Dhritiman Das is a highly accomplished computer scientist with a strong background in bioengineering. He holds a Ph.D. in Computer Science from the Technical University of Munich, where he focused on developing innovative machine learning algorithms for medical imaging applications. More specifically, he developed applied machine learning and computer vision tools for accelerated processing and analysis of large-scale brain imaging (MRSI) data. Prior to his doctoral studies, Dhritiman earned a Master of Science in Bioengineering from Arizona State University and a Bachelor of Engineering in Biomedical Engineering from Manipal Institute of Technology. Throughout his academic career, Dhritiman has demonstrated a strong passion for research and has published several papers in top computer science and biomedical engineering journals. He has also presented his work at numerous international conferences and workshops, gaining recognition from the scientific community. In addition to his academic achievements, Dhritiman has gained valuable industry experience through various internships and research positions. He has worked as a Postdoctoral Researcher at the Massachusetts Institute of Technology, where he collaborated with leading researchers to develop cutting-edge technologies for healthcare applications. His work here focused on self-supervised learning, generative models and neuroinformatics. He has also held positions at GE Healthcare and Siemens Limited, where he applied his expertise in information theory, computer vision and machine learning to solve real-world challenges in the field of medical imaging. Dhritiman is a skilled researcher and problem-solver with a strong background in both computer science and bioengineering. He is dedicated to using his knowledge and expertise to make a positive impact in the field of healthcare and beyond.

Dhritiman Das, Ph.D.

Postdoctoral Researcher at Massachusetts Institute of Technology : AI | Computer Vision | Signal Processing | Healthcare

Research Expertise

Machine Learning

Medical Image Analysis

Computer Vision

Signal Processing

Electronic, Optical and Magnetic Materials

Condensed Matter Physics

Neurology (clinical)

Neurology

Radiology, Nuclear Medicine and imaging

Molecular Medicine

Spectroscopy

Electrical and Electronic Engineering

Computer Science Applications

Software

Radiological and Ultrasound Technology

About

Publications

Convolutional Neural Networks for Direct Inference of Pharmacokinetic Parameters: Application to Stroke Dynamic Contrast-Enhanced MRI

Frontiers in Neurology / Jan 08, 2019

Ulas, C., Das, D., Thrippleton, M. J., Valdés Hernández, M. del C., Armitage, P. A., Makin, S. D., Wardlaw, J. M., & Menze, B. H. (2019). Convolutional Neural Networks for Direct Inference of Pharmacokinetic Parameters: Application to Stroke Dynamic Contrast-Enhanced MRI. Frontiers in Neurology, 9. https://doi.org/10.3389/fneur.2018.01147

Quantification of Metabolites in Magnetic Resonance Spectroscopic Imaging Using Machine Learning

Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 / Jan 01, 2017

Das, D., Coello, E., Schulte, R. F., & Menze, B. H. (2017). Quantification of Metabolites in Magnetic Resonance Spectroscopic Imaging Using Machine Learning. In Lecture Notes in Computer Science (pp. 462–470). Springer International Publishing. https://doi.org/10.1007/978-3-319-66179-7_53

Velocity-To-Pressure (V2P) - Net: Inferring Relative Pressures from Time-Varying 3D Fluid Flow Velocities

Lecture Notes in Computer Science / Jan 01, 2021

Shit, S., Das, D., Ezhov, I., Paetzold, J. C., Sanches, A. F., Thuerey, N., & Menze, B. H. (2021). Velocity-To-Pressure (V2P) - Net: Inferring Relative Pressures from Time-Varying 3D Fluid Flow Velocities. In Information Processing in Medical Imaging (pp. 545–558). Springer International Publishing. https://doi.org/10.1007/978-3-030-78191-0_42

An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation

International Journal of Biomedical Imaging / Feb 03, 2019

Kanberoglu, B., Das, D., Nair, P., Turaga, P., & Frakes, D. (2019). An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation. International Journal of Biomedical Imaging, 2019, 1–14. https://doi.org/10.1155/2019/9435163

Spatially Adaptive Spectral Denoising for MR Spectroscopic Imaging using Frequency-Phase Non-local Means

Lecture Notes in Computer Science / Jan 01, 2016

Das, D., Coello, E., Schulte, R. F., & Menze, B. H. (2016). Spatially Adaptive Spectral Denoising for MR Spectroscopic Imaging using Frequency-Phase Non-local Means. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 (pp. 596–604). Springer International Publishing. https://doi.org/10.1007/978-3-319-46726-9_69

Joint Automatic Metabolite Identification and Quantification of a Set of 1H NMR Spectra

Joint Automatic Metabolite Identification and Quantification of a Set of 1H NMR Spectra. (n.d.). American Chemical Society (ACS). https://doi.org/10.1021/acs.analchem.0c04232.s001

Reliability of MRSI brain temperature mapping at 1.5 and 3 T

NMR in Biomedicine / Nov 24, 2013

Thrippleton, M. J., Parikh, J., Harris, B. A., Hammer, S. J., Semple, S. I. K., Andrews, P. J. D., Wardlaw, J. M., & Marshall, I. (2013). Reliability of MRSI brain temperature mapping at 1.5 and 3 T. NMR in Biomedicine, 27(2), 183–190. Portico. https://doi.org/10.1002/nbm.3050

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

IEEE Transactions on Medical Imaging / Oct 01, 2015

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.-A., Arbel, T., Avants, B. B., Ayache, N., Buendia, P., Collins, D. L., Cordier, N., … Van Leemput, K. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024. https://doi.org/10.1109/tmi.2014.2377694

Education

Technical University of Munich

Ph.D., Computer Science

Munich

Arizona State University

M.S., Bioengineering

Tempe, Arizona, United States of America

Manipal Institute of Technology

B.E., Biomedical Engineering

Manipal

Experience

Massachusetts Institute of Technology

Postdoctoral Researcher

Technical University of Munich

Scientific Staff / 2015 — 2020

GE Healthcare

Early Stage Researcher / 2015 — 2019

Siemens Limited

Computer Vision Intern / May, 2014 — August, 2014

Links & Social Media

Google Scholar

ORCID

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