Rashin Gholijani Farahani

MSc AI Student & Researcher · M2L 2025 Scholar · Machine Learning • Deep Learning • LLMs • Reinforcement Learning • Trustworthy & Explainable AI • Healthcare AI • Speech • NLP • Vision

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📍 Karaj, Iran

🎓 MSc Artificial Intelligence

🔬 Open to PhD positions — Fall 2027

Hi! I’m Rashin Gholijani Farahani, an MSc Artificial Intelligence student and researcher working on data-efficient, explainable, and trustworthy machine learning for high-stakes, real-world problems. I completed my BSc in Computer Engineering in three years as the Top Graduate of 2024, currently hold a 19.27/20 GPA in my master’s program, and teach programming and AI fundamentals to 100+ students at the Tehran Institute of Technology.

I was selected as a fully funded participant of the M2L (Mediterranean Machine Learning) Summer School 2025 at the University of Split, Croatia, focused on machine learning and learning theory. I also serve as a peer reviewer for the Asian Research Journal of Mathematics, and contribute machine learning methods to an international scientific research collaboration working on large-scale experimental data.

My research interests are deliberately broad and interdisciplinary — spanning deep learning, reinforcement learning, natural language processing, computer vision, speech and audio processing, and multimodal learning. I’m most drawn to problems where rigorous methodology meets genuine societal value: AI for healthcare and well-being, explainable and trustworthy AI, AI safety, and the responsible use of large language models and foundation models in complex, data-scarce domains.

Across my work I’ve tackled audio-based classification, medical diagnosis support, mental-health modeling (stress detection, ADHD), financial decision systems, plant disease classification, reinforcement learning agents, and acoustic steganalysis for AI-generated speech. I enjoy owning the full stack of an ML project — from signal and feature engineering, through model design and training, to evaluation, interpretability, and clear scientific writing.

I’m preparing to pursue a PhD starting Fall 2027 in machine learning, with a focus on data-efficient, explainable, and trustworthy AI for human-centered applications. I’m actively looking to collaborate with research groups whose work aligns with these interests — please feel free to reach out.

news

May 01, 2026 🌍 Preparing PhD applications for Fall 2027 — focused on trustworthy AI, multimodal learning, and ML for healthcare. Open to collaboration.
Feb 01, 2026 🔬 Joined an international research collaboration as a funded research fellow, applying machine learning to large-scale scientific data analysis.