A. J. Alsahaf, PhD

Computer scientist and researcher based in the Netherlands
Email: ahmadalsahaf@gmail.com / LinkedIn: aj-alsahaf / GitHub: amjams

Experience

Post-doctoral researcher - University Medical Center Groningen (2021 - 2024)
I work at the Giepmans group where my primary focus is the analysis of cellular images from large-scale electron microscopy.

• Hyperspectral analysis of X-ray dispersive data (recently published in npj Imaging).
• Segmentation of large-scale electron microscopy data in life sciences (paper).
• Supervision of MSc and PhD students.
• FAIR data management of EM data and experience in open-source microscopy formats.
• Lead the publishing of following dataset, which won the RUG Open Research award.

PhD student in computer science - University of Groningen (Feb 2016 - Dec 2020)
I did my doctoral studies at the Intelligent systems group under supervision of Prof. Nicolai Petkov and Dr. George Azzopardi, as part of an STW-funded project in collaboration with Wageningen University & Research.
My thesis covered the following topics:

1. Feature selection through the mechanism of boosting (paper, repo).
2. Interpretable machine learning applied to livestock data.

Research fellow - Politecnico di Milano (2015)
I was a post-graduate research fellow and member of the EU project SmartH2O for urban water management.

Education

• PhD in Computer Science, University of Groningen, the Netherlands, thesis titled "Feature selection and intelligent livestock management". (2016-2020)
• MSc in Automation and Control Engineering, Politecnico di Milano, Italy, thesis titled "Projection vs. selection-based model reduction for emulation modeling in water resources planning and management problems". (2012-2014)
• BSc in Electrical Engineering, Kuwait University, Kuwait. (2006-2011)

Teaching and supervision

Teaching

• Designed and taught lectures on computer vision and data science for students of biology and biomedical science (UMC Groningen and Hanzehogeschool Groningen) (2021-2024).
• Guest lecturer on the topic of pre-processing techniques for machine learning for the MSc course Introduction to Data Science (5 ECTS, University of Groningen) (2020-2021).
• Teaching assistant for the BSc course parallel computing (5 ECTS, University of Groningen) (2020).
• Designed and gave a lecture on tree-based ensemble learning at the winter school on machine learning (WISMAL 2019), Las Palmas, Spain (2019).
• Guest lecturer for the MSc pattern recognition (5 ECTS, University of Groningen) (2018-2019).
• Teaching assistant for the MSc course pattern recognition, and helped with course digitization (5 ECTS, University of Groningen) (2017-2018).
• Teaching assistant for the BSc course introduction to computing science (5 ECTS, University of Groningen) (2017-2018).

Supervision

• PhD project "Automatic identification of cellular structures in electron microscopy images". Anusha Aswath (University of Groningen, UMCG) (2021).
• Five student group project on the "Hyperspectral analysis of STEM-EDX images in biology". Hanzehogeschool Groningen (2023).
• MSc project titled "Evaluating the performance of supervised feature ranking algorithms on tabular feature datasets", J. Overschie (University of Groningen) (2021).
• BSc project (ongoing) titled "Speeding up the feature selection algorithm FeatBoost", N. Dijkema (University of Groningen) (2020).
• Research internship project titled "Feature selection performance assessment", J. Overschie (University of Groningen) (2020).
• BSc thesis project titled "A data-driven approach to calving ease prediction", Radu Gheorghe (University of Groningen) (2020).
• Research internship project on iterative feature selection, V. Shenoy (University of Groningen) (2018).
• BSc thesis project titled "Prediction of Carcass Weight from Kinect Image Data", N. Micallef (University of Malta, University of Groningen) (2017).

Publications

Journal articles and conference proceedings

• Duinkerken, B. P., Alsahaf, A. M., Hoogenboom, J. P., & Giepmans, B. N. (2024). Automated analysis of ultrastructure through large-scale hyperspectral electron microscopy. npj Imaging (DOI).
• Aswath, A., Alsahaf, A., Giepmans, B. N., Azzopardi, G. (2023). Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey. Medical image analysis.
• Alsahaf, A., Gheorghe, R., Hidalgo, A. M., Petkov, N., Azzopardi, G. (2023). Pre-insemination prediction of dystocia in dairy cattle. Preventive Veterinary Medicine.
• Overschie, J. G., Alsahaf, A., Azzopardi, G. (2022). fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms. Journal of Open Source Software.
• Alsahaf, A., Petkov, N., Shenoy, V., and Azzopardi, G. (2021) "A framework for feature selection through boosting." Expert Systems with Applications.
• Alsahaf, A., Azzopardi, G., Ducro, B., Hanenberg, E., Veerkamp, R. F., and Petkov, N. (2019). Estimation of Muscle Scores of Live Pigs Using a Kinect Camera. IEEE Access.
• Alsahaf, A., Azzopardi, G., Ducro, B., Hanenberg, E., Veerkamp, R.F. and Petkov, N. (2018). Prediction of slaughter age in pigs and assessment of the predictive value of phenotypic and genetic information using random forest. Journal of animal science.
• Alsahaf, A., Ducro, B., Veerkamp, R., Azzopardi, G., and Petkov, N (2018). Assigning pigs to uniform target weight groups using machine learning. In World Congress on Genetics Applied to Livestock Production (WCGALP).
• A. Alsahaf, G. Azzopardi, B. Ducro, R.F. Veerkamp, N. Petkov. (2018) Predicting Slaughter Weight in Pigs with Regression Tree Ensembles. In Proceedings of the 1st International APPIS Conference, IOS press.

Lectures, talks and posters

• Invited speaker at Microscopy & Microanalysis 2024 Annual Scientific Meeting in Cleveland, Ohio, US (July 2024).
• "Hperspectral Analysis of Large-scale EM" (poster) - Artificial Intelligence for Bio-oriented Imaging Analysis (AIBIA) Conference, Utrecht (May 2024).
• Co-chair and panelist in session on FAIR data at the Euro-BioImaging All-Hands meeting and the BioImaging and EOSC workshop at EMBL Heidelberg (2023).
• A. Alsahaf. Sustainable long-term archiving and publishing of large scale EM data. NEMI day (Netherlands Electron Microscopy Infrastructure. Leiden, the Netherlands (2022).
• A. Alsahaf. Invited lecture on tree-based ensemble learning at the winter school on machine learning, Las Palmas, Spain (2019).
• Alsahaf, A., Azzopardi, G., and Petkov, N. (2018). Estimation of live muscle scores of pigs with RGB-D images and machine learning. Abstract from FAIR Data Science for Green Life Sciences, Wageningen, Netherlands.
• A. Alsahaf, G. Azzopardi, B. Ducro, R.F. Veerkamp, N. Petkov. SmartBreed: On the use of Machine Learning in Animal Breeding. Abstract from Challenges in Data Science, Matera, Italy. (2016).
• Galelli, S., Alsahaf, A., Giuliani, M. and Castelletti, A., (2015), December. Yielding physically-interpretable emulators- A Sparse PCA approach. In AGU Fall Meeting Abstracts.
• Alsahaf, A., Giuliani, M., Galelli, S. and Castelletti, A., (2015), April. Model reduction of process-based hydro-ecological models: a comparison between projection-and selection-based methods. In EGU General Assembly Conference Abstracts.