C++, Python, Java, C, LISP, SQL, TypeScript, JavaScript, MATLAB, C#, HTML, CSS,
Angular, Lenskit, JUnit, Flask, Django, Sikuli, Git, JIRA, ZooKeeper,
Unity, Unreal, Hive, Hadoop, Kafka, REST, Agile Scrum, AWS, Azure, Redis, Docker,
Software Engineering, Database Systems, Data Mining
Software Developer Intern
09/2018-Present
Software Developer Intern
05/2018 - 08/2018
Summer Intern
05/2016 - 06/2016
Dispersed data-driven computing
Spatial Computing
DB systems
Data Mining
Recommender Systems
Artificial Intelligence
I am a Computer Science graduate student at the University of Minnesota, Twin Cities. I love to solve problems.
MS, Computer Science | CGPA: 3.78
2017 - 2019
B.Tech, Computer Science and Engineering | CGPA: 9.07/10
2013 - 2017
Designed a system for efficient synchronization of IoT streamed data points with cloud. Edge repositories act as a ubiquitous middle layer, performing the tasks of device data filtration and subsequent synchronization. Developed system facilitates efficient utilization of network bandwidth and compute heterogeneity.
Implemented a game bot using a model-free reinforcement learning technique called Q-learning. After training for 3 hours, the bot consistently crossed a score of 200.
Implemented content based filtering, nearest neighbor collarborative filtering, and summary statistics algorithms for recommending movies based on user and movie datasets, using Lenskit toolkit.
Implemented a stroke rehabilitation game for upper limb motor training of stroke patients.
Proposed a location-aware mobile data offloading technique to prolong network lifetime in an energy efficient manner. Lifetime enhancement through fair energy expenditure in the network is obtained by engaging additional nodes beside cluster heads in an intracluster data distribution process.
Implemented a novel routing technique for common content distribution in MANETs using teaching learning based optimization (TLBO). Proposed approach performs better than a standard K-means clustering approach in terms of average as well as total energy consumption in the network