2024 Reading List

As 2024 draws to a close, it’s time for another retrospective list of the most interesting books I’ve read this year.

The list is decidedly shorter than usual, a result of my focus on completing my Master’s degree — a year spent diving into academic papers more than leisure reading. (I’ve not included the papers I read for my thesis here).

Interestingly, not a single physical book made the cut this year — a sign of the times, perhaps? Additionally, most of the titles are recent publications, with the obvious exception of the timeless wisdom found in Marcus Aurelius’ writings (!).

Finally, a delightful addition to my reading routine this year was to attend the excellent Reading Rhythms events, in which people gather together to individually read a book of their choice, to curated background music; turning reading into a communal experience.

Audiobooks

• Why Can’t I Just Enjoy Things?: A Comedian’s Guide to Autism (Novellie, 2024)
• Friends: Understanding the Power of our Most Important Relationships (Dunbar, 2021)
• Move: The Forces Uprooting Us (Khanna, 2021)
• The Body Keeps the Score: Brain, Mind, and Body in the Healing of Trauma (van der Kolk, 2014)
• Meditations (Aurelius, 180)
• How to Become Famous: Lost Einsteins, Forgotten Superstars, and How the Beatles Came to Be (Beyer, 2024)

Papers

• Vaswani, A. et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems.
• Sculley, D., et al. (2015). Hidden technical debt in Machine learning systems. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 (NIPS’15). MIT Press, Cambridge, MA, USA, 2503–2511.
• Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958.
• Chen, H., Gilad-Bachrach, R., Han, K. et al. Logistic Regression over Encrypted Data from Fully Homomorphic Encryption. BMC Med Genomics 11 (Suppl 4), 81 (2018).
• T. Li, A. K. Sahu, A. Talwalkar and V. Smith (2020). Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50-60, May 2020.
• Rigaki, Maria, and Sebastian Garcia. (2023). A Survey of Privacy Attacks in Machine Learning. ACM Computing Surveys, vol. 56, no. 4, Nov. 2023, pp. 1–34.