The UW Computational Neuroscience Center and the Shanahan Fellowship program have an exciting paid opportunity for pre-doctoral researchers at the interface of data and neuroscience. These roles are open to both current undergraduate and post-baccalaureate students.
Student fellows will be mentored by a current Shanahan Fellow, and they will gain hands-on research experience in neural computation, neural networks, and computational modeling/method development. Students will join a vibrant interdisciplinary research community with the opportunity to work with researchers at all levels at University of Washington and Allen Institute for Brain Science. As appropriate, fellows will be supported in opportunities to present and publish their work.
The fellowship will be full time during the summer quarter, with potential opportunities to continue into the academic year. The salary range for this role is $20.19 – $26.98/hour and is eligible for benefits.
To apply, please send your CV and cover letter to cncadmin@uw.edu. Please include a description of your career goals, research interests, and which projects interest you in your cover letter. Applications will be reviewed on a rolling basis.
Current Fellows and Project Descriptions
Lu Mi
Bio:
Lu Mi joined the Allen Institute and the University of Washington as a Shanahan Foundation Fellow in the fall of 2022. She has a Ph.D. in Computer Science at MIT CSAIL. In her Ph.D. study with Prof. Nir Shavit, she developed advanced deep learning tools for fast and scalable automatic connectomics pipelines to discover the brain, and linking the anatomical structure and neural activity with whole-brain modeling. In the current collaboration with investigators at the Allen Institute and the University of Washington, she is developing interpretable deep learning approaches including probabilistic modeling and representation learning for biological and artificial systems. Her research topics broadly include developing fast and scalable automatic pipelines to discover the brain; modeling brain with multi-modal neural data; understanding the robustness and efficiency of coding, computation and learning in biological and artificial systems; and building brain-inspired AI frameworks. She had multiple mentorship experiences during her PhD, the mentees were admitted by PhD programs of top-tier graduate schools (Harvard, Cornell, Yale, etc).
NeuroAI, which stands at the intersection of neuroscience and deep learning, is an exciting field with great amounts of opportunities and potential. The RA position (renewable quarterly up to a year) between the Allen institute and UW will investigate: 1. apply state-of-the-art deep learning tools to large-scale and multi-modal neural data; 2. perform mechanistic studies towards understanding the robustness and efficiency of coding, computation and learning in biological networks and artificial neural networks. The research focus will have great flexibility and could be determined by the RA’s interest and experience. Potential directions are listed as follows,
- Apply latent generative models (diffusion models, VAE, GAN, etc.) for neural data: building latent dynamics models with functional recording and anatomical connectivity; exploring the diverse solution space of preferred stimuli; improve temporal or spatial resolution of neural data.
- Study biologically plausible mechanisms (coding, computation, learning) in network models: dynamical motifs in neural data constrained RNNs; superposition in sensory coding; attention in hippocampus; training-free in-context learning; scaling-law in biological networks.
Skills and qualifications:
- Self-motivated pre-graduate (undergraduate, post-bac) applicants who are interested in NeuroAI are encouraged to apply.
- Required: interest in neuroscience and AI, data analysis and programming skills.
- Preferred: relevant research experience.
Anamika Agrawal
Bio:
Anamika Agrawal joined the Allen Institute and the University of Washington as a Shanahan Foundation Fellow in August 2022. She received her Ph.D. in Physics from UC San Diego, working with Prof. Elena Koslover. During her graduate studies, she became interested in the interplay of intracellular dynamics and neuronal morphology, with her work focusing on mitochondrial organization. Drawing inspiration from her specialization in Quantitative Biology at UCSD, she enjoys working with simple analytical and computational models that still preserve the relevant biological complexity of the involved data. During the fellowship, she is interested in understanding how cell-type specific neuronal properties, such as morphology and transcriptomics, contribute to neuronal function and dysfunction.
Project Description:
Note: projects can be tailored according to the selected applicant’s interests and skill sets.
Neuropath data analysis and modeling exploration
Alzheimer’s Disease is a neurodegenerative disorder that can have debilitating effects on patients through its progression in the brain. Through the Seattle Alzheimer’s Disease Brain Cell Atlas initiative (SEA-AD), several pathological markers such as amyloid-Beta plaques, neurofibrillary tau filaments, and a-Syn have been imaged in brain tissues across donors. It is not well understood how pathological progression occurs across the brain over time, and how the differences in progression across individual patients could be linked to different phenotypes of the disorder. On a biophysical level, it is also not well understood how the interactions between the chief pathological proteins contribute to the progression course. In this project, the selected applicant will get a chance to analyze neuropathological data and make inferences about possible courses of pathology. The applicant will also get experience in how kinetic biophysical models of aggregate growth and multiplication could be leveraged to understand region-specific neurodegeneration patterns.
Other possible research areas: dendritic integration and computation, analysis of local dendritic activity and its role in somatic AP, quantification of morphological diversity across neuronal cell types and potential implications to function.
Skills and qualifications:
Applicants looking to expand their interdisciplinary expertise are encouraged to apply.
Required: General programming experience, broad neuroscience and neurobiology interests
Preferred: Programming in python, data handling, data analysis of biological data
Matthew Bull
Bio:
Matthew joined the Allen Institute and the University of Washington as a Shanahan Family Foundation Fellow in the autumn of 2022. His previous experience spans: a Ph.D. in Applied Physics from Stanford University (with Prof. Manu Prakash), an internship at Google research exploring privacy-preserving ‘user-focused epidemiology’, and three years building precision measurement tools for single bio-polymers at JILA (with Prof. Thomas Perkins at University of Colorado, Boulder, and NIST). Matthew is enthusiastic about seeking unexpected simplicity within the high dimensional dynamics of a behaving brain.
Project description:
There is still much to learn about how the symphony of neurons underlies an animal’s perception and actions. This RA position (renewable quarterly up to a year) between the Allen institute and UW will use task-trained spiking neural networks as hypothesis generation tools and neural data analysis of massive data sets (both publicly released and in production) to seek signatures of three postulated mechanisms underlying how networks of spiking neurons may navigate the famed exploration-versus-exploitation tradeoff in a dynamic environment. We aim to embrace the role of cellular diversity, neuromodulation, and embodiment. A successful research program will teach RAs to integrate models by construction into their scientific process as a tool for wading through the near-paralyzing complexity and beauty of neural data.
Skills:
All interested students/postbacs are encouraged to apply independent of qualification or background.
Required: self-motivated, creativity, curiosity, interest in neuroscience and data,
Preferred: python, optimization