Arshia Hemmat

Arshia Hemmat

Researcher at University of Oxford

Researches

Counterfactual Modelling Using Vision-Linguistic Models

August 01, 2023

Research Experience, Technical University of Munich, Computer Aided Medical Procedures (CAMP), Munich, Germany

Investigates generating counterfactual scenarios through integration of visual and linguistic data to enhance AI interpretability and reliability, focusing on applications such as autonomous driving and medical diagnostics. This research highlights the potential of vision-linguistic models in creating diverse, hypothetical scenarios for advanced problem-solving and decision-making processes.

3D Vessel Segmentation Using Geometric Approaches

May 01, 2023

Research Experience, Technical University of Munich, Computer Aided Medical Procedures (CAMP), Munich, Germany

This research focuses on leveraging geometric deep learning techniques for the accurate segmentation of blood vessels in 3D medical images. The aim is to enhance diagnostic and therapeutic procedures in medicine, showcasing the potential of geometric approaches in complex image segmentation tasks.

Vision-Linguistic Models

February 01, 2023

Research Experience, Sharif University of Technology, Robotics and Machine Intelligence Lab (RIML), Tehran, Iran

This project aims to enhance the synergy between visual perception and language processing in AI systems, exploring the boundaries of machine understanding and interpretation of complex visual-textual information. The work under Prof. Mohammad Hossein Rohban investigates innovative methodologies to advance the capabilities of vision-linguistic models.

Text Summarization Using Graph Neural Network

February 01, 2022

Research Experience, University of Isfahan, Isfahan, Iran

Under Dr. Hamidreza Baradaran’s guidance, this project employs graph neural networks for efficient text summarization. The objective is to enhance the coherence and relevance of automated summaries, facilitating improved information retrieval and understanding across large text corpora.

Zero-shot Learning in Medical Domain

August 01, 2021

Research Experience, University of Isfahan, Isfahan, Iran

Focusing on developing models capable of recognizing unseen medical conditions from imaging data, this research tackles the challenge of improving diagnostic tools without extensive labeled datasets. Dr. Peyman Adibi guides the project, aiming to revolutionize diagnostic methodologies through zero-shot learning.