• Fall 2015: Algorithms for Modern Computer Systems, Information Retrieval, Distributed Systems, Computer Security.
• Spring 2016: Operating Systems, Machine Learning, Applied Cryptography and Computer Security.
• Object Oriented Programming (F.E), Data Structure and Algorithms (F.E),Distributed Systems (S.E), Microcontroller and Microprocessor (S.E), Computer Networks (T.E), Digital Telephony (T.E), Neural Networks (B.E), Data Compression and Encryption (B.E), Image Processing (B.E), Digital Signal Processing (B.E)
Completed Infosys training at Mysore, India.
The training involved subject and practical understanding of Software Engineering, RDBMS(Relational Database Management Systems), Oracle Database, Core-Java, .NET which includes learning C#, ASP.NET, ADO.NET, MSSQL, Visual Studio.
Completed the training with 4.7/5 GPA.
Single Page Application
Experience working in an Agile development environment using Test Driven Development concepts, SEO friendly, and A/B testing concepts.
Build Automation Project
Worked on build automation system to debug build and test related failures using tools such as Microsoft Build Tracker, Visual Studio, Team Foundation server and source depot.
• Collected tweets in 5 different languages by crawling the Twitter API using twitter4j. Tokenized and Indexed ~12000 tweets in Apache Solr.
• Performed cross lingual analysis by leveraging language detection, query translation, query expansion and field boosting.
• Implemented the UI using Bootstrap to display query results based on ranking scheme selected by user.
• Designed and implemented a simplified Amazon Dynamo like Android application which covers ID space partitioning/repartitioning, ring based routing, node joins, Quorum replication with replication degree 3 and data recovery from replicated storage after failure.
• The main goal is to provide both availability and linealizability at the same time.
• Developed an android group messenger application implementing Basic Multicast, Total and FIFO ordering of messages.
• Implemented node failure handling to preserve ordering of messages.
•Predicted the probability of patient having Diabetes using five machine learning techniques a) LDA &QDA, b) Linear Regression, c) Ridge Regression, d) Ridge Regression using Gradient Descent and e) Non-Linear Regression
• Implemented a Multilayer Perceptron Neural Network to classify handwritten digits in MNIST dataset. Applied parametric variation in terms of hidden nodes and learning rate.
• Feed forward and back propagation algorithm incorporating regularization were used to fine tune the neural network layers iteratively resulting in an optimal accuracy of 94.4% on test dataset.
Feel Free to reach me out at:
101 E San Fernando St #100, Apt #306, San Jose, CA-95112, US