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My scientific work centers on extracting insights from complex neuroscience data, especially high-dimensional time-series signals recorded from the brain. During my PhD, I developed strong analytical and quantitative research skills, strengthened through coursework in computer science (machine learning track).




This is how my data look like:
Real-time, simultaneous imaging of raw jRGECO1a fluorescence dynamics (with overlaid regions of interest), neural activity (∆F/F), and changes in hemodynamic signals. Neural activity ∆F/F signals from each ROI are plotted over time on the right.




I combine a deep understanding of brain physiology with advanced data science techniques, enabling me to turn raw biological signals into meaningful scientific insights. 


As an initial step, I designed, trained and validated machine learning / deep learning models to investigate
how cortical activity encodes movement:
Neural activity recorded from 500 cortical regions (top right) was analyzed using both a linear model and a 1D convolutional neural network (1D-CNN) (top left) to decode mouse behavioral dynamics. Although the linear model performed adequately during active movement periods, the 1D-CNN significantly outperformed it during resting (non-locomoting) states (bottom left, bottom right). These results highlight that neural encoding of behavior involves distinct nonlinearities, especially prominent during periods without overt movement.









I thrive when exploring complex and challenging questions, balancing data-driven exploration with hypothesis-driven rigor:

   · I build customized pipelines, dashboards and interactive data visualization tools that facilitate intuitive exploration of complex, multi-dimensional datasets. 
   · I regularly apply statistical analyses, dimensionality reduction algorithms, and other advanced analytical techniques to uncover latent patterns hidden within my data.
   · I design and develop creative solutions to overcome technical and methodological constraints.
   · I craft clear and effective data visualization that communicate complex findings with clarity. Ensuring that my work is accessible and easy to understand for diverse audiences is a priority throughout my scientific process.




These approaches enabled me to uncover precise, body-part-specific motor topographies directly from functional brain data –– patterns typically challenging to detect using traditional structural imaging methods, which are often costly, labor-intensive, and time-consuming.

Using correlation maps seeded from sensory cortex (S1), I track the synchrony of spontaneous neural activity across the cortex. Each map clearly identifies distinct motor regions (solid outline in the medial frontal cortex; secondary motor cortex M2) specifically correlated with their corresponding sensory areas (star markers in S1) . Comparing across these maps demonstrates clear functional segregation for different body parts. The neural activity traces shown below each map are from the same brief resting-state period (6 seconds), further illustrating tight sensory-motor coupling within the same body part and distinct patterns across different body parts.




           

Further insights into the cortical organization became evident when contours from multiple correlation maps were overlaid, revealing a homologous spatial arrangement between sensory (S1) and secondary motor cortex (M2):                            
Applying a simple spatial transformation (rotation and compression) aligned the sensory (S1) and motor (M2) maps, demonstrating a homologous organization between the two topographies.







Extending this analysis by placing seed regions uniformly across posterior cortical areas revealed a continuum of functional correlation between S1/V1 and M2: 
Seed regions used to compute correlation maps were color-coded according to their positions across S1 and visual cortex V1. Contours representing regions of high correlation are outlined with matching colors. Overlaying these contours highlights a continuous, graded pattern of connectivity between sensory/visual regions and M2 motor areas in the medial frontal cortex.