, we developed a learning algorithm based on orthogonal matching pursuit that automatically
learns a score system type model, which enjoys the benefits of both worlds: like other ML methods,
it is adaptive; like standard score systems, it is easily interpretable by the clinicians. Our experiments
confirm the efficacy of our approach in comparison to standard score systems and ML methods.</p-->
</div>
</div>
<div class="yui-u">
<div class="job">
<h2>Revisiting Simple Neural Networks for Learning Representations of Knowledge
Graphs</h2>
<h3>Srinivas Ravishankar, Chandrahas, Partha Pratim Talukdar</h3>
<h3>Automated Knowledge Base Construction (AKBC) Workshop at NeurIPS - 2017</h3>
<h3><a href="https://arxiv.org/abs/1711.05401">Paper</a></h3>
<!--p> In this work, we developed a learning algorithm based on orthogonal matching pursuit that automatically
learns a score system type model, which enjoys the benefits of both worlds: like other ML methods,
it is adaptive; like standard score systems, it is easily interpretable by the clinicians. Our experiments
confirm the efficacy of our approach in comparison to standard score systems and ML methods.</p-->
</div>
</div>
<!--// .yui-u -->
<div class="yui-u">
<div class="job last">
<h2>Learning Score Systems for Predicting Patient Mortality in ICUs via Orthogonal
Matching Pursuit</h2>
<h3>Aadirupa Saha, Chandrahas Dewangan, Harikrishna Narasimhan, Sriram Sampath,
Shivani Agarwal</h3>
<h3>International Conference on Machine Learning and Applications (ICMLA) - 2014
</h3>
<h3><a
href="http://shivani-agarwal.net/Publications/2014/icmla14-ICU-mortality-prediction.pdf">Paper</a>
</h3>
<!--p> In this work, we developed a learning algorithm based on orthogonal matching pursuit that automatically
learns a score system type model, which enjoys the benefits of both worlds: like other ML methods,
it is adaptive; like standard score systems, it is easily interpretable by the clinicians. Our experiments
confirm the efficacy of our approach in comparison to standard score systems and ML methods.</p-->
</div>
</div>
<!--// .yui-u -->
</div>
<!--// .yui-gf -->
<div class="yui-gf">
<div class="yui-u first">
<h2>Projects</h2>
</div>
<!--// .yui-u -->
<div class="yui-u">
<div class="job">
<h2>Optimization for Machine Learning</h2>
<h3>Study of Parallel Coordinate Descent Algorithms</h3>
<h4>2015</h4>
<p>Coordinate Descent Algorithms form a class of simple optimization algorithms
which has received attention of many researchers in last decade. There has been
significant advancements in adapting these algorithms in parallel (multi-core)
settings. In this project, we focused on studying parallel versions of
Coordinate Descent Algorithms. We also implemented and conducted experiments
with some of these algorithms.</p>
</div>
<div class="job">
<h2>Natural Language Processing</h2>
<h3>Entity Linking</h3>
<h4>2015</h4>
<p>Entity linking(EL) is a process of mapping textual mentions of named-entities in
text to an entity in some knowledge base. EL is used in numerous areas of
natural language processing to automate structured information retrieval from
raw corpus.
In this project, we focused on D2W (Disambiguation to Wikipedia) task, where we
map textual mentions to corresponding Wikipedia pages. Specifically, we studied
the effects of co-reference resolution (using Stanford CoreNLP) on the
performance of <a
href="https://cogcomp.cs.illinois.edu/page/software_view/Wikifier">Wikifier</a>
system for D2W task.
</p>
</div>
<div class="job">
<h2>Machine Learning</h2>
<h3>Application of Machine Learning for predicting mortality in ICUs</h3>
<h4>2012-2013</h4>
<p> This project aims to develop a technique for estimating the probability of
patients' mortality in the Indian intensive care units. We apply different
machine learning techniques (specifically, linear and non-linear logistic
regression) to this problem. We also propose a boosting-style approach for
predicting patient mortality rates, which automatically builds a score-based
system for Indian patient data. </p>
</div>
<div class="job">
<h2>Program Analysis</h2>
<h3>Null Dereference Analysis in Java Programs</h3>
<h4>2012</h4>
<p>Null derefence is a common bug in programs. This project applies the abstract
interpretation framework for the analysis of null dereferences in Java programs
using <a href=http://www.sable.mcgill.ca/soot />Soot framework</a>. </p>
</div>
<div class="job">
<h2>Expert Examination System</h2>
<h3>An automated question paper generation system</h3>
<h4>2011</h4>
<p>This project automates the question paper generation process for examinations. It
covers the process of creation of questions database, selection of questions for
exams meeting certain criteria and generation of encrypted paper and its
decryption. </p>
</div>
<div class="job last">
<h2>Graphics and OpenGL</h2>
<h3>Implementation of a Tetrahedral Mesh Viewer</h3>
<h4>2012</h4>
<p>The aim of the project was to implement a basic viewer which can render
tetrahedral meshes read from a file. It also supports rendering of individual
meshes and group of meshes at different scaling levels.</p>
</div>
</div>
<!--// .yui-u -->
</div>
<!--// .yui-gf -->
<div class="yui-gf ">
<div class="yui-u first">
<h2>Education</h2>
</div>
<div class="yui-u">
<div class="job">
<h2>Indian Institute of Science, Bangalore</h2>
<h3>PhD, Computer Science and Engineering </h3>
</div>
<div class="job">
<h2>Indian Institute of Science, Bangalore</h2>
<h3>Master of Engineering, Computer Science and Engineering </h3>
</div>
<div class="job">
<h2>Bhilai Institute of Technology, Durg</h2>
<h3>Bachelor of Engineering, Computer Science and Engineering </h3>
</div>
<div class="job last">
<h2>JRD Higher Secondary School, Durg</h2>
<h3>AISSCE ( 12<sup>th</sup> ), Physics, Chemistry, Mathematics </h3>
</div>
</div>
</div>
<!--// .yui-gf -->
<div class="yui-gf ">
<div class="yui-u first">
<h2>Positions of Responsibility</h2>
</div>
<div class="yui-u">
<div class="job">
<h2>Reviewer for EMNLP 2020-22, NAACL 2021, ICON 2020</h2>
</div>
<div class="job">
<h2>Student volunteer for EMNLP 2020, ACL 2018, 2020</h2>
</div>
<div class="job last">
<h2>Led the publicity team for department (CSA) Open-days 2013 and CSA Summer School
2013</h2>
</div>
</div>
</div>
<!--// .yui-gf -->
<div class="yui-gf ">
<div class="yui-u first">
<h2>Achievements</h2>
</div>
<div class="yui-u">
<div class="job">
<h2>Google travel grant for attending ACL 2018</h2>
</div>
<div class="job">
<h2>Received Special Recognition Award while working at Veveo R&D</h2>
</div>
<div class="job">
<h2>AIR-44 in GATE-2011</h2>
</div>
<div class="job">
<h2>Honours in Bachelor of Engineering</h2>
</div>
<div class="job last">
<h2>Certificate of Excellence in Mathematics in 12<sup>th</sup></h2>
</div>
</div>
</div>
<!--// .yui-gf -->
<div class="yui-gf ">
<div class="yui-u first">
<h2>Talks</h2>
</div>
<div class="yui-u">
<div class="job">
<h2>Representation Learning for Text</h2>
</h3>CSA Summer School 2016</h3>
</div>
<div class="job last">
<h2>Introduction to Machine Learning </h2>
</h3>CSA Summer School 2013</h3>
</div>
</div>
</div>
<!--// .yui-gf -->
<div class="yui-gf last">
<div class="yui-u first">
<h2>Hobbies</h2>
</div>
<div class="yui-u">
<div class="job ">
<h2>Music (Playing Guitar)</h2>
</div>
<div class="job last">
<h2>Origami</h2>
</div>
</div>
</div>
<!--// .yui-gf -->
</div>
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</body>
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