I’m a final year Ph.D. candidate and research associate at the Interaction Lab at the University of Stuttgart. My research focus lies at the intersection of interactive systems and applied machine learning. I invent, build, and evaluate novel interaction techniques and devices that notably improve the input efficiency for users.
Most recently, I am working on novel touch-based interaction techniques driven by capacitive sensing processing with deep learning. Model-based input processing enables new application scenarios such as finger-aware input, grip sensing, and the inference of further input properties to solve the current limitations of touch input. I am also interested in modeling hand ergonomics and touch interaction in general to find new ways for improving interaction with mobile devices.
Prior to my Ph.D. work starting in late 2015, I completed a Bachelor’s and Master’s degree in Software Engineering at the University of Stuttgart. Moreover, I worked as a software engineering intern at Daimler TSS and as a visiting researcher at the School of Computing and Communications at the Lancaster University. Don’t hesitate to contact me in case of any questions!
- I’ll present a paper about finger identification on capacitive touchscreens using deep learning at IUI ’19 in Los Angeles, CA, USA. (Paper | Project Page)
- Presented a paper about deep learning for finger-aware interaction on fully touch sensitive smartphones at UIST ’18 in Berlin, Germany. (Paper | Project Page)
- Co-organized a tutorial on “Machine Learning For Intelligent Mobile User Interfaces using Keras” and presented a demo at MobileHCI ’18 in Barcelona, Spain. (Tutorial | Demo Proposal)
- Presented two papers and my PhD work at the Doctoral Consortium at CHI ’18 in Montréal, Canada. (Paper 1 | Project Page of Paper 1 | Paper 2 | DC Proposal)
- Gave a public talk of my Milestone Presentation about Hand-and-Finger-Aware Touch Interaction on Mobile Devices at the University of Stuttgart.
- Co-organized a tutorial on “Machine Learning with Tensorflow for Mobile and Ubiquitous Interaction” at MUM ’17 in Stuttgart, Germany. (Tutorial Proposal)
- Presented a paper about reducing touchscreen latency using neural networks and IMUs at ISS ’17 in Brighton, England. (Paper | Project Page)
- Machine Learning and Computer Vision for HCI (Lab Course)
- Machine Learning for Human-Computer Interaction (Lab Course)
- Machine Learning for Intelligent Mobile User Interfaces using TensorFlow (Lab Course)
- Media Informatics (Lecture)
- Physical Computing (Lab Course)
- Multimodal Interaction for Ubiquitous Computers (Lecture)
- Mobile Multi-Device Interaction (Lab Course)
- Selected as Reviewer for CHI, UIST, MobileHCI, IMWUT, ISS, NordiCHI, TEI, AH, MUM, Multimedia
- Program committee member at CHI 2019 (Late-Breaking Work)
- Student volunteer at MobileHCI ’16, NordiCHI ’16, MUM ’17, NordiCHI ’18
- Co-organized tutorials at MobileHCI ’17, Mensch und Computer ’17, MUM ’17, PerDis ’18, and MobileHCI ’18
- My research prototypes were featured internationally, e.g. by Arduino (2018), Hackster.io (2018), and open-electronics.org (2018)