Liyuan Gao
Texas Tech University. PhD candidate in Computer Science.
Hello! I’m Liyuan, a Ph.D. candidate in Computer Science at Texas Tech University, delving into the fascinating realms of Explainable AI and Neuro-symbolic AI. In our world, where AI increasingly influences critical sectors like healthcare and finance, understanding the “why” behind AI decisions is crucial. Yet, as AI models grow in complexity, their inner workings often become more opaque. My research focuses on demystifying these complexities, ensuring that AI remains both powerful and understandable. I delve into how diverse data types – from text and biological sequences to images – are processed and interpreted by AI, aiming to make these systems as transparent as they are effective.
I am currently exploring opportunities in both academia and industry to further advance my research and contribute to the evolving landscape of AI. If you share similar interests or see potential collaborations, please feel free to reach out via email!
news
| Sep 17, 2024 | The paper “Exploring Task-Specific Dimensions in Word Embeddings Through Automatic Rule Learning” has been accepted for publication in the International Conference on Artificial Neural Networks (ICANN 2024)!  |
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| Jul 20, 2024 | The paper “Deep-Learning Uncovers certain CCM Isoforms as Transcription Factors” has been accepted for publication in Frontiers in Bioscience-Landmark! |
| Jun 30, 2024 | The paper “Privacy-Preserving Unsupervised Spherical Text Embeddings” has been accepted for publication in 2024 International Joint Conference on Neural Networks (IJCNN)! |
| Oct 30, 2023 | The poster “Enhancing Transcription Factor Prediction Through Multi-Task Learning (Student Abstract)” has been selected for a 3-minute oral presentation at AAAI-24! |
| Oct 12, 2023 | The paper “Explainable Transcription Factor Prediction with Protein Language Models” has been accepted at 2023 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2023)! |