Hello! My name is Aldo. I am a PhD student at Stanford University in the Institute for Computational and Mathematical Engineering (ICME). I have the privilege of being advised by Professor Susan Athey. My research focuses on topics in personalized decision-making and recommendation. In my core research, I explore topics in bandit learning, federated learning, causal inference, differential privacy, and language models. I’m always eager to broaden my understanding in many areas of machine learning.

Please find my CV here.

Publications & Preprints

• Privacy and Efficiency in Personalized Decision-Making and Recommendation
  Ph.D. Dissertation, Stanford University, 2023

• Federated Offline Policy Learning with Heterogeneous Observational Data
  Aldo G. Carranza, Susan Athey

• Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Language Models
  Aldo G. Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin, Matthew Jagielski, Milad Nasr

• Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles
  Aldo G. Carranza, Sanath Krishnamurthy, Susan Athey
  Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)

• Higher-order Clustering in Complex Heterogeneous Networks
  Aldo G. Carranza, Ryan A. Rossi, Anup Rao, and Eunyee Koh
  Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2020)

• Heterogeneous Graphlets
  Ryan A. Rossi, Nesreen K. Ahmed, Aldo G. Carranza, David Arbour, Anup Rao, Sungchul Kim, and Eunyee Koh
  ACM Transactions on Knowledge Discovery from Data (TKDD), 2020