Where Mathematics Meets Markets: Dr. Dushyant Kumar’s Economics Journey

Dr. Dushyant Kumar is Assistant Professor and former Head of the Department of Economics and Finance at BITS Pilani, Hyderabad, an alumnus of the Indian Statistical Institute, Delhi and a former post-doctoral fellow at the Centre for Development Economics of the Delhi School of Economics (DSE). In this interview, Professor Dushyant reflects on his journey into economics, shaped by a strong inclination toward mathematics and analytical thinking. Drawing from his experiences at institutions like ISI Delhi and BITS, he discusses teaching, research, curriculum design, and how students can navigate the evolving landscape of economics in the age of AI. 

Q) Can you share what inspired you to pursue economics academically, and how your journey progressed from your bachelor’s to your PhD?

After my 10th and 12th, I found myself in a somewhat unusual position. I was very interested in mathematics and statistics, but I wasn’t particularly drawn to the natural sciences-physics, chemistry, or biology. So I started looking for a discipline where I could use my comfort with mathematics in a way that felt meaningful and connected to the real world. That’s how economics became a natural choice. I ended up taking a combination of mathematics and economics in my higher secondary education-which is relatively rare in the arts stream. Fortunately, my family was open to experimentation, so they supported this decision. What really excited me was the ability to apply mathematical ideas-even in abstract or hypothetical settings-to understand real-world phenomena. For instance, in areas like game theory or microeconomics, you can model behavior using equations and explore outcomes systematically. That interplay between theory and application kept me engaged and is what ultimately led me to continue in the field.

Q) You served as Head of Department from 2020 to 2023. What were the biggest challenges and achievements during that period?

There were two broad challenges. First, the department itself was relatively new. Around 2015–2016, several of us joined within a short span-essentially forming a young department without senior faculty at higher ranks. This created a unique dynamic. On one hand, there was a lot of freedom-no rigid hierarchy telling you what to do. On the other hand, we lacked established networks and institutional experience.We had to build everything from scratch: academic direction, collaborations, and even internal recognition. This also meant engaging with the institute’s  leadership-explaining how economics differs from engineering or science disciplines, and why certain policies (like support for PhD students or funding structures) needed to be adapted accordingly.The second challenge was the COVID-19 pandemic. Like everyone else, we had no prior experience dealing with something of that scale. Transitioning to remote teaching, managing evaluations, addressing student concerns, and maintaining academic continuity-everything had to be figured out in real time. It was a collective learning process.

Q) How would you contrast your experience at ISI Delhi and BITS?

They are fundamentally different environments. At ISI Delhi, the scale is extremely small. The total student population during my time was around 60–70. A master’s batch might have 10–15 students, and similar numbers existed across statistics and PhD programs. This naturally creates a very informal and close-knit environment-almost like a family. There’s minimal bureaucracy, and interactions are highly personal. More importantly, ISI is primarily a research institute. Teaching exists, but it is not driven by placement concerns. You can design courses with a long-term intellectual focus, without worrying about immediate employability outcomes.BITS, on the other hand, operates at a completely different scale-thousands of students across multiple campuses. This brings structure, coordination, and complexity. Decisions-like introducing a new course-require approvals across campuses and departments. Scheduling, curriculum design, and administration all involve multiple layers.There are trade-offs. ISI offers flexibility and depth; BITS offers scale, visibility, and institutional strength. At BITS, placement considerations also play a much larger role. Students are understandably concerned about how a course translates into job outcomes, which influences both teaching and curriculum design.

Q) You mentioned getting approval of a course is complex, can you briefly explain the process of designing and getting a course approved at BITS?

It’s a multi-stage process.

1) Department Level: The course proposal is first reviewed within the department.

2) Cross-Campus Approval: The proposal is then sent to other campuses and related departments.

3) Senate Approval: Finally, the proposal goes to the institute-level academic senate.In practice, delays often happen at intermediate stages. For example, while we successfully introduced game theory, proposals like information economics and auction theory, both of which are major fields in applied economics,  took years of effort and were ultimately not approved.

Q) What do you look for in undergraduate and PhD students?

For PhD students, I primarily look for conceptual clarity. They don’t need to know everything, but whatever they know, they should understand deeply. A solid mathematical foundation is important.For undergraduates, the emphasis is similar: strong fundamentals and logical clarity. In today’s world, where AI tools are widely available, accessing information is easy. But verifying and critically evaluating that information requires a strong conceptual base.Skills can be learned over time. What matters is whether the student has the ability and discipline to learn and adapt.

Q) What advice would you give students regarding skill development, especially in the age of AI?

I would like to give two major pieces of advice. First, students should actively use AI. It’s a powerful tool for processing information quickly. Second, verification is critical. AI outputs must be evaluated carefully. If your fundamentals are weak, AI can become a crutch. So the ideal approach is to use AI efficiently while maintaining strong conceptual understanding.

Q) What teaching methods do you use that work best for abstract concepts?

I always use examples. They are irreplaceable tools, but must be used carefully. If I solve every example in class, it leads to spoon-feeding. Instead, focus on a few core categories. Once students understand these, they can extend the logic themselves. The goal is to build independent thinking, not just problem-solving replication.

Q) In your experience, how has the economics curriculum evolved, and what are the emerging frontiers?

Economics has become increasingly data-driven over the past few decades. Today, it is difficult to find research that does not involve data. There is also growing convergence with computer science, especially AI and machine learning. On the theoretical side, mechanism design and market design have gained prominence-focusing on designing systems to achieve desired outcomes.

Q) Where do you see the field of economics heading in the future?

I see continued growth in data-driven research and stronger integration with data science. At the same time, there is a need to make theoretical economics more accessible. Future work should focus on problem-oriented research integrating multiple branches of economics rather than rigid subfields.