Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis
- Niharika DSouza
- Hongzhi Wang
- et al.
- MICCAI 2022
Niharika is a research scientist working at IBM Research, Almaden since 2022. Her research interests lie at the intersection of geometric deep learning , graph signal processing, computational neuroscience, and medical computer vision.
In recent news, her work from 2022 on multiplexed graph neural networks for multimodal fusion was recognized as a finalist for the Young Scientist Award for MICCAI 2022 and was invited as a special issue submission to MedIA.
Between 2016-2021, she obtained her doctoral degree from the Electrical and Computer Engineering at Johns Hopkins University under the supervision of Dr. Archana Venkataraman. In collaboration with researchers from the Malone Center for Engineering in Healthcare and Kennedy Krieger Institute, she developed a suite of mathematical models of brain and behavior spanning network optimization models, deep-generative hybrids, graph neural networks and manifold learning approaches for analyzing functional and structural connectomics data. Her research has been prominently featured in top tier conference venues such as MICCAI, IPMI, MIDL, and journals such as NeuroImage. Her google scholar profile can be found here.
She also holds a Masters Degree in Applied Mathematics and Statistics (Johns Hopkins University, 2019-2021) and Bachelor's Degree (with Honours) in Electrical Engineering along with a minor in Electronics and Electrical Communications Engineering from the Indian Institute of Technology, Kharagpur (2012-2016). During her undergraduate years, she worked with Dr. Debdoot Sheet on developing deep learning frameworks for deblurring and denoising Fluorescence Microscopy images.