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


Oct 2021 Work on XAI facial affect analysis presented at ICCV Workshop on Responsible PR&MI! Watch our video about the work here.
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. Extracting Motion-Related Subspaces from EEG in Mobile Brain/Body Imaging Studies Lukas Gehrke*, Luke Guerdan*, and Klaus Gramman Gramman 9th International IEEE/EMBS Conference on Neural Engineering, 2019 [PDF] [Poster]
  2. TigerAware: An Innovative Mobile Survey and Sensor Data Collection and Analytics System Will Morrison, Luke Guerdan, Jayanth Kanugo, Tim Trull, and Yi Shang Shang IEEE DSC, 2018
  3. Augmented Resource Allocation Framework for Disaster Response Coordination in Mobile Cloud Environments Luke Guerdan, Olivia Apperson, and Prasad Calyam Calyam IEEE MobileCloud, 2017 [PDF] [Poster]
  4. ADA - Automatic Detection of Alcohol Usage for Mobile Ambulatory Assessment Peng Sun, Nick Wergeles, Cheng Zhang, Luke Guerdan, Tim Trull, and Yi Shang Shang IEEE SmartComp, 2016

Workshops & Preprints

  1. Toward Affective XAI: Facial Affect Analysis for Understanding Explainable Human-AI Interactions Luke Guerdan, Alex Raymond, and Hatice Gunes Gunes ICCV Workshop on Responsible Pattern Recognition and Machine Intelligence, 2021 [PDF] [Video]
  2. Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction Luke Guerdan*, Peng Sun*, Connor Rowland, Logan Harrison, Zhang Tang, Nick Wergeles, and Yi Shang Shang MICCAI Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge, 2019 [PDF]