Andrew Spielberg

AI-Powered Design And
Manufacturing For Embodied AI

Postdoctoral Fellow
Harvard University
Mechanical Engineering & Materials Science


Google Scholar:

Morphological   Intelligence
Meets Artificial Intelligence

My mission is to enable anyone to be able to design functional artifacts across scales and domains, and with a special emphasis on robotics and other cyberphysical machines.  I want to empower novices and accelerate experts' workflows.

I research simulation methods, design algorithms, digital manufacturing processes, and methods for overcoming the sim-to-real gap, for inventing in both virtual and physical worlds.  With the right tools, we can augment human creativity, advance more capable computer creativity, and build computational systems that work for everybody, not just a select few.

My work has been published at top venues and featured in popular science periodicals such as, well, Popular Science, and others like Scientific American, TechCrunch, and more.  I am a recipient of the Unity Global Fellowship, the DARPA I2O Fellowship, the MIT Sandbox Innovation Fund, and a Harvard GRID $100K award.

Presently, I am a Postdoctoral Associate at Harvard University, where I work with Prof. Jennifer A. Lewis in the aptly named Lewis Lab and collaborate closely with Prof. Karen Liu at Stanford.  I received my PhD from MIT's Computer Science and Artificial Intelligence Lab, where I was advised by Daniela Rus and Wojciech Matusik.  During my PhD, I did two stints at Disney Research, Pittsburgh and Zürich.  In Olden Days of Yore, I did my undergrad/Master's at Cornell.

I am on the academic job market and will be applying FA '23. 
Feel free to get in touch!  These might be useful to you (generic versions):
[CV] [Research Statement] [Teaching Statement] [Diversity Statement]

(✈️ = Spiel's On Wheels, i.e. travel)

Funding and Collaborations

Research Funders  (They like the shoutout)

Industry Collaborators

Highlighted Research Projects

For all core research projects, please see the publications page.

Research Questions

Generative yet optimal, fabricable, certifiable robot co-design

User-Centric AI-Powered Co-Design Algorithms

Digital Fabrication Powered By Physical Intelligence

Modeling For Design across Domains and Morphologies 

This last part "closes the loop," allowing data from designs to improve and adapt modeling (and future designs) to novel domains.


I take systems-, algorithmic-, user-, and society-centric approaches to research for impact.

Building modular design technology stacks.

🤖 Methods that learn from the data they produce:
i.e. supervise on the simulator, not an external dataset.

🖱️ Keep the practitioner's wants and needs in mind:
Research is most useful when it is usable!

🤓 Make core research accessible through open-sourcing/
                releasing what we can.