Stark / Zhicheng Guo
I’m a fourth-year Ph.D. student in the Department of Electrical and Computer Engineering at Duke University, under supervision of Professor Cynthia Rudin. Before Duke, I received my Bachelor’s degree in Computer Science from Rensselaer Polytechnic Institute.
My research focuses on developing robust and interpretable deep learning models for high-stakes applications such as disease detection and diagnostics. I aim to advance the transparency and reliability of deep learning algorithms.
news
| Aug 30, 2025 | Back from Texas Instruements! |
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| May 30, 2025 | I will attend CVPR 2025 at Nashville. See you there! |
| May 14, 2025 | Started my summer internship as a Machine Learning Researcher at Kilby Labs @ Texas Instrument, Dallas, TX. I will be working on LLM and Embed AI. |
| Mar 01, 2025 | Hurray! Our “What is Different Between These Datasets?” A Framework for Explaining Data Distribution Shifts is accepted at JMLR! |
| Feb 26, 2025 | Our Rashomon Sets for Prototypical-Part Models: Editing Accurate Interpretable Models in Real-Time is published at CVPR! |
selected publications
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"What is Different Between These Datasets?" A Framework for Explaining Data Distribution ShiftsJournal of Machine Learning Research, 2025 -
Rashomon sets for prototypical-part networks: Editing interpretable models in real-timeProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025 -
Improving Clinician Performance in Classifying EEG Patterns on the Ictal–Interictal Injury Continuum Using Interpretable Machine LearningNEJM AI, 2024 -
SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation DetectionHarvard Data Science Review, 2024 -
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia AlarmsIEEE Journal of Biomedical and Health Informatics, 2024 -
Sparse learned kernels for interpretable and efficient medical time series processingNature Machine Intelligence, 2024 -
A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearablesPhysiological Measurement, 2021
