Himanshu Kumar

Bangalore, India.

Iam intersted in ML, AI and it's applications. Currently, am pursuing masters in Artificial Intelligence from Indian institute of Science, Bangalore.
I have completed projects in NLP and Computer vision. have worked in developing various ML/DL models in the field of Computer vision as well as NLP. For my final Mtech project, I am working on SLAM based problems using an event camera. I have completed implementation of Depth estimation using a RCNN model for real world environment.
I have familiarity with ML frameworks and tools such as Pandas, Keras, TensorFlow, scikit-learn, and learning PyTorch.

Experience

Technical Officer

ISTE VESIT

  • Head of the design and development team for website and android application projects.

  • Conducted technical workshops in Android Studio and Adobe Photo shop with more than 200 attendees.

Aug 2016 - March 2017

Executive Head

PRAXIS TAC-TECHNICAL FESTIVAL

  • Worked along with 2 other team members to develop Android application for College Tech-fest Praxis 2016.

  • Responsible for managing technical assistance committee and coordinating with Organizing and Executive committee.

May 2016 - Sep 2016

Education

Indian Institute of Science, Bangalore

Master of technology
Artificial Intelligence

August 2019 - Present

VESIT, Mumbai

Bachleor of Engineering
Computer Engineering

August 2014 - May 2018

Projects

Deep Learning
  • Natural Language Inference

    • The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE). Implemented a LSTM and Logistic regression based model on SNLI. Also, fine tuned a pre trained small version of BERT model for SNLI dataset.

  • Depth Estimation using Neuromorphic Camera

    • Working on Monocular Depth Estimation for SLAM by processing subsequent non-overlapping windows of events/frames over an interval. Training will be done based on data obtained by Conventional and Event based Vision cameras, using deep learning methods.

  • Sentiment Classification using Tree structured LSTM

    • Using LSTMs in a tree structured manner, performed binary and 5-class sentiment classification on Stanford Sentiment Treebank dataset. Used Glove embeddings for word representation.

  • Text to Image Synthesis using GAN

    • Using a RNN and Deep Convolutional GAN implemented an image synthesis models, which translates sentence text into image pixels. Using GLOVE word embeddings trained the model to generate images of birds and flowers.

Data Structures
  • Persistent Data Structures

    • Implemented persistent data structures as part of Coursework assignment for Data Structure and Algorithms course. Both Partially and fully persistent data structures were implemented in C. Used the application of Persistent Stack for solving a maze.

Courses

  • E0 251
    Data Structures and Algorithms
  • E0 230
    Computational Methods of Optimisation
  • E0 299
    Computational Linear Algebra
  • E1 222
    Stochastic Models and Applications

  • E1 213
    Pattern Recognition and Neural Networks
  • E0 250
    Deep Learning
  • E1 277
    Reinforcement Learning
  • E9 261
    Speech Information Processing

  • E9 253
    Neural Networks and Learning Systems
  • E9 208
    Digital Video: Perception and Algorithms
  • E9 309
    Advanced Deep Learning

Acheivements & Extra-Curricular

  • 107th AIR - GATE 2019 - Computer Science secured 99.89 percentile amongst around 1 lakh students.
  • Reliance Ode2Code Hackathon, Secured 1st place in Genius Unleashed, a coding challenge in NLP from Reliance.