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.
    [arXiv]

  • 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.
    [proceedings]

  • 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.
    [proceedings]

  • 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.
    [arXiv]

Miscellaneous

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.