About

Arpita Agrawal is an experienced NLP and Search Science Engineer currently working at Adobe Systems. She holds a Master of Science in Information Technology from Carnegie Mellon University, where she developed a keen interest in fields such as information retrieval, machine learning, and cloud computing. Arpita's current role at Adobe involves working on the Query Intent Service in Adobe Sensei and Search team, where she uses machine learning algorithms such as Entropy-based and Naive Bayes Classification to detect and predict the topmost intents for given queries. She also works on the Search Autocompletion Service, where she retrieves and scores the autocompletion for the search service by analyzing the search click log and ranking the suggestions using machine learning algorithms. Arpita's expertise extends to content analysis for Adobe Stock Images/Videos (Sensei), where she applies and creates machine learning models for content analysis use-cases, such as face detection, text detection, and human detection. She is also working on an image duplicate detection algorithm to diversify the content on Adobe stock search page and query-based best frame selection for Adobe stock videos. Arpita has previously worked at Adobe as an Applied Engineering Intern, where she was involved in developing the Faceted Search to provide a uniform search experience across all the Adobe products. She also worked on understanding the importance of 11 aesthetic features linked with an image to enhance the image search experience for users. Arpita has also worked as a Research Assistant at Carnegie Mellon University, where she worked on the CrowdVerify project to extract the Terms and Conditions for various Ecommerce websites to feed them into a classifier to find patterns of user behavior for different policies. Arpita is skilled in various technologies, including Search, NLP, ML, Information Retrieval, Naive Bayes, Elastic Search, Research Scientist, and Web. With 8.54 years of relevant experience, Arpita is a highly capable engineer with a passion for using machine learning and data analysis to improve search experiences.