madamala revanth

Software Engineer

Kore.ai•  July 2020 - July 2021

• Communicated Efficiently across multiple teams, of 50 people and invented client-NLP SDK to perform chatbot actions at client-side & reduced response time from 3s to 0.01s and documented it. Created modules for graph database and usage logs to utilize employee activities to derive intelligence to kora. • Presented and trained various Deep Learning Models with Platform team for question answering on Stored Notes and Implemented POC for table question answering models with ML resulting huge appreciation, gaining new clients • Collaborated with Service team(Size 10) to devise sentiment analysis on communication and project management boards • Built modules for keyword extraction and categorization for resource suggestions from mails and messages. • Worked with service team for import, synchronization and Zapier integration with external applications to Table-app in kora

Data Analyst and ML Engineer

Lifemudra•  May 2018 - April 2020

--Worked on Data Analytics part mostly on visualisation and feature dependency part --Built pre-processing pipeline, ML classification models for Bio-metric data, tested and developed on AWS. -- Worked on developing novel computer vision-based feature recognition model for fingerprint analysis

Data Science Intern

CDAC•  May 2019 - September 2019

-Conducted background research on various methods available for web page categorization and Implement and evaluating them in for the current scenario. -Worked on gathering data to build required dataset for building a model and implemented web page scraping using beautiful soup in python to retrieve information related to web page and performed data munging techniques on dataset -Implemented various machine learning techniques including LSTM, DNN, combination models, etc.., with different model paradigms where I have modeled automated system linked with the database which will gather new data from database to perform scraping and predicts the category on its own in turn it will train the machine learning model with the existing data on a timely basis and can be monitored for performance -Fine Tuned the model to increase the optimum utilization of memory resources. -Evaluated the model on various metrics and visualized the model metrics and documented the findings

University of Southern California (USC), Los Angeles

Computer Science, MS•  August 2021 - Present

Pursuing Masters in Computer Science -Data Science @USC