Bahar Dibaei Nia

Bahar Dibaei Nia

B.Sc. Computer Engineering Student

Research

Research Assistant at Chinese University of Hong Kong

July 01, 2024

Research Experience, Chinese University of Hong Kong, Hong Kong

During my time at the Chinese University of Hong Kong, I worked as a Research Assistant under the supervision of Professor Farzan Farnia. Our research focused on investigating the diversity of text-to-image generative models, specifically how they are conditioned on text descriptions. We explored differential clustering methods as a potential approach to better capture and quantify this diversity. Through this experience, I became familiar with the world of generative models and their evaluation metrics, broadening my understanding while refining my research skills in machine learning and artificial intelligence.

Remote Research Assistant at University of South Carolina

July 01, 2023

Research Experience, Univrsity of South Carolina, South Carolina, USA

As a Remote Research Assistant at the University of South Carolina under the supervision of Prof. Pooyan Jamshidi, I conducted experiments on Centered Kernel Alignment (CKA) and its variations, such as dCKA, along with other similarity metrics. My research aimed to evaluate and compare these methods for measuring similarity between neural networks, identifying their strengths and proposing improvements to enhance their performance and interpretability.

Research Assistant at RIML Lab

October 01, 2022

Research Experience, Sharif University of Technology, Tehran, Iran

During my time as a Research Assistant at the Robust and Interpretable Machine Learning Lab under the supervision of Dr. Mohammad Hossein Rohban, I focused on advancing machine learning robustness and interpretability. My initial work involved conducting literature reviews and experiments on out-of-distribution (OOD) detection, particularly addressing spurious correlations. I tested various OOD benchmarks on spurious samples, analyzed the limitations of existing methods, and proposed improvements. This phase of research significantly contributed to understanding the complexities of OOD detection in real-world applications.