MaxCorrMGNN: A Multi-Graph Neural Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction
- Niharika DSouza
- Hongzhi Wang
- et al.
- ICML 2023
Niharika is a research scientist working at IBM Research, Almaden since January 2022. Her research interests lie at the intersection of geometric deep learning , graph signal processing, computational neuroscience, and medical computer vision.
In recent news:
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. She has also been the recipient of multiple awards and honors such as the MINDS Data Science Fellowship 2021 (JHU), Rising Stars in Data Science 2021 (U. Chicago), Rising Stars in EECS 2020 (UC Berkeley), Best Paper Award (MLCN at MICCAI 2020), IPMI conference scholarship (2019) and NIH travel awards for MICCAI (2018, 2020, 2022).
For a complete list of publications, her google scholar profile can be found here 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.