I am a 2021 Computing Innovation Fellow and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University and in the Booth School of Business at the University of Chicago. I am fortunate to be co-mentored by Bryon Aragam and Pradeep Ravikumar.

Previously, I received my PhD in Computer Science from Purdue University, where I was advised by Jean Honorio. Before my PhD, I completed my BSc in Mechatronics Engineering from the National University of Engineering in Lima, Peru.

I serve as Production Editor of the Journal of Machine Learning Research (JMLR), the flagship journal for the field of machine learning. For JMLR related inquiries please reach out to bello@jmlr.org.

I’m in the job market for the 2024 year. If you see a fit, please reach out.

Research

My research goal is to develop next-generation ML systems that will tackle some of the current major challenges, such as robustness, interpretability, and fairness. These systems necessitate a shift from standard statistical models that are susceptible to capture undesired nonlinear correlations to ones that can potentially unearth causal relations from data. To this end, I develop, analyze, and apply principled algorithms for causal ML, with a focus on causal discovery (NeurIPS’22, ICML’23, NeurIPS’23), which is a crucial step for developing the new wave of ML models, and learning invariant/causal representations (NeurIPS’23).

I also work in structured prediction, studying efficient learning with latent variables (NeurIPS’18), minimax bounds (AISTATS’20), and exact inference (NeurIPS’19, NeurIPS’20).

News

• 09/23: Two papers accepted to NeurIPS.
• 09/23: Giving a talk on Oct. 17 about recent advances in continuous structure learning at the INFORMS Annual Meeting.
• 09/23: Presenting iSCAN at the Bay Area Machine Learning Symposium (BayLearn) on Oct. 19.
• 09/23: Code for iSCAN is now available on GitHub: Efficient identification of distribution shifts of causal variables in nonlinear SCMs.
• 07/23: Talk about iSCAN at the Max Planck Institute for Intelligent Systems, Tübingen.
• 07/23: New preprint: iSCAN: New identifiability results of causal mechanism shifts among nonlinear SCMs without individual structure learning! (Accepted to NeurIPS).
• 06/23: New preprint: First set of results on global optimality for gradient-based DAG learning. (Accepted to NeurIPS).
• 06/23: Code for TOPO is now available on GitHub: Improved continuous optimization for DAG learning with optimality guarantees.
• 05/23: New preprint: TOPO: Optimization theory for continuous structure learning. (Accepted to ICML).
• 04/23: One paper accepted to ICML.
• 04/23: Awarded the DAAD AInet fellowship. A fund for visiting German institutions.
• 12/22: Code for DAGMA is now available on GitHub: Continuous optimization for structure learning with faster and more accurate log-det constraint.
• 09/22: Our paper “DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization” has been accepted to NeurIPS.

Older News

• 09/22: Presenting DAGMA at the Bay Area Machine Learning Symposium (BayLearn) on Oct. 20!
• 05/22: One paper accepted to ISIT.
• 01/22: One paper accepted to AISTATS.
• 09/21: Started a joint postdoc at UChicago and CMU.
• 09/21: One paper accepted to NeurIPS.
• 06/21: Excited to have been awarded the Computing Innovation Fellowship!
• 04/21: One paper accepted to ISIT.
• 04/21: Talk at Pradeep Ravikumar’s lab at CMU on April 15.
• 04/21: Talk at David Sontag’s lab at MIT CSAIL on April 14.
• 04/21: Talk at Tomaso Poggio’s lab at MIT CBMM on April 5.
• 03/21: Honored to have been awarded the Bilsland Dissertation Fellowship at Purdue!
• 01/21: Talk at Peru’s 3rd Symposium of Deep Learning on Jan 29.
• 09/20: Talk at TECHSUYO’20 on October 29.
• 09/20: One paper accepted to NeurIPS.
• 05/20: Summer internship at Facebook AI.
• 01/20: One paper accepted to AISTATS.
• 09/19: One paper accepted to NeurIPS.
• 05/19: Summer internship at Facebook Ads Ranking team.
• 09/18: Two papers accepted to NeurIPS.

Service

• Production Editor of the Journal of Machine Learning Research (JMLR)
• Mentor in the 2023 Data Science Institute Summer Lab (UChicago)
• Web Chair of LatinX AI Workshop at ICML 2020
• Reviewer:
  • NeurIPS: 2023, 2022, 2021, 2020, 2019
  • ICML: 2024, 2023, 2022, 2021
  • ICLR: 2024, 2023, 2022, 2021
  • CLeaR: 2024
  • AISTATS: 2021
  • AAAI: 2022
  • LATIN Symposium: 2022
  • IJCAI: 2020
  • Journal of Machine Learning Research (JMLR)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • Transactions on Machine Learning Research (TMLR)
  • Journal of Computational and Graphical Statistics (JCGS)