Award

NRT: NeuroTech - Bringing Technology to Neuroscience

Description: Deciphering how the brain works could have untold impacts on medicine, technology, commerce, and our understanding of ourselves. For example, advances in neurotechnology could lead to brain-machine interfaces to overcome sensory impairments and loss of movement due to neurodegenerative disease. Many of the most important advances in neuroscience have required interaction with technical fields such as physics, electrical and chemical engineering, bioengineering, statistics, and computer science, and this will increasingly be the case as the field advances. However, the path for top students from these disciplines to enter the field of neuroscience has always been challenging because they lack the appropriate background and awareness of key questions and technological limitations in the field. This National Science Foundation Research Traineeship (NRT) award to Stanford University will accelerate fundamental developments in neuroscience by attracting promising young talent from these technical disciplines to neuroscience and training them to be leaders in the field. The program will allow students to apply technological developments in diverse fields to the most important problems in neuroscience today and train a new generation of neuroscientists who will bring these technologies to fruition in academia, medicine, and the private sector. The project anticipates training thirty (30) PhD students, including twelve (12) funded trainees, from physics, electrical and chemical engineering, bioengineering, materials science, computer science, and other technical fields. This traineeship program consists of a novel integrated curriculum of coursework, internship and training experiences, and outreach to achieve its goals. The program will emphasize training for acquiring and analyzing vast data sets, enabling an understanding of nervous system circuitry at a scale that was unimaginable just a few years ago, and connecting the novel data to Stanford's strength in theory, inference from large data sets, and computational modeling. The program will introduce a rigorous multi-year curriculum for trainees, building on their home-discipline training and allowing them to collaborate with each other and with the members of the Neurosciences PhD program. Training will leverage the highly successful Stanford ADVANCE program that supports new PhD students with a special summer program prior to the start of graduate training, and build on it with several approaches customized to this program. The program will be specifically designed to optimize trainee preparation for a career in academia or in a technology industry setting, utilizing internship placements with both startups and established corporations. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Amount: $3,000,000
Funding type: grant
Funder scheme: Standard Grant
Start date: 2018-09-01
End date: 2024-08-31
Investigator: Eduardo Chichilnisky
Institution: Stanford University
Funded outputs count: 28
Funder award ID: 1828993
OpenAlex ID: G2062940092
2026 · Gale, Mary Kate, Shobayashi, Shujiro, et al. · arXiv (Cornell University)
2026 · Bibudha Parasar, Siavash Moghadami, et al. · Trends in Cell Biology
2026 · Bibudha Parasar, Achuthan Raja Venkatesh, et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2
2026 · Alice Tor, Yuxin Wu, et al. · Journal of Neural Engineering
2025 · Ava Lakmazaheri, Steven H. Collins · IEEE Transactions on Neural Systems and Rehabilitation Engineering · 4
2025 · Shumao Xu, Han Cui, et al. · ACS Nano · 13
2025 · Mary Kate Gale, Kailana Baker-Matsuoka, et al. · IEEE Transactions on Biomedical Engineering · 3
2025 · Ava Lakmazaheri, Steven H. Collins · IEEE Transactions on Medical Robotics and Bionics · 1
2024 · Emily Anaya, Paul Schleyer, et al. · IEEE Transactions on Radiation and Plasma Medical Sciences
2024 · Lauren Cooper, Marigold Gil Malinao, et al. · Accounts of Chemical Research · 14
2024 · Syamantak Payra, Gabriel Loke, et al. · Optimizations in Applied Machine Learning
2024 · Ava Lakmazaheri, Seungmoon Song, et al. · Journal of NeuroEngineering and Rehabilitation · 75
2023 · Praful K. Vasireddy, Alex R. Gogliettino, et al. · International IEEE/EMBS Conference on Neural Engineering · 2
2023 · Amrith Lotlikar, Nishal P. Shah, et al. · International IEEE/EMBS Conference on Neural Engineering · 2
2023 · Xiang Wu, Fan Yang, et al. · Electromagnetic waves · 1
2020 · Emily Anaya, Craig Levin · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · 2
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