Fall 2020

Alex Berman

Alex Berman

Ph.D. Candidate
Texas A&M University
alexander.n.berman@gmail.com

Time

Friday, 11/13/20 at 12:00 PM - 1:00 PM (Central)

Title

Anyone Can Print

Zoom ICS Calendar Invite

Abstract

The emergence of affordable digital fabrication technologies like 3D printing may drastically change how physical goods can be created anywhere, shifting the focus of fabrication from the distribution of physical goods towards the distribution of digital designs. However, there are many challenges toward fabricating, modifying, and creating digital designs that inhibit many from utilizing 3D Printing. Observations of university printing services and results from a more-controlled lab study reveal how anyone can print by learning how to specify 3D printing ideas (What to Print) without requiring learning printing as a practice through the direct operation of machinery (How to Print). This analysis of printing services reveal that while anyone can print by collaboratively specifying ideas with printing practitioners, there are many barriers and challenges 3D printing newcomers face before they plan to print. Creation and Utilization of the Big Multimedia 3D Printing Dataset ThingiPano helped inform and facilitate the development of HowDIY, a website to introduce anyone to 3D printing. An evaluation of newcomers utilizing HowDIY revealed future directions for supporting newcomers to 3D Print anywhere online.

Bio

Alexander Berman is a Ph.D. Candidate in Computer Science working with Dr. Francis Quek at the Texas A&M Institute of Technology-Infused Learning (TITIL), where he investigates how to facilitate broader participation with DIY and 3D Printing technologies through Human-Computer Interaction and Machine Learning research. He received a B.S.E. in Computer Science at the University of Michigan in 2015, and has interned with John Deere Headquarters and twice with the U.S. Department of Defense. He actively participates in K-12 education-oriented projects and research, sharing his enthusiasm for Making, Robotics, and Machine Learning.

https://alexander-berman.github.io/