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Spectral Encoding. Develop advanced neural architectures (e.g., adapted Vision Transformers or 1D sequence models) capable of fusing multiple distinct analytical "views" (e.g., combining MS2 and NMR data
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Spectral Encoding. Develop advanced neural architectures (e.g., adapted Vision Transformers or 1D sequence models) capable of fusing multiple distinct analytical "views" (e.g., combining MS2 and NMR data
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data. Yet, their reliability remains a serious concern. This project aims to first explore current techniques such as fine-tuning, model alignment, prompt engineering and Retrieval Augmented
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Additive exPlanations): Ranks feature importance globally across the patient cohort, validating that age, CA125, HE4, CEA, and systemic inflammatory or protein markers drive predictions in alignment with
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Additive exPlanations): Ranks feature importance globally across the patient cohort, validating that age, CA125, HE4, CEA, and systemic inflammatory or protein markers drive predictions in alignment with
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, and accountability are essential. Recent developments in Agentic AI offer a promising paradigm shift. Rather than relying on a single monolithic model, multiple specialised AI agents can collaborate to
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"I am legitimate member of X agency" (without revealing exactly who), or "My software is not on the blacklist" (without revealing the exact software). There are multiple aspects of this project. For
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: Research Focus : Implementing federated learning to optimize resource management across multiple sites without compromising data privacy. Skills Gained : This research offers experience in federated
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with diverse background and skill sets in machine learning, cybersecurity, and digital health to work in the above directions in a collaborative manner. This project will provide multiple competitive