Seema Singh Saharan, PhD
Postdoctoral Scholar
Clinical Pharmacy
School of Pharmacy
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.
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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.
Education & Training
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- Ph.D. Statistics Focused on Data Science algorithms . Ph.D. Thesis Research with Dr John Kane ,CVRI, UCSF University of Rajasthan 5/2023
Publications (6)
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Logistic Regression and Statistical Regularization Techniques for Risk Classification of Coronary Artery Disease using Cytokines transported by high density lipoproteins.
Proceedings. International Conference on Computational Science and Computational Intelligence 2024 Saharan SS, Nagar P, Creasy KT, Stock EO, Feng J, Malloy MJ, Kane JP -
Optimization of Smoking Classification by Applying Neural Network with Variable Importance Using Cytokine Biomarkers.
Proceedings. International Conference on Computational Science and Computational Intelligence 2024 Saharan SS, Nagar P, Creasy KT, Stock EO, Feng J, Malloy MJ, Kane JP -
Smoking Classification Using Novel Plasma Cytokines by implementing Machine Learning and Statistical Methods.
Proceedings. International Conference on Computational Science and Computational Intelligence 2024 Saharan SS, Nagar P, Creasy KT, Stock EO, Feng J, Malloy MJ, Kane JP -
Application of Machine Learning Ensemble Super Learner for analysis of the cytokines transported by high density lipoproteins (HDL) of smokers and nonsmokers.
Proceedings. International Conference on Computational Science and Computational Intelligence 2022 Saharan SS, Nagar P, Creasy KT, Stock EO, Feng J, Malloy MJ, Kane JP -
Implementation of PCA enabled Support Vector Machine using cytokines to differentiate smokers versus nonsmokers.
Proceedings. International Conference on Computational Science and Computational Intelligence 2022 Saharan SS, Nagar P, Creasy KT, Stock EO, Feng J, Malloy MJ, Kane JP -
Machine learning and statistical approaches for classification of risk of coronary artery disease using plasma cytokines.
BioData mining 2021 Saharan SS, Nagar P, Creasy KT, Stock EO, Feng J, Malloy MJ, Kane JP