Bio

Alisa is a Computer Science Ph.D. student jointly in the Stanford Neural Prosthetics Translational Lab and Linderman Lab, advised by Frank Willett, Jaimie Henderson and Scott Linderman. Her research focuses on cross-brain transfer methods to reduce the data collection burden on new handwriting and speech intracortical BCI users. She is supported by the NSF Graduate Research Fellowship and the Stanford Interdisciplinary Graduate Fellowship. Prior to coming to Stanford, Alisa received her B.S. in Computer Science from Trinity College in 2021, where she conducted research on heart murmur detection and classification.

Education & Certifications

  • Bachelor of Science, Trinity College, Computer Science (2021)

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Current Research and Scholarly Interests

Robust and generalizable intracortical brain-computer interfaces

All Publications

  • Restoring rapid natural bimanual typing with a neuroprosthesis after paralysis. Nature neuroscience Jude, J. J., Levi-Aharoni, H., Acosta, A. J., Allcroft, S. B., Nicolas, C., Lacayo, B. E., Card, N. S., Wairagkar, M., Levin, A. D., Brandman, D. M., Stavisky, S. D., Willett, F. R., Williams, Z. M., Simeral, J. D., Hochberg, L. R., Rubin, D. B. 2026

    Abstract

    Here, recognizing keyboard typing as a familiar, high information rate communication paradigm, we developed an intracortical brain-computer interface (iBCI) typing neuroprosthesis providing bimanual QWERTY keyboard functionality for people with paralysis. Typing with this iBCI involves only attempted finger movements, which are decoded accurately with as few as 30 calibration sentences. Sentence decoding is improved using a 5-gram language model. This typing neuroprosthesis performed well for two iBCI clinical trial participants with tetraplegia-one with amyotrophic lateral sclerosis and one with spinal cord injury. Typing speed is user-regulated, reaching 110 characters per minute, resulting in 22 words per minute with a word error rate of 1.6%. This resembles able-bodied typing accuracy and provides higher throughput than current state-of-the-art hand motor iBCI decoding. In summary, a typing neuroprosthesis decoding finger movements, provides an intuitive, familiar and easy-to-learn paradigm for individuals with impaired communication due to paralysis.

    View details for DOI 10.1038/s41593-026-02218-y

    View details for PubMedID 41840138

    View details for PubMedCentralID 5319839

https://orcid.org/0009-0003-0517-4843