Dr Fatemeh Najibi (alumnus)
Fatima received her PhD in Computer Science from the City University of London in late August 2021. During her PhD, she was a member of CitAI and worked at the interface of power system operations, machine learning and optimization. Her thesis title was “Enhanced power system operation with coordination and forecasting techniques”. Her main research interests are machine learning, optimization, power system and renewable energy.
Fatima is currently a Senior Energy Strategy Consultant in WSP, contributing her knowledge to the transitioning to the net-zero emission power system. She is also an artist who paints, takes photos, and writes in her free time.
Before doing her PhD, she worked as a researcher for the Iran Power Transmission, Generation and Distribution Company (TAVANIR). She is experienced with different industrial software such as CYMDIST, DIgSILENT, ArcGIS, AutoCAD, NEPLAN, DIALux as well as academic software such as MATLAB and GAMS.
She has published among others in the International Journal of Electrical Power and Energy Systems, the Proceeding of 12th International UKACC Conference on Control (CONTROL2018), the Proceedings of the IEEE PowerTech 2021 Conference, Energy and Energy Conversion and Management.
I am a PhD candidate at the Artificial Intelligence Research Centre, CitAI at City, University of London, working on computer vision for medical image analysis under the supervision of Giacomo Tarroni and Aidan Slingsby. I work on algorithms to learn medical images representations without using labels (i.e. unsupervised and self-supervised) and with limited labels (semi-supervised) and applications of these algorithms for the detection and segmentation of anomalies and organs. I have a vested interest in problems related to unsupervised learning such as compression, image generation and transfer learning and, given that medical images are generally high-dimensional, I am also researching on data efficient sparse image representations such as point cloud or implicit fields learning.
Before starting my PhD I completed a MSc in Data Science in City, University of London and worked for more than 10 years in consulting supporting businesses on analytics and forecasting projects. In my free time I enjoy reading, cooking, travelling and scuba diving.
I have published my research on unsupervised anomaly detection in conference papers such as IEEE International Symposium on Biomedical Imaging (ISBI 2021) and Medical Image Computing and Computer Assisted Interventions (MICCAI 2021). I also achieved 2nd and 3rd positions in the Medical Out-of-Distribution Challenge held at MICCAI 2020.