Mohammadreza Samadi

My name is Mohammadreza, and I am an AI Researcher at Huawei Canada, specializing in optimizing Generative AI models—such as Large Language Models (LLMs) and Diffusion Models—for efficient deployment. Passionate about making AI accessible and efficient, I focus on developing advanced technologies that are lightweight and fast for real-world applications.

Before joining Huawei, I worked as a Machine Learning intern at the Alberta Machine Intelligence Institute (Amii), where I created customized AI solutions to meet the unique needs of various companies.

I hold an M.Sc. in Computing Science from the University of Alberta, where I was advised by Prof. Davood Rafiei. My research focused on Natural Language Processing and Information Retrieval, particularly on query performance prediction in multi-hop question answering. Prior to that, I completed my Bachelor’s in Computer Engineering at Amirkabir University of Technology (Tehran Polytechnic), where I developed a fake news detection system for Persian news under the guidance of Prof. Saeede Momtazi.

Selected Publications

Deep contextualized text representation and learning for fake news detection

Mohammadreza Samadi, Maryam Mousavian, and Saeedeh Momtazi

Information Processing & Management, 2021

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In recent years, due to the widespread use of social media and broadcasting agencies around the world, people are extremely exposed to being affected by false information and fake news, all of which have negative impacts on both collective thoughts and governments’ policies. In recent years, the great success of pre-trained models for embedding contextual information from texts motivates researchers to utilize these embeddings in different natural language processing tasks. However, in a complex task like fake news detection, it is not determined which contextualized embedding can assist the classifier with more valuable features. Due to the lack of a comparative study about utilizing different contextualized pre-trained models besides distinct neural classifiers, we aim to dive into a comparative study about using different classifiers and embedding models. In this paper, we propose three classifiers with different pre-trained models for embedding input news articles. We connect Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) after the embedding layer which consists of novel pre-trained models such as BERT, RoBERTa, GPT2, and Funnel Transformer in order to benefit from deep contextualized representation provided by those models as well as deep neural classifications. We evaluate our proposed models on three well-known fake news datasets: LIAR (Wang, 2017), ISOT (Ahmed et al., 2017), and COVID-19 Patwa et al. (2020). The results on these three datasets show the superiority of our proposed models for fake news detection compared to the state-of-the-art models. The results show 7% and 0.1% improvements in classification accuracy compared to the proposed model by Goldani et al. (2021) on LIAR and ISOT, respectively. We also achieved 1% improvement compared to the proposed model by Shifath et al. (2021) on the COVID-19 dataset.
FunEditor: Achieving Complex Image Edits via Function Aggregation with Diffusion Models

Mohammadreza Samadi, Fred X Han, Mohammad Salameh, and 4 more authors

The 39th Annual AAAI Conference on Artificial Intelligence, 2025

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Diffusion models have shown exceptional performance in generative tasks, making them suitable for image editing. Recent research highlights their ability to apply edits based on textual instructions, but two key challenges persist. First, these models struggle to perform multiple edits simultaneously, causing computational inefficiencies due to sequential processing. Second, using textual prompts to specify editing regions can result in unintended changes to the image. We introduce FunEditor, an efficient diffusion model designed to learn basic editing functions and combine them to perform complex edits. This method allows tasks like object movement by merging multiple functions and applying them simultaneously to designated areas. Our experiments show that FunEditor significantly outperforms recent inference-time optimization techniques and fine-tuned models, both quantitatively across various metrics and through visual comparisons, on complex tasks like object movement and object pasting. Moreover, with only four inference steps, FunEditor achieves a 5-24x speedup compared to existing popular methods. Codes and datasets are available online.