Raghavendra Addanki

I am a Research Scientist at Adobe Research (San Jose, California). I completed my Ph.D. in Computer Science from the College of Information and Computer Sciences at the University of Massachusetts Amherst in May 2022, where I was co-advised by Prof. Andrew McGregor and Prof. Cameron Musco. I received a Dissertation Writing Fellowship for my Ph.D. thesis. I was a visiting student in the Causality program at the Simons Institute for the Theory of Computing, Berkelely in 2022. Prior to this, I completed my Bachelors and Masters degrees (dual degree) in Computer Science from the Indian Institute of Technology Madras in 2016.

Contact Email: raddanki AT adobe.com

Internships: If you are a PhD student looking for internship opportunities in theory or causal inference, please apply here and send me an email with your research interests.

Research Interests

I am broadly interested in the design and analysis of algorithms for data science, causal inference, theoretical computer science, and machine learning. During my Ph.D., among other things, I have worked on identifying new connections between combinatorial optimization and causal inference.

Publications (author ordering for the papers below is alphabetical unless marked *)

  • Sample Constrained Treatment Effect Estimation
    Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, and Anup Rao
    Conference on Neural Information Processing Systems (NeurIPS) 2022.

  • Non-Adaptive Edge Counting and Sampling via Bipartite Independent Set Queries
    Raghavendra Addanki, Andrew McGregor, and Cameron Musco
    European Symposium on Algorithms (ESA) 2022.
    [arXiv, proceedings]

  • Improved Approximation and Scalability for Fair Max-Min Diversification
    Raghavendra Addanki, Andrew McGregor, Alexandra Meliou, and Zafeiria Moumoulidou
    International Conference on Database Theory (ICDT) 2022.
    [arXiv, proceedings]

  • Collaborative Causal Discovery with Atomic Interventions
    Raghavendra Addanki, Shiva Prasad Kasiviswanathan
    Conference on Neural Information Processing Systems (NeurIPS) 2021.
    [arXiv, proceedings][12 min video at NeurIPS]

  • Intervention Efficient Algorithms for Approximate Learning of Causal Graphs
    Raghavendra Addanki, Andrew McGregor, and Cameron Musco
    International Conference on Algorithmic Learning Theory (ALT) 2021.
    [arXiv, proceedings][1hr video at MIT, 12 min video at ALT]

  • How to Design Robust Algorithms using Noisy Comparison Oracle
    Raghavendra Addanki, Sainyam Galhotra, and Barna Saha
    International Conference on Very Large Data Bases (VLDB) 2021.
    [arXiv, proceedings]

  • Efficient Intervention Design for Causal Discovery with Latents
    Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, and Cameron Musco
    International Conference on Machine Learning (ICML) 2020.
    [arXiv, proceedings][15 min video at ICML, 1 hr video at NUS]

  • Search Result Diversification with Guarantee of Topic Proportionality*
    Sheikh Muhammad Sarwar, Raghavendra Addanki, Ali Montazeralghaem, Soumyabrata Pal, and James Allan
    International Conference on the Theory of Information Retrieval (ICTIR) 2020.

  • Dynamic Set Cover: Improved Algorithms and Lower Bounds
    Amir Abboud, Raghavendra Addanki, Fabrizio Grandoni, Debmalya Panigrahi, and Barna Saha
    Symposium on Theory of Computing (STOC) 2019.

  • Embed as you need: Evaluation of Random Walk and Poincare Embeddings for Healthcare Tasks*
    Khushbu Agarwal, Tome Eftimov, Raghavendra Addanki, Sutanay Choudhury, Suzanne Tamang, and Robert Rallo
    Workshop on Applied Data Science for Healthcare, Knowledge Discovery and Data Mining (KDD) 2019.


Theoretical Computer Science group at UMass Amherst. There is a weekly theory seminar every semester – if you are interested in giving a talk, please let one of the organizers know.