Research Interests

Natural Language Processing (NLP)

I find NLP to be an incredibly fascinating field, as it deals with the complexities of human language and its representation in the digital world. During my time as a research assistant and NLP intern, I've had the chance to work on various projects, including text multi-label classification using deep neural networks. I believe that mastering NLP will allow us to create more intelligent and intuitive AI systems that can communicate and understand human language effectively.

Natural Language Understanding (NLU)

As an extension of NLP, NLU focuses on the semantics and meaning behind the text, which I find crucial for making AI systems more human-like. My interest in NLU has led me to explore various projects, such as studying language models with a focus on psycholinguistic aspects of computation. By delving deeper into NLU, I aim to bridge the gap between AI and human cognition.

Cognitive Science

I am passionate about cognitive science because it provides a multidisciplinary approach to understanding the human mind and its processes. By examining the psychological, neurological, and computational aspects of cognition, I believe that we can gain valuable insights into the intricacies of human learning and thinking. This knowledge, in turn, can be harnessed to develop more advanced and human-like AI systems.

Computational Psycholinguistics

My interest in computational psycholinguistics stems from the desire to understand the underlying mechanisms of language processing and acquisition in the human brain. In my academic experience, I've had the opportunity to explore this field as a research assistant, studying human learning based on psychological theories, such as Freudian ideas. By combining the principles of psycholinguistics with computational models, I hope to contribute to the development of AI agents that can mimic human language processing and learning abilities more closely.

Research Groups

Sharif Psychoanalysis of Artificial Intelligence

The Psychoanalysis of Artificial Intelligence is an emerging research area that seeks to integrate insights from the field of psychoanalysis into the development and understanding of AI systems. By examining the underlying psychological theories behind human cognition, language, and behavior, researchers in this field aim to create more human-like AI models that can better understand and interact with the world. Key concepts from psychoanalytic theory, such as Freud's theory of personality and Jacques Lacan's philosophy, are explored to investigate the foundations of human thought and its potential implications for artificial intelligence.

Multimodality and Language Models

Multimodality and Language Models is a research domain focused on the intersection of visual and textual information in language modeling. By leveraging the complementary strengths of different modalities, such as text, images, and video, researchers aim to build more robust and versatile AI systems that can comprehend and generate content across various formats. Projects in this area often involve the development of text-to-image models, the creation of multimodal datasets, and the investigation of the relationships between different modalities. These efforts pave the way for a new generation of AI systems capable of understanding and generating complex, context-rich information spanning multiple sensory modalities.