Portfolio#
Education#
Degree: Ph.D.
Advisor: Rajiv Khanna
Focus: Computer Science, AI / ML
Relevant Coursework: Foundations of Deep Learning, Randomized Algorithms, Statistical Machine Learning, Computer Networks
GPA: 3.85
Degree: B.S.
Major: Computer Science - Machine Learning Track
Minor: Statistics
Relevant Coursework: Artificial Intelligence (AI), Machine Learning (ML), Computer Vision (CV), Natural Language Processing (NLP), Data Science (DS), Parallel Computing, Calculus 1, 2, & 3, Statistics, Linear Algebra, Compilers, Algorithms
GPA: 4.0, Summa Cum Laude
Experience & Projects#
Graduate Teaching Assistant
Assisting with CS240: Programming in C
Leading lab section to guide students to success
Holding office hours to assist students with their coursework
Grading assignments, proctoring exams, and assisting with course materials
Undergraduate Research Assistant
Created a neural video codec to surpass state of the art compression algorithms for image and video data
Models were fitted to decode the original video from input pixel coordinates efficiently
Used 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 sklearn and torch 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 for the Intro to AI class
Supports many differentiable n-dimensional tensor operations such as matmul, ConvNd, element-wise, reductions, etc.
Created MNIST demo using convolutional, dense, and softmax layers
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