Abstract: Ubiquitous computing requires that mobile and wearable devices are aware of our surroundings so as to augment the real world with contextual information that enriches our interactions with them. For this to work, the objects around us need to carry machine-readable tags, such as barcodes and RFID labels, that describe what they are and communicate this information to devices. While barcodes are inexpensive to produce, they are typically obtrusive, less durable, and less secure than other tags. Regardless of their type, most conventional tags are added to objects post hoc as they are not part of the original design. I propose to replace this post-hoc augmentation process with tagging approaches that extract objects’ integrated hidden features and use them as machine-detectable tags to make the real world more informative. In this talk, I will introduce three projects: (1) InfraredTags are invisible fiducial markers embedded into 3D printed objects using infrared-transmitting filaments, and detected using cheap infrared cameras. (2) G-ID marks different 3D printed copies of the same object by using unique printing (“slicing”) settings, which result in unobtrusive, machine-detectable surface artifacts. (3) SensiCut is a smart laser cutting platform that leverages speckle imaging and deep learning to distinguish visually similar workshop materials. It adjusts designs based on the chosen material and warns users against hazardous ones. I will show how these methods assist users in creative tasks and enable new interactive applications for augmented reality (AR), object traceability, and user identification. Bio: DoğaDoğan is a Ph.D. candidate at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and currently an intern at Adobe Research, where he builds novel identification and tagging techniques. At CSAIL, he works with Stefanie Mueller as part of the HCI Engineering Group. Doğa’s research focuses on the fabrication and detection of unobtrusive physical tags embedded into everyday objects and materials. His work has been nominated for best paper and demo awards at CHI, UIST, and ICRA. He is a past recipient of the Adobe Research Fellowship and Siebel Scholarship. Prior to MIT, Doğa conducted research in the Laboratory for Embedded Machines and Ubiquitous Robots at UCLA, and the Physical Intelligence Department of the Max Planck Institute for Intelligent Systems. His website: https://www.dogadogan.com/.