Research

My research streams center around the application of machine learning and deep learning to analyze large-scale unstructured data, including audio, text, and video, with the goal of inferring consumer behavior and enhancing business decision-making. These streams can be broadly categorized as three areas: Developing Deep Learning Artifacts from Unstructured Data: In this stream, I focus on developing novel deep learning algorithms inspired by computer science literature, which are tailored to business contexts by considering the specific characteristics of the data and the business domain Applying Deep Learning Artifacts for Consumer Behavior Inference and Business Decision-Making: This research stream involves utilizing the deep learning artifacts developed in the first stream. The developed algorithms are applied to large-scale datasets gathered from social media and crowd-based platforms Assessing the Impact of State-of-the-Art (SOTA) Algorithms on Business: The last research stream focuses on evaluating the impact of cutting-edge algorithms on business decision-making such as ChatGPT. Additionally, I explore how these algorithms can be fine-tuned or adapted based on specific business contexts and requirements to enhance overall business outcomes.