Anurag Roy

Anurag Roy

pursuing PhD in Multimodal Machine Learning

CSE, IIT Kharagpur

Biography

I graduated from the Computer Science and Technology department(with Hons.) of IIEST Shibpur in the year 2017. I worked as a software engineer at Polaris Networks for a year(July 2017 - Jul 2018). Currently I am a Ph.D. scholar at IIT Kharagpur under the supervision of Prof. Saptarshi Ghosh and Prof. Abir Das of Computer Science and Engineering Department. I am also a member of the Complex Networks Research Group(CNERG)

Interests

  • Machine Learning
  • Multimodal Learning
  • Information Retrieval
  • Meta Learning
  • Continual Learning

Education

  • PhD in Computer Science and Engineering, 2019

    Indian Institute of Technology Kharagpur

  • B.E.(Hons) in Computer Science and Technology, 2017

    Indian Institute of Engineering Science and Technology, Howrah, West Bengal

  • CBSE XII Science with Computer Application, 2013

    Army Public School, Ballygunge, Kolkata, West Bengal

Skills

Programming Skills

Python

90%

C

90%

Java

70%

Experience

 
 
 
 
 

Senior Research Fellow

CNeRG Lab, CSE Department, IIT Kharagpur

Jul 2020 – Present Kharagpur, West Bengal
 
 
 
 
 

Applied Scientist Intern

India ML Team, Amazon Development Center India Pvt. Ltd.

May 2019 – Jul 2019 Bengaluru, Karnataka
– Predicted Product Trust Score – Developed machine learning models to Predict Trust Score
 
 
 
 
 

Ph.D. Research Scholar

CNeRG Lab, CSE Department, IIT Kharagpur

Jan 2019 – Present Kharagpur, West Bengal
Under the Supervision of Prof. Saptarshi Ghosh
 
 
 
 
 

Junior Research Fellow

CNeRG Lab, CSE Department, IIT Kharagpur

Jul 2018 – Jul 2020 Kharagpur, West Bengal
 
 
 
 
 

Software Developer

Polaris Networks

Jul 2017 – Jul 2018 San Jose, California

Responsibilities include:

  • Development of 5g core network stack
  • Development of MCPTT tester
 
 
 
 
 

Research Intern

IIT Kanpur

May 2017 – Aug 2017 Kanpur, Lucknow

A LANGUAGE INDEPENDENT MORPHOLOGICAL CLUSTERING ALGORITHM

Responsibilities include:

  • Understaning the different kind of morphological variations existing over different languages.
  • Developing of an un-supervised clustering algorithm to capture the morphological variants of a word.
 
 
 
 
 

Research Intern

IIEST Shibpur

Dec 2016 – Jan 2017 Howrah, West Bengal
Developed using python and scikit-learn a program which identified rumor tweets in real-time by learning from previous data.
 
 
 
 
 

Research Intern

CNeRG Lab, CSE Department, IIT Kharagpur

May 2016 – Jul 2016 Kharagpur, West Bengal

UNDERSTANDING RELATIVE IMPORTANCE OF CONTENT AND RESPONSES ON COMMUNITY SURVIVAL IN REDDIT.

Responsibilities include:

  • Developin an on-line survey application in flask and jinja.
  • Using machine learning models to evaluate accuracy
 
 
 
 
 

Undergraduate Student

IIEST Shibpur

Aug 2013 – May 2017 Howrah, West Bengal
BE in Computer Science and Technology

Recent Publications

Quickly discover relevant content by filtering publications.

ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions

Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to …
PDF Code DOI

Distributed Representation of Tags for Active Zero Shot Learning [short paper]

Extreme multi-labeled classification (XMLC) refers to the problem of tagging items to its most relevant subset of class labels from an …
PDF Code DOI

An Unsupervised Normalization Algorithm for Noisy Text: A Case Study for Information Retrieval and Stance Detection

A large fraction of textual data available today contains various types of ‘noise’, such as OCR noise in digitized …
PDF Code

Retrieving Information from Multiple Sources [poster]

The Web has several information sources on which an ongoing event is discussed. To get a complete picture of the event, it is important …
PDF DOI

Combining Local and Global Word Embeddings for Microblog Stemming [short paper]

Stemming is a vital step employed to improve retrieval performance through efficient unification of morphological variants of a word. …
PDF Code DOI
See all publications

Recent Posts

Introduction to Continual Learning

In this section we will introduce you to Continual Learning Contents Contents Definition Overwiew Features Catastrophic Forgetting Stability-Plasticity Dilemma Approaches Regularization Based Dynamic Architectures Memory Replay References Definition A continual learning system can be defined as an adaptive algorithm capable of learning from a continuous stream of information, with such information becoming progressively available over time and where the number of tasks learned are not pre-defined.
Jul 31, 2021 3 min read

MetaSegnet

Summary While Existing methods on few-shot image segmentation focus on 1-way segmentation, this paper focuses on k-way segmentation tasks. Existing Few-shot learning algorithms suffer from: Distribution Divergence: Most existing methods require to be pre-trained on ImageNet.
Jun 27, 2020 3 min read meta learning, deep learning, image segmentation

From CPP to Java

Some differences between c++ and java: Java compiled code is platform independent whereas c++ compiled code is platform dependent Java interpreter reports the run-time error that caused the execution to halt unlike in c/c++ programs which may simply crash
Apr 30, 2020 2 min read cpp to java cheatsheet

Stats 101

Sampling Theory Data scientists are required to draw conclusions about a group, a.k.a population from a few samples of it because getting the entire population is intractable. This process of drawing samples is called sampling.
Dec 4, 2019 2 min read statistics, mathematics, probability

Sampling Theory and Distributions

Sampling Theory Data scientists are required to draw conclusions about a group, a.k.a population from a few samples of it because getting the entire population is intractable. This process of drawing samples is called sampling.
Nov 5, 2019 2 min read statistics, mathematics, probability
See all posts

Popular Topics

C c++ coding Continual Learning CPP deep learning hacks hypothesis testing Incremental Learning Java Linux maml meta learning Neural Networks Python Reinforcement Learning Reinforcement Learning-An Introduction samples statistics Tensorflow

Contact

  • 7278389228
  • Dept. of Computer Science and Engineering, IIT Kharagpur, Kharagpur, West Bengal 721302