Capacitive sensing is a core component of interactive devices, powering the input interface of touchscreen devices, keyboards and mice, as well as controllers with applications in mobile computing, wearable devices, as well as Mixed Reality and Virtual Reality. In addition to its applications in interactive systems, capacitive sensing is an active research topic in the domains of embedded systems, robotics, computer vision, and human-computer interaction.

In this project, we will develop novel capacitive sensors and processing algorithms to imbue the curved surfaces on real-world objects with touch sensitivity to inform novel smart embedded devices that will be capable of adapting to user behavior during direct and indirect use. We will apply methods in fabrication to create surface-conforming sensors and develop processing methods to detect and infer understand user input.

At the Sensing, Interaction & Perception Lab (Prof. Christian Holz), we are looking for a PhD candidate with an interest in conducting cutting-edge research, with a strong motivation to work on challenging topics, and a strong desire to learn.

We have completed several projects on capacitive sensing, from designing novel sensors to creating novel machine learning-based processing methods for traditional touch detection as well as interactive purposes, such as

• TouchPose: Hand Pose Prediction, Depth Estimation, and Touch Classification from Capacitive Images (video, ACM UIST 2021)
• CapContact: Super-resolution Contact Areas from Capacitive Touchscreens (video, ACM CHI 2021)
• Finding Common Ground: A Survey of Capacitive Sensing in Human-Computer Interaction (ACM CHI 2017)
• Duoskin: Rapidly Prototyping On-Skin User Interfaces Using Skin-Friendly Materials (video, ACM ISWC 2016)
• Bodyprint: Biometric User Identification on Mobile Devices Using the Capacitive Touchscreen to Scan Body Parts (video, ACM CHI 2015)

You should have a background in Computer Science, Electrical Engineering, Robotics, Bioengineering, or Mechanical Engineering. Most importantly, you should have experience in:

• digital fabrication or 3D graphics, including experience with
  • geometric modeling, discrete curvature, and geometry processing
  • fabrication using laser cutting and 3D printing
  • experience with 3D graphics programming is beneficial
• work with capacitive sensing
  • familiarity with common configurations of capacitive sensors
  • ideally should have experience in signal processing related to capacitive sensors, including event detection, noise suppression, etc.
• electronics (ideally)
  • comprehend, process, and evaluate hardware datasheets and schematics
  • design PCB layouts using ECAD tools (use of simulation tools is beneficial though not required)
  • assemble prototypes (soldering, component assembly, deployment)
• optional: experience with machine learning basics and
  • an understanding of underlying deep learning and machine learning concepts
  • experience in common toolkits, e.g., TensorFlow, PyTorch

If available, you should include a link to their portfolio, such as

• a website showing past projects (example1, example2) or
• link to a Github profile, blog, etc.

• an excellent master's degree (M.Sc., M.Eng. or equivalent) in Electrical Engineering, Computer Science, Robotics, or closely related
• written and spoken fluency in English
• demonstrated experience working with digital fabrication
• experience implementing research prototypes
• strong interpersonal and communication skills

Prior experience in conducting research and experiments using human-centered approaches in the fields of HCI, machine learning, or applied computer vision are a plus.