I will join MIT Mechanical Engineering department as an Assistant Professor in Fall 2020. Currently, I am a Postdoctoral Fellow at the IDEAL Lab, working with Dr. Wei Chen.
I completed my Ph.D. from the University of Maryland, College Park, where I worked with Dr. Mark Fuge. Prior to my Ph.D., I worked as a Reliability Engineer at Rio Tinto in Western Australia. I earned my undergraduate and master's degree from the Indian Institute of Technology Kanpur, where I worked with Dr. Bishakh Bhattacharya and Dr. Kalyanmoy Deb. I believe in reproducible and open-source science whenever possible, and do my part by making most of my research code available online.
I am interested in AI-driven design problems, which encompasses the following areas:
1. Design for everyone, by everyone: How can we enable distributed teams of people to create new products?We are interested in transforming the way people design products by democratizing design — where teams of globally distributed people work together to design physical products. This will enable designers to express complex ideas from anywhere in the world and firms to process thousands of ideas efficiently. To achieve this goal, we worked on principled methods for representation, learning, and optimization of discrete problems occurring in design. In the past, we investigated three main questions to help organizations and distributed teams:
a) Computational Creativity Estimation: How does one reliably measure the creativity of ideas?
b) Diverse Bi-partite Matching: How does one form teams to evaluate design ideas?
c) Idea Filtering Algorithms: How does one select the most diverse ideas out of hundreds of submissions?Details on these questions are provided on my past research page.
2. AI for Engineering Material Systems: How can we design, develop, and deploy advanced engineering material systems?Our goal in this area is to do fundamental research to enable the accelerated design and development of materials by developing and creatively integrating theory, manufacturing, and computational approaches with rigorous engineering design principles. Existing topology optimization methods cannot handle these requirements. To address this gap, we work on combining representation learning of material unit cells (metamaterials) with surrogate modeling and combinatorial search methods to solve very large inverse design problems. A typical example is designing a new prosthetic arm with thousands of unit cells, spatially varying properties, complex non-linear physics and manufacturing constraints.