Hi! My name is Ram Vegiraju and I am currently a Solutions Architect at Amazon. I am passionate about understanding the theory behind Machine/Deep Learning algorithms and implementing full-stack Machine Learning solutions at scale for interesting problems in fields such as NLP and Computer Vision. In my free time I love writing, reading, and playing basketball or tennis. Read my latest article here. Feel free to check out my Resume/Skills below and some of my projects and hobbies in the other sections!
B.A. Degree in Statistics, Minor in Computer Science• Sept 2017 - Dec 2020
Relevant Coursework: Machine Learning, Linear Algebra, Software Development Methods Java, Data Analysis with Python, Data Science with R, Mathematical Statistics & Probability, Regression Analysis
R&D Software Engineering Intern • June 2020 - August 2020
Worked on building and presenting prototypes as an R&D Engineer over 12 weeks for two different customers.
Data Science Intern • June 2019 - August 2019
Worked on the backend for the Targeted Agent and Profiling Utilization Registry (TAPUR) team.
I have experience with implementing various Supervised and Unsupervised models such as MLR, Logistic Regression, k-Means Clustering, PCA, CART/Random Forest, and SVM. In addition, I am well versed with deep learning architectures such as ANN's, CNN's, and RNN/LSTM's as well as their application in fields such as NLP and Time-Series. Currently learning more about GANs and Reinforcement Learning.
Created a live informative time-series forecasting dashboard predicting COVID-19 cases using Deep Learning with an LSTM model. Worked with Bootstrap and Javascript to host visualizations and predictions based off of the Machine Learning algorithms. Also used Facebook's Prophet Library to develop a SARIMA model to compare to deep learning performance in forecasting cases.
Skills/Tools: Python, Tenorflow/Keras, Javascript, HTML/CSSA music-themed web application that takes in user features such as favorite genre/mood, artists, language, and playlist size to curate a playlist. Scraped data using Spotify's Developer API, currently incorporating more languages for users to input. Developed a K-Nearest Neighbors based Recommender System for song suggestions, used Flask for ML model inference on backend, with HTML/CSS/JS on the frontend for the user form.
Skills/Tools: Python, Flask, HTML/CSS/JS, Sci-Kit LearnDeveloped a web application that takes in text of any length and returns a summary with the entities extracted and highlighted. Preprocessed and cleansed data for analysis with NLTK and used Spacy for the Entity Extraction. Built web application with Flask on backend, HTML/CSS on the frontend.
Skills/Tools: Python, Flask, NLTK, Spacy, HTML/CSSDeveloped a simple web application that takes in an image and returns a black & white sketch using the OpenCV library.
Skills/Tools: Python, OpenCV, HTML/CSSProin gravida nibh vel velit auctor aliquet. Aenean sollicitudin, lorem quis bibendum auctor, nisi elit consequat ipsum, nec sagittis sem nibh id elit.
Branding, WebdesignProin gravida nibh vel velit auctor aliquet. Aenean sollicitudin, lorem quis bibendum auctor, nisi elit consequat ipsum, nec sagittis sem nibh id elit.
PhotographyProin gravida nibh vel velit auctor aliquet. Aenean sollicitudin, lorem quis bibendum auctor, nisi elit consequat ipsum, nec sagittis sem nibh id elit.
Branding, Illustration