CV
Education
- Ph.D. in Artificial Intelligence, Teesside University, United Kingdom (2019 - 2023)
- Thesis: Radiogenomics and fluxomics data integration using machine learning and deep learning to improve ovarian cancer survival prediction
- Supervisor: Prof. Claudio Angione
- M.Sc. in Artificial Intelligence, Ferdowsi University of Mashhad, Iran (2014 - 2017)
- Thesis: Microaneurysm detection in fundus images using deep neural networks
- Supervisor: Prof. Hamid-Reza Pourreza
Work Experience
- Research Associate, Alan Turing Institute, London, UK
- Fields: Deep Neural Networks (Transformers, Diffusion Models, Foundation Models)(2023 - Present)
- Projects: Real-time plankton monitoring with the RAPID tool suite, supporting biodiversity and ecosystem studies
- Researcher, National Oceanography Centre, Southampton, UK (March 2023 - Sep 2023)
- Fields: Machine Learning, Convolutional Neural Networks (CNNs), Diffusion Models
- Projects: Developed sensor-agnostic models for marine particle classification and stratification
- Researcher, Computational Systems Biology and Data Analytics Research Group, Teesside University, UK (2019 - 2023)
- Fields: Machine Learning, Computer Vision, Medical Image Analysis, Computational Biology
- Projects: Developed models for ovarian cancer survival prediction, CT image segmentation, and integrated breast cancer fluxomics data
- Part-time Lecturer, School of Computing, Engineering and Digital Technologies, Teesside University, UK
- Courses:
- Artificial Intelligence Bootcamp (July-August 2021)
- Machine Learning & AI for Business Applications Bootcamp (Oct-Feb 2021-2022)
- Artificial Intelligence Foundations (Sep-Dec 2021)
- Machine Learning (Jan-May 2022)
- Courses:
Skills
- Programming Languages: Python, R
- Libraries & Tools: TensorFlow, Keras, Scikit-learn, OpenCV, SimpleITK
- Machine Learning Techniques: CNNs, Transformers, U-Net, Diffusion Models, Survival Models
- Data Tools: MySQL, SQL Server
- Operating Systems: Linux (Ubuntu, Fedora), Windows
Publications
Improving In Situ Real-Time Classification of Long-Tail Marine Plankton Images for Ecosystem Studies
N. Eftekhari, S. Pitois, M. Masoudi, R. E. Blackwell, J. Scott, S. L. C. Giering, "Improving in situ real-time classification of long-tail marine plankton images for ecosystem studies," ECCV, CV4E Workshop, 2024.
Cross-Attention Enables Deep Learning on Limited Omics-Imaging-Clinical Data of 130 Lung Cancer Patients
S. Verma, G. Magazzù, N. Eftekhari, A. Occhipinti, C. Angione, "Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients," Cell Reports Methods, 2024.
Optimizing Plankton Image Classification with Metadata-Enhanced Representation Learning
M. Masoudi, S. Verma, N. Eftekhari, M. Massot-Campos, J. O. Irisson, B. Thornton, "Optimizing plankton image classification with metadata-enhanced representation learning," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024.
Microaneurysm Detection in Fundus Images Using a Two-Step Convolutional Neural Network
N. Eftekhari, H. Pourreza, M. Masoudi, K. G. Shirazi, and E. Saeedi, "Microaneurysm detection in fundus images using a two-step convolutional neural network," Biomedical Engineering Online, 2019.
Talks
Improving In Situ Real-Time Classification of Long-Tail Marine Plankton Images for Ecosystem Studies
Poster Presentation at ECCV, CV4E Workshop, Milan, Italy
AI Biodiversity Monitoring: Developing Computer Vision Tools to Assess Ecosystem Health
Poster Presentation at AI UK, United Kingdom
Plankton Classification Using Edge Artificial Intelligence
Oral Presentation at 7th Zooplankton Production Symposium, Hobart, Australia
Real-time Particle Analysis
Oral Presentation at AI/ML Symposium, Imperial College London, London, United Kingdom