Development Intern Software Engineer

SUSMITHA RAJA

+1-801******* • **************@*****.*** • https://www.linkedin.com/in/susmitha-raja-37576419a/ Education

University of Utah • Salt Lake City, Utah Aug 2022 – present Masters • Computer Science • CGPA: 3.56/4.0

JSS Science and Technology University • Mysuru, India Aug 2016 – May 2020 Bachelor of Engineering • Computer Science • CGPA: 9.3/10.0 Technical Skills

• Programming language: Python, C++, C, Java.

• Tools and Technologies: Perforce, Git, LabVIEW, Apache Spark, Docker, Kubernetes, Flask, Keras, Pandas, .NET framework, MySQL.

Work Experience

Software Engineer I & II July 2020 – July 2022

National Instruments RnD, Bengaluru, India.

• Aided in the implementation of an algorithm for detecting burst locations in RF signal (waveform) and designed API for the same.

• Designed and implemented features which load and download the waveform configuration and waveform data to/from RFSG driver memory. These features were implemented using Object- Oriented concepts in C++.

• Collaborated with cross-functional teams in designing and developing features.

• Resolved bugs and customer escalation issues within timeline.

• Conducted code reviews and Knowledge-Sharing sessions across teams. Software Development Intern Jan 2020 – Jun 2020

National Instruments RnD, Bengaluru, India.

• Written python scripts to automate updating hierarchy information of waveform properties in RFSG driver codebase.

• Researched and implemented Read and Write APIs for RF TDMS waveform files. Projects

Speeding up LSM Compaction in LevelDB code

• Worked on speeding up merging of data using ML in Log Structured Merge (LSM) tree that is used in Google’s open source database-LevelDB. Using Piece-wise Linear Regression model, effectively reduced the number of merge comparisons by 30% and 75% for random and Zipfian data distributions, respectively. Logging and Recovery

• Implemented write-ahead logging (WAL) and recovery in a write-optimized key-value store-B epsilon tree to bring the index back to a consistent state after a crash . Internet Traffic Monitoring System

• Monitoring the activity on the internet by tracking the internet usage at the application layer of the data packets, using deep packet inspection.

• Streaming the data collected from packets using Big Data technologies and displaying the duration spent on different applications or websites, along with the incoming and outgoing bandwidth utilization per application, in the form of graphs, to facilitate easy visualisation. Movie recommendation System

• Implemented Alternating Least Squares(ALS) for collaborative filtering, to fill in the missing entries in the user-rating matrix(MoviesLens 1M dataset).

• Used Spark to perform big data analytics on MovieLens 1M dataset and using k-means clustering displayed the 10 best movie recommendations based on the user’s preferences and generic movie ratings. Internet Traffic Classification

• Designed a model to perform classification on internet traffic dataset with 246 features. Preprocessed the data, selected features based on Pearson Correlation and Mutual Information methods and achieved peak accuracy of 96% for Logistic Regression.

Extra-Curricular Activities

• Volunteered at Samarthanam, a non-governmental organization in India, that empowers people with disabilities and the underserved.

• Co-organized technical talks, and workshops as part of the student club-Computer Society of India-SJCE(Executive Committee member).

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