Hi! I am a Graduate Student at Carnegie Mellon University. My current areas of interest include Machine Learning, Distributed Systems and Data Infrastructure. Having said that, I enjoy solving real-world problems irrespective of the tools, technologies and domains. I am currently a Teaching Assistant for the Machine Learning at Scale course offered by Dr. Heather Miller. I am also a Machine Learning Research Assistant at Dr. Giulia Fanti's Research Group where I am working on Collaborative Anomaly Detection. I interned with the Emerging Products Group in the Digital Media Org at Adobe Inc. during Summer 2020 where I worked on Backend Development, Data Engineering and Machine Learning. Prior to graduate school, I worked as a Software Development Engineer at GE where I built Scalable Cloud Native Data Pipelines and REST APIs. I also interned as a Deep Learning Researcher at the University of Southern California, Viterbi School of Engineering during Summer 2017.
As an intern at Adobe, I successfully designed and developed an Information Retrieval System from scratch using BERT for Language Modelling, Spark for Data Processing, Flask for REST API development and MongoDB as a NoSQL database. The product was successfully deployed to production and presented at the Adobe Photoshop Developer Meetup.
As a Software Development Engineer at GE, I primarily worked on Backend Engineering between August 2018 - June 2019. Prior to my full time role, I interned as a Data Engineer between January 2018 to June 2018. I made significant contributions to the development of a Data Streaming pipeline using Java, AWS Kinesis and AWS DynamoDB. The platform was built for change data capture and near real-time streaming of GE Aviation asset data. I also developed REST APIs for numerous asset information retrieval tasks using Go. In addition, I actively delivered seminars on Data Science at Digital Bytes - a GE forum where knowledge and ideas are shared.
I worked on Deep Learning for Computer Vision at the USC, Media Communications Lab under the supervision of Dr. C.-C. Jay Kuo. Other than conducting extensive literature survey, I contributed to the generation of realistic labeled urban scene images using Generative Adversarial Networks (GAN). I particularly developed a Cycle GAN model for image-to-image translation of labeled Synthetic images (training data obtained from GTA dataset) to their realistic counterpart (training data obtained from Cityscapes dataset). The focus of my research was to generate realistic labeled urban scene images that can be used as training data for applications such as Pedestrian Detection, Scene Understanding and Autonomous Vehicles training. I had the opportunity of presenting my work at the 2018 9th International Conference on Computing, Communication and Networking Technologies held at the Indian Institute of Science (IISc).
GPA: 3.71/4.0
GPA: 9.23/10.0
Apart from technology, I enjoy playing sports. I particularly enjoy racquet sports and I am always up for a game of Badminton or Table Tennis. I had the opportunity of leading my high school badminton team to the finals of the Inter-School State Championships! A memory that I will always cherish.
I am also a foodie and enjoy trying various cuisines and dining at restaurants in the city. Oh, and I absolutely love dogs!