I have a background in machine learning and signal processing for biomedical applications. I do research in the field of personalised medicine by proposing computational methods that involve network-based machine learning and drug response prediction.
SLE Map: a consensus map of SLE patients into a low dimensional space
Human transcriptome profiles typically contain gene expression values for many thousands of genes, thus representing points in a high-dimensional feature space. A principal technical challenge in understanding the pathways involved in the disease and […]
PhD Student in Computational Biology
Computational Biology Department, Institut Pasteur, Paris, France;
École Doctorale Complexité du Vivant, Sorbonne Université, Paris, France
Thesis: Integrative modeling of cancer drug response and auto-immune disease base on network-based machine learning techniques (under the supervision of Dr. Benno Schwikowski).
Master in Biomedical Engineering, Bioelectric
Iran University of Science and Technology (IUST), Tehran, Iran, Sept. 2015- Feb. 2019.
GPA: 18.18/20 (Top Student)
Bachelor in Electrical Engineering, Electronics
Azad University (Abhar branch), Abhar, Zanjan, Iran, 2009-2014.
GPA: 19.14/20 (Top Student)
Machine learning: deep learning, graph neural networks, consensus clustering.
Network analysis: graph representation learning, multilayer and heterogenous graph analysis, graph-based clustering.
Data science: data preprocessing and normalization, statistical tests, low dimensional representation.
Systems and signals modelling: noise reduction, adaptive filters, spectral analysis, wavelet analysis.
Chaos and nonlinear dynamics: phase space reconstruction, Poincare section, complexity and entropy measures.
Bioinformatics: network biology, gene expression data analysis, gene module detection, gene enrichment analysis.
Neural data analysis: spike sorting, spike train and LFP signal analysis, neural decoding.
Programming languages: R, Python, MATLAB, LabVIEW, C/C++, HTML,…