Rizwan Qureshi

Dr. Rizwan Qureshi is a Postdoctoral Researcher specializing in Computer Vision, Natural Language Processing and Deep Learning, with a focus on Large Language Models (LLMs), Vision-Language Models (VLMs), and Responsible AI. He is currently affiliated with the Center for Research in Computer Vision at the University of Central Florida. With a PhD from City University of Hong Kong, Hong Kong and a former graduate of MUET, Jamshoro, Dr. Qureshi had held multiple postdoctoral appointments across globally recognized institutions including MD Anderson Cancer Center (USA), Houston, TX, Chinese Academy of Sciences (Hong Kong), and HBKU (Qatar Foundation), Doha, Qatar, Dr. Qureshi brings over 10 years of interdisciplinary expertise spanning biomedical AI, computational biology, vision-language models, and generative AI. He was a Lecturer at the Department of Electrical Engineering, COMSATS University Islambad, Wah Cantt, and an Assistant Professor at the National University of Computer and Emerging Sciences, Karachi, Pakistan. He is a Senior Member of IEEE, an editorial board member for several AI and bioinformatics journals, and has published in high-impact venues including The Lancet Digital Health, IEEE Transactions on Image Processing, IEEE Transactions on Medical Imaging, Nature Scientific Reports, and CVPR. He is also the recipient of several prestigious fellowships, including the AI-Net DAAD Fellowship (Germany), and has led or co-led projects funded by Qatar Foundation, RGC Hong Kong, and NIH USA. With over 2,000 citations and multiple competitive grants to his name, Dr. Qureshi is leading efforts to make AI not just intelligent—but explainable, trustworthy, and transformative. His keynote will reflect on the evolving role of LLMs and generative models in enabling data-driven scientific discovery, shaping the future of research and innovation in the AI era.

Keynote Speech on:
Scientific Discovery in AI Era

Artificial Intelligence is rapidly redefining how science is done. From drug discovery and genomics to climate modeling and materials science, AI—especially large language and generative models—is accelerating the pace of discovery by enabling hypothesis generation, complex simulations, and data-driven insights at unprecedented scale. This keynote explores how foundation models are transforming scientific research, with real-world case studies spanning biomedicine, smart systems, and computational biology. We will discuss the opportunities, challenges, and ethical imperatives of using AI as a true collaborator in the scientific process—and outline a vision for a future where human intuition and machine intelligence co-create the next wave of scientific innovations.

-->