M.Tech Research Areas: Machine Learning, Deep Learning, Sequence Modelling, GRU, LSTM, Word Embeddings

Although I worked extensively on sequence modelling during my master’s, I have an urge to explore the further possibilities of using deep learning on digital images and videos as a part of computer vision.


Papers selected as Book Chapters

  • Artificial Intelligence and Early Warning Systems
    Rabindra Lamsal and T.V. Vijay Kumar
    Global Symposium on Artificial Intelligence in Governance and Disaster Management, New Delhi, 2019

  • Artificial Intelligence based Disaster Response Systems
    Rabindra Lamsal and T.V. Vijay Kumar
    Fourth World Congress on Disaster Management. Indian Institute of Technology (IIT) Bombay, 2019

  • Artificial Intelligence Based Early Warning System for Coastal Disasters
    Rabindra Lamsal and T.V. Vijay Kumar
    International workshop on ‘Reinforcing Coastal Zone Management: Saving Lives, Habitats and Livelihood of People’, New Delhi, 2019

Journal articles

  • Improving Twitter based Disaster Response using Deep Learning (to be communicated)
    Rabindra Lamsal and T.V. Vijay Kumar

  • Twitter based Disaster Response using Machine Learning (to be communicated)
    Rabindra Lamsal and T.V. Vijay Kumar

  • Classifying Emergency Tweets for Disaster Response (communicated)
    Rabindra Lamsal and T.V. Vijay Kumar


  • Determining Optimal Number of k-Clusters based on Predefined Level-of-Similarity
    Rabindra Lamsal and Shubham Katiyar
    arXiv preprint arXiv:1810.01878, 2018

  • Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning
    Rabindra Lamsal and Ayesha Choudhary
    arXiv preprint arXiv:1809.09813, 2018


  • Twitter Based Disaster Response System
    Disaster Response System targeted for Coastal disasters
    Projects completed as a part of M.Tech Dissertation

  • Word Embeddings (Word2Vec Implementation) for Nepali Language [GitHub Repo]
    Word2Vec implementation of a Nepali language corpora having 100 million running words. The text corpora was designed by scrapping publicly available news content of various Nepali online news portals. The model has Word Embeddings for 0.5 million Nepali words.

  • Indian Premier League (IPL) Matches Prediction Model
    A machine learning model capable of predicting the outcome of an IPL match, 15 minutes before the gameplay, immediately after the toss results. The prediction model was able to correctly predict 43 out of 60 matches of 2018 season. Project carried out as a part of the course Artificial Intelligence.