Profile
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 Computer Science and Automation, Indian Institute of Science Bangalore under the guidance of Dr. Partha Pratim Talukdar. 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 Dr. Shivani Agarwal in the area of Machine Learning at the Machine Learning and Learning Theory Lab.
Interests
Knowledge Graphs
Natural Language Understanding
Machine Learning
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.
Experience
Applied Scientist
Amazon, India
November 2022 - Present
Working on Product Information Extraction.
Visiting Researcher
Google Research, India
September 2021 - October 2022
Working on Language Models.
Intern
Facebook, London
September 2018 - November 2018 (3 months)
Worked on Search Query Recommendation.
Research Intern
IBM Research Lab, Bangalore
June 2016 – August 2016 (3 months)
Worked on Task Specific Knowledge Graph Construction.
Member of Technical Staff
Veveo India Private Limited, Bangalore
August 2013 – July 2015 (2 years)
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.
Publication
OKGIT: Open Knowledge Graph Link Prediction with Implicit Types
Chandrahas, Partha Pratim Talukdar
Findings of the ACL: ACL-IJCNLP 2021
Paper
Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph Embeddings
Chandrahas, Nilesh Agrawal, Partha Pratim Talukdar
International Conference on Natural Language Processing (ICON) - 2020
Paper
Inducing Interpretability in Knowledge Graph Embeddings
Chandrahas, Tathagata Sengupta, Cibi Pragadeesh, Partha Pratim Talukdar
International Conference on Natural Language Processing (ICON) - 2020
Paper
Towards Understanding the Geometry of Knowledge Graph Embeddings
Chandrahas, Aditya Sharma, Partha Pratim Talukdar
Association for Computational Linguistics (ACL) - 2018
Paper
Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs
Srinivas Ravishankar, Chandrahas, Partha Pratim Talukdar
Automated Knowledge Base Construction (AKBC) Workshop at NeurIPS - 2017
Paper
Learning Score Systems for Predicting Patient Mortality in ICUs via Orthogonal Matching Pursuit
Aadirupa Saha, Chandrahas Dewangan, Harikrishna Narasimhan, Sriram Sampath, Shivani Agarwal
International Conference on Machine Learning and Applications (ICMLA) - 2014
Paper
Projects
Optimization for Machine Learning
Study of Parallel Coordinate Descent Algorithms
2015
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.
Natural Language Processing
Entity Linking
2015
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 Wikifier system for D2W task.
Machine Learning
Application of Machine Learning for predicting mortality in ICUs
2012-2013
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.
Program Analysis
Null Dereference Analysis in Java Programs
2012
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 Soot framework.
Expert Examination System
An automated question paper generation system
2011
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.
Graphics and OpenGL
Implementation of a Tetrahedral Mesh Viewer
2012
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.