Sebastian "Sebo" Diaz
sebodiaz@csail.mit.edu | sdd@mit.edu | seboddiaz@gmail.com
I am a MIT PhD candidate working with Prof. Elfar Adalsteinsson and Prof. Polina Golland on machine learning problems.
Research interests
- Incentivizing generalization beyond the training (source) distribution.
- Exploring synthetic data for improved or otherwise unattainable representations.
- Representation learning and optimal use of embeddings.
- Exploiting numerical structure to enhance algorithms.
Funding
I am indebted to the following sources for supporting my research (exhaustive list in my CV):
- NSF Graduate Research Fellowship Program (GRFP)
- MathWorks Fellowship
- MIT SoE Distinguished Engineer Fellowship
Education
I received my B.S. from the University of Arizona in 2023 where I was fortunate to be mentored by Prof. Arthur Gmitro, Prof. Jennifer Barton, and Prof. Nan-kuei Chen.
News
- Sep 2026“Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It” accepted to NeurIPS 2026.
- Dec 2025Invited MIT IMES Seminar talk.
- Sep 2025Invited MIT IMES Retreat talk.
- Jun 2025“Robust Fetal Pose Estimation across Gestational Ages via Cross-Population Augmentation” accepted to MICCAI 2025.
- Apr 2024Awarded NSF GRFP.
Publications
Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It
Invariance alone can limit domain generalization. MaskGen combines invariant and domain-specific information for sample-efficient biomedical segmentation across domains.
Robust Fetal Pose Estimation across Gestational Ages via Cross-Population Augmentation
Cross-population augmentation improves pose estimation in younger fetuses using only older fetuses for training. Our method achieves state-of-the-art results on a challenging clinical dataset.
Design of Novel RF Pulse for Fetal MRI Refocusing Trains using Rank Factorization (SLfRank) to Reduce SAR and Improve Image Acquisition Efficiency
SLfRank optimizes RF pulses to accelerate fetal MRI by 22% over the industry standard. The approach also applies to other turbo spin echo sequences.
Fetuses Made Simple: Modeling and Tracking of Fetal Shape and Pose
We introduce a 3D articulated fetal body model based on SMPL to track shape and pose. It captures body structure and motion beyond isolated keypoints or segmentations.
Stochastic-offset-strategy enhanced RF pulse optimization with auto-differentiation
Random spatial offsets improve RF pulse optimization with automatic differentiation. This approach outperforms fixed-point representations at coarse spatial resolutions.