Francesco Iori, PhD
About Francesco Iori, PhD
Francesco Iori, PhD, is the Director of Software Engineering at Pear Bio, where he applies his expertise in machine learning and deep learning. He has a strong academic background in Medical Engineering and Aerospace Engineering, alongside extensive experience in software development and engineering roles across various organizations.
Work at Pear Bio
Francesco Iori currently serves as the Director of Software Engineering at Pear Bio, a position he has held since 2023. His role involves overseeing software development initiatives and managing engineering teams to enhance the company's technological capabilities. Prior to this, he worked as a Senior Computer Vision Engineer at Pear Bio for two years, where he focused on applying advanced computer vision techniques to real-world problems.
Education and Expertise
Francesco Iori holds a Doctor of Philosophy (PhD) in Aerospace, Aeronautical and Astronautical Engineering from Imperial College London, which he completed from 2014 to 2018. He also earned a Master's degree in Medical Engineering and a Bachelor's degree in the same field from the University of Rome Tor Vergata, studying from 2007 to 2013. His academic background supports his expertise in signal and image processing, computational modeling, and data analysis.
Background
Francesco Iori has a diverse professional background, having worked in various engineering roles across different organizations. He began his career as a UROP Placement at Imperial College London from 2012 to 2013. He later served as a Project Engineer at CHAM Ltd for five months in 2014, and as a FEM/CFD Analyst at NIER Ingegneria SpA in 2019. Additionally, he worked as a Software Developer and Mechanical Engineer at Oxford Heartbeat from 2019 to 2022, and as a Postgraduate Researcher at Imperial College London from 2014 to 2018.
Achievements
Francesco Iori has contributed to the scientific community through multiple oral and poster presentations at leading international conferences. He has also published several papers in peer-reviewed scientific journals, showcasing his research and findings in the fields of engineering and technology. His work emphasizes the application of machine learning and deep learning to address real-world challenges.