Seema Saharan is a highly skilled Software Engineer, Data Scientist and Biostatistics researcher specializing in Big Data, Machine Learning, and Artificial Intelligence (AI) techniques applied to healthcare, precision medicine, and translational research. Her expertise spans data Science, biostatistics and AI-driven methodologies, with a particular focus on multimodal signal data integration, medical imaging, and AI-powered diagnostic tools using deep learning.

At the MOC, Seema conducts cutting-edge research on multi-modality big data, leveraging AI and deep learning models to analyze medical imaging, sensor-derived physiological signals, and high-dimensional biomolecular data. She develops advanced diagnostic tools that integrate computer vision, deep neural networks, and AI-driven multimodal fusion techniques, enabling early disease detection, risk assessment, and personalized treatment strategies. Her work is particularly focused on Alzheimer’s disease and related dementias, where AI-driven pattern recognition enhances clinical decision-making and treatment evaluation.

Seema holds a Ph.D. in Statistics with a Data Science Algorithm focus, where she optimized statistical exploratory analyses of proinflammatory cytokine cascades transported by HDL/Plasma. Her research provides critical insights into cardiovascular diseases, Alzheimer’s, and cancer, advancing AI applications in biomedical signal processing, medical imaging analytics, and AI-assisted diagnostics.

She is passionate about building standardized AI ecosystems for healthcare and bioinformatics, ensuring scalable, secure, and interpretable AI solutions. As an educator and mentor, she actively leads research initiatives, secures project funding, and guides students in AI, deep learning, multimodal data integration, and AI-based diagnostic tool development.