Dr. Naveen Kumar Muthumanickam is a research engineer at the National Renewable Energy Laboratory, specializing in developing computational design technologies to leverage industrialized and robotic construction techniques for rapid volume production of energy-efficient buildings, as well as automated retrofitting of existing build stock.

He received his doctoral degree in architecture along with a concurrent specialization in architectural engineering from Penn State University. He was part of the Penn State team at the finals of the NASA 3D Printed Mars Habitat Centennial Challenge and worked on the building information modeling-based optimization and digital twin simulation for robotic concrete 3D printing. His doctoral research focused on leveraging a range of techniques like generative modeling algorithms and machine learning-based metamodels for multidisciplinary design optimization of buildings for autonomous robotic construction. He is originally from Chennai, India, with a bachelor’s degree in architecture, and he holds an M.S. in architectural science and building technology from the University of Michigan.

Previously, Naveen served as a research specialist at the University of Michigan 3D Lab, where he worked on building physics simulations and research projects that leveraged cutting-edge technologies such as LiDAR-attached drones to obtain 3D point cloud scans of urban-scale building stock, computer vision-based object detection, and classification of building roofs from the obtained 3D point cloud data. He also held summer internship appointments with construction technology firms Autodesk and Higharc, Inc.

Naveen was awarded the 2021 Thomas and June Beaver Fund Award for excellence in industry-sponsored research and collaboration by Penn State University. He is a reviewer in major technical journals like the Journal of Architectural Engineering and serves as the secretary of the American Society of Civil Engineers Space Engineering & Construction Technical Committee. 

Building information modeling

Building performance analysis

Design for manufacturing and assembly

Multi-objective design optimization

Machine learning-based metamodeling

3D process modeling for industrialized and robotic constructability analysis

Computer vision for object recognition, classification, and digital twin modeling

Robotics for systems integration in buildings