I am a 4th year PhD student in Cognitive Science at Brown Univeristy’s Laboratory of Neural Computation and Cognition and am interested broadly in computational models of human learning and decision-making. I investigate how the brain learns structure and creates useful representations in order to generalize in new contexts. In my research, I aim to integrate multiple levels of analysis, for example by developing reinforcement learning algorithms (i.e. how to learn by trial-and-error) that draw from biological details of learning and memory.
I received my Bacherlor’s in Computer Science and a minor in Cognitive Science from Princeton in 2017. I also completed a Fulbright Student fellowship as an English Teaching Assistant in Rivne, Ukraine.
Jaskir, A. & M.J. Frank. 2022 “On the normative advantages of dopamine and striatal opponency for learning and choice.” bioRxiv.
Jaskir, A., L. Lehnert, M.J. Frank (2022) “Sleep’s role in analogous transfer for sequential reinforcement learning”. Winter Conference on Brain Research.
Jaskir, A. & M.J. Frank (2019) “Computational advantages of dopaminergic states for decision making.” Computational Cognitive Neuroscience Conference (CCN)
A Jaskir & Y. Niv. “Modeled learning weights predict attention and memory in a multidimensional probabilistic task.” Reinforcement Learning and Decision Making Conference (RLDM) 2017.
(Undergraduate) Jaskir, A. (2017). Learning how to learn: the interaction between attention and learning as a mechanism for dimensionality reduction in the brain. Department of Computer Science, Princeton University.