I am an Applied Research Scientist at Pinterest Labs where I build machine learning algorithms for inclusive and diverse recommendations at scale. In 2021, I completed my PhD in Computer Science at Cornell University in Ithaca, NY where I was advised by Thorsten Joachims along with Solon Barocas, Karthik Sridharan, and David Mimno on my dissertation committee.
My research spans the broad areas of Machine Learning, Recommender Systems, and Information Retrieval. My research focuses on:
- Building machine learning models and algorithms to learn from interactive user feedback in user-facing platforms such as search and recommendation.
- Fairness and Responsibility aspects of Recommender Systems considering fair distribution of opportunity for both users as well as the items.
Through my research, I envision search and recommendation systems to form the foundation of economically sustainable multistakeholder online platforms that ensure utility, fairness, and safety for the users as well as the creators and producers.
During my Ph.D., I have completed internships at Google Brain, Facebook Research, and Microsoft Research (NYC and Montreal) where I had the opportunity to collaborate closely with Alex Beutel, Fernando Diaz, Khalid El-Arini, John Langford. Previously, I was an undergraduate student at the Indian Institute of Technology (IIT) Kanpur and also spent a summer at Carnegie Mellon University doing research on Natural Language Processing. You can find more information on the resume and cv.
|Sep 28, 2022||Hannah Korevaar (Meta), Manish Raghavan (MIT), and I are presenting a tutorial at NeurIPS 2022 on Fair and Socially Responsible ML for Recommendations.|
|Sep 29, 2021||Our paper “Fairness in Ranking under Uncertainty” has been accepted to NeurIPS 2021. This is joint work with my advisor Thorsten Joachims (Cornell) and David Kempe from USC. 📄|
|Aug 1, 2021||In August, I joined the Applied Science team at Pinterest. 💼|
|Jun 25, 2021||I successfully defended my PhD thesis. A big thanks to my advisor and the thesis committee. 🎓|
|Jun 1, 2021||I will be joining Pinterest Labs to build machine learning algorithms for inclusive and diverse recommendations at scale. 📌|
selected publicationsFind more information on the publications page and google scholar.
* contributed equally.
- KDDFairness of Exposure in RankingsIn In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK, 2018.
- NeurIPSPolicy learning for fairness in rankingIn Neural Information Processing Systems (NeurIPS), 2019.
- SIGIRControlling Fairness and Bias in Dynamic Learning-to-RankIn Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, 2020. Best Paper Award
- RecSys WorkshopBuilding Healthy Recommendation Sequences for Everyone: A Safe Reinforcement Learning ApproachIn FAccTRec Workshop at ACM RecSyS, 2020.
- NeurIPSFairness in Ranking under UncertaintyIn Neural Information Processing Systems (NeurIPS), 2021.