Short Bio

Dr. Salamata Konate is a postdoctoral researcher in Computer Science at York University, where she focuses on advancing the interpretability and explainability of machine learning models, particularly in the fields of computer vision and healthcare. Her work aims to bridge the gap between complex AI systems and human understanding, ensuring that machine learning models can be more transparent and trustworthy in critical sectors like medical diagnostics and imaging. She earned her Ph.D. from Queensland University of Technology (Australia), where her research centered on developing visual interpretability techniques for machine learning models. Her work aimed to create methods that make it easier to explain the decision-making processes of AI systems, particularly in complex areas such as computer vision and medical imaging. During her Ph.D., she contributed to several key projects that focused on explaining how machine learning models process and analyze visual data, helping to enhance both the accuracy and reliability of AI-driven tools in healthcare and related fields.

Area of Research

• Interpretability and Explainability of AI models
• Fairness of AI models
• AI in medical imaging

Selected awards and achievements

• VISTA scholarship (declined).
• Connected Minds Postdoc Scholarship.

Short Bio

Dr. Mojtaba Kolahdouzi is a postdoctoral fellow in the Department of Computer Science at York University and a visiting researcher at the Vector Institute for Artificial Intelligence. His research focuses on fair and safe artificial intelligence, with a particular emphasis on understanding how training dynamics influence fairness and safety, developing label-free fairness-enhancing frameworks, and creating fairness-enhancing deepfake detection and facial expression recognition methods. Mojtaba earned his Ph.D. in Computer Engineering from Queen’s University (Canada), where his dissertation introduced novel fairness-enhancing methods and explored how optimization algorithms affect group fairness in deep learning. His research has been published in top venues such as NeurIPS, TMLR, IEEE Transactions on Artificial Intelligence, ICMI, FG, IJCB, etc. His work has been recognized through several awards, including the Connected Minds Postdoctoral Fellowship and the VISTA Postdoctoral Award, as well as a NeurIPS 2025 Spotlight selection, an honor reserved for the top 3% of submissions among over 21,000 papers.

Area of Research

• Fair and safe machine learning
• Fairness and safety in large language models
• Deepfake detection
• Facial expression recognition

Selected awards and achievements

• Connected Minds Postdoctoral Fellowship, York University, 2025.
• VISTA Postdoctoral Fellowship, York University, 2025.

Short Bio

Hassan is a PhD student in Computer Science at York University, specializing in Computer Vision, with a particular emphasis on diffusion models and their applications in medical image analysis. His current research aims to enhance the performance and explainability of medical image classifiers through the use of synthetic data, thereby addressing key challenges in the healthcare field. He completed his Master’s degree at Sharif University of Technology in Artificial Intelligence, where he focused on semantic segmentation, 3D computer vision, and knowledge distillation. During his Master’s, He proposed a novel framework to improve point cloud semantic segmentation models in data-scarce scenarios by integrating additional modalities, such as images, and distilling their knowledge into a point cloud-based network. This work contributed to the development of more accurate and data-efficient models for 3D semantic segmentation.

Area of Research

• Generative AI
• Fairness of AI models
• AI in medical imaging

Selected awards and achievements

• $40,000 from the VISTA program.

Short Bio

Matin Tavakoli is a PhD student in Computer Science at York University, specializing in Responsible AI with a focus on bias and fairness in multimodal systems. He earned his MSc in Software Engineering and Intelligent Systems from the University of Alberta and his BSc in Computer Engineering from Amirkabir University of Technology. His research spans autonomous driving, object detection, and multimodal learning, aiming to address critical issues of reliability and transparency in AI models used in high-stakes applications. Matin’s current work includes investigating fairness in multimodal large language models, with an emphasis on modality gap reduction and bias analysis. He has published in leading journals, including the IEEE Transactions on Intelligent Vehicles, where his master’s research introduced a novel approach to improving dataset quality for Autonomous Driving Systems (ADSs) by leveraging multimodal information processing and confident learning.

Area of Research

• Multimodal Learning
• AI Fairness
• Medical Imaging

Selected awards and achievements

• $40,000 from the Connected Minds program.

Short Bio

Amirreza Naziri is a Machine Learning Engineer with over two years of experience, focusing on developing and optimizing models in drug discovery, data analysis, and big data. Currently pursuing an MSc in Computer Science at York University, his work centers on machine learning solutions for drug discovery. Amirreza earned his bachelor's degree in computer engineering from Amirkabir University of Technology. He has been recognized with the Interdisciplinary AI Scholarship worth $20,000 for excellence in AI research and academic performance and was awarded a Top 5% Student Award at Amirkabir University for outstanding academic achievements.

Area of Research

• AI in drug discovery
• Bioinformatics

Selected awards and achievements

• $20,000 from Connected Minds.

Short Bio

After graduating with a Bachelor degree in Physics from York University, worked at RBC Capital Markets as a Lead Developer. Now pursuing Masters in Computer Vision, and AI at York University.

Area of Research

• Computer Vision
• Deep Learning
• Medical Imaging

Selected awards and achievements

• Ontario Graduate Scholarship ($15,000, declined).

Short Bio

Arash Asgari, an MSc student at York University's Responsible AI Lab, is advancing multi-modal Retrieval-Augmented Generation (RAG) models for processing text and image data. He has also achieved an MSc in Computer Engineering from Sharif University, where he optimized AI inference for cloud environments. Arash’s focus on responsible AI spans areas like fairness and medical imaging applications in NLP, underscoring his commitment to ethical AI innovation. His industry experience includes developing a chatbot for car dealerships and designing high-precision vehicle segmentation models using a combination of image enhancement and depth estimation models as an AI engineer at We Dryve Technologies in California. Previously, he worked at the Iranian Telecommunication Company, where he built an EEG-based Brain-Computer Interface for Smart Home applications to assist individuals with disabilities. Arash’s expertise in NLP, computer vision, and data mining, paired with his academic and practical insights, positions him as a versatile researcher dedicated to responsible AI development.

Area of Research

• Multimodal Large Language Models (LMMs)
• Medical Image Processing
• AI Fairness

Selected awards and achievements

• DRI EDIA Champions Award ($27,000) for pioneering research on fairness metrics in LLMs.
• Vector Scholarship in AI ($17,500) awarded to top AI master's students in Ontario.

Short Bio

Ryan's research focuses on AI fairness, generative AI, and natural language processing (NLP). Aiming to develop more ethical and responsible AI systems.

Selected awards and achievements

• Vector Scholarship in AI ($17,500) awarded to top AI master's students in Ontario.
• Erasmus Award $11,000 from MOST (overseas) mobility.

Thesis

• Large Language Models Fairness Evaluation, Capstone Project.

Thesis

• Performance and fairness analysis of vector embedding Chest X-ray representations.

Selected awards and achievements

• Erasmus Award $11,000 from MOST (overseas) mobility.