Research Awareness in Artificial Intelligence

Artificial Intelligence has evolved from early symbolic reasoning systems to today’s data-driven and deep learning–based models that influence science, industry, and everyday life. This talk traces the historical development of AI, highlighting key milestones that shaped modern intelligent systems. I will discuss why AI has become such a powerful tool—enabling advances in automation, pattern discovery, and decision support—while also examining its limitations and risks, including bias, robustness, and trustworthiness. Finally, I will present examples from my own research on applying AI to real-world problems, illustrating both the promise and the practical challenges of deploying AI systems responsibly.

This talk introduces undergraduate students to practical applications of artificial intelligence across cybersecurity, intelligent systems, and cyber-physical environments. The presentation connects foundational ideas from mathematics, computer science, and engineering to real-world AI systems used for security, decision-making, and resilience. Through research-informed examples, the talk illustrates how AI moves from theory to deployment, highlighting technical challenges, system design considerations, and emerging directions in applied AI.

With the advent of the internet and the popularity of social media, a vast amount of textual data is now available for researchers. However, the unstructured nature of most text makes it difficult to directly use for downstream tasks. In this workshop, students will implement large language models and iteratively design prompts to automatically extract structured information from raw text. Students will then use this structured data to construct a knowledge graph.

Magnetic Resonance Imaging (MRI) is one of the most powerful tools in modern medicine—but it is inherently slow due to physical and hardware constraints. In this talk, we will explore how machine learning is transforming MRI by dramatically accelerating image acquisition and improving reconstruction quality. We will trace the evolution of learning frameworks in medical imaging—from model-based methods to data-driven approaches and emerging physics-informed hybrid techniques, highlighting how AI is reshaping the future of medical imaging technology and opening exciting opportunities for the next generation of engineers and scientists.