HeyJaredDr. Michail (Mike) Mamalakis
Job: Research Assistant Professor - CRUK-CI and University of Cambridge Field: Artificial Intelligence - Machine Learning - Deep learning, Medical Image Analysis - Computer vision Athlete: Powerlifter, Boxer, Intermediate Weightlifter Country: Greece - Crete Country of…
- Indexed videos, last 90 days
- 6
- Latest publication
- Aug 3, 2026
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- ~37 subscribers
- Earliest in this view
- Aug 3, 2026
Latest videos
Lecture 6: Attributional Interpretability Approaches in Neuroimaging and Neurobiology (opens the original)
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Lecture 6: Attributional Interpretability Approaches in Medical Large Language Models (m-LLM), Neuroimaging and Neurobiology This session will highlight various Explainable AI (XAI) techniques in attributional interpretability, such as GradCam, SHAP and GNNExplainer. [9-11, 24]. This lecture will highlight how we can use the XAI to verify patterns and potentially identify new biomarkers [16,17,26] in Large Language Models, Transformers and GNNs in medical applications, neurobiology and neuroimag
Lecture 7: Mechanistic Interpretability in Neuroscience part 1 (opens the original)
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This lecture will cover key terminology related to superposition, polysemantic representations, and the privileged basis. Additionally, it will address the problem of mechanistic interpretability and explore how the sparse autoencoder attempts to provide explanations for various deep learning applications in the medical domain, such as clinical assignment (e.g., medical large language models, m-LLMs) and neurobiology (e.g., multi-omics data, protein language models, PLM) [12-14,18, 33, 34]. This
Lecture 7: Mechanistic Interpretability in Neuroscience part 2 (opens the original)
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This lecture will cover key terminology related to superposition, polysemantic representations, and the privileged basis. Additionally, it will address the problem of mechanistic interpretability and explore how the sparse autoencoder attempts to provide explanations for various deep learning applications in the medical domain, such as clinical assignment (e.g., medical large language models, m-LLMs) and neurobiology (e.g., multi-omics data, protein language models, PLM) [12-14,18, 33, 34]. This
Lecture 5: Large Language Models, Catastrophic Forgetting and Neuroscience (opens the original)
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This lecture introduces catastrophic forgetting and model collapse in artificial neural networks, focusing on challenges arising from sequential and large-scale learning. It covers core concepts in continual learning, including replay mechanisms, parameter and functional regularization (e.g., Elastic Weight Consolidation), and optimization-based strategies to preserve prior knowledge . The course also examines state-of-the-art approaches such as Hard Attention to the Task (HAT), gradient-based m
L205: Introduction Lent semester 2026 (opens the original)
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This module aims to provide students with a coherent, multi-scale understanding of how advanced AI methods—including CNNs, transformers, graph neural networks, and LLMs—can be applied to neuroscience and translational biomedicine. It integrates brain anatomy, connectomics, neuroimaging, multi-omics, clinical big data, and model interpretability to support biologically meaningful discovery and reliable clinical applications. https://www.cl.cam.ac.uk/teaching/2627/L205/ Principles of AI-driven Neu
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