Ashudeep Singh

Applied Research Scientist, Pinterest..
Ph.D., Computer Science, Cornell University.
Résumé · CV

Areas of Interest: Machine Learning, Responsible AI, Ranking & Recommender systems, Information Retrieval, AI Safety.

I am an Applied Scientist at Pinterest Labs where I work on topics related to Responsible AI and ML fairness, ensuring that the AI and ML systems provide utility to users while also guaranteeing fairness and safety to all stakeholders.

I completed my PhD in Computer Science at Cornell University in 2021 where I was advised by Thorsten Joachims. My PhD thesis focused on building machine learning models and algorithms to learn from interactive user feedback in user-facing platforms such as search and recommendation. I developed a notion of fairness in recommender and ranking systems to ensure fairness to users and creators/producers.

During my PhD, I was fortunate to have Solon Barocas, Karthik Sridharan, and David Mimno on my dissertation committee. I also 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 doing NLP research at Carnegie Mellon University.

You can find more information on my resume and CV.


May 17, 2024 Paper on measuring user impact of diversification in recommendations titled “Inclusive Recommendations and User Engagement: Experimental Evidence from Pinterest” accepted at ACM EC 2024.
Oct 17, 2023 Presented a guest lecture for the Operations Management class (BUAD 311) at USC Marshall School of Business on “Responsible ML for Real-World Search and Recommender Systems: A Multistakeholder Perspective” (slides).
Sep 9, 2023 Ongoing research with Madhav Kumar(MIT) on the impact of diversification in recommender systems on its users was recently accepted to Workshop on Information Systems and Economics (WISE) 2023.
Jun 15, 2023 Our work on interpretability for recommender systems called “RecRec: Algorithmic Recourse for Recommender Systems” was accepted to ACM CIKM 2023. This is joint work with co-authors from University of Washington and University of Maryland. Here is the arXiv link for the paper.
Jun 11, 2023 Our recent work on diversification in search and recommender systems across the Pinterest platform was accepted to ACM FAccT 2023. See you in Chicago!
It was recently also published on the Pinterest Engineering blog.
Dec 5, 2022 The slides to the NeurIPS 2022 tutorial on Fair and Socially Responsible ML for Recommendations are public. Download them here. Visit the NeurIPS portal to access the recording video if you are registered.
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. 📄

selected publications

Find more information on the publications page and google scholar.

* contributed equally.

  1. KDD
    Fairness of Exposure in Rankings
    Ashudeep Singh, and Thorsten Joachims
    In In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, London, UK, 2018.
  2. NeurIPS
    Policy Learning for Fairness in Ranking
    Ashudeep Singh, and Thorsten Joachims
    In Neural Information Processing Systems (NeurIPS), 2019.
  3. SIGIR Best Paper Award
    Controlling Fairness and Bias in Dynamic Learning-to-Rank
    Marco Morik*, Ashudeep Singh*, Jessica Hong, and Thorsten Joachims
    In Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
  4. FAccTRec
    Building Healthy Recommendation Sequences for Everyone: A Safe Reinforcement Learning Approach
    Ashudeep Singh, Yoni Halpern, Nithum Thain, Konstantina Christakopoulou, Ed H. Chi, Jilin Chen, and Alex Beutel
    In FAccTRec Workshop at ACM RecSyS, 2020.
  5. FAccT
    Representation Online Matters: Practical End-to-End Diversification in Search and Recommender Systems
    Pedro Silva, Bhawna Juneja, Shloka Desai, Ashudeep Singh, and Nadia Fawaz
    In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023.