Dhritiman Das

Former Scientific staff and doctoral candidate at the Image-Based Biomedical Modeling Group and GE Global Research.

Update: I am now a postdoctoral research associate at the Massachusetts Institute of Technology working at the McGovern Institute for Brain Research. I continue to work with Image-Based Biomedical Modeling Group as a visiting scientist.

My work focuses on machine learning architecture development towards brain imaging research, bayesian neural networks, signal processing and distributed learning infrastructures with an emphasis on computational neuroscience and open research.

Contact

• Technical University of Munich
  Fakultät für Informatik
  Germany
  

• Office : IMETUM, Technical University of Munich
  

• Email :
  
  

Research Interests

• Machine learning : Bayesian Neural Networks, Generative Models, Federated Learning.
• Image processing, computer vision : Denoising methods, super-resolution, optical flow
• Applications : Neuroimaging, Computational Neuroscience, MR Spectroscopic Imaging, Cardio-Vascular flow .

Teaching & Student Projects

*Projects* are available on machine learning applications to MR Spectroscopic Imaging - classification, parameter estimation and denoising of spectral data.

Feel free to contact me for further information or if you have your own project ideas!

Publications

2019
	Berkay Kanberoglu, D. Das, Priya Nair, Pavan Turaga, David Frakes An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation International Journal of Biomedical Imaging, vol. 2019, Article ID 9435163, 14 pages, 2019. (bib)
	C. Ulas, D. Das, M.J. Thrippleton, M. del C. Valdes Hernandez, P.A. Armitage, S.D. Makin, J.M. Wardlaw, B. Menze Convolutional Neural Networks for Direct Inference of Pharmacokinetic Parameters: Application to Stroke Dynamic Contrast-Enhanced MRI Frontiers in Neurology, 9:1147 (bib)
2018
	D. Das, M.E. Davies, Jeremy Chataway, Siddharthan Chandran, B. Menze, I. Marshall Metabolite Quantification of 1H-MRSI spectra in Multiple Sclerosis: A Machine Learning Approach Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM), Paris, France, June 2018. (bib)
	D. Das, E. Coello, A. Sekuboyina, R. Schulte, B. Menze Direct Estimation of Model Parameters in MR Spectroscopic Imaging using Deep Neural Networks Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM), Paris, France, June 2018. (bib)
	D. Das, M.J. Thrippleton, M.E. Davies, R. Schulte, B. Menze, I. Marshall MRSI Brain Temperature Mapping Using Machine Learning Proceedings of the International Society of Magnetic Resonance in Medicine (ISMRM), Paris, France, June 2018. (bib)
2017
	D. Das, E. Coello, R. Schulte, B. Menze Quantification of Metabolites in Magnetic Resonance Spectroscopic Imaging using Machine Learning Accepted to Proceedings of the 20th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Quebec, Canada, September 2017. (bib)
2016
	D. Das, E. Coello, R. Schulte, B. Menze Spectral Denoising for MR Spectroscopic Imaging using Non-Local Means Proceedings of the ISMRM Workshop on MR Spectroscopy, Lake Constance, Germany, August 2016 (bib)
	D. Das, E. Coello, R. Schulte, B. Menze Spatially Adaptive Spectral Denoising for MR Spectroscopic Imaging using Frequency-Phase Non-Local Means Proceedings of the 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Athens, Greece, October 2016 (bib)