Niharika S D'Souza

Niharika is a research scientist working at IBM Research, Almaden since January 2022. Her research interests are statistical representation learning, geometric deep learning , graph signal processing, computational neuroscience, and medical computer vision.

In recent news:

• She will serve as an Area Chair for MIDL 2025
• Her work on Encoded Representations and Modern Hopfield Networks was accepted into the workshop on Unifying Representations in Neural Models at NeurIPS 2024 and selected for an oral spotlight. This work was also selected as an oral presentation (top 13 percent of submission abstracts) at BayLearn 2024. This work was led by her colleague Satyananda Kashyap
• Her work on Geometrically Constrained U-Nets for segmentation in Radial Imaging modalities was presented at the Machine Learning with Medical Imaging workshop at MICCAI 2024. This was a joint collaboration between the MIT-IBM Watson Lab (with the Medical Vision group, CSAIL, EECS, MIT) and Boston Scientific and a part of her mentee, Yiming Chen's M. Eng. thesis.
• She was recognised with an Outstanding Technical Achievement Award by IBM Research
• She is serving as an Area Chair for MICCAI 2024
• She is serving as an Organising Committee Member for the 6th workshop on GRaphs in biomedicAl Image anaLysis- GRAIL 2024
• Her work on Multiplexed Graph Neural Networks for multimodal fusion appeared as special issue published at MedIA and is now available online.
• Her team was recognised by with an A-Level Technical Accomplishment for fundamental advances to the science of multimodal fusion IBM Research.
• 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 served as a session chair for the session on Brain Connectomics at IPMI 2023
• 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 an NIH Travel Award.
• 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.

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.