- Web Scraping and Data Analysis- Simulation Tools Development- Database Management

Comparison of Dimensionality Reduction Methods for Large Scale Neuronal RecordingsSimulation development: Designed and implemented biologically plausible stochastic model of neural population activity.Dimensionality reduction: Applied dimensionality reduction machine learning methods, such as PCA, LEM, UMAP, etc., to simulation results to produce low-dimensional latent manifolds of neural population activity.Results analysis and visualization: Compared performance of dimensionality reduction methods with respect to ground truth data. Performed Euclidean distance and visual analysis of the resulting manifolds.

Interactive track: Took part in a selective summer school in computational neuroscience and machine learning, organized and taught by leading experts in the field and sponsored by companies including Meta, Google AI, DeepMind etc. The intensive curriculum combined the latest advances in machine learning and causality research with state-of-the-art computational modelling approaches in neuroscienceCourse project: Worked as a part of a three-member team on a neural data analysis project. Repeatedly met and consulted with domain experts and incorporated their feedback. Presented the results to an international audience of students and researchers.

Co-developed and independently executed research protocol to study impact of Alzheimer's disease neurodegeneration on electrophysiological properties of cortical neurons. Managed state-of-the-art electrophysiological equipment. Applied blind whole-cell patch clamp in-vivo. Performed craniotomy surgeries on mice. Created animal model using adeno-associated virus. Transformed raw input signals into interpretable data. Performed pilot study and suggested further research.

Shadowing of PhD researchers. Performed experiments in 2-photon imaging in-vivo, tetrode recording and immunohistochemical staining.