Topics for Collaboration

Embodied AI & Interfaces

Embodied AI, such as automated vehicles, service robots, and planetary rovers, acts in the physical world. People perceive and control this AI through interfaces, from vehicle displays to mixed reality and brain-computer interfaces. Established interface assumptions often break down when systems move into vehicles, public spaces, mixed reality, or extreme environments.

• Automated vehicles and future mobility: Future mobility systems must communicate capability, uncertainty, intent, and failure clearly enough for people to trust, monitor, and intervene when needed.
• Extended reality and spatial interaction: Augmented and virtual environments can change what users see and attend to, raising questions about how interfaces should reveal, simplify, or restructure complex surroundings.
• Robots, teleoperation, and shared human-machine environments: Autonomous and semi-autonomous machines need interfaces that make their behavior legible, controllable, and usable for people nearby or operating them remotely.
• Brain-computer interfaces, eye tracking, and novel embodiment paradigms: New input and feedback channels create opportunities to study how people control, sense, and experience interactive systems beyond conventional mouse, touch, and screen interaction.
• Serious games and gameful interactive systems: Gameful systems can make complex risks, behaviors, and social situations experiential, enabling projects that study learning, awareness, and behavior through interaction.
• Space and extreme-environment interaction: Extreme environments challenge basic assumptions about perception, movement, feedback, and control, creating design problems that cannot be solved by standard interface patterns.

Human Factors

Human factors decide whether people accept and safely use embodied AI. I study how interfaces shape trust, mental workload, situation awareness, agency, and accessibility in real use, and how these effects develop over time.

• Trust calibration: Trust should match the AI’s actual capabilities, what it can perceive, predict, and do, especially when these are uncertain or imperfect.
• Situation awareness and uncertainty: Interfaces should help users understand what is happening, what may happen next, and how certain the system is, without creating information overload.
• Agency, control, and contestability: Adaptive systems should keep users meaningfully involved when they change, recommend, intervene, or make consequential decisions.
• Accessibility, inclusion, and diverse user needs: Interfaces should fit people with different abilities, backgrounds, preferences, and situational constraints.
• Long-term use and sustainable behavior: Interactive systems shape everyday decisions over weeks and months and should support socially and environmentally responsible behavior where design has real influence.

Computational Design & Simulation

I develop computational methods for designing and evaluating interactive systems when manual design iteration is too slow, too narrow, or too difficult to scale. These methods help explore larger design spaces, personalize interfaces, and test future systems before deployment.

• Human-in-the-loop optimization: Human feedback can guide optimization methods to search large interface design spaces and personalize parameters for individual users.
• Implicit and multimodal feedback loops: Behavior, physiology, interaction traces, language, and context can provide design-relevant signals when explicit ratings are sparse, costly, or disruptive.
• User simulation and synthetic users: Computational models can approximate user behavior to pretest interface concepts, compare design alternatives, and identify promising directions before empirical studies.
• Simulation-based evaluation of interaction concepts: Virtual and computational environments enable early testing of interaction concepts that are difficult, unsafe, expensive, or premature to study in the real world.

Where the Areas Meet

Many strong projects sit where two areas overlap. The diagram names three such directions.

• Context-adaptive mediation: Interfaces mediate what a person perceives of the AI and adapt it to the situation, for example mediated reality in vehicles.
• Experience-guided optimization: Optimization methods adapt an interface to what people report and how they behave, for example human-in-the-loop optimization for trust or mental workload.
• Scalable interface evaluation: Simulated users and environments test interfaces for embodied AI before and beyond lab studies, for example traffic simulations with many road users.

HCI foundations

• Stanford HCI Qual Exam Reading List: Google Doc
• Introduction to Human-Computer Interaction (by Kasper Hornbæk, Per Ola Kristensson, and Antti Oulasvirta): Oxford University Press (open access)
• The Encyclopedia of Human–Computer Interaction: Interaction Design Foundation
• The Design of Everyday Things (by Don Norman): Nielsen Norman Group
• Foundations and Trends® in Human–Computer Interaction series: Emerald Publishing

Research methods and evidence

• Research Contributions in Human-Computer Interaction (by Jacob O. Wobbrock and Julie A. Kientz): ACM DL
• Research Methods in Human-Computer Interaction (by Jonathan Lazar, Jinjuan Heidi Feng, and Harry Hochheiser): Science Direct
• Modern Statistical Methods for HCI (edited by Judy Robertson and Maurits Kaptein): Springer Nature

Interaction and computational design

• Pick, Click, Flick!: The Story of Interaction Techniques (by Brad A. Myers): ACM DL
• Computational Interaction (edited by Antti Oulasvirta, Per Ola Kristensson, and Xiaojun Bi): Oxford University Press
• Bayesian Methods for Interaction and Design (edited by John H. Williamson, Antti Oulasvirta, Per Ola Kristensson, and Nikola Banovic): Cambridge University Press
• 3D User Interfaces: Theory and Practice (by Joseph J. LaViola Jr., Ernst Kruijff, Ryan P. McMahan, Doug A. Bowman, and Ivan P. Poupyrev): Google Books