Niharika S D'Souza

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:

• She was recognized as an Outstanding Reviewer (one among the top 12 reviewers) for MICCAI 2023
• She presented her work on Maximal Correlation informed Multi-Layered GNNs for Multimodal Fusion at the ML4MHD workshop at ICML 2023 as an oral.
• Her contributed chapter Network Comparisons and their applications in Connectomics appeared in Connectome Analysis: Characterization, Methods, and Analysis
• She presented her work on Geodesic Mean Estimation for Functional Connectomics manifolds at IPMI 2023 as an oral.
• 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.
• She was recognised as one of the top 10 % of reviewers for ICML 2022.

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.