Jinyung Hong

Hello, I'm Jinyung Hong, a Ph.D. candidate in computer science at Arizona State University.

I belong to the SEADS lab, supervised by Dr. Theodore P. Pavlic.

My research aims to apply principles and techniques from neuroscience, biology, and cognitive science to implement new interpretable Intelligence that can collaborate with humans and help them make decisions. To this end, I conduct research that spans the areas of neuro-symbolic computing, continual learning, and interpretable AIs, which are crucial components to building human-like AI systems with cognitive abilities.

Research Interests: Bio-inspired Computing, Neuro-symbolic Computing, Continual Learning, Model Interpretability.

News

Spring 2024: Received an ASU School of Computing and Augmented Intelligence (SCAI) Doctoral Fellowship. 🎉

Nov 28, 2023: Received an ASU Graduate and Professional Student Association (GPSA) Travel Grant Award. 🎉

Nov 27, 2023: Received the ASU School of Computing and Augmented Intelligence (SCAI) Conference Funding Award. 🎉

Nov 20, 2023: Received the Best UniReps Proceeding Paper Award. 🎉

Recent Publications

Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks

Hong, Jinyung, Jeon, Eun Som, Kim, Changhoon, Park, Keun Hee, Nath, Utkarsh, Yang, Yezhou, Turaga, Pavan and Pavlic, Theodore P. 

arXiv.org (2024)

Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace

Hong, Jinyung, Park, Keun Hee, and Pavlic, Theodore P. 

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2024

Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks

Hong, Jinyung, and Pavlic, Theodore P. 

UniReps: Unifying Representations in Neural Models Workshop @ NeurIPS 2023

Selected the Best UniReps Proceeding Paper