Research Interests and Publications📜/Patents💡

Interested in / Focusing on :

I have practical expertise and experience in :

  • Generative AI
  • Graph Neural Networks
  • Natural Language Processing
  • Reinforcement Learning
  • Large Language Models
  • Recommenders
  • Information Extraction
  • Image Generation

Here is my : 1) Google Scholar 2) Google Patents

Please click the links below ⬇️ to explore the papers further and access the pdf and support materials

InAppropriate Content Detection System

Published in US Patents, 2021

Built an end-to-end automated pipeline for “Inappropriate content detection” for UGC(user generated content) items on Walmart eCommerce using Vader Sentiment, Topic Modelling, Snorkel and BERT. Built as part of the Trust and Safety framework. This framework creates auto-generated keyword based rules that are obtained through Topic Modelling, over a multi-processed scraper that pulls in RSS feeds of news articles and summaries

A Multi-Objective Optimization for Clearance in Walmart Brick-and-Mortar Stores

Published in INFORMS Journal on Applied Analytics, 2021

Using RL to simulate price markdowns in stores, personalized to region and demographic based optimal policy in order to balance contradictory multi-objective optimization scenario. We need to reduce the price to increase sell through rate of bad performing products, but this decay has to be done in a slow controlled duration, so as to avoid triggering a replenishment before shelf change cycle. This involves reward design with multiple cost penalties. For single price change scenario we devised a variant of the Black Scholes Equation

Recommended citation: Nitin Kishore Sai Samala, Yixian Chen, Prakhar Mehrotra, Kamilia Ahmadi, Viresh Jivane, Linsey Pang (2021) A Multiobjective Optimization for Clearance in Walmart Brick-and-Mortar Stores. INFORMS Journal on Applied Analytics 51(1):76-89.

Exploring the Efficiency of Capsule Networks in GANs

Published in Umass Amherst, 2018

Used Capsule Network as discriminator for a generative adversarial network (GAN), trained using several hacks, which outperformed CNN-GANs at modeling image data distribution of mnist, cifar10 and celebA

Recommended citation: