Alireza Motlagh
Doctoral Assistant at EPFL
Lausanne, Vaud, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolAlireza Motlagh is a computational biology PhD student and Doctoral Assistant at EPFL with six years of experience developing machine-learning methods for single-cell and spatial omics. He combines a strong electrical engineering background with hands-on research in stochastic variational inference and scRNA-seq method development, having interned at the Wellcome Sanger Institute and contributed to lipidomics analysis at EPFL. At Sharif University of Technology he led multiple teaching assistant roles across signals, machine learning, and bioinformatics courses, reflecting both deep technical expertise and clear communication skills. His open-source work includes enhancing an SVM-focused machine learning course notebook, demonstrating practical scikit-learn and gradient-descent implementations for noisy data. Based in Lausanne, he thrives at the interface of quantitative method development and biological discovery, often translating classroom concepts into reproducible research tools. Colleagues note his ability to bridge theory and practice, tackling complex data modalities with principled ML approaches.
5 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Computational Biology, Doctor of Philosophy - PhD, Computational Biology at EPFLBachelor's degree, Electrical Engineering, Bachelor's degree, Electrical Engineering at Electrical Engineering Department, Sharif University of Technology
Persian, English
Github Skills (12)
scikit10
jupyter-notebook10
machine-learning10
svm10
python10
scikit-learn10
linear-algebra9
ai9
artificial-intelligence9
gradient-descent9
python-course8
data-analysis7
Programming languages (2)
Github contributions (5)
SharifiZarchi/Introduction_to_Machine_Learning
Sep 2022 - Nov 2022
Machine Learning Course, Sharif University of Technology
Role in this project:
Data Scientist / ML Engineer
Data Scientist / ML EngineerContributions:23 commits, 41 pushes in 2 months
Contributions summary:Alireza primarily contributed to a machine learning course by adding and updating an SVM (Support Vector Machine) notebook. Their work involved implementing the core concepts of SVM, including hyperplanes, margins, and the maximal margin classifier. The user demonstrated knowledge of Scikit-learn and incorporated gradient descent implementation. The notebook also covered the impact of noisy data on decision boundaries.
pythonmachine-learningdepartmentengineeringsharif-university
alireza7agm/Machine-Learning-ESW
Sep 2022 - Aug 2023
Contributions:14 pushes, 1 branch in 11 months
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