Darshan Sarojini has joined as an assistant professor in the department. He is a graduate from the Georgia Institute of Technology, and most recently served as a postdoctoral scholar at the Large Scale Design Optimization Lab at the University of California, San Diego. Sarojini’s research interests include multidisciplinary design optimization, probabilistic and robust design, multiphysics topology optimization, and high-performance computing. He is leading the aircraft design section of the department’s senior capstone design course this academic year.

Sarojini completed his Ph.D. in Aerospace Engineering from the Georgia Institute of Technology, with a focus on structural analysis and optimization of aircraft wings through dimensional reduction. He holds a master’s in computational science and engineering, and a master’s in aerospace engineering from the Georgia Institute of Technology, and a bachelor’s in mechanical engineering from the B.M.S. College of Engineering in Bangalore, India. 

Sarojini is a member of the American Institute of Aeronautics and Astronautics.


What drew you to Virginia Tech? Share with us what excites you about the department and our students? 

I was drawn to Virginia Tech because of its strong commitment to interdisciplinary research, which is essential for my work in system-level analysis, design, and optimization. The university’s collaboration-driven environment, through centers and institutes like FAA NEXTOR, the Virginia Tech Transportation Institute, and the National Security Institute, fosters an atmosphere where complex, multidisciplinary problems can be addressed. Within the department, I’ve seen an eagerness among both colleagues and students to collaborate. The combination of aerospace and ocean engineering is also particularly exciting to me, as, while my experience has primarily been in aerospace, I’m eager to broaden my work to ocean vehicles as well.

What does your research entail? What do you hope will come of it?

My research focuses on developing advanced computational methods and tools to accelerate the design and analysis of multidisciplinary systems, with a specific emphasis on aircraft design. As the aviation industry moves towards electric and more sustainable aircraft to lower its carbon footprint, my work seeks to develop methods that not only improve efficiency but also ensure safety and reliability, particularly under adverse or off-nominal conditions. Ultimately, I hope that my research will contribute to the development of the next generation of aircraft, including urban air mobility vehicles with electric vertical takeoff and landing capability, regional aircraft with hybrid-electric propulsion, and interstate aircraft using sustainable aviation fuels. My goal is to transform the future of mobility and transportation by enabling safer, greener, and more versatile aircraft.

What originally got you interested in your work? Tell us about the ‘spark’ that pulled you to your area of research.

Growing up near an air force base and influenced by my grandfather—who was a member of the cadets—I spent a lot of my childhood around planes. From visiting cockpits during flights to attending aviation open days at a local science center, I was involved with airplanes early on. I started building paper airplanes, rubber-band-powered planes, and RC planes. As I pursued engineering, I became intrigued by how so many disciplines—aerodynamics, materials, propulsion, dynamics, and more—come together to make something as complex as an aircraft fly. My interest grew further as I explored the mathematical methods that can handle hundreds of variables and scenarios, helping to filter and search for optimal designs.

Please share with us what you’d like engineering students to know about your lab and research group? 

My lab operates at the intersection of mathematical optimization, computer science, and engineering design. We focus on developing novel algorithms capable of analyzing and designing complex systems, or systems-of-systems. We leverage modern high-performance computing and machine learning to help reduce the computational costs of simulating such systems, which is essential for embedding these simulations into optimization loops to enhance the design processes. Students in my group will have the chance to apply these methods to real-world design challenges, collaborating with industry and government partners.