• Ruixue Liu

    PhD student in Computer Science at Worcester Polytechnic Institute

  • Hi, my name is Ruixue (Rachel) Liu. I am currently a PhD student at WPI working with Dr. Erin solovey. My main PhD research area is human-computer interaction (HCI), with a focus on brain-computer interfaces and physiological computing. In this work, we use machine learning to detect patterns in brain and physiological data that indicate aspects of the user’s cognitive state. The goal is to develop adaptive user interfaces that better support the user in various tasks, based on the detected brain state.

  • Projects

    A selection of projects I am working on

    Adaptive Brain Interface

    Using machine learning, we aim to develop a robust system that can classify a user’s cognitive state during a learning activity, using brain data collected with functional near-infrared spectroscopy, an emerging non-invasive neuroimaging tool.

    Mindful Shopping

    Through an iterative design process, we design web-based prototypes to provide in-the-moment mindfulness-based interventions for compulsive buying behavior. The goal of our designs is to help users shop with intention and attention, be aware of themselves and the present, and thus be more in control of their shopping behavior.

    Backdoor Convolutional Networks

    In this work, we perform several experiments to explore the potential of controlling the output of the CNN classifier by introducing small perturbations in training data under our three threat models. We find that convolutional networks are highly susceptible to malicious training data.

  • Experiences

    Microsoft Research Cambridge

    April 2018-June 2018

    Research Intern (Mentors: Dr. Advait Sarkar, Dr. Cecily Morrison)

    • Worked at the Human Experience and Design group on interactive machine learning for notification design
    • Designed context-aware notifications for people with visual impairment and conducted user studies to evaluate the designs

    U.S. Army Research Laboratory

    July 2016-Sep 2016

    Research Intern (Advisor: Dr. Richard Harang)

    • Worked on deep Learning in adversarial setting
    • Designed and performed experiments to investigate the impact of malicious training data on Convolutional Networks
  • Publication

    R. Liu, A. Sarkar, E.T. Solovey, S. Tschiatschek (2019). Evaluating Rule-based Programming and Reinforcement Learning for Personalising an Intelligent System. In Proc. of the 2019 ACM IUI Workshop on Explainable Smart Systems.

    M. Kim, T. Park, R. Liu, A. Forte (2019). Understanding Learning Curves and Trajectories in CSS Layout, In Proc.of the 50th ACM Technical Symposium on Computer Science Education. ACM.

    L. Friedman, R. Liu, E. Walker, E. Solovey (2018). Integrating Non-Invasive Neuroimaging and Computer Log Data to Improve Understanding of Cognitive Processes, In Proc. of the 2018 ICMI Workshop on Modeling Cognitive Process from Multimodal Signals. ACM, New York, NY, USA

    L. Friedman, R. Liu, A. Kim, E. Walker, E. Solovey (2018). Towards Neuroadaptive Personal Learning Environments: Using fNIRS to Detect Changes in Attentional State, Proc. 2nd International Conference on Neuroergonomics, Frontiers

    R. Liu, E. Walker, E.T. Solovey (2017). Toward Neuroadaptive Personal Learning Environments. In Proceedings of 2017 Conference on Neuroadaptive Technology

    R. Liu, G. Marcu, E.T. Solovey. Mindful Shopping: A Compulsive Buying Disorder Management Tool. In Proceedings of ACM CHI 2017 Workshop on Interactive Systems in Healthcare (WISH 2017).

    J. Chan, P. Siangliulue, D. Q. McDonald, R. Liu, R. Moradinezhad, S. Aman, E.T. Solovey, K. Gajos & S.P. Dow. (2017). Semantically far inspirations considered harmful? Accounting for cognitive states in collaborative ideation. In Proceedings of 2017 ACM Conference on Creativity and Cognition.

    E. T. Solovey, R. Liu, R. Moradinezhad. (2016). Advanced Interaction Research in Autonomous Vehicles. In Proc. ACM CHI 2016 Workshop on HCI and Autonomous Vehicles: Contextual Experience Informs Design. San Jose, CA.

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