Hi, I'm Noor. 

I am a theoretical neuroscientist; focused on understanding and emulating natural decision-making.

My research is grounded in developing generative models for understanding and mimicking natural decision-making to investigate how artificial agents can adapt to and learn from environmental perturbations, similar to humans and animals. Importantly, these models can serve as a useful tool to understand neural (dys)function and design causal interventions post-neurological damage.

Briefly, I investigate the computations necessary for (1) designing appropriate generative models for decision-making, and (2) and use them to investigate decision-making in health and damage.

(Fountas, Sajid, et al, NeurIPs, 2020; Yuan*, Sajid*, et al, Nature Machine Intelligence, 2023; Gumbsch, Sajid, et al, ICLR Spotlight, 2024)

(Sajid, Ball, et al, Neural Computation, 2021; Sajid, Tigas, et al, ICML URL, 2021)

(Sajid*, Faccio*, et al Neural Computations, 2022; Sajid, Convertino, Friston, Entropy, 2021; Parr, Sajid, Friston, Entropy, 2020)


(Sajid, Parr, et al, Cerebral Cortex, 2020; Sajid, Gajardo-Vidal, et al, Biological Communications, 2023)

(Sajid, Holmes, et al, Scientific Reports, 2021, Sajid, Parr, et al, Brain Communications, 2021)


Highlighted Papers (full list here)

Sajid, N., Ball, P. J., Parr, T., & Friston, K. (2021). Active inference: demystified and compared. Neural Computation, 33(3), 674-712. 


[paper, code]

Fountas, Z., Sajid, N., Mediano, P. A., & Friston, K. (2020). Deep active inference agents using Monte-Carlo methods. Part of Advances in Neural Information Processing Systems. 


[paper, code]

Sajid, N*, Faccio, F*, Da Costa, L., Parr, T., Schmidhuber, J., & Friston, K. (2022). Bayesian brains and the Rényi divergence. Neural Computation. 

[paper, code]

Featured Talks 

Workshops