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 Ph.D. student looking for internship opportunities for Summer 2024 in theoretical computer science or causal inference, please send me an email with your research interests in Sept 2023.
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 *)
Causal Discovery in Semi-Stationary Time Series*
Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan A. Rossi, and Murat Kocaoglu
Neural Information Processing Systems (NeurIPS) 2023.
Anytime-Valid Confidence Sequences in an Enterprise A/B Testing Platform*
Akash Maharaj, Ritwik Sinha, David Arbour, Ian Waudby-Smith, Simon Liu, Moumita Sinha, Raghavendra Addanki, Aaditya Ramdas, Manas Garg and Viswanathan Swaminathan
ACM Web Conference (WWW), Industry Track, 2023.
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, proceedings, code]
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.
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]
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.