We combine creative engineering and experimental science to enhance the lives of individuals within the neural disorder spectrum. As part of a dynamic team, I'm responsible for developing the data analysis pipeline and engineering backend features. These efforts aim to provide insightful statistics for both users and care homes.

During my three-month internship at Neurometry, I accomplished several important milestones. I worked with Power BI to deliver analytics and insights about our app users, creating a pipeline from raw SQL data to business metrics for Power BI dashboards. Additionally, I developed and adapted metrics to analyze users' behavior in our app. This involved implementing various tests and experiments to ensure the robustness of these metrics, accompanied by comprehensive documentation for each. Importantly, the internship provided me with the opportunity to engage with our clients and potential partners at the CogX festival in London, interact with scientists during a visit to the British Neuropsychological Society at UCL, and meet many other brilliant minds. Overall, this internship was an exceptional opportunity to develop my professional skills and expand my network.

Currently, we're developing a comprehensive naturalistic fMRI and eye-tracking database. This unique collection forms the basis for my PhD research and will facilitate the exploration of research questions related to multisensory integration and the interplay between linguistic and non-linguistic cues.

Researcher at the International Laboratory of Algebraic Topology and Its Applications:We employed topological methods to explore the fundamental mechanisms of brain organization. I managed a substantial database, procuring functional magnetic resonance imaging (fMRI) data from the open-source OASIS-3 database. Subsequently, I implemented a preprocessing pipeline and constructed functional connectomes, enabling my colleagues to test various hypotheses within our lab.Further, we used the normalized Laplacian to characterize fMRI connectomes. I developed a classification framework to differentiate patients with ADHD from healthy controls. The outcomes of this work were published in the Medical Image Analysis journal as part of a machine learning challenge (scroll down for publication details).

I contributed to several fascinating projects in cognitive neuroscience:1. In collaboration with a hospital, we explored the temporal and spatial dynamics of nested oscillations in human non-REM sleep using intracranial EEG data from epilepsy patients with implanted electrodes. I was responsible for data analysis, specifically detecting slow waves and tracking their pattern of propagation across various brain regions.2. In a separate project, we investigated price perception using an event-related design and EEG method. Our goal was to understand how humans perceive price and make judgments about costliness. My responsibilities included the development of experimental design and data collection protocol.3. In another study, we focused on the fundamental mechanisms of fear. Using galvanic skin response measurements, we aimed to develop a protocol to alleviate fear in people with arachnophobia. My role involved data analysis and implementing the data collection protocol.This work significantly refined my data analysis and research skills and provided opportunities to collaborate with top-tier professionals from diverse fields and countries.

Our start-up specialized in commercial neuromarketing research, utilizing EEG, eye-tracking, and behavioral methods to delve into consumer experiences. We partnered with significant clients from the banking and IT sectors, boasting a fully mobile lab equipped with all the necessary tools for on-site research.My role encompassed the entire research process, from conceptualizing experimental designs to performing data analysis and interpretation.