Resume
Sai Adithya Reddy Chinthalapani
I am a Student from Arizona State University with a Master’s in computer science (Big Data specialization).
Education
Arizona State University 2019 – 2021
Vellore Institute of Technology 2015 – 2019
Experience
Data Scientist intern - Verticross India Pvt.Limited
- Built dashboards to effectively visualize the findings like anomalies and missing data.
- Performed time series analysis and forecasting for a company that specializes in meter reader design and data collection.
- Wrote several scripts for data simulation, to calculate performance metrics and also to analyze data.
Android Developer Intern - Grepthor Software Solutions pvt. ltd
- Developed an application that connects volunteers and managers to each other using an android application.
- Developed splash screen, authentication features and core functionality of displaying data and images.
- Followed the material design principles for design and connected to the company internal API for data.
Projects
- Deployed a deep learning classifier and front‐end app on AWS using flask to classify the input image.
- Scaled the app by implementing custom autoscaling application using SQS, S3, EC2 in AWS.
- Developed a program that parses relevant data from resume and creates a video using ffmpeg and flask.
- Deployed it on GCP using google app engine, cloud store, pub/sub and cloud run.
- Implemented different versions of k-means algorithm and Naive Bayes algorithm and compared accuracy.
- Detected anomalous activity in videos with an accuracy of 85 % for 2 classes and 55% for 5 classes.
- Used FFmpeg to extract images from video data and Keras for fine-tuning deep learning models.
- Collect large amount of text data using data scraping tools and manual methods.
- Used novel methods to generate required data using pandas and spacy.
- Finetuned the huggingface transformers BERT model on collab to achieve an accuracy of 69%.
- Built a garbage collection robot using robot car chassis, motors, camera and ultra sonic sensor.
- Trained a garbage detection model using Keras and wrote RaspberryPi scripts to handle all the robot components and deployed the deep learning model on it.
Machine Learning - Gesture recognition using personalized page rank
- Generated files to store gesture data at different levels of abstraction using statistical features and dimensionality reduction with help of pandas, sklearn, scipy and custom functions.
- Visualized and analyzed the data using heatmaps and charts with libraries like matplotlib.
- Implemented personalized page rank and used it with generated data to recognize gestures.
- Visualized data and uncovered patterns in the sales dataset using Tableau.
- Performed sentiment analysis on comments for each product to visualize the customer sentiment.
- Retrieved images closest to the given medical image to aid in diagnosis. Project based on peer reviewed publication.
- Used feature engineering, dimensionality reduction and machine learning techniques to achieve an accuracy of upto 60% and precision of 0.55.
- Implemented different noise detection and reduction techniques based on multiple peer reviewed publications to find out the best performing method.
- Calculated different quality estimation metrics to find the technique producing image with least noise.
- Improved the speed of computation using parallel processing with OpenMp.
- Automated parts of video editing pipeline using FFMPEG at global launch to save many hours in production time.
- Produced result 100x faster when compared to traditional methods.
- Performed primary data visualization and analysis to understand the data and then selected relevant features based on different methods.
- Classified the data points between haze and fog, predicted temperatures using different machine learning and dimensionality reduction techniques.
- Implemented fake currency detection method during introduction of new currency in India.
- Researched multiple peer reviewed publications on different currencies to implement an effective method to detect fake currency using image from normal camera.
Database - Distributed database implementation
- Implemented round robin and range partition using PostgreSQL and psycopg2.
- Implemented range and point queries.
- Implemented parallel sort and parallel join.
Database - Google like Big Table Implementation
- Implemented low level code for Map insert, Batch insert for Bigtable and improved its efficiency using btrees.
- Implemented indexing, querying, row sort and row join operations on Bigtable database.
- Analyzed NYC Taxi data by implementing range and distance queries in order to identify statistically significant spatial hot spots areas using Apache spark.
- Reduced CPU and memory utilization by up to 50% by distributing the load across Hadoop cluster on AWS.
Machine Learning - Predicting meal/no meal data from CGM data
- Extracted different types of features from the given time-series readings and selected relevant features using forward, backward selection.
- Predicted the meal/ no-meal from the time-series data with an mean accuracy of 70%
Skills
- Programming languages: Java, Python, Ruby, JavaScript, SQL
- Libraries: OpenCv, Scikit-Image, Scikit-learn, Keras, Pandas, Numpy, Mongoose
- Frameworks: Ruby on Rails, React, Express, Django
- Databases: MySQL, PostgreSQL
- Tools: Git, unix terminal