PhD Student
About Me
I’m a first-year PhD student in Computer Science at McMaster University, co-supervised by Stephen Kelly and Reuven Dukas. My research explores how animal-like behaviour emerges and evolves in artificial living systems. I’m especially interested in the evolutionary origins of biological complexity and how it manifests across levels of organization, from microbes like E. coli to multicellular organisms.
Before starting the PhD, I completed my MSc at McMaster. Outside the lab, you’ll find me reading, watching films, or playing the violin.
- Evolutionary Computation
- Artificial Life
- Bio-Inspired Computing
MSc in Computer Science
McMaster University
BCS(H) in Computer Science
University of Windsor
Naqvi, A., & Kelly, S. (2026). Path-Local Learning in Reward-Modulated Tangled Program Graphs. In W. Banzhaf & T. Hu (Eds.), Recent Advances in Linear Genetic Programming (Chapter 9, pp. 197–217). Springer. Forthcoming.
Naqvi, A., & Kelly, S. (2026). Dynamic Vector and Matrix Memory for Tangled Program Graphs. In L. Manzoni, S. Cussat-Blanc, & Q. Chen (Eds.), Genetic Programming: EuroGP 2026 (Lecture Notes in Computer Science, Vol. 16521). Springer, Cham.
Roy, X. H., Naqvi, A., Shao, X., & Kelly, S. (2026). In Search of Larger Populations: Rethinking GPU Execution for Genetic Programming in Artificial Life. In Proceedings of the 2026 Artificial Life Conference. MIT Press.
Djavaherpour, T., Naqvi, A., Zhuang, E., & Kelly, S. (2025). Evolving Many-Model Agents with Vector and Matrix Operations in Tangled Program Graphs. In S. M. Winkler, W. Banzhaf, T. Hu, & A. Lalejini (Eds.), Genetic Programming Theory and Practice XXI (Genetic and Evolutionary Computation). Springer, Singapore.
Naqvi, A., Djavaherpour, T., Vacher, Q., & Kelly, S. (2025). Integrating Neuroplasticity into Genetic Programming Agents for Adaptive Decision Making. In Proceedings of the Artificial Life Conference 2025: Ciphers of Life (p. 69). ASME.
Vacher, Q., Kelly, S., Naqvi, A., Beuve, A., Djavaherpour, T., Dardaillon, M., & Desnos, K. (2025). MAPLE: Multi-Action Programs through Linear Evolution for Continuous Multi-Action Reinforcement Learning. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ‘25) (pp. 1062–1071). Association for Computing Machinery, New York, NY, USA.
Djavaherpour, T., Naqvi, A., Norouziani, R., Vacher, Q., & Kelly, S. (2025). Genetic Encoding and Shared Knowledge in Reinforcement Learning with Structured Memory. In Proceedings of the Artificial Life Conference 2025: Ciphers of Life (Kyoto, Japan, October 6–10, 2025, p. 30). ASME.