Mustafa Doğa Doğan


I am a PhD candidate in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). I work with Prof. Stefanie Mueller as part of the Computer Science & Artificial Intelligence Laboratory (CSAIL). I am a 2021 Adobe Research Fellow and a 2020 Siebel Scholar.

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email: doga [at] {,,,}

My research is about developing novel physical tagging mechanisms to embed unobtrusive fingerprints and data in everyday objects/materials for seamless, ubiquitous identification. I use computer vision, machine learning, and digital fabrication to achieve my human-computer interaction (HCI) vision.

Check out FabPub, an online directory of related work in digital fabrication research maintained by our group.

Sign up for the MIT EECS Graduate Application Assistance Program, which provides assistance to students from underrepresented groups applying for PhD.


SensiCut: Material-Aware Laser Cutting Using Speckle Sensing and Deep Learning

Mustafa Doga Dogan, Steven Vidal Acevedo Colon, Varnika Sinha, Kaan Akşit, Stefanie Mueller
2021 ACM User Interface Software and Technology Symposium (UIST)

Laser cutter users face difficulties distinguishing between visually similar materials. This can lead to problems, such as using the wrong power/speed settings or accidentally cutting hazardous materials. To support users in identifying the sheets, we present SensiCut, a material sensing platform for laser cutters. In contrast to approaches that detect the appearance of the material with a conventional camera, SensiCut identifies the material by its surface structure using speckle sensing and deep learning. SensiCut comes with a compact hardware add-on for the laser cutter and a user interface that integrates material sensing into the cutting workflow. In addition to improving the traditional workflow, SensiCut enables new applications, such as automatically partitioning the design when engraving on multi-material objects or adjusting the shape of the design based on the kerf of the identified material. We evaluate SensiCut’s accuracy for different types of materials under different conditions, such as with various illuminations and sheet orientations.

Full paper and project page.
Featured on The Next Web and MIT News.

G-ID: Identifying 3D Prints Using Slicing Parameters

Mustafa Doga Dogan, Faraz Faruqi, Andrew Day Churchill, Kenneth Friedman, Leon Cheng, Sriram Subramanian, Stefanie Mueller
2020 ACM CHI Conference on Human Factors in Computing Systems

G-ID is a method that utilizes the subtle patterns left by the 3D printing process to distinguish and identify objects that otherwise look similar to the human eye. The key idea is to mark different instances of a 3D model by varying slicing parameters that do not change the model geometry but can be detected as machine-readable differences in the print. As a result, G-ID does not add anything to the object but exploits the patterns appearing as a byproduct of slicing, an essential step of the 3D printing pipeline. We introduce the G-ID slicing & labeling interface that varies the settings for each instance, and the G-ID mobile app, which uses image processing techniques to retrieve the parameters and their associated labels from a photo of the 3D printed object. Finally, we evaluate our method’s accuracy under different lighting conditions, when objects were printed with different filaments and printers, and with pictures taken from various positions and angles.

Full paper on ACM DL and project page.
Featured on  3DPrintCom, logo Hackster.ioand ITMediaNews ITmedia (Japanese). 

DefeXtiles: 3D Printing Quasi-Woven Fabric via Under-Extrusion

Jack Forman, Mustafa Doga Dogan, Hamilton Forsythe, Hiroshi Ishii
2020 ACM User Interface Software and Technology Symposium (UIST)
Best Demo Honorable Mention

We present DefeXtiles, a rapid and low-cost technique to produce tulle-like fabrics on unmodified fused deposition modeling (FDM) printers. The under-extrusion of filament is a common cause of print failure, resulting in objects with periodic gap defects. In this paper, we demonstrate that these defects can be finely controlled to quickly print thinner, more flexible textiles than previous approaches allow. Our approach allows hierarchical control from micrometer structure to decameter form and is compatible with all common 3D printing materials. In this paper, we introduce the mechanism of DefeXtiles, establish the design space through a set of primitives with detailed workflows, and characterize the mechanical properties of DefeXtiles printed with multiple materials and parameters. Finally, we demonstrate the interactive features and new use cases of our approach through a variety of applications, such as fashion design prototyping, interactive objects, aesthetic patterning, and single-print actuators.

Full paper on ACM DL and on project page.
Featured on Gizmodo and MIT News.

FoldTronics: Creating 3D Objects with Integrated Electronics Using Foldable Honeycomb Structures

Junichi Yamaoka, Mustafa Doga Dogan, Katarina Bulovic, Kazuya Saito, Yoshihiro Kawahara, Yasuaki Kakehi, Stefanie Mueller
2019 ACM CHI Conference on Human Factors in Computing Systems

FoldTronics is a 2D-cutting based fabrication technique to integrate electronics into 3D folded objects. The key idea is to cut and perforate a 2D sheet to make it foldable into a honeycomb structure using a cutting plotter; before folding the sheet into a 3D structure, users place the electronic components and circuitry onto the sheet. The fabrication process only takes a few minutes enabling users to rapidly prototype functional interactive devices. The resulting objects are lightweight and rigid, thus allowing for weight-sensitive and force-sensitive applications. Finally, due to the nature of the honeycomb structure, the objects can be folded flat along one axis and thus can be efficiently transported in this compact form factor. We describe the structure of the foldable sheet, and present a design tool that enables users to quickly prototype the desired objects. We showcase a range of examples made with our design tool, including objects with integrated sensors and display elements.

Full paper on ACM DL and project page.
Featured on logo

Magnetically Actuated Soft Capsule Endoscope for Fine-Needle Aspiration

Donghoon Son, Mustafa Doga Dogan, Metin Sitti
2017 IEEE International Conference on Robotics and Automation (ICRA)
Max Planck Institute for Intelligent Systems
Best Medical Robotics Paper Award Nomination

This paper presents a magnetically actuated soft capsule endoscope for fine-needle aspiration biopsy (B-MASCE) in the upper gastrointestinal tract. A thin and hollow needle is attached to the capsule, which can penetrate deeply into tissues to obtain subsurface biopsy sample. The design utilizes a soft elastomer body as a compliant mechanism to guide the needle. An internal permanent magnet provides a means for both actuation and tracking. The capsule is designed to roll towards its target and then deploy the biopsy needle in a precise location selected as the target area. B-MASCE is controlled by multiple custom-designed electromagnets while its position and orientation are tracked by a magnetic sensor array. In in vitro trials, B-MASCE demonstrated rolling locomotion and biopsy of a swine tissue model positioned inside an anatomical human stomach model. It was confirmed after the experiment that a tissue sample was retained.

Full paper on IEEE Xplore.
Featured on Engadget Engadget and IEEE SpectrumIEEE Spectrum.

Research & Teaching Experience


Graduate Research Assistant, Massachusetts Institute of Technology (MIT)
Computer Science and Artificial Intelligence Lab (CSAIL)
Human-Computer Interaction (HCI) Engineering Group
Advisor: Stefanie Mueller (Cambridge, MA)


Visiting Researcher, Massachusetts Institute of Technology (MIT)
Computer Science and Artificial Intelligence Lab (CSAIL)
Human-Computer Interaction (HCI) Engineering Group
Advisor: Stefanie Mueller (Cambridge, MA)


Research Assistant, Max Planck Institute for Intelligent Systems
Physical Intelligence Department
Medical Millirobots Group
Advisor: Metin Sitti (Stuttgart, Germany)


Undergraduate Researcher, UCLA
Electrical and Computer Engineering
Laboratory for Embedded Machines and Ubiquitous Robots
Advisor: Ankur Mehta (Los Angeles, CA)


Undergraduate Researcher, Bogazici University
Haptics & Robotics Lab & Intelligent Systems Lab
Advisor: Evren Samur & Işıl Bozma (Istanbul, Turkey)

Undergraduate Teaching Assistant, Bogazici University
EE142 Introduction to Digital Systems
Fall 2016 & Fall 2017 (Istanbul, Turkey)

Conference Service

Organizing Committee


  • ACM CHI Conference on Human Factors in Computing Systems (’21, ’20)
  • ACM UIST: Symposium on User Interface Software and Technology (’21)
  • IEEE ICRA: International Conference on Robotics and Automation (’21)
  • ACM TEI: International Conference on Tangible, Embedded and Embodied Interaction (’21)
  • IEEE ISMAR: International Symposium on Mixed and Augmented Reality (’20)
  • ACM EICS: Symposium on Engineering Interactive Computing Systems (’21)
  • ACM IDC: Interaction Design and Children Conference WIP (’21)
  • IEEE Sensors Journal (’20, ’21).

Student Volunteer



M.Sc. in Electrical Engineering & Computer Science (’18 – ’20)
Massachusetts Institute of Technology (MIT)
Cambridge, Massachusetts


B.Sc. in Electrical & Electronics Engineering (’14 – ’18)
Bogazici University
Past Chairman of the IEEE Student Branch (’15-’16)
Istanbul, Turkey


Exchange Student, Electrical and Computer Engineering (’17)
University of California, Los Angeles (UCLA)
Los Angeles, CA


Email: doga [at], doga [at]
LinkedIn: /in/dogadogan
Twitter: @mdogadogan

Here are a few pictures that highlight some of my favorite moments.

President Reif visits grad dorm Sidney-Pacific - MIT President L. Rafael Reif and Sr. Associate Dean of Graduate Education Blanche Staton help SP officers and volunteers prepare Sunday brunch (Cambridge, MA, 2019)


2021 Mustafa Doğa Doğan