Today, practical skills are not just desirable—they’re absolutely essential for engineers: Dr Vikas Upadhyay

“Gone are the days when a faculty member's role was just to teach in the classroom and leave,” says Dr. Vikas Upadhyaya, Associate Professor, Electronics and Communications Engineering, NIIT University highlighting a powerful shift in the role of educators in shaping future technologists. In this interview, Dr. Upadhyaya—Professor at NIIT University and a mentor to multiple industry-backed student innovations—discusses with Education Post’s Prabhav Anand about the evolving landscape of engineering education. From redefining Very-Large-Scale Integration (VLSI) curriculum to embracing simulation-physical hybrid models and embedding research from the second year onward, he provides a blueprint for integrating theory with real-world applications. The conversation also talks about the ethics in AI, interdisciplinary startup ecosystems, and the transformative power of mentorship. Dr. Upadhyaya's vision offers not just commentary, but direction for institutions striving to align with India’s strategic tech missions while cultivating agile, innovation-driven graduates.

1. With your deep involvement in VLSI design and real-world student projects, how do you see Indian engineering curricula evolving to close the gap between theoretical knowledge and industry-grade implementation—especially in high-precision domains like semiconductor design?

I’d like to begin by saying this gap between theoretical knowledge and practical application isn’t new—it's been part of engineering education for decades. However, what's changed drastically in the 21st century is the pace and complexity of technological evolution. Especially in domains like VLSI and semiconductor design, we’re seeing a level of innovation and precision that we've never experienced before in human history.

Given this rapid advancement, the expectation from an engineering graduate has also transformed. Today, it's no longer enough for students to just grasp theoretical concepts—they must know how to apply them. Practical skills are now not just desirable; they’re essential.

In most engineering curricula, including ours at NIIT University, every course traditionally comes with an associated lab. But what’s encouraging is that these labs themselves are evolving. It's not just about doing an experiment anymore—it’s about thinking innovatively, solving real-world problems, and in many cases, integrating multiple disciplines into a single project.

We now see more project-based learning, where students go beyond the scope of a textbook or a single subject. They’re expected to think critically, collaborate, and innovate—skills that align much more closely with industry expectations. And in high-precision fields like VLSI, this hands-on, integrated approach makes a world of difference in bridging the gap between classroom learning and real-world application.

2. UGC has also implemented a policy regarding the Professor of Practice (PoP), which is based on industry experience. What are your thoughts on this?

I think it's a very welcome move. Professors of Practice bring real-world industry experience directly into the classroom, which significantly enhances students’ practical learning. In academia, we now have two strong pillars—academic researchers and industry practitioners. While researchers contribute to theoretical depth, PoPs add the applied perspective. This dual support helps shape students' thinking in both innovative and implementation-driven ways, especially when it comes to hands-on lab work and real-world problem solving.

3. You’ve guided projects that received backing from premier institutions like DRDO. What do you believe are the core qualities in mentorship that foster a culture of breakthrough innovation at the undergraduate level?

Today, I believe the role of a faculty member goes beyond delivering knowledge—we’re here to inspire. When I involved second-year students in a DRDO-supported project, the work naturally became multidisciplinary—combining optical communication, electronic devices, circuit design, and more. That kind of integration is powerful.

As mentors, our job is to present real challenges to students—not just answers—so they’re motivated to solve problems. And more importantly, we must encourage curiosity. When students start asking the right questions, that’s when true innovation begins.

4. As India pushes forward with semiconductor self-reliance under initiatives like “India Semiconductor Mission,” how should engineering institutions reimagine VLSI education beyond traditional CAD tools to meet this strategic national vision?

Traditionally, India’s role in VLSI was largely focused on testing rather than design. But with the India Semiconductor Mission and strong industry collaboration, we’re now seeing progress in fabrication as well.

This shift means VLSI education must evolve. It’s no longer enough to teach just CAD or EDA tools for design and synthesis—we now need to prepare students for the fabrication side too. That requires building the right infrastructure, even at the university level, to simulate real fabrication environments and support hands-on learning aligned with the national vision.

5. In an era where digital simulation often replaces physical prototyping, how do you strike a balance between hands-on experimentation and virtual design environments when training students in electronics and robotics?

We’re training students in electronics and robotics here, and when we look at current trends—especially in robotics—we see the growing use of digital twin technology. In this, we simulate the entire environment where the robot will operate, test everything virtually, and only then move to deployment.

But deploying directly into the real world is never a good idea. That’s where the balance comes in. Whether in academia or industry, the first step is simulation—where the risk of failure is low. But once that’s done, we must take the next step: prototyping and product development. The simulation must be converted into a real, meaningful application, tested on the ground. Only then can it evolve into a usable product.

6. Considering your work on sensor-based automated vehicle detection, how do you address the ethical dimensions and privacy implications of deploying image processing and AI in surveillance and smart infrastructure?

The kind of work we did was essentially in the public domain—detecting vehicles on roads—which means it involves public spaces. So placing surveillance cameras there cannot really be called unethical.

However, when we move into AI, especially areas like computer vision and machine learning—which I’ve been working on—the ethical dimension becomes more critical. In such applications, user consent must be taken, and that’s where ethics come into play.

You’ll notice that wherever CCTV cameras are placed, there’s usually a sign indicating that the area is under surveillance. That transparency is important. Also, AI systems shouldn't be a black box to the user. The process of how data is being collected and used should be open and understandable. That’s something we’re experiencing now and definitely need to take seriously.

7. Your mentorship spans electronics, robotics, and image processing. In your view, how can universities foster truly interdisciplinary project ecosystems without diluting domain depth—especially in rapidly converging tech spaces?

If I talk about my own research journey, I started in VLSI, then moved into computer vision, machine learning, and working with different sensors. This transition helped me develop a broader perspective—using technologies from one discipline in another. That’s exactly the kind of approach we’re encouraging here at the university.

We’re incubating several student-led startups on campus, and I’m actively contributing in that space too. The idea is to build interdisciplinary teams—bringing together students from computer science, electronics and communication, and even those with an understanding of management.

This kind of ecosystem—where domain expertise is combined rather than diluted—is the reason we’ve seen successful innovations emerge, even though the university was only established in 2009.

8. Given NIIT University’s model of integrating sustainability, innovation, and technology, how do you see the role of a professor evolving in shaping not just curriculum, but student mindsets and future tech leadership?

This is something I was trying to highlight earlier. Gone are the days when a faculty member's role was just to teach in the classroom and leave. In the 21st century, students are looking more for mentors than traditional teachers—because knowledge is now available everywhere, whether it’s Google or AI-based systems.

So, as a faculty member, I see my role as presenting problems to students—challenges that make them think, apply their minds, and evolve in their understanding. And while we encourage interdisciplinary learning, we must ensure that the foundation is very strong. Only with a solid base can students confidently move from one discipline to another without losing depth.

9. What institutional or pedagogical changes do you believe are essential to nurture a stronger research mindset in undergraduate engineering students, especially those entering core domains like electronics and embedded systems?

Developing a research-driven mindset shouldn't be something we wait to introduce in the third or final year, which has been the traditional approach in engineering. Things are changing now.

At NIIT University, for example, we start involving students in research right from the undergraduate level. Recently, I worked on a consultancy project with a company in Maharashtra, where we developed a computer vision-based system for a biomedical application. The students involved with me were in their second and third years of engineering.

This early involvement helps students not only understand research better, but also prepares them for the industry from day one. They learn how to apply research and also how to build products and processes—skills that are crucial when they step into the professional world.

10. Like artificial intelligence, including chatbots and GPT, there are many AI tools that are commonly used today. For tech students interested in entrepreneurship or starting their own startups, what advice would you give? What important factors should they keep in mind to succeed in their entrepreneurial careers and ventures?

The role of AI, in my view, is similar to the advent of computers. When computers first emerged, it became essential for everyone to learn how to use them in order to perform meaningful tasks and enhance their capabilities. We are experiencing a similar shift with artificial intelligence today. Regardless of the field—be it engineering or another domain—having a basic understanding of AI is becoming crucial.

Whether using platforms like ChatGPT or exploring entrepreneurship and innovation, it’s important for everyone to familiarize themselves with these tools. However, I would like to offer a word of caution: while it’s beneficial to utilize AI, one must be careful not to let it stifle your creative thinking. Instead of merely typing a prompt and copying the response, treat AI as a helpful resource. Use it for guidance and information, and then apply your own innovative ideas to build upon that foundation.