Welcome! I am Ning Dai, a third-year PhD student in the School of Electrical Engineering and Computer Science at Oregon State University. I am privileged to work under the supervision of Prof. Liang Huang.
Prior to my PhD journey, I served as an undergraduate research assistant with the FudanNLP Group, under the mentorship of Prof. Xipeng Qiu.
My research is anchored in the intersections of Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL). My journey through these fields has led me to explore a spectrum of topics within these areas, ranging from Reinforcement Learning, Syntactic Parsing, Text Style Transfer and Self-supervised Representation Learning. As I continue to delve deeper into these domains, my current research focuses have shifted and expanded as follows:
Text Generation & Generative Models for Sequential Data
Here, I study generative models and their algorithms that span across NLP and Computational Biology. For NLP, I’m exploring models that can produce coherent, contextually-relevant, and diverse textual outputs. In Computational Biology, I’m interested in how generative models can simulate biological sequences, predict structures, or design novel sequences for therapeutic applications.
Fine-grained Human Supervision for Aligning Large Language Models
While Large Language Models possess groundbreaking potential, ensuring their alignment with nuanced human perceptions and intentions is still challenging. My research in this space revolves around leveraging fine-grained human supervision through reinforcement learning. This approach aims to steer LLMs, enhancing their adaptability and performance in various applications.
Efficient ML Algorithms & DL Models for Computational Biology
The nexus of ML and computational biology unfolds a rich tapestry of challenges and prospects. I am particularly focusing on challenges in RNA folding and design problems. Here, pioneering ML algorithms and DL models stand to provide trailblazing solutions, offer unprecedented insights, potentially reshaping our understanding of complex biological matrices.
LinearCoFold and LinearCoPartition: linear-time algorithms for secondary structure prediction of interacting RNA molecules (Nucleic Acids Research) [paper]
He Zhang, Sizhen Li, Ning Dai, Liang Zhang, David H Mathews, Liang Huang
RNA design via structure-aware multifrontier ensemble optimization (Bioinformatics) [paper]
Tianshuo Zhou, Ning Dai, Sizhen Li, Max Ward, David H Mathews, Liang Huang
LinearSankoff: Linear-time Simultaneous Folding and Alignment of RNA Homologs (arXiv preprint) [paper]
Sizhen Li, Ning Dai, He Zhang, Apoorv Malik, David H. Mathews, Liang Huang
Ning Dai, Jianze Liang, Xipeng Qiu, Xuanjing Huang
A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation [paper]
Qipeng Guo, Zhijing Jin, Ning Dai, Xipeng Qiu, Xiangyang Xue, David Wipf, Zheng Zhang
Pre-trained Models for Natural Language Processing: A Survey [paper]
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, Xuanjing Huang
Tencent AI Lab Seattle
Jul 2023 ~ Sept 2023 ‖ Research Intern
Baidu Research USA
Jul 2022 ~ Sept 2022 ‖ Research Intern
ByteDance AI Lab
Sept 2020 ~ Aug 2021 ‖ Research Intern, MLNLC Group
AWS Shanghai AI Lab
Nov 2019 ~ Aug 2020 ‖ Applied Scientist Intern, Prof. Zheng Zhang’s Group
Montreal Institute for Learning Algorithms (Mila)
Jul 2019 ~ Oct 2019 ‖ Research Intern, Prof. Christopher Pal’s Group