349, Bill and Melinda Gates Hall
107 Hoy Rd
I am a Ph.D. Candidate in Computer Science at Cornell University in Ithaca, NY. I am advised by Thorsten Joachims. I am fortunate to have Solon Barocas, Karthik Sridharan and David Mimno on my dissertation committee.
My research focuses on Fairness and Responsibility aspects of Machine Learning algorithms for Search and Recommendation systems. Through my research, I envision these search and recommendation systems to form the foundation for building economically sustainable multistakeholder platforms. In my research, I have developed notions and algorithms for fair distribution of opportunity and benefits for the both the stakeholders–users as well as the content providers.
During my Ph.D., I have completed internships at Google Brain, Facebook Research and Microsoft Research where I had the opportunity to collaborate closely with Alex Beutel, Fernando Diaz, John Langford. Previously, I was an undergraduate student at Indian Institute of Technology (IIT) Kanpur, and also spent a summer at Carnegie Mellon University. You can find more information in the resume.
|Sep 20, 2020||Our new work on Building Healthy Recommendation Sequences for Everyone: A Safe Reinforcement Learning Approach has been accepted at FAccTRec workshop on Responsible Recommendation at ACM RecSys 2020 to be held from September 22-26, 2020. This is joint work with my collaborators at Google Research.|
|Aug 19, 2020||Communications of the ACM (CACM) and Cornell Chronicle covered a story about our ACM SIGIR 2020 paper Controlling Fairness and Bias in Dynamic Learning-to-Rank.|
|Jul 22, 2020||New: Our paper Controlling Fairness and Bias in Dynamic Learning-to-Rank has been awarded the Best Paper Award at the ACM SIGIR 2020 conference that was held virtually.|
publicationsFind more information on google scholar.
* contributed equally.
- 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.
- NeurIPSPolicy learning for fairness in rankingIn Neural Information Processing Systems (NeurIPS), 2019.
- KDDFairness of Exposure in RankingsIn In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK, 2018.
- NeurIPS WorkshopEquality of Opportunity in RankingsIn Workshop On Prioritizing Online Content (WPOC) at NeurIPS, 2017.
- NeurIPS WorkshopLearning item embeddings using biased feedbackIn NeurIPS Workshop on Causal Inference and Machine Learning for Intelligent Decision Making, 2017.
- ICMLRecommendations as treatments: Debiasing learning and evaluationIn International Conference on Machine Learning (ICML), 2016.
- PreprintA Semantic Approach to SummarizationarXiv preprint arXiv:1406.1203 2014.
- ITSPredicting student learning from conversational cuesIn International Conference on Intelligent Tutoring Systems (ITS), 2014.
- AIEDAutomatically generating discussion questionsIn International Conference on Artificial Intelligence in Education (AIED), 2013.