I am a CS Ph.D. student in the SAIL lab advised by Dr. Xintao Wu at the University of Arkansas.

My research focuses on graph machine learning with an emphasis on trustworthy learning, especially privacy and fairness. I am also broadly interested in trustworthy foundation models, graph foundation models and applications of LLMs on graph data. Currently, I am also working on applications of graph machine learning in physical systems such as critical infrastructure networks.

📝 Preprints

• DP-TabICL: In-Context Learning with Differentially Private Tabular Data, Alycia N. Carey, Karuna Bhaila, Kennedy Edemacu, Xintao Wu, ArXiV 2024. [Paper]

📝 Publications

• Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach, Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu, SDM 2024 and GLFrontiers @ NEURIPS 2023. [Paper] [Code]

• Cascading Failure Prediction in Power Grid Using Node and Edge Attributed Graph Neural Networks, Karuna Bhaila, Xintao Wu, IJCNN 2024 and AI2ASE @ AAAI 2024. [Paper] [Code]

• Randomized Response Has No Disparate Impact on Model Accuracy, Alycia N. Carey, Karuna Bhaila, Xintao Wu, IEEE Big Data 2023. [Paper]

• Fair Collective Classification in Networked Data, Karuna Bhaila, Yongkai Wu, Xintao Wu, IEEE Big Data 2022 and EAI-KDD 2022. [Paper]

🎖 Honors and Awards

• 2024.04: SIAM Student Travel Award
• Reginald R. ‘Barney’ & Jameson A. Baxter Graduate Fellowship
• College of Engineering Graduate Fellowship
• Webster International Scholarship

💻 Experience

• 2020.09 - 2021.07: Developer, ITSutra
• 2019.08 - 2020.05: Data Analyst, FDC Webster University
• 2019.05 - 2019.07: Intern, Jenzabar

💬 Teaching

• Lecturer, Python and Machine Learning @ Multidisciplinary Bootcamp (MDaS), University of Arkansas
• Guest Lecturer, CSCE 4143 Data Mining, University of Arkansas
• Math and CS Tutor, Webster University