Adithya Data Science and ML Projects

Resume

Sai Adithya Reddy Chinthalapani

Student from Arizona State University with a Master’s in computer science (Big Data specialization).

Education

Arizona State University 2019 – 2021

  • GPA 3.56

Vellore Institute of Technology 2015 – 2019

  • GPA 8.34

Skills

  • Programming languages: Java, Python, Ruby, JavaScript, SQL, AWS, OpenCv, Scikit-Image, Scikit-learn, Keras, Pandas, Numpy, Mongoose, Ruby on Rails, React, Express, Django, MySQL, PostgreSQL, Git, unix terminal

Certifications and Apps

  • AWS Certified Solutions Architect Associate SAA-C02
  • Developed an android game using GDevelop and Cordova plugin which got 15000 installs.

Hackathons

Action.ML Hackathon - Fox

  • Trained the model on AWS Sagemaker by getting data from s3 bucket hosted by FOX.
  • Experimented by finetuning models like RESNET and VGG pretrained models in Keras to classify sports scenes

Android Hackathon – Dev Savant

  • Developed a quiz app for cricket enthusiasts that randomizes questions based on selected difficulty level in 24 hours.

Experience

Global Launch, ASU : Student Video editor

  • Produced videos by following instructions form professors and delivered them within deadlines.
  • Developed ffmpeg scripts that automated parts of video editing and saved 100’s of hours in production time.

Data Scientist intern - Verticross India Pvt.Limited

  • Created visualizations to find anomalies and missing data using matplotlib and plotly.
  • Performed time series analysis and forecasting on data collected from substation meter readers using keras.
  • Transformed data using SQL queries, psycopg2
  • Wrote several scripts to for data simulation, to calculate performance metrics and to analyze data using numpy, scipy and pandas.

Android Developer Intern - Grepthor Software Solutions pvt. ltd

  • Developed an application that connects volunteers and managers using an android application.
  • Developed splash screen, authentication features and displayed data, images using Picasso library.
  • Followed the material design principles for design and connected to the APIs for data using volley.

Projects

Machine Learning - Naive Bayes and KNN algorithms from scratch

  • Implemented different versions of k-means algorithm and Naive Bayes algorithm from scratch using numpy and compared accuracy of both algorithms.

Scalable image classifier – Aws, Python/h3>
  • 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.

Deep Learning - Anomalous activity detection in surveillance videos

  • 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.

Coreference resolution using BERT

  • 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%.

Robotics - Garbage collection using raspberry pi robot

  • Built a garbage collection robot using robot car chassis, motors, camera and ultra sonic sensor.
  • Scraped garbage photos data from internet using beautiful soup.
  • 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.

Big Data - Geo Spatial Data Hotspot Analysis

  • 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 - 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.

Data Visualization - Analysis of sales dataset

  • Visualized data and uncovered patterns in the sales dataset using Tableau.
  • Performed sentiment analysis using nltk on comments for each product to visualize the customer sentiment.

Image Processing/ML - Tuberculosis image retrieval

  • 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 with opencv, scikit-image and keras to achieve an accuracy of upto 60% and precision of 0.55.

Image Processing - Noise reduction using parallel processing

  • Implemented different noise detection and reduction techniques using opencv and C++ 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 using matlab.
  • Improved the speed of computation using parallel processing with OpenMp in C++.

FFMPEG - Automated video title replacement

  • 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.

Machine Learning/ Visualization - Delhi weather dataset

  • Performed primary data visualization and analysis using numpy, matplotlib 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 using scikit-learn.

Image Processing - Fake Currency detection using MATLAB

  • 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 using Matlab libraries.

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 by modifying minibase code in Java.

Geo Spatial Data Hotspot Analysis

  • 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% using scikit-learn, numpy and pandas.