Shanahan Undergraduate and Post-bac Fellows

2023 Research Assistants

Janna Hong

mentor Anamika Agrawal

Janna is a recent grad with a degree in Neuroscience and Education. She also studied computational neuroscience and leads UW’s only Neuro/AI organization to democratize interdisciplinary education. For her research, she is analyzing neuropathological data from the hippocampus and MTG (Medial Temporal Gyrus). She is analyzing quantitative data from post-mortem brain samples from donors in Python. Central to her research on Alzheimer’s Disease, characterized by plaques (such as dense core and diffuse) and tangles, are the AT8 and 6e10 markers aligned with Braak and Thaal stages. Additionally, she’s beginning to explore the distribution of α-synuclein in relation to Lewy body disease. Her quantification efforts aim to illuminate disease severity, progression, and inform the pseudotime inference model. Overall, Janna hopes to augment the ongoing SEA-AD project

Andrew Smith

Mentor Matthew Bull

Andrew completed his BS in Computer Science at the University of Virginia before moving to Seattle as a software engineer with Microsoft developing solutions for the internet of things (IOT). Motivated by the revolution in computing happening on the edge with low power, and sample efficient learning technologies, Andrew decided to shift his focus toward research at the intersection of neuroscience and machine learning. His current goal is to gain admission into graduate school at the intersection of computer science and biologically inspired learning rules. The Shanahan RA position has provided him the opportunity to dive deeply into emerging models for credit assignment and their comparison with neurobiological phenomena. Currently, Andrew is building a comprehensive open-source implementation (with plans to submit an accompanying article to the Journal of Open Source Software) of a recently proposed architecture called the Forward-Forward model which merges ideas from contrastive objective functions with locally accessible activations. By inverting the locally defined objective function in this model, he is exploring connections with predictive coding. In one ongoing numerical experiment, he is comparing the flow of top-down and bottom-up information in the activations of his trained networks, and he hopes to build explicit links with observed hierarchical ordering in the visual cortex. This Forward-Forward foundation merges local credit assignment rules with hierarchical networks in a way which is conducive to integrating spiking neurons (and other non-differentiable components), curiosity-based learning, sparse activations, and neuromorphic accelerators. As we enter the age of embodied, power-hungry, language models embedded within real life environments, Andrew is thrilled to have the opportunity to make humble contributions to our understanding of a low-power, efficient learning paradigm with potential to further advance our understanding of both living and artificial learning.

Wuwei Zhang

Mentor Lu Mi

I am currently enrolled in the combined BS/MS program, pursuing a major in computer science at the University of Washington. My research interests revolve around computational biology, particularly focusing on developing and applying machine learning and statistical methods to study biology, including mechanisms of human health and disease. I began my research journey with Prof. Sara Mostafavi, working on projects related to regulatory genomics, where I use deep learning to decode gene expression from DNA sequences under different methylation conditions. As I am still in the early stages of my academic career, I am eager to broaden my research horizons and gain exposure to other related fields. I have a strong passion for NeuroAI, an exciting field that leverages the potential of artificial intelligence to study the intricate workings of the brain. Currently, I am mentored by Lu Mi and working on a research project to infer neural synaptic connectivity from neural activity data. Looking ahead, I plan to pursue a Ph.D. in computer science, where I aim to merge machine learning, computational biology, and algorithmic research. By embracing interdisciplinary collaborations and staying abreast of the latest advancements, I seek to develop transformative solutions that have a lasting impact on both the scientific community and society as a whole

2022 Research Assistants

Miles Mahon

mentor Praveen Venkatesh

I’m a research assistant working with Praveen Venkatesh, moving between UW’s Computational Neuroscience Center and the Allen Institute. I’m currently working on measures of directed information in recurrent networks, looking at targeted interventions and context integration in decision-making tasks. I’m also broadly interested in social cognition and the development of moral reasoning. Outside of research I like to play music, chess, and indie video games.

Mozes Jacobs

Mentor Ryan Raut

I graduated with a B.S. in Computer Science from the University of Washington. I am interested in deep generative modeling and studying how it can be used to help understand the dynamics of the world. My current research is on the problem of system identification for dynamical systems. Specifically, I am studying the applications of deep learning methods and approximate Bayesian inference methods (e.g. VAEs, hypernetworks, implicit distributions) to discovering stochastic differential equations from data. By developing these techniques further, we may be able to pull out SDE representations for some brain dynamics. In my free time, I enjoy playing with cat, watching TV, playing video games. I also do powerlifting.

A’Dawnah Pangelinan

Mentor Monica Liu

I double major in Biology and Psychology and minor in Neural Computation and Engineering. My research interests in computational neuroscience are: modeling of neural coding and computation, simulation development, and neuroinformatics. During this fellowship, I have worked closely with Dr. Monica Liu on modeling ideal observer theory principles then comparing this model to behavioral representations of certainty. Currently, I am working with Dr. Saskia de Vries on quantifying and defining previously overlooked tuning properties of GABAergic interneurons in the mouse primary visual cortex. Outside of research, you’ll find me trying new restaurants with friends/family, finding pretty PNW views, and expanding my music palate. I also unapologetically adore dogs.