Assistant Professor

Bio

Dr. Sevim Cengiz joined the College of Technological Innovation at Zayed University (ZU) in 2024. Before this, she was part of Mohammed Bin Zayed University of Artificial Intelligence (MBZUAI).

With over seven years of combined research and business experience and a Ph.D. in engineering, she has developed strong skills in writing, analysis, research, multitasking, and effective communication and collaboration across different fields.

Professionally, she (as a data science consultant) helped with the adaptation of deep learning (DL) techniques for human activity recognition in a company. In academia, she focused on examining healthcare problems using DL to analyze medical data.

She has published several peer-reviewed research papers with local and international co-authors in highly ranked and well-recognized journals. (Over 10 articles indexed in the Science Citation Index and Scopus, 3 book chapters, 1 online book, and over 20 oral presentations and abstracts.

Office

Dubai Academic City, D-L1-041

Phone:

+971 4 402 1713

Email:

Teaching Areas

IT project management

Data analytics

Data science

Research and Professional Activities

Research Area(s)

Data Visualization

Deep learning adaptation for business cases (WatchOS)

Real healthcare data analysis using deep learning

Developing, supporting open-source software

Human activity recognition using deep learning

Open-source software developing

Deep learning for computer vision

Google Scholar:

https://scholar.google.com/citations?user=yHYxNMcAAAAJ&hl=en

Orcid ID:

https://scholar.google.com/citations?user=yHYxNMcAAAAJ&hl=en

Oryx-MRSI (Open-Source MRSI Data Analysis Software):

https://sevimcengiz.github.io/oryx/

Online MRS Educational Book:

https://mrshub.github.io/mrs-educational-book/

Research and Professional Activities 

Articles:

• Cengiz S et al., FUSQA: Fetal Ultrasound Segmentation Quality Assessment. (In publication process.)
• Identification of metabolic correlates of mild cognitive impairment in Parkinson’s disease using magnetic resonance spectroscopic imaging and machine learning. Magn Reson Mater Phy. 2022.
• Cengiz S et al., ORYX-MRSI: A fully automated open-source software for proton magnetic resonance spectroscopic imaging data analysis. Int J Imaging Syst Technol. 2022.
• Cengiz S et al., Deep Learning-based Quality Assessment of Clinical Protocol Adherence in Fetal Ultrasound Dating Scans. arXiv:2201.06406. 2022.
• Ozturk-Isik E, Cengiz S, et al., Identification of IDH and TERTp mutation status using 1H-MRS in 112 hemispheric diffuse gliomas. Journal of Magnetic Resonance Imaging. 2021.
• Azamat S, Arslan DB, Erdogdu E, Kicik A, Cengiz S, et al. Detection of visual and frontoparietal network perfusion deficits in Parkinson’s disease dementia. Eur J Radiol. 2021.
• Arslan DB, Gurvit H, Genc O, Kicik A, Eryurek K, Cengiz S, et al., The cerebral blood flow deficits in Parkinson’s disease with mild cognitive impairment using arterial spin labeling MRI. J Neural Transm. 2020.

Book Chapters:

• Cengiz S et al., Automatic Quality Assessment of First Trimester Crown-Rump-Length Ultrasound Images. ASMUS 2022. Lecture Notes in Computer Science, vol 13565. Springer, Cham.
• Cengiz S et al., Automatic Fetal Gestational Age Estimation from First Trimester Scans. Lecture Notes in Computer Science.
• Cengiz S et al., Super-Resolution Convolutional Neural Networks for Increasing Spatial Resolution of 1H Magnetic Resonance Spectroscopic Imaging. Communications in Computer and Information Science, vol 723. Springer, 2017

Posters - Oral Presentations:

• Cengiz S et al., ORYX-MRSI: A data analysis software for multi-slice 1H-MRSI. In ISMRM. May 15-20, 2021.
• Cengiz S et al., MRSI Based Biomarkers of Parkinson’s Disease with Mild Cognitive Impairment at Resting-State fMRI Based Brain Parcellation.
• +15, more conference abstracts

Completed Grants:

• UK-based Funded project: Completed the Royal Society Newton Mobility Grant
• Turkey-based Funded project: Completed TUBITAK 1001 Grant 240.000$