Working on tools and platforms to empower R&D teams in making data-driven decisions. Focused on building and enhancing the Data Science Platform, internal Datalake, and automated data ingestion pipelines to streamline data analysis, preprocessing, and storage.

As a model developer at Ørsted, my primary responsibility is to design and develop innovative software using Python. In this role, I utilize my extensive programming knowledge and experience to create robust and scalable applications.

Project title: Network models of EEG responses to speech.In this project, I investigated the neural mechanisms underlying the ability to attend to a specific talker in a multi-talker conversation. I recorded electroencephalography (EEG) signals from participants while they engaged in a multi-talker conversation task and used advanced statistical and machine-learning techniques to analyze the data.The human auditory system can selectively attend to one speaker's voice in the presence of other competing sounds, a process known as auditory selective attention. I used a ridge regression model and a deep neural network to decode the attended talker based on the neural activity recorded from the participants' brains.My findings enhanced our understanding of the neural mechanisms underlying auditory selective attention and may have implications for the development of treatments for individuals with hearing impairments or difficulties in understanding speech in noisy environments.

I was responsible for conducting EEG signal processing to extract relevant information from EEG data obtained during experiments. I developed software for analyzing EEG data and conducted experiments. I also designed and developed Python packages for EEG data analysis, visualization, and connectivity. As part of my role, I troubleshooted and resolved technical issues related to EEG signal processing and software development.My key skills include a strong background in EEG signal processing and analysis, proficiency in software development using Python, and experience in developing Python packages for EEG data analysis, visualization, and connectivity. I possess strong problem-solving and analytical skills.

I completed the "EEG Signal Response Extended beyond RGB" course, which focused on designing and analyzing a preliminary EEG pilot study in Alzheimer's disease. I gained a comprehensive understanding of EEG signal processing techniques, including data analysis and cleaning. Through hands-on exercises, I developed the skills to manipulate and interpret EEG signals effectively. The course also covered the intricacies of conducting EEG experiments, including setup, electrode placement, and stimulus selection. I explored various EEG paradigms and protocols used to interpret signals related to cognitive impairments.

I was responsible for overseeing a six-month project related to internal processes. I conducted a thorough analysis of data using SQL databases and Microsoft Excel pivot tables and functions. Through close collaboration with associates, I gained valuable insights and developed a deep understanding of the operation's inner workings. I worked independently, organizing my time and prioritizing tasks to meet project goals. My flexibility and ability to adjust to new work environments allowed me to seamlessly integrate with the team and contribute to the project's success.