Portfolio#
Education#
Degree: Bachelor of Science
Major: Computer Science - Machine Learning Track
Minor: Statistics
Relevant Coursework: Artificial Intelligence, Machine Learning, Computer Vision, Natural Language Processing, Data Science, Parallel Computing, Calculus 1, 2 & 3, Statistics, Linear Algebra, Compilers, Computer Systems, Algorithms, Organization of Programming Languages, Object-Oriented Programming 1 & 2, Discrete Math
GPA: 4.0
Experience & Projects#
Undergraduate Research Assistant
Creating a neural video codec to surpass state of the art compression algorithms for image and video data
Models are fit to decode the original video from input pixel coordinates efficiently
Using methods such as model quantization and meta learning to achieve ideal reconstruction quality with high compression, high encoding, and high decoding speeds
Undergraduate Research Assistant
Created manuscripts for machine learning research projects related to identifying and managing turfgrass related diseases
Used methods such as transfer learning and gradual unfreezing to train highly accurate nematode image classifiers
Performed automatic hyperparameter optimization using Ray Tune to train scikit-learn and PyTorch models to achieve highest metrics
Performed parallelized automatic image dataset preprocessing using OpenCV and NumPy
Created a library which provides Tensors with reverse-mode automatic differentiation capabilities using only numpy arrays for the Intro to Artificial Intelligence (CMSC421) class
Supports many differentiable n-dimensional tensor operations such as matrix multiplication, convolution, element-wise functions, aggregate functions, and arithmetic operations, with support for operations along any axes
Created MNIST demo using convolutional, dense, and normalization layers and used techniques such as Xavier/Glorot initialization and residual connections
Fully documented using sphinx at https://vikramrangarajan.github.io/SimpleTensor/
Data Strategy Engineer
Gained advanced experience with relational databases, Docker, Linux, Python, and Pandas
Learned to use Azure Data Factory (ADF) to transform and move data on the Azure Cloud Platform
Used Apache Airflow to orchestrate ETL pipelines between on-prem databases and Azure
Accelerated a data pipeline’s execution time from 90 minutes down to 6 minutes using ADF
Technical Skills#
Programming Languages
Technologies