Raakesh Premkumar

Indiana University Bloomington

Master’s Degree, Data Science

January 1, 2016 – January 1, 2018

Anna University

Engineer's Degree, Computer Sciene and Engineering

January 1, 2011 – January 1, 2015

Capital One

Senior Lead Machine Learning Engineer

May 1, 2026 – Present

Chewy

Lead Machine Learning Scientist

September 1, 2024 – May 1, 2026

The Home Depot

Lead Machine Learning Engineer

March 1, 2023 – September 1, 2024

Wayfair

Senior Machine Learning Scientist

June 1, 2022 – March 1, 2023

Amazon Web Services (AWS)

Software Engineer - Machine learning

October 1, 2019 – May 1, 2022

New York, United States

Nordstrom

Data Science Engineer

July 1, 2018 – October 1, 2019

Greater Seattle Area

Indiana University Bloomington

Associate Instructor

August 1, 2016 – May 1, 2018

Bloomington, Indiana

Zoho Corporation

Software Engineeer

December 1, 2014 – July 1, 2016

Knowledge Graph Representation on an Ontology

August 1, 2017 – November 1, 2017

Python, Neo4J - Cypher, AWS – EC2, Spacy, StanfordCoreNLP, OpenNLP, Kafka • Contributed to a pipeline in which natural text of about 1TB from Wikipedia is parsed, saved in a graph (Neo4J) and can be queried upon. • One of the two AWS instance parses the data and extracts the NLP information using Spacy, Stanford Core NLP & Open NLP which is stored in Neo4J in the other instance. • Contributed in storing the data in Neo4J by defining the Ontology using OWL, writing dynamic Cypher queries and then querying the data.

Zappos – Search Functionality – Android

April 1, 2017 – May 1, 2017

• Collaborated with the Android team at Zappos to work on the Search functionality and product display page of the Zappos Android app. • Collaborated with the API team and used retrofit to fetch data from the server. Enabled offline mode by caching data. • Enabled product sharing using a magic link and worked on animations ensure great user experience thereby following the material design guidelines.

Sarcasm Detection

February 1, 2017 – April 1, 2017

• Collected twitter data with the hashtag #sarcasm and manually validated the tweets to be used as my training data set. • Developed n-gram model, performed sentiment analysis, generated parts of speech tags & collected the bag of words from the dataset. • The overall accuracy of the model is 0.65 (F-score) in which N-gram performed better when tested individually (0.61) followed by sentiment analysis (0.46).

Speech Separation, Identification and Automatic Speech Recognition

February 1, 2017 – Present

• Implemented Deep feed forward Neural Network and Long Short-term memory neural network to separate speech from background noise using Complex ratio masks improving the accuracy of the algorithms to 3.073 PESQ with a training set of about 14,400 generated noisy speech and a test set of 1400 samples. • Performed Automatic Speech Recognition using Hidden Markov Model Toolkit and Kaldi on the denoised data.

Approval Ratings – US President, Canadian Prime Minister

January 1, 2017 – May 1, 2017

• Collected a total 1.5 million tweets for both the leaders containing certain keywords and pre-processed the data on an AWS instance (Amazon Web Services Server). • Performed Sentiment Analysis using Affin, SentiwordNet and custom lexicon check on the 400,000 tweets collected every month for almost four months. • Used Cross Validation to calculate the accuracy (90%).The results are more detailed and accurate than the famous Gallop poll.

Artificial Intelligence Projects

November 1, 2016 – December 1, 2016

• Implemented a Map search algorithm for a USA dataset using BFS, DFS, IDS and A-star algorithm to find the optimal, shortest, longest and fastest path between places. • Implemented n-k-coh-coh (variant of X-O) game using alpha beta pruning and minimax algorithm. • Implemented machine learning algorithms such as Naïve Bayes and Decision trees for Spam/Non-Spam mail filtering.

Fast Learn

May 1, 2016 – Present

• As a side project, collaborated with my team and the computer science department of my university to design and implement a student-teacher community portal. • Contributed to the controller part of the application by designing and coding the servlets enabling proper logic flow within the application.

Eliminating Redundancy in File System Using Data Compression and Secured File Sharing

April 1, 2015 – Present

There are many files of same type-size-quality and content being shared across social media in the recent times. To ensure that the redundancy is prevented we designed a storing and sharing software wherein we implemented this redundancy check by comparing files and ensuring redundancy is avoided. Similar use case can also be used in messaging applications or e-mailing applications. When the same image is being forwarded by multiple people and when downloaded these applications don’t make sure redundancy is prevented. Hence, we went ahead and implemented this solution.

Multiclip

October 1, 2014 – Present

In present day mobile phones, the users can copy and paste only one item at a time. Since, app switching in mobile phones is quite costly in terms of processing and the number of times app switching is needed to copy multiple stuff from one app to another from multiple sources is quite a lot of times. We solved this issue by helping user copy multiple stuff at a time and paste all of them at a time between applications by using content provider. Also, to ensure sensitive data has not been copied and persisted control check has been programmed.

Mutual Data protection in cloud computing Environment

January 1, 2013 – Present

To ensure and enhance the security of the customer’s data in a third party cloud provider we proposed four techniques over time such as Colouring Technique, Login-out code, Messaging Code, Binary Code conversion Technique.