Kunal Khadilkar
Data Scientist at Adobe
Kunal Khadilkar is a Data Scientist working at Adobe, working on personalization, recommendation and user understanding for Photoshop, the most popular photo editing app. Prior to Adobe, Kunal graduated from Carnegie Mellon University, with a Masters Degree in Data Science. He has been recognized as a ‘Rising Star of Social Impact’ by Harvard Rising Stars Workshop 2020. Kunal’s work in the field of AI for Social Good has been featured in leading journals & Conferences like AAAI, IJCAI, Cell, Nature and 100+ media outlets like BBC, Vice, Times of India, Hindustan Times, WION etc. Kunal is a 2 time TEDx Speaker, talking about how technology can be used to tackle social issues. He was also a Guest Lecturer at Carnegie Mellon’s School of Computer Science. Kunal was the opening speaker at the world’s largest AI Conference. Kunal has reviewed 40+ papers for ICDM, O’Reilly, DSAA, NSF etc.
In the past, Kunal has been felicitated by the Prime Minister of India, Mr. Narendra Modi and the Education Minister of Singapore, for prototyping solutions in the field of AI for Social Good, for the citizens of both the countries. He has represented India at an international hackathon and has been a winner of the world’s largest hackathon, Smart India Hackathon.
Watch on-demand: February 21
Product Led Growth, or PLG is a new and upcoming concept, created by data driven startups and nowadays being used by most major tech companies to better grow their products. The main essence of PLG is to drive growth through amazing user experiences, personalized content and insightful recommendations. The foundation of PLG relies on collecting lots of user data, and a close collaboration between Data Scientists, Product Managers, Designers and Engineers. In this talk, I will explain what PLG is, how it works in tech companies and share few insights. I will also talk about how the traditional role of Data Scientists has changed with the rise of PLG, and how Data Scientists can upskill themselves to support PLG. This talk will cover my personal research+industry experiences working with large scale data.