As technology continues to reshape industries at an unprecedented pace, the role of engineering education is undergoing a fundamental redefinition. No longer is it sufficient to train students for specific tools or immediate job roles; institutions must now prepare graduates for careers that will evolve multiple times over their working lives. Prof. S K Shukla believes that the key lies in strong fundamentals, research-driven learning, ethical system design, and a deep focus on student well-being. He outlines IIITH’s vision for future-ready education, the importance of cybersecurity and emerging technologies, why placements should be viewed beyond salary, and how interdisciplinary learning can help students build resilient, purpose-driven careers. Our vision is to prepare students not just for today’s technologies, but for a future that will be defined by constant and often unpredictable change. To remain relevant in such an environment, students must be grounded in strong fundamentals—systems thinking, algorithmic problem-solving, information and systems security–centric design, and ethical system development.

With AI taking on an increasing role in writing code, a new and pressing question has emerged: how can we be sure that automatically generated code is safe, reliable, and correct? “The process of developing software itself is now automated,” Prof. Abhishek Singh explains. “But in the process, there are a lot of bugs that get generated as well.” His research focuses on building automated systems that can catch these bugs early – especially in modern software that runs multiple tasks at the same time, a category known as parallel or concurrent programs. According to Prof. Singh, many software bugs originate long before code is written. “Many software bugs find their roots in the transition from informal intent to formal implementation. They begin with how humans describe what they want software to do. “The problem arises because you never describe your intent clearly,” he says. “You provide inputs in natural language and then these AI agents produce code for you.” But, he explains, natural language is ambiguous. English sentences may have multiple meanings.

The researchers at IIITH used visual learning models to analyse and reach the conclusion about the variations. The dataset used by the researchers included 12 distinct types of biryanis: Ambur, Bombay, Dindigul, Donne, Hyderabadi, Kashmiri, Kolkata, Awadhi, Malabar, Mughlai, Sindhi and Thalassery. The researchers tried to understand what makes the biryanis unique and how: “By comparing the cooking process for different types of biryani, we can identify common patterns and variations in the cooking methods, ingredients and techniques used. This can help us understand the unique characteristics of each biryani recipe and how they differ.” The paper titled ‘How Does India Cook Biryani’ – authors – C.V. Rishi, Farzana S., Shubham Goel, Aditya Arun and Prof. C.V. Jawahar presented at ICVGIP conference at Mandi in December 2025. “We began the research more than a year ago as we wanted to understand the differences in Indian cooking process versus, health values, nutrition and cultural variations among other things,” informed Mr. Jawahar when asked about the research.