Pedro R. C. Dall'Antonia
Rio de Janeiro, Brazil
I am a first-year Ph.D. student in Applied Mathematics at the School of Applied Mathematics, Getulio Vargas Foundation (FGV EMAp), in Rio de Janeiro, where I also obtained my B.Sc. and M.Sc. degrees. Currently, I am working under the supervision of Diego Mesquita.
Previously, I worked as a Partner / Quantitative Researcher at Kadima Asset Management, following my service as a Midshipman in the Brazilian Navy.
My research lies broadly in Probabilistic Machine Learning. Most recently, I have been focusing on Generative Flow Networks (GFlowNets), investigating methods to help them explore sparse combinatorial spaces more effectively while mitigating mode collapse.
Recently, I have expanded my research to Uncertainty Quantification and rigorous statistical inference with black-box models, working with Conformal Prediction and Prediction-Powered Inference (PPI). Additionally, I am currently working on methodological developments in Computer Vision in collaboration with researchers at the Vision and Computer Graphics Laboratory (Visgraf) of IMPA.
news
| Oct 20, 2026 | Happy to share that Functional Gradient Descent with Adaptive Representations was accepted to NeurIPS 2026! |
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| Apr 30, 2026 | Excited to share that Extending Prediction-Powered Inference through Conformal Prediction was accepted to ICML 2026! |
| Apr 30, 2026 | Happy to share that Avoid What You Know: Divergent Trajectory Balance for GFlowNets was accepted to ICML 2026! |
| Apr 27, 2026 | Presented our work Avoid What You Know: Divergent Trajectory Balance for GFlowNets at the DeLTa Workshop at ICLR 2026 in Rio de Janeiro, Brazil. |
| Jan 22, 2026 | Excited to share that Boosted GFlowNets was accepted to AISTATS 2026! I will be presenting our work on boosting for generative flow networks in Morocco. See you there! |