Rashin Gholijani Farahani

Hi! I’m Rashin Gholijani Farahani, an MSc Artificial Intelligence student and aspiring researcher passionate about building data-efficient, explainable, and trustworthy machine learning systems with real-world impact. I completed my BSc in Computer Engineering in three years as the Top Graduate of 2024 and currently maintain a 19.27/20 GPA in my master’s program, while teaching programming and AI fundamentals to over 100 students at the Tehran Institute of Technology.

I am honored to be a fully funded START Research Fellow at the Joint Institute for Nuclear Research (JINR), Dubna, contributing to the TAIGA astroparticle physics collaboration through applied machine learning for large-scale physics data analysis. I was also 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 additionally serve as a peer reviewer for the Asian Research Journal of Mathematics.

My research interests are broad and interdisciplinary, spanning deep learning, reinforcement learning, natural language processing, computer vision, speech and audio processing, and multimodal learning. I am especially drawn to problems where rigorous methodology meets societal value, particularly AI for healthcare and well-being, explainable and trustworthy AI, AI safety, and the responsible application of large language models and foundation models to complex, data-scarce domains.

Across my projects, I have worked on a wide range of applied ML problems including audio-based classification, medical diagnosis support, mental-health-related modeling (stress detection, ADHD), financial decision systems, plant disease classification, reinforcement learning agents, and acoustic steganalysis for AI-generated speech. I enjoy working at the full stack of an ML project — from signal and feature engineering, through model design and training, to evaluation, interpretability, and clear scientific communication.

I am 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, including health, language, and multimodal understanding. I’m actively seeking opportunities to collaborate with research groups whose work aligns with these interests. Please feel free to reach out!