Jaspreet Arora is a graduate student at UCLA studying computer science with a GPA of 3.9/4.0. She has work experience as a graduate research assistant at UCLA where she improved code optimization techniques. Previously, she was the CTO and cofounder of BrownCode Software where she developed a student performance tracking system. She has also worked as an associate consultant at Software AG where she led projects to optimize systems performance. Her skills include languages like Java, C, Python and frameworks like AngularJS, Node.js. She has conducted research projects in areas like sentiment analysis, gender classification from images, and social trait prediction from images.

1. JASPREET ARORA
Web: jay-ucla.github.io · (310)500-5620 · jasa92@cs.ucla.edu · LinkedIn: jasaora · Github: jay-ucla
EDUCATION
University of California, Los Angeles Sept 2018 - March 2020
Masters in Computer Science GPA : 3.9/4
Guru Nanak Dev University, Amritsar 2011 - 2015
B.Tech in Computer Science & Engineering GPA : 9.57/10
SKILLS
Languages Java, C, Python(scikit-learn, scipy, numpy), SQL, Shell Scripting
Web Technologies AngularJS, Node.js, Ruby on Rails, Javascript, HTML, CSS
Tools/Frameworks Jenkins, ANT, Maven, WebMethods, Git, SVN, AWS (EB, EC2, RDS)
WORK EXPERIENCE
Software Engineering and Analysis Laboratory June 2019 - Sept 2019
Graduate Research Assistant with Prof. Miryung Kim UCLA
• Code size reduction using call graph analysis to remove unused code in a Java application & its dependencies
• Conducted comparative study of bytecode dynamic profiling methods - JVM modification, instrumentation, heap/thread dumps
• Improved semantic preservation from 80% to 95% by developing and incorporating Java JVMTI agent into project
• Extended bytecode transformations like class collapser, method inliner by discovering and categorizing unique use cases
• Submitted research paper to ICSE 2020(International Conference on Software Engineering) (Under Review)
BrownCode Software LLP Dec 2015 - June 2017
Cofounder & CTO Bengaluru, India
• Architected and developed Student Performance Tracking and Analysis service(web & mobile) from concept to production
• Deployed first pilot project with MVP within 3 months
• Successfully deployed the product in various schools, serving more than 10,000 users (Demo - jay-ucla.github.io/web)
• Designed low-latency scalable back-end APIs for 20+ modules using RoR and UI/UX for web application using AngularJS
• Facilitated offline usage by developing a novel caching framework with automated syncing, using Java and AngularJS
• Integrated live notifications to SMS, Whatsapp & Facebook APIs by building notification engine using RoR and Redis
Software AG July 2017 - Jan 2018
Associate Consultant Bengaluru, India
• Led architecture redesign and end-to-end development of Project Lifecycle Management platform
• Optimized response time by 60% by devising real-time stream processing infrastructure scaling to 20,000+ projects
• Full Stack development to add metrics dashboard and delegation modules using Java and AngularJS for 25 resources
• Awarded quarterly award Going Above and Beyond for performance in project
Software AG Aug 2015 - June 2017
Assistant Consultant Bengaluru, India
• Implemented CI/CD across 10 projects increasing production deployment frequency from bimonthly to biweekly.
• Reduced CI/CD migration from months to days by authoring generic build, test and deployment scripts in Jenkins & Ant
• Conducted CI/CD workshop in the Software AG All India technical event, Spectrum 2017
PROJECTS
Sentiment Analysis and Emotion detection in text conversations (SemEval 2019)
• Achieved 88% accuracy in sentiment classification by developing LSTM classifier using pytorch
• Compared performance of word embeddings like word2vec, Glove, ELMO, BERT, deepmoji in capturing emotional context
• Used deepmoji and clustering to discover 4 new emotion classes in unclassified text data with average silhouette score of 0.65
Gender classification from images
• Achieved 99% accuracy in gender classification using FLD on features extracted from images using PCA in python
Social traits and election prediction using SVM
• Implemented SVM classifier to predict winners of previous U.S. elections with the accuracy of 67.9% (Governors)
and 65.5% (Senators) by deriving social traits (eg. perceived intelligence, wealth, etc.) from images of politicians.
Face Detection in images
• Achieved 93% accuracy in face detection by implementing the Adaboost algorithm and HAAR filters in python