Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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Published in GitHub Journal of Bugs, 2024
This paper is about fixing template issue #693.
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Published in GitHub Journal of Bugs, 2024
This paper is about a famous math equation, \(E=mc^2\)
Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Undergraduate course, University of Oxford, 2020
This course offers a rigorous introduction to the theoretical foundations of Artificial Intelligence, centered on Turing machines, computation, and the principles that define what intelligent systems can and cannot compute.
Undergraduate course, University of Oxford, 2022
This course offers a rigorous introduction to the theoretical foundations of Artificial Intelligence, centered on Turing machines, computation, and the principles that define what intelligent systems can and cannot compute.
Graduate course, University of Oxford, 2023
This course provides a unified geometric and mathematical foundation for modern deep learning—deriving architectures such as CNNs, GNNs, Transformers, DeepSets, and LSTMs from symmetry and invariance principles—while equipping students with both theoretical understanding and practical insights into their applications.
Graduate course, Harvard University, 2025
This course provides a comprehensive introduction to artificial intelligence methods—from classical statistical models to modern deep learning and foundation models—with a focus on their practical applications in medicine.