Amjad Seyedi
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Doctoral researcher, |
About me
I am currently a Doctoral Researcher in Matrix Theory and Optimization at the University of Mons under the supervision of Prof. Nicolas Gillis. Previously, as a graduate research assistant at the University of Kurdistan, I worked on representation learning with a focus on robustness and generalization. I also led the Algebraic Machine Learning (AML) team, a group that explores fundamental methods in unsupervised machine learning. I have a Master’s degree in Artificial Intelligence from the same university, where I worked with Dr. Fardin Akhlaghian and Dr. Parham Moradi on matrix factorization and low-rank approximation for various applications such as semi-supervised learning, multi-label classification, and recommendation systems. I also have an Associate and a Bachelor’s degree in Software Engineering.
Research interests
Machine Learning: representation learning, deep learning, unsupervised learning
Trustworthy ML: robustness, generalization, interpretability, fairness
Applications: healthcare, recommender systems, remote sensing
Applied Mathematics: linear algebra, optimization, low-rank approximation
News
Upcoming
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Honestly, not much, my supervisor thinks I should be writing papers.
Recent news
I attended the “Autumn School: Scientific Machine Learning and Numerical Methods”, at the Centrum Wiskunde & Informatica (CWI), Netherlands, October 27-31, 2025.
Our paper, “A Deep Latent Factor Graph Clustering with Fairness-Utility Trade-off Perspective”, has been accepted as a regular paper for the 2025 IEEE International Conference on Big Data.
Our paper, Distributionally Robust NMF with Self-paced Learning, has been accepted for publication in Information Sciences journal 2026.
My three Master's students successfully defended their theses in Artificial Intelligence at the Algebraic ML Team, University of Kurdistan, September-October 2025.
I attended the “SOCN: Stochastic variance-reduced optimization algorithms and applications to federated learning” course, UCLouvain, Belgium, October 7-9, 2025.
I attended the Systems, Optimization, Control and Networks (SOCN) Workshop, UMONS, Belgium, September 26, 2025.
I was on the organizing committee of the third workshop on Low-Rank Models and Applications (LRMA 25), Mons, September 11-12, 2025.
I attended the “PhD School: Machine Learning and Optimization”, at the Centrum Wiskunde & Informatica (CWI), Netherlands, September 2-4, 2025.
Our paper, Encoder-Decoder NMF with β-Divergence, has been accepted for publication in Pattern Recognition journal 2026.
Our paper, Instance-wise Distributionally Robust NMF, has been accepted for publication in Pattern Recognition journal 2026. It's my first collaboration with Prof. Nicolas Gillis!
I attended the “Learning Theory from First Principles” course (Prof. Francis Bach), KU Leuven, Belgium, April 1-3, 2025.
I attended the Masterclass: “Graphs, Data and AI” (Prof. Jure Leskovec), University of Antwerp, Belgium, March 20, 2025.
I delivered a speech on Deep Matrix Factorizations for Data Representation at the STADIUS Center, KU Leuven, Belgium, February 20, 2025.
I attended the Graduate School in Systems, Optimization, Control and Networks (SOCN) Study Day, UCLouvain, Belgium, October 29, 2024.
I attended the Autumn School on Constrained Optimization and Machine Learning, Trier University, Germany, October 9-11, 2024.
I attended the ALGOPT2024 Workshop on Algorithmic Optimization: Tools for AI and Data Science, UCLouvain, Belgium, August 27-30, 2024.
My poster was accepted for presentation at the ALGOPT2024 Workshop on Algorithmic Optimization: Tools for AI and Data Science.
I started my PhD in Matrix Theory and Optimization at Polytech MONS, University of Mons, Belgium, June 18, 2024.