- Part of the Perception unit at Advance Research and Development team.- Contribute in creating Fully Autonomous Forklifts. - Work on creating vision based Deep learning models converting to the ONNX models and TensorRT engine for inference.

I will be working under Dr. Stephanie Griggs on Young adults with Type 1 Diabetes and Sleep Deprivation during COVID 19. • Engineered the wavelet analysis model used to find the correlation between glucose data, activity patterns, and sleep deprivation in Type 1 Diabetic patients in Python and MATLAB.• Designed an in-house algorithm for time-bands creation to simplify the data analysis in C++ and used OpenGL for 3D visualizations.• Applied various machine learning techniques by deploying Python to build time series analysis visa low-rank matrix factorization of the sleep patterns.

• Created analytical datasets by extracting, manipulating, and consolidating huge datasets with >4 million records using Mongo Client in Python for FedEx warehouse by mapping MySQL to MongoDB.• Conducted regression tests on the data to validate the Weighted Average Cost of Capital (WACC) and its implications for Incentive Plan Design for the FedEx drivers.• Analyzed the 16.8% performance increase in driver's Lease activation ROI performance based on online training versus in-person training using Alteryx on Docker system with a dataset of 500k drivers.• Conducted a data regression analysis of the relationship between company stock prices and industry trends, achieving a 15% more accurate prediction of performance than in previous years.• Utilized web scraping techniques to extract and organize competitor data.• Updated company data warehousing techniques such as data recall and segmentation, resulting in a 20% increase in usability for non-technical staff members.• Updated data streamlining processes, resulting in a 25% redundancy reduction.

•Conceptualized & implemented an Aspect based sentiment analysis dashboard used by Amazon India using AWS Cloud9 IDE, data management using Amazon S3 Console and data visualization using AWS QuickSight.• K-NN model development, search ranking & validation of 100+ financial products and services using gathered data to rank the reviews & evaluate sentiment using POS tagging via NLTK (Stanford NLP).• Developed a Flask API to understand customer behavior leading to 95% success in aspect words generation.• Video and image processing using GPU parallel computing, and stream analysis with audio/video codecs like MPEG, AVI using ffdshow by creating a Speech-to-Text API synchronous recognition request.• Developed parallel image/video processing algorithms in depth image-based rendering (DIBR), scale-invariant feature transform (SIFT), and region labeling for E-commerce reviews.• Optimized these GPU-based real-time data analyses and speed up more than 10 times.

• Developed Knowledge Extraction pipeline for public documents with Semantic Extraction using NER, Constituent Parsing with Rasa NLU & Spacy and built a Knowledge Graph using Dgraph, GraphQL to represent the extracted knowledge.• Maintaining, training and deployed text classifiers API with Word Embeddings, LSTM, BERT using TensorFlow, Keras on AWS Sagemaker.