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Hi there, I'm Arian AmaniπŸ‘‹

πŸ’¬ I'm a Computer Science student at the Sapienza University of Rome, studying, working on, and researching deep learning applications in Life Sciences such as Single-Cell Genomics and Drug Discovery with a focus on Generative Models as a remote research assistant at the Wellcome Sanger Institute. I'm also quite interested in Adversarial Examples and trying to make more robust and generalized deep learning models as well.

πŸ“« Contact me

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⚑ A Few Quick Facts

  • 🌱 I'm currently studying causality in deep learning, specifically, Causal Representation Learning.
  • πŸš€ My recent focus has been on creating cutting-edge Generative Models and pioneering Multi-Modal Contrastive Learning approaches.
  • πŸ”¬ Single-Cell Genomics, Drug Discovery, Drug Optimization, and Gene-Drug Interaction Analysis
  • πŸ“™ Check out my resume.
  • βœ’οΈ I might start to write articles on my blog regularly, for now there's a roadmap to starting Deep Learning Medium logo Blog logo

πŸ“ Recent Posts

Arian's GitHub stats

Arian Amani's Projects

ai-course-aut icon ai-course-aut

Codes for the assignments of the Artificial Intelligence Course at Amirkabir University of Technology

face-alter icon face-alter

Latent Representation and Exploration of Images Using Variational AutoEncoders

preimutils icon preimutils

All you need to preprocess and prepare your annotated dataset

scanpy icon scanpy

Single-cell analysis in Python. Scales to >1M cells.

scpa icon scpa

The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates. Added causal representation via sparsity.

scrna-analysis-project icon scrna-analysis-project

Final project preparation for the graduate course "ML for Bioinformatics" at Sharif University of Technology | Spring 2023

scvi-tools icon scvi-tools

Deep probabilistic analysis of single-cell omics data

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