Danielle Gruber

Danielle Gruber is a dedicated and enthusiastic researcher with a strong passion for neuroscience, computer science, and mathematics.

Her expertise lies in programming, particularly in MATLAB and Python, advanced mathematics, computational modeling, statistical analysis, and experimental design.

With over three years of experience in research labs, Danielle has honed her skills and knowledge in collecting, analyzing, and interpreting EEG data.

Danielle played a crucial role in various projects at the Laboratory for Computational Neurodiagnostics, demonstrating her proficiency in task design, processing pipelines, and developing her own research inquiries.

Her recent focus has shifted towards technology-based innovation and Brain-Computer Interfaces (BCI), particularly in the application of deep learning to BCI data.

She is currently working on leveraging reinforcement learning to simulate how the brain transitions between states, drawing inspiration from the work of Danielle Bassett.

As she embarks on her academic journey at Yale University Class of 2025, Danielle aims to delve deeper into neuroscience and BCI, potentially majoring in physics or electrical engineering to complement her interests.

Danielle's past roles include not only her research position at the Laboratory for Computational Neurodiagnostics but also experiences as a writer, student, innovator, fellow, research assistant, and teacher, showcasing her diverse skill set and interests.