Shreesh Ladha

I am a graduate student in the department of Computer Science at University of Massachusetts Amherst. I am primarily interested in Deep Learning, Reinforcement Learning and their application to Computer Vision and Natural Language Processing.

Before coming here, I worked for a year at Samsung Research Institute, Bangalore in their S-Voice(Now Bixby) NLU Research division. My work revolved around intent recognition and entity extraction using deep learning technologies. Prior to that, I did my undergraduation from Indian Institute of Technology Kanpur with a major in Mathematics and Scientific Computing.

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Notable Projects

OCR of conjunct characters in Devanagari Script
Supervisors: Dr. Harish Karnick, Indian Institute of Technology Kanpur
Dr. Amit Mitra, Indian Institute of Technology Kanpur

Devised algorithms for conjunct character recognition within scanned documents. Experimented with pre-trained and self-trained CNN architectures.

Obtained significant improvement over preexisting systems with a decrease in average word error rate from 19.5% to 12.5%.


A survey of Zero Shot Learning
Supervisor: Dr. Piyush Rai, Indian Institute of Technology Kanpur

Studied different methods of performing Zero Shot Learning(ZSL) - prediction of a label that has been not seen during the training procedure.

Implemented two contemporary papers from this area which required learning a common semantic space for embedding images and labels, to perform ZSL task. Focused on dictionary learning as a way to resolve the PDS issue and found that CNN based features drastically improve the classification accuracy.

report | poster


Visualization of high dimensional data
Supervisor: Dr. Ketan Rajawat, Indian Institute of Technology Kanpur

Explored applications of convex optimization for dimensionality reduction, especially over non linear manifolds.

Compared performance based on visualizations, computational complexities, and error rates obtained in classification tasks. Selected as the best project in the course comprising of over 80 students.



Domain-invariant Transfer Kernel Learnning
Supervisor: Dr. Harish Karnick, Indian Institute of Technology Kanpur

Implemented a learning model which generalizes across training and testing data from different distributions.

Minimizing the Nystrom Approximation error, obtained a domain-invariant kernel which is plugged into an SVM for transfer learning.


Samsung Research and Development Institute, Bengaluru (SRIB)
Supervisor: Singaravel Ramalingam, Principal Engineer, SRIB

Created a system for analyzing and suggesting improvements in Samsung's voice assistant.

Used Apache Spark and Drill to work with terabytes of data and applied classification and clustering algorithms for better insights. Wrote queries to gather insights using SQL and Map-Reduce code, in Spark and Drill.


m.Paani, Mumbai
Supervisor: Akanksha Hazari, Founder & CEO

m.Paani is a Hult-Prize winning social startup working on loyalty programmes for people living at the bottom of the pyramid. Built an interactive map, for spatial data analytics, using web technologies and open source javascript libraries.

Presented the above tool to a CEO of a large company that is an m.Paani partner. Designed the front-end interfaces of m.Paani's loyalty management application.

inspired from this website