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

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

• Generative AI
• AI Fairness
• Representation Learning

Selected awards and achievements

• $40,000 from the Connected Minds program.

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 (MLLMs)
• 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 is a researcher specializing in AI fairness, generative AI, and natural language processing (NLP), with a particular focus on understanding and mitigating bias in large language models. His work spans dialectal variation, stereotype amplification, and multi-metric evaluation of model behavior, aiming to build systems that treat all language users with equal respect. Ryan aims to develop more ethical, transparent, and socially responsible AI systems, bridging algorithmic performance with real-world linguistic diversity.

Area of Research

• Natural Language Processing
• AI Fairness

Selected awards and achievements

• Vector Scholarship in AI ($17,500) awarded to top AI master's students in Ontario.

Short Bio

Noran is pursuing an MSc in Computer Science at York University and is a member of the Responsible AI Lab, where her research focuses on AI fairness and mitigating dialect bias in state-of-the-art large language models.

Area of Research

• AI Fairness
• Natural Language Processing

Selected awards and achievements

• Vector Scholarship in AI ($17,500).
• Lassonde Undergraduate Research Award ($10,000).

Short Bio

Souheab is a Computer Science student at York University and an undergraduate researcher at the Responsible AI Lab. His research interests include responsible AI, multimodal learning, vision-language models, and cross-lingual AI. He is interested in developing AI systems that are fair, robust, transparent, and accessible across languages.

Area of Research

• AI Fairness and Responsible AI
• Multimodal and Vision-Language Models
• Cross-Lingual AI

Short Bio

Kiavash is an Honours Computer Science undergraduate student at York University and a research assistant at the Responsible AI Lab. His research focuses on responsible artificial intelligence, particularly fairness in medical imaging and the use of generative AI to improve the performance and fairness of deep learning models.

Area of Research

• AI Fairness
• Medical Imaging
• Generative AI
• Computer Vision

Short Bio

Robel is an International Visiting Research Trainee at York University and an MSc student in Artificial Intelligence and Computer Science at the University of Calabria, Italy. His research interests include generative AI, AI in medical imaging, and AI for healthcare, with a focus on developing responsible and robust AI methods for medical image analysis.

Area of Research

• Generative AI
• AI in Medical Imaging
• AI for Healthcare

Postdoctoral Researcher, Fall 2025 – 2026
(Position: Machine Learning Researcher at RBC Borealis)

Master's Student, Winter 2024 – Fall 2025
(Position: AI Engineer at BMO Capital Markets)

Research Associate, Summer 2023 – Summer 2025
(Position: Lecturer at University of Bath)

Research Assistant, Fall 2025
(Position: Data Science Intern at Bough Biosciences)

Master's Student, Summer 2024 – Winter 2024
(Position: Software and AI Engineer at York University)

Bachelor's Student, Summer 2023
(Position: Founder and AI Engineer at FuteraAI)