Mission:
We study computational and data-driven methods that will augment or transform the activities of engineering and industrial design. Formally, we consider design activities the process of defining and solving optimization problems that are potentially high-dimensional, and seek search methods that leverage human knowledge and heuristics, learning algorithms, physics-based models, and computing hardware to achieve scalable design beyond the capacity of experienced human designers and existing algorithms. Challenges lie across multiple disciplines including optimization, machine learning, crowdsourcing, and engineering system design and control. Key real-world applications include computational material design and manufacturing, vehicle and robotics system design and control, and the automation of industrial design for functionalities and preferences.

Projects:


People:

Max Yi Ren,
Assistant Professor, project: Stay hungry. Stay foolish.

Ruijin Cang,
MAE PhD, project: Computational material design

Hope Yao,
MAE PhD, project: Adversarial attack and robust classification

Malcolm Regan,
EE Undergrad, project: Adversarial attack and robust classification

Saurabh Animesh,
EE Master, project: FPGA-accelerated computing

Andrew Leaton,
MAE Undergrad, project: Sensor topology design
Alumni:

Thurston Sexton,
NIST, MAE Master (2016), project: Learning to optimize

Paul Stobinske,
Orbital ATK, MAE Bachelor (2016), project: CNC additive manufacturing

Fabian Gadau,
Ford, MAE Bachelor (2015), project: Racetrack optimization

Aditya Vipradas,
MSC Software, MAE Master (2017), project: Computational material design

Adithya Venugopal,
MAE Master (2017), project: FPGA-accelerated computing