Computer Science Student @ UW | Software Engineer
My name is Vikram Balaji, and I am a third-year student at the University of Washington studying computer science. My areas of interest include distributed systems, algorithms, and ML theory. In my free time, I enjoy reading books about technology, taking a look at engineering blogs, and watching math videos on YouTube. I am currently reading The Google Story and Distributed Systems: Principles and Paradigms.
When I'm not doing schoolwork (or leisurely reading about computer science), I love playing tennis, going to the gym, and exploring the beautiful outdoors.
Java, C, C++, Python, HTML, CSS, JavaScript, TypeScript
Computer Vision, NLP, PyTorch, Keras, TensorFlow, OpenCV
AWS Elastic Load Balancer, Amazon S3, EC2, SQL, Microsoft Active Directory
Linear Algebra, Probability, Algorithmic Analysis, System Design
A rudimentary operating system written in C that supports file management (opening, closing, duplicating files; reading/writing to files, providing metrics), process management (forking new processes/inter-process communication), expanding the stack/heap, and concurrent file system operations.
A deep learning model that leverages machine learning techniques (train/test split, multiclass cross-entropy loss, epochs, hyperparameter tuning through grid search, convolutional filters, etc.) to predict images belonging to the CIFAR-10 dataset. Achieved 69% accuracy despite limited compute resources.
A website that uses Natural Language Processing techniques (Transformers, LSTMs, RNNs) to generate computer-written text and analyze the produced result. Demonstrates advanced NLP capabilities for text generation.
B.S. Computer Science | Sep. 2022 – Jun. 2026
June 2025 - September 2025 (Incoming)
Will contribute to the PyTorch Distributed team, focusing on checkpointing strategies for large-scale deep learning models. Work will involve improving reliability, efficiency, and scalability of distributed training workflows.
June 2024 - September 2024
Designed and developed an API to provide order delivery estimates (early/late/on-time) for 175 million daily order events, to be used by downstream fulfillment teams. Resulted in an 8% reduction in average request latency, a 14% decrease in request failures, and 1.5% increase in an internal customer experience metric compared to previous implementation.
September 2023 - Present
Develop and maintain infrastructure supporting 1,000+ students and faculty for educational and research use cases. Duties involve performing wide-scaled software installations, hardware component repair, and in-house server maintenance. Implementing code to support secure and scalable storage of sensitive information, including budgeting and records.