Shashwath Hosur Ananthakrishna

Machine Learning Research Engineer

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About Me

A Master's student in Computer Science at the University of Massachusetts Lowell focusing on Machine Learning/Deep Learning for problems in Computer Vision and Natural language processing. My passion is to build intelligent machines to solve complex problems in Artificial Intelligence. I aspire to combine my Machine Learning research experience and software engineering skills to contribute as a research software engineer.

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17 Marshall Terrace, Apt 5, Lowell, Massachusetts 01854

Phone Number

(978) 421-5090


Alternate Email



Machine Learning Research Engineer


Machine Learning Intern

Unsupervised Anomaly Detection
  • Developed an anomaly detection system for the company’s expense data using Machine Learning.
  • Used Kernel Density Estimation and a one-class Support Vector Machine in a hybrid approach that led to achieving an F1 score of 0.87.
  • Other project - Developed python programs to keep track of and validate Cost Center and G/L Accounts mapping changes for BW data conversion to SAP S/4 HANA system

University of Massachusetts Lowell

Research Assistant

  • Worked on an automatic dietary monitoring system which classified incoming audio streams from the Bluetooth headset worn by the user into different food categories.
  • Used a semi-supervised approach with Deep Boltzmann Machine for unsupervised pre-training and a deep feedforward neural network for fine-tuning. We achieved an accuracy of 94% with the DBM-DNN model which outperformed previous systems at the time.


University of Massachusetts Lowell

Sept 2016 - May 2018

Master of Science in Computer Science

Visvesvaraya Technological University

Sept 2012 - May 2016

Bachelor of Engineering in Computer Science


Semi supervised Learning with Deep Convolutional Generative Adversarial Networks

Aims to leverage the representations learnt by the discriminator network during the adversarial training process and use them to achieve one/few shot learning in supervised CNNs in the task of image classification using MNIST and CIFAR 10/100 datasets. Python, Keras, Tensorflow

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Clinical Concept Classification using Bidirectional Long Short Term Memory Network(LSTM)

Clinical concept classification system for medical notes in the 2010 I2B2/VA challenge dataset using Bidirectional LSTM with GloVe word embeddings trained on the MIMIC II dataset. Python, Tensorlfow, Keras

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In-one file manager

Desktop application that logically aggregates files based on their file extension. It helps to store information regarding file organization in storage media like DVDs, HDDs etc. It reminds the user to backup important documents and photos. It suggests the user to rename vaguely named documents like PDF files, word files etc. Java, JavaFX, SQLite

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Relation Extraction using Convolutional Networks

A Chainer implementation of a Convolutional Network model for relation classification in the SemEval Task 8 dataset. This model performs Multi-Way Classification of Semantic Relations Between Pairs of Nominals in the SemEval 2010 task 8 dataset.

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Sequence labeling using word embeddings

A repository for performing NER, POS tagging and Chunking using the CoNLL-2003 dataset using a simple window based concatenation of word embedding

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Programming Languages

  • Python
  • Java
  • C
  • C++
  • HTML
  • PHP
  • SQL
  • MySQL


  • Tensorflow
  • Chainer
  • PyTorch
  • Keras
  • sckit learn
  • Numpy
  • Pandas
  • Matplotlib


  • Git
  • Pycharm
  • Eclipse
  • Netbeans
  • Tableau
  • Visual Studio Code
  • Ipython notebooks

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