Work Experience

• Student Research Intern - VP Lab,IIT Madras

  May 2015-July 2015

  I worked at Visualization and Perception Lab(VP Lab) of IIT Madras on Face Recognition under the supervision of Prof. Sukhendu Das. My project contributions were the following:-

  • Developed a Online Face Recognition Software using basic algorithms such as Eigenfaces and Fisherfaces
  • Software was developed over .NET framework
  • Maximum Reliability and modularity

• Summer Research Intern- NIT,Rourkela

  May - July 2014

  I worked as an intern at the Computer Vision Lab of NIT Rourkela for the duration May,2014-July,2014. My target in this project were broadly the following:-

  • Developed a highly accurate shadow removal system, a method to remove shadows from images and videos
  • Explored many classification algorithms and recorded their accuracy over 5 different UCI datasets

• PHP Developer - NIC,Govt. of India

  Nov - Dec 2013 & Nov-Dec 2014

  I worked at NIC as a PHP programmer to develop a Project Information Tracking System. My major responsibilites were:-

  • Developed a back-end model to track project information
  • Maximum Modularity, minimum Ambiguity and improved Maintainability

• Explorer

  1994-Till Death
  • Regular programmer over several online programming websites such as Codechef.com and SPOJ
  • In my non-academic sessions of the year, I visit different schools to give tutorials on basic Computer Science topics
  • Currently involved in Data Visualization using R
  • A regular Open Source Contributor

Education

• Carnegie Mellon University

  AUG 2016-
  • MS Biotechnology Innovation & Computation
  • School of Computer Science

• The LNM Institute of Information Technology

  AUG 2012-JUN 2016
  • B.Tech (Major: Computer Science Engineering)
  • CGPA : 9.29 (on a scale of 10.00)

• Sir Padampat Sighania School,Kota

  2010-2012
  • Class XII (CBSE) with 82.60% marks.

• Army Public School,Kirkee,Pune

  2005-2010
  • Class X (CBSE) with 9.8 CGPA(on the scale of 10).

• 1. Recommender's System

  • • Deconstructing Recommender Systems
    • RecSys 2014

• 2. Computer Vision

  • • 16-385 Computer Vision : Robotics Institute
    • Awesome Papers from Fernando De la Torre(CMU)
    • Python for geospatial data processing

• 3. Interview Stuff

  • • How does a browser work???
    • How to store passwords in databases?
    • Difference between OSI and TCP model
    • Operating System

  • 4. Dynamic Programming

    • • Dynamic Programming
      • Dynamic Programming Tutorial
      • Dynamic Programming Practice
      • More Dynamic Programming
      • Dynamic Programming Problems
      • Dynamic Programming Problems

    • Awesome YouTube Channels

      • • Statistics - 1
        • Mathematical Monk
        • The new Boston
        • CS50
        • Google for Entrepreneurs
        • Google for Developers
        • Best for interviews

      • 5. Trees and Graphs

        • • Graph Algorithm Repo
          • Course on Graph Algorithms
          • Grpah Problem Solving
          • Graph Basic Algo Implementation
          • Graph Basics
          • Awesome Coursera
          • Graph Basic Algo Implementation
          • Graph Basics
          • Awesome Coursera

        • 6. JavaScript

          • • Javascript tutorial
            • Javascript cool stuff
            • Javascript: Douglas Crockford

          • 7. Python

            • • Inputs in Python
              • Regular expression in Python
              • Files in Python
              • Python- Awesome Tutorial
              • NLP : Basics
              • Tensorflow

            • 8. Natural Language Processing

              • • Problem Repository
                • Learning Tips
                • Some basics of NLP
                • Algo Directory
                • NLP Lectures : Manning
                • Peter Norvig's Spell Corrector

              • 9. Machine Learning: Techincal Paradigm Change

                • • Visual Familiarity of ML
                  • ML Introductory
                  • Neural Networks & Deep Nets

                • 10. Logical Thinking: Most important skill

                  • • Sample Questions
                    • Big O CheetSheet
                    • Crazy Coders
                    • More Problems
                    • How to approach a brain teaser?
                    • Bit Manipulation Tricks
                    • Binary Indexed Tree - You Beauty!!
                    • Boyer Moore Majority Voting

                  • 11. Deep Learning

                    • • Stat 212b : Topics in Deep Learning
                      • Deep Learning by CSAIL, MIT
                      • Deep Learning in NLP
                      • Deep Learning in NLP
                      • Convolutional Neural Networks
                      • Machine Learning in Oxford U
                      • Optimization Algorithms - Overview
                      • Hackers Guide to NN
                      • Convolutional Neural Networks for Visual Recognition
                      • Karpathy's Blog
                      • Yoshua Bengio
                      • Best Guys in Machine Learning
                      • Stanford Tutorials
                      • Uber Chief Scientist - Rockstar
                      • Ian Goodfellow recommended
                      • Restricted Boltzman Machines
                      • Hugo Deep Learning Tutorials
                      • Hinton on BMs
                      • Cheat Sheet for DL by YB,IG
                      • Introduction to Boltzmann Machine
                      • ELI5 : Its simply awesome .
                      • Long Short Term Memory Great Resource to start

                    • 12. Big Data Analytics

                      • • Map Reduce Starter Tutorial
                        • Map Reduce Awesome Description
                        • Begginer Tutorial on Hadoop Ecosystem
                        • Top Visualization Tools

                      • 13. Design

                        • • Software Design
                          • Hacks to improve design skills
                          • Product Design
                          • [Interview Prep]Infix, Prefix and Postfix
                          • Infix to Prefix
                          • Generators Python - Flow Explained
                          • Curl, Divergence and Gradient
                          • Tensors

                      • Bitcoins - Emerging Disruption

                        • • Bitcoin Original Paper - From where it all began
                          • Lot of resources
                          • Explain Bitcoin Like I'm five

                      • Things to know:-

                        • • Amazon Web Servies
                          • Character Encoding
                          • Garbage Collection Java
                          • [Interview Prep]Infix, Prefix and Postfix
                          • Infix to Prefix
                          • Generators Python - Flow Explained
                          • Curl, Divergence and Gradient
                          • Tensors

Legacy NLP & Stat Resources

• • Natural Language Processing (Almost) from Scratch
  • Foundations of statistical natural language processing
  • [Springer]A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling
  • Math needed for Deep Learning

Most Cited DL NLP Papers

• Year 2013
  
  • On the difficulty of training Recurrent Neural Networks

• Year 2015
  
  • Teaching Machines to Read and Comprehend
  • Effective Approaches to Attention-based Neural Machine Translation
  • Conditional Random Fields as Recurrent Neural Networks

• Year 2016
  
  • Neural Architectures for Named Entity Recognition
  • Exploring the Limits of Language Modeling

• Year 2017
  
  • Attention Is All You Need

• Year 2018
  
  • [SOTA] BERT
  • Phrase-Based & Neural Unsupervised Machine Translation
  • [DeepMind] Conditional Neural Processes
  • [DeepMind] Fast Parametric Learning with Activation Memorization
  • [DeepMind] Automatic Goal Generation for Reinforcement Learning Agents
  • [DeepMind] Successor Features for Transfer in Reinforcement Learning
  • [DeepMind] Measuring abstract reasoning in neural networks

• Year 2019
  
  • [SOTA] BERT
  • TRANSFORMER-XL: ATTENTIVE LANGUAGE MODELS BEYOND A FIXED-LENGTH CONTEXT
  • Assessing BERT’s Syntactic Abilities
  • Analysis Methods in Neural Language Processing: A Survey
  • Identifying and Correcting Label Bias in Machine Learning

April 4 2019

• • Neural Machine Translation of Rare Words with Subword Units
  • Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates

Burning Hot Ones

• • LOOKING FOR ELMO’S FRIENDS: SENTENCE-LEVEL PRETRAINING BEYOND LANGUAGE MODELING
  GAN Mania
  • Keeping up with the GANs

My research focus : Language Modelling

• • [SOTA] BERT
  • ULM FiT
  • ELMo
  • AWD LSTM
  • Multi-Task Learning
  • Attention is all you need
  • Bi-DAF
  • GLoMo
  • QANet
  • QRNN
  • Char-CNN
  • Morphological Embeddings
  • Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings
  • DiSAN
  • Character-Level Language Modeling with Deeper Self-Attention
  • A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
  • LOOKING FOR ELMO’S FRIENDS: SENTENCE-LEVEL PRETRAINING BEYOND LANGUAGE MODELING
  • TRANSFORMER-XL: ATTENTIVE LANGUAGE MODELS BEYOND A FIXED-LENGTH CONTEXT
  • On the State of the Art of Evaluation in Neural Language Models
  • Assessing BERT’s Syntactic Abilities

Great Blogs

• • A3C RL Policy Gradient Medium Article
  • BERT, ELMo and Co.
  • BERT Google Blog
  • ULMFiT Analitics Vidya Blog
  • Reddit - Link to BERT resources
  • BERT explained
  • Allen Ins. tutorials on Elmo
  • Rudder's [Amazing Guy for NLP] Intro to Multi-task Learning
  • CMU ML Blog on Parallel HPO
  • Progress in NLP
  • Optimization algorithms : Overview
  • Best Practices of DL in NLP
  • Selecting data for Transfer Learning
  • Approximating Softmax in Word Embeddings
  • [GAN] PAPER READING ROAD MAP

Great Githubs and Data Sources

• • Datasets by Percy Liang
  • SQUAD
  • SNLI Dataset
  • WMT
  • WordNet
  • Pre-trained Word Embeddings : Glove Stanford , Spacy Large , Google wrd2vec, Fast Text
  • Stanford NLP Reading Group
  • Awesome deep Learning Papers
  • Deep Learning Papers Git 2
  • NLP Deep Learning Papers Git 1
  • GAN Zoo
  • [Google] Attention and Augmented Recurrent Neural Networks
  • [Google] Sequence Modelling with CTC
  • [SEAS] The Annotated Transformer
  • Generalized Language Models

Things to remember

• • Phrase Structure Rules
  • Dependency Grammar