Title: Machine Generation and Evaluation of Design Concepts Using Large-Scale, Publicly-Available Data

Abstract:

The objective of this research is to develop machine learning methods that predictively improve the outcome of design solutions through the acquisition, fusion and mining of large-scale, publicly-available data. It has been reported that 70-80% of the manufacturing costs of a product are determined during the design phase. Towards enhancing the efficiency of the design process and creating personalized design solutions, our research focuses on three core thrusts:

Research Thrust 1: Feature Discovery and Quantification: Develop machine learning algorithms that mine large scale, publicly-available data in order to identify and quantify relevant design features.

Research Thrust 2: Scalable Generative Design: Develop machine learning models (currently based on Deep Neural Network principles) that result in the automatic, scalable generation of design concepts (both 2D sketches and 3D CAD models).

Research Thrust 3: Scalable Design Evaluation: Develop design evaluation methods (currently based on Deep Reinforcement Learning principles) that result in the automatic evaluation of design concepts, based on computer-learned physics of an environment.

The fundamental concepts of machine learning-driven design extend well beyond consumer products and include the design of more efficient manufacturing processes (e.g., machine learning models for improving the efficiency of additive manufacturing processes), healthcare systems (e.g., machine learning models for early detection, as well as long-term management of patients’ health-related abnormalities), and educational experiences (e.g., advancing personalized learning through adaptive machine learning models).

Bio:

Dr. Conrad Tucker holds a joint appointment as Associate Professor in Engineering Design and Industrial and Manufacturing Engineering at The Pennsylvania State University. He is also affiliate faculty in Computer Science and Engineering. Dr. Tucker is the director of the Design Analysis Technology Advancement (D.A.T.A) Laboratory. His research focuses on the design and optimization of systems through the acquisition, integration and mining of large scale, disparate data.

Dr. Tucker has served as PI/Co-PI on federally/non-federally funded grants from the National Science Foundation (NSF), the Air Force Office of Scientific Research (AFOSR), the Defense Advanced Research Projects Agency (DARPA), the Army Research Laboratory (ARL), the Office of Naval Research (ONR) through the NSF Center for e-Design, and most recently, the Bill and Melinda Gates Foundation (BMGF). He is currently serving as PI and Site Director of the NSF Center for Health Organization Transformation (CHOT), an NSF Industry/University Cooperative Research Center at Penn State. In February 2016, he was invited by National Academy of Engineering (NAE) President Dr. Dan Mote, to serve as a member of the Advisory Committee for the NAE Frontiers of Engineering Education (FOEE) Symposium. Dr. Tucker is the recipient of the American Society of the Engineering Education’s (ASEE) Summer Faculty Fellowship Program (SFFP) award and conducted research at the Air Force Institute of Technology at the Wright Patterson Air Force Base during Summer 2014 and Summer 2015. He received his Ph.D., M.S. (Industrial Engineering), and MBA degrees from the University of Illinois at Urbana-Champaign, and his B.S. in Mechanical Engineering from Rose-Hulman Institute of Technology. Dr. Tucker is part of the inaugural class of the Gates Millennium Scholars (GMS) program, funded by a $1 billion grant from the Bill and Melinda Gates Foundation.