Yasaswi Currently pursuing my Masters (MS by research) in Computer Science and Engineering at IIIT-Hyderabad, India. I work at Center for Visual Information Technology (CVIT) under the guidance of Prof. C. V. Jawahar. My co-advisor is Dr. Suresh Purini.




Research Interests

  • Deep Learning, Machine Learning and Computer Vision.

  • My current research is on Deep Learning, specifically on the convergence analysis of training Deep Neural Networks. Apart from this I enjoy working on real world Machine Learning applications.


Publications

  • Jitendra Yasaswi, Sri Kailash, Anil Chilupuri, Suresh Purini, C. V. Jawahar. Unsupervised Learning Based Approach for Plagiarism Detection in Programming Assignments. ACM ISEC 2017. PDF


Experience

Teaching Assistant. IIIT Hyderabad
Monsoon 2016

I worked as a Teaching Assistant for Machine Learning Course.
GE Capital. Hyderabad, India
June 2014 - July 2014
Intern, Research and Data Analytics.
Risk and Fraud prediction.

Projects



Aug 2015 - July 2016


Predicting the Training Time of Deep Neural Networks [PDF]
Proposed a method to estimate the time required to train a deep neural network to achieve a given accuracy, given the neural network type and a task at hand. One of the main applications of having such prediction capacity is to determine beforehand whether some on-going training procedure is heading in the right direction or not. If this were predicted early, it would be possible to stop the training procedure and save important computational resources.

Aug 2016 - Sept 2016


Plagiarism detection using Code-Based Features
Proposed a method which uses static and dynamic source code based features for automatic plagiarism detection in programming assignments.

Aug 2014 - Nov 2014


Replacing CCTV Cameras With Cheap Smartphones
Built an end-to-end scalable cloud system to support capture, storage and retrieval of imagestaken by smartphone running on android OS. Used multiple services from Amazon Web Service (AWS) to power the system.

Aug 2014 - Nov 2014


Bike Sharing Demand
Used Machine Learning techniques like Regression, Random Forests and Gradient Boosted Trees to forecast the bike rental demand based on historical weather patterns and bike sharing data.

Aug 2014 - Nov 2014


Sentiment Analysis On Movie Reviews
Used Naive-Bayes classifier, logistic regression, Support Vector Machines to predict the sentiment or polarity class for given movie reviews from the Rotten Tomatoes dataset.

Education

Master of Science
(MS by research)
2014 - present
International Institute of Information Technology - Hyderabad
Computer Science
Bachelor of Technology (B.Tech.)
2010 - 2014
JNTUK University College of Engineering - VZM
Information Technology

Hobbies

  • In my free time I like to listen melodies, try to play them on my guitar
  • Playing Cricket

Contact

Email: jitendra.katta@research.iiit.ac.in, yasaswikatta@gmail.com

LinkedIn Twitter GitHub

CVIT,
IIIT-Hyderabad,
Gachibowli, Hyderabad
India - 500032