Jun 2020 - CurrentFoxx Equipment Company
Web Developer Intern
May 2020 - Dec 2020National Science Foundation
Working on Open Collaborate Experimental Learning (OCEL.AI) project which is funded by NSF. I have developed an End to End ML tool which has Storyteller, Data Sharing, Data Visualization, ML Experiences, Ethics. Using this tool I have developed a Spatial Mismatch project using PyLDA topic modelling, Tableau and Plot.ly on OCEL.AI tool. Also worked on Data visualization on structured, unstructured, text, image data using D3.js, T-SNE, Tableau.
Jul 2020 – Aug 2020Google
CSSI Teaching Assistant (Volunteer)
Teaching Assistant of Computer Science Summer Institute (CSSI) Course. I have mentored approx. 20 incoming first-year college students from around the globe throughout their completion of a Google-Sponsored coursera Specialization course – specifically Web Development.
Mar 2020 - May 2020University of Missouri - Kansas City
Graduate Research Assistant
Working on a ML tool to collect data from users and perform ML experiences on dataset. Data collected are use cases which will be further used for Data Visualization, using Flask Framework. I have used Amazon AWS for deploying this application, D3.js for Data Visualization and using deep learning for data processing.
2019 - 2021University of Missouri - Kansas City
MS in Computer Science
Location: Kansas City, United States
Grants: Dean’s International Student Award (DISA), Chandra Scholarship
Relevant Coursework: Design Analysis of Algorithms, Deep Learning, Cloud Computing, Big Data Analytics, ADBMS, Unity (AR/VR)
2015 - 2019Graphic Era University
B.Tech in Computer Science and Engineering
Location: Dehradun, India
Research & Publications
2020IEEE International Conference
A Machine Learning Based Approach for Progeria Syndrome Detection
Proposed a generic framework for diagnosis of Progeria syndrome among the newborns. We proposed a novel framework architecture of VGG-16. We have used multiple machine learning algorithms and feature extraction tools and we got best results in Logistic Regression algorithm. Disease identification helps us in early-stage diagnosis of a disease. It also helps us to find out effective medicines or treatments for it. Our model gives us the best accuracy of 99.8%.
Indoor Object Classification Using Higher Dimensional MPEG Features
Propose a generic model to classify a given image by detecting a specific image patch. Employing MPEG-7 features along with feature selection populates the feature space which is used to train using SVM classier. Our work target towards classifying objects in an unclassified image. We propose a model that can detect objects through generic framework for larger classes. Our model gives an overall accuracy of over 97%.