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COMPANY · ENTITY #1953

CSAIL

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RESEARCH · 1 SOURCE · MIT News AI

E(3)-Pose: physics-aware deep learning to track fetal head motion in MRI

Researchers at MIT, Boston Children’s Hospital, and Inria developed E(3)-Pose, a deep learning model that embeds physical priors (notably left–right symmetry/equivariance) into its architecture to track fetal head motion during MRI and help assemble higher-resolution volumetric scans; the work was presented at ECCV 2026. The approach aims to reduce coverage gaps and orientation errors in fetal MRI that can lead to missed or incorrect prenatal brain diagnoses.

7.0

RESEARCH · 1 SOURCE · MIT News AI

MIT’s Christina Delimitrou applies machine learning to improve data‑center efficiency

Christina Delimitrou, an associate professor at MIT and member of CSAIL, leads research applying machine learning to make large-scale data centers more efficient, secure, and reliable by redesigning cloud systems, managing shared hardware resources, and developing tools (such as Seer) that anticipate and prevent application problems. Her work aims to increase utilization of existing servers so operators can deliver predictable performance while reducing the need to build new, power-hungry data centers.

6.0

RESEARCH · 1 SOURCE · MIT News AI

MIT researchers develop xvr — an AI that rapidly aligns surgical X‑rays with patients’ 3D scans

Researchers at MIT’s CSAIL and clinical collaborators developed xvr, an AI system that adapts to an individual patient in about five minutes and automatically registers intraoperative X‑rays to preoperative 3D scans in seconds with reported sub‑millimeter precision. The method, published in Nature, outperformed prior AI approaches by roughly an order of magnitude across diverse patients, body parts, and procedures.

8.0