Hi, I'm Luke!

I am a first-year PhD student in the Human-Computer Interaction Institute (HCII) within the School of Computer Science at Carnegie Mellon University. I am advised by Steven Wu, and also work closely with Haiyi Zhu and Ken Holstein.

My work is in human-centered machine learning. In particular, I aim to create systems that augment human abilities (human-AI complementarity), while also fostering partnerships that are transparent, fair, and aligned with stakeholder values. My research is generously supported by an NSF Graduate Research Fellowship.

Previously, I did my Master’s (MPhil) in Computer Science at Cambridge, where I worked under Hatice Gunes. I also studied Computer Science and Psychology at the University of Missouri, where I worked with Yi Shang and Tim Trull, and co-founded TigerAware, a mobile-based research platform.

Luke Guerdan
lguerdan [at] cs.cmu.edu


Jul 2021 Work on XAI facial affect analysis accepted to ICCV Workshop on Responsible PR&MI! 📜
Jun 2021 Graduated from the Cambridge MPhil program! My thesis on Federated Continual Learning for Human-Robot Interaction received distinction 🎓
Dec 2020 Getting hands-on with federated learning 🔎

Conference Papers

  1. NER
    Extracting Motion-Related Subspaces from EEG in Mobile Brain/Body Imaging Studies Gehrke, L*., Guerdan, L*., and Gramman, K 2019 [Abs] [PDF] [Poster]
  2. DSC
    TigerAware: An Innovative Mobile Survey and Sensor Data Collection and Analytics System Morrison, M., Guerdan, L., Kanugo, J., Trull, T., and Shang, Y. 2018
  3. MobileCloud
    Augmented Resource Allocation Framework for Disaster Response Coordination in Mobile Cloud Environments Guerdan, L., Apperson, O., and Calyam, P 2017 [PDF] [Poster]
  4. SmartComp
    ADA - Automatic Detection of Alcohol Usage for Mobile Ambulatory Assessment, Sun, P., Wergeles, N., Zhang, C., Guerdan, L., Trull, T., and Shang, Y 2016

Workshops & Preprints

    (to appear) Toward Affective XAI: Facial Affect Analysis for Understanding Explainable Human-AI Interactions Guerdan, L., Raymond, A., and Gunes, H 2021
    Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction Guerdan, L*, Sun, P*, Rowland, C, Harrison, L, Tang, Z, Wergeles, N, and Shang, Y 2019 [Abs] [PDF]