We are currently looking for strong candidates for the following internship topics:
Evaluating advanced interaction techniques in (semi-)field experiments
IconHK is an novel interaction technique published at CHI’17. The goal of this internship is to better understand how users use/customize this interaction technique and evaluate its performance in the field with a semi-field experiment. The student has to (1) implement this interaction technique in a real application as well as a panel to customize the technique interaction technique; (2) conduct a field study.
Keywords: (semi-) field experiment; experimental design; Interaction design; Transition novice to expert behavior
HCI and Time series analysis
We collected a large amount of data on how users transition from menus to shortcuts with different interaction techniques. These data can be represented a multi-sequence multi-variate time series and we are interested in analyzing the transition period. The goal of this internship is to identify, implement and compare different algorithms to analyze these data.
Keywords: HMM; DTW; Change point algorithms; Transition novice to expert behavior.
This internship addresses a major challenge in Human-Computer Interaction: How to favor the transition from novice to expert behavior? In your favorite applications, you can select commands either with menus or through shortcuts (keyboard or gesture shortcuts). Shortcuts are significantly faster. However, we observe that many users do not transition from novice behavior (menus) to expert behavior (shortcuts). This has serious implications on productivity.
The goal of the internships is to better understand how users transition from novice to expert behavior and to develop a model of performance and knowledge focusing on subset of key factors such as the IconHK technique published at CHI’17 or the impact of gesture vocabulary.
The project consists of augmenting the surgical cockpit to facilitate the execution of gestures/commands during robotic laparoscopic surgery. The goal is to implement and evaluate multimodal interaction techniques at the cross-road between HCI and Robotics. For instance, the student can create devices using non-standard body parts such as the head or the feet to let the hands of the surgeon free to interact with the patient.
Today, most research in InfoVis focuses on the classic desktop environment with a screen, a mouse and a keyboard. However, people now receive information via a large variety of interactive technologies such as smart watches, phone screens, tablets, projectors, large public displays, and also newly-available consumer-grade augmented reality (AR) and virtual reality (VR) glasses.
These technologies offer novel opportunities. For instance, VR glasses favor immersion by providing a rich 3D environment. Tangible visualizations can facilitate interaction and data exploration. At the same time these technologies introduce more variabilities in the context of interaction. For instance, in desktop environments we can expect that users are sitting in front of their computer facing their screen. In contrast, we cannot make such assumptions with these new technologies as people can walk around and likely analyze visualizations with large variations in viewing distances and angles, occlusion, or lighting conditions. These external factors can lead to false interpretation that can have strong consequences on decision making.
The goal of this internship is to study the effects of these factors to determine how to best encode data for these new devices and environments.
Gauging Uncertainty Through Incentivized Elicitation of Interval Estimates
Advised by: Yvonne Jansen and Pierre Dragicevic (contact us if you are interested in this topic)
Eliciting interval estimates is useful in many domains. In Infovis, it could be very useful for measuring confidence in tasks that admit a numerical answer, although it is rarely used. But psychology research has shown that eliciting reliable interval estimates is hard.
A good elicitation method should (in order of importance):
Produce accurate results (meaning, produce intervals that are close to 95% CIs or BCIs = Bayesian Credible Intervals)
Be fast (allowing to collect more observations for the same amount of time/money)
Be enjoyable to use (for utilitarian reasons)
The goal of this internship is to use an incentivized scheme for eliciting intervals and to evaluate the efficiency of such a scheme through quantitative studies.
Collaboative work and Augmented Reality (with Sagula)
Haptic feeback and Augmented Reality (with Sagula)