Introducing “Meet the Team”: Our new video interview series
Colive Voice is proud to present “Meet the Team,” a new video interview series featuring our team members and their innovative work in the field of vocal biomarkers. In this episode, we sit down with Abir to discuss her crucial role in analyzing and interpreting the vast amount of data collected by Colive Voice.
Learn about the essential role of our data scientist Abir
Abir’s expertise in designing and implementing machine learning algorithms has been instrumental in developing models that can accurately identify vocal biomarkers relevant to diabetes, cancer, and mental health conditions. She has always been interested in combining data science and healthcare. In this interview, she shares with great enthusiasm her motivations for pursuing a career in the health sector and explains how she became involved with the Colive Voice project.
Through collaboration with other team members, Abir’s work has contributed significantly to the success of the project. She emphasizes the importance of teamwork in research and how it motivates her to work on such an innovative project.
Join us in this exciting new series as we dive deeper into the world of vocal biomarkers and the team behind Colive Voice’s research.
Could you introduce yourself and your background in a few words?
Sure! My name is Abir Elbeji, I am from Tunisia and I have a background in Bioengineering. I am currently pursuing a PhD in digital health at LIH, focusing on the development of vocal biomarkers.
What drew you to the world of data analytics? Why the health sector?
I have always been fascinated by the power of data and artificial intelligence to drive insights and inform decision-making. As for why I specifically chose to work in the health sector, I am passionate about leveraging technology and data to improve health outcomes and make a positive impact on people’s lives.
Can you describe a typical day at work as a data scientist for Colive Voice?
My typical day involves a mix of tasks such as data analysis and machine learning. I begin by collecting and preprocessing data to ensure its quality and consistency. I collaborate with the team to design and implement machine learning models to analyze and interpret voice data.
What impact would you like Colive Voice’s research findings to have on the future of healthcare?
We work on Colive Voice to develop innovative biomarkers based on voice data that can help diagnose and monitor a range of health conditions. Ultimately, we hope that our research will revolutionize the way healthcare is delivered, making it more personalized, efficient, and effective for patients and providers alike.
To conclude, I encourage everyone to consider donating their voice to help advance the field of voice-based biomarkers and improve both the future of healthcare and the quality of life of people around the world.
Support Colive Voice by donating your voice