Golzar’s academic background is in neuroscience and psychology, providing a foundation in cognitive processes and human language understanding. During her PhD, she shifted her research direction toward computational linguistics and natural language processing, developing skills in machine learning, language models, and multimodal AI. Her work increasingly focuses on how computational models capture linguistic phenomena and meaning beyond literal language. Currently, her doctoral research focuses on figurative language understanding, particularly idioms, in multimodal and cross-lingual settings. A central aim of her work is to understand how vision-language models integrate linguistic and visual information when interpreting figurative meaning, and to identify the limitations and biases that affect their performance. Her research also explores cross-lingual transfer and cognitive-inspired approaches to language understanding. Alongside her research, she contributes to teaching in deep learning.