Gizeaddis Simegn

Gizeaddis Simegn

Steve Hui

Postdoctoral Fellow
Johns Hopkins University School of Medicine

Email: gsimegn1 [at] jh [dot] edu
GoogleScholar: scholar.google.com
LinkedIn: LinkedIn

Biography:

Gizeaddis received his Bachelor's degree in Electrical Engineering from Jimma University and later completed his Master's degree in Biomedical Engineering at Addis Ababa University in Ethiopia. Gizeaddis began his PhD at the University of Cape Town (South Africa) in 2016 under the supervision of Prof. Frances Robertson, Dr. Ali Alhamud and Prof. Andre Van der Kowue (MGH, Boston, USA), focusing on development of motion and shim robust CEST MRI method for detection of muscle glycogen based of double volumetric navigators.

Upon successfully completing his Ph.D. in 2019, Gizeaddis joined Jimma University as an assistant professor. In just three years, he achieved the rank of associate professor through the university's accelerated promotion program. During his time there, he established his own research lab (www.biomai.et) and conducted multiple research projects, primarily employing machine learning and deep learning techniques to detect various diseases such as breast cancer, cervical cancer, lung cancer, skin cancer, and heart disease. In 2022, he also secured a Google grant for his work on developing an AI-based Integrated English and Amharic text-to-speech synthesis system with OCR, designed to assist visually impaired individuals in reading hardcopy texts. He has a track record of supervising more than 20 M.Sc. students and a Ph.D. student on various Biomedical Engineering research topics Additionally, he has extensive experience teaching Biomedical Engineering courses at both the postgraduate and undergraduate levels.

In pursuit of a research career in MRI/MRS, Gizeaddis made a significant move in October 2023, when he joined Johns Hopkins University School of Medicine as a postdoctoral research fellow. In this role, he is working under the supervision of Prof. Richard Edden, with a focus on developing motion-corrected edited MRS pulse sequences to study brain metabolite levels in children, from birth to young childhood.

Contact:

gsimegn1 [at] jh [dot] edu