Adrian Vasile Dalca

Postdoctoral Fellow     curriculum vitae | Linkedin

Computer Science and Artificial Intelligence Lab
EECS, Massachusetts Institute of Technology

A.A. Martinos Center for Biomedical Imaging
Massachusetts General Hospital, Harvard Medical School

32 Vassar St, 32-G904, Cambridge, MA, 02139 
adalca at mit dot edu

My research focuses on machine learning techniques and probabilistic models for healthcare, with an emphasis on the analysis of medical images. I am a postdoctoral fellow at CSAIL, MIT and MGH, Harvard Medical School, working with Mert Sabuncu and John Guttag. I completed my PhD in the Medical Vision Group, CSAIL, EECS, MIT, advised by Polina Golland.

My wife, Monica, completed her PhD at MIT in the Biology department doing exciting research in cancer biology.

Recent Updates

Mar 2019 Check out a new strategy to learn data augmentation for one-shot medical image segmentation, led by Amy Zhao. Accepted as Oral Presentation at CVPR 2019.
Feb 2019 Check out a joint learning framework for acquisition and reconstruction, led by Cagla Bahadir. Accepted at IPMI 2019.
Dec 2018 Our analysis of the Voxelmorph framework is accepted at IEEE TMI: Transactions on Medical Imaging.
Nov 2018 Several papers accepted at NeurIPS Workshops: ML4H: Machine Learning for Health and MED-NeurIPS: Medical Imaging Meets NeurIPS.
Sep 2018 Voxelmorph (Probabilistic Diffeomorphic registration) is a Finalist for best paper (Young Scientist) award at MICCAI!
Sep 2018 Our paper, led by Paolo Casale, on using Gaussian Process Priors in Variational Autoencoders, accepted at NeurIPS!
Jul 2018 Our paper on Medical Image Imputation from Image Collections accepted at IEEE TMI: Transactions on Medical Imaging.
Jul 2018 Our paper led by Danielle Pace on Iterative Segmentation from Limited Training Data accepted at MICCAI-DLMIA: Deep Learning in Medical Image Analysis!
Jun 2018 News articles about VoxelMorph and pose warping [1] [2] came out during the week of cvpr where we presented three papers.
Apr 2018 Our paper "Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration" is an early-accept at MICCAI 2018, where we will present it as an oral presentation! We develop a connection between classical probabilistic diffeomorphic models and learning to register with Convolutional Neural Networks.
Apr 2018 Our paper led by Katie Bouman on Reconstructing Video of Time-varying Celestial Objects accepted at IEEE TCI: Transactions on Computational Imaging!
Mar 2018 We will be organizing a new technical workshop at MICCAI this year focusing on integrating medical imaging and non-imaging modalities to answer novel clinical and healthcare challenges.
Feb 2018 Three papers accepted at CVPR 2018!

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