About me

I am Ujwal Karippali Chandran, a Master’s student in Computer Science at the University of Colorado Boulder, with a passion for building efficient, scalable systems.

With over three years of experience as a Software Engineer at JPMorgan Chase, I specialized in designing and developing microservices using spring boot and user interfaces using react. My technical expertise spans multiple programming languages, including Java, Python, and SQL, as well as frameworks like Spring Boot, React, and TensorFlow.

I’m also experienced in cloud platforms, holding an AWS certification and proficiency in Google Cloud. My fast-track promotion to Software Engineer II awarded to the top 4% globally at Chase reflect my strong technical abilities and dedication to driving results.

I have worked on innovative projects, such as developing algorithms to optimize police patrol routes and building automation systems for hospitals. My project experience is supported by strong accomplishments in hackathons, including being a Smart India Hackathon winner and receiving awards from GE HealthCare and Reverie Technologies.

Currently, I am diving deeper into areas like algorithms, data-center scale computing, and natural language processing. In addition to my academic and professional work

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Honors and Awards

Smart India Hackathon : Winner of National-Level Hackathon, awarded by Govt of India under National Crime Records Bureau.

Recognition from JPMorgan Chase & Co : Rewards and Recognition Recipient from Head of Technology for Chase Digital Banking.

GE HealthCare Precision Hack Top 8 : Awarded by GE HealthCare for building a hospital automation system for hassle-free booking of appointments and resource allocation inside a hospital.

Reverie Hack Winner: Awarded by Reverie Technologies for developing a flutter app to remove the language barriers between the retailers & customers using Natural Language Processing.

FAER McAfee Scholar : Developed a web app to identify authorship, prevent fraudulent transactions and cyber crimes by using efficient machine learning algorithms.

Projects and Publications

Detection of Covid-19 through cough and breathing sounds using CNN: By employing an ensemble of Convolutional Neural Networks, cough, breathing, and speech sounds were analyzed to determine if a person is infected with COVID-19, achieving an accuracy of 88.75%.

Police Patrolling Optimization : Developed an algorithm for optimizing police patrol routes by prioritizing locations based on crime records, the presence of banks and schools. Integrated data from a machine learning model into a customized Travelling Salesman Algorithm to determine the most efficient patrol path.

Optimizing Hospital-Patient Interactions through Advanced ML and NLP Engineered a hospital automation system using Feed forward neural network and a BERT-based NLP summarizer to streamline appointment booking and optimize resource allocation within the healthcare facility.