Robust and Interpretable Machine Learning
Welcome to the RIML Lab at Sharif University of Technology, led by Dr. Mohammad Hossein Rohban. We focus on developing robust and interpretable machine learning solutions, addressing challenges in anomaly detection, adversarial robustness, and computational biology. Let's grow together.


About the Lab
At RIML Lab, we focus on creating machine learning algorithms that can withstand adversarial conditions and provide insights into their decision-making processes. Our research aims to bridge the gap between theoretical advancements and practical implementations, ensuring that AI systems are both effective and trustworthy.
Our Goals
By embodying these principles, the RIML Lab aims to be a beacon of excellence in machine learning research, contributing to a more vibrant, growing, self-actualized, and sustainable world.
🌟 Vibrant: Cultivating an Energetic and Inclusive Research Environment
🌱 Growing: Advancing Knowledge and Expanding Impact
🧠 Self-Actualized: Empowering Individual Potential
♻️ Sustainable: Committing to Long-Term Responsibility







Our Research Fields
Our Latest Articles

Sample of anomaly detection post
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the
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2025-05-14
Techniques for Image-Based Anomaly Detection
When it comes to detecting anomalies in images, several powerful techniques stand out: 1️⃣ Autoencoders – These
sina
2025-05-14
Introduction to Anomaly Detection in Images
Anomaly Detection in images is all about identifying data points or patterns that deviate from the norm.