Zhen Yang    杨珍

I am a PhD student at Knowledge Engineering Group (KEG), Department of Computer Science and Technology of Tsinghua University, fortunately working with Prof. Jie Tang. I obtained my master and bachelor degree from Tsinghua University and Xidian University, respectively.

My current research interests lie in specialized mathematical models, large language models, multi-modal large language models, and graph representation learning. If these areas align with your interests, I welcome you to reach out via email. I am always open to discussing potential collaborations and exploring new ideas together.

Email: yangz21 [at] mails.tsinghua.edu.cn  /  CV_ZH  /  CV_EN  /  Google Scholar  /  Github

We aim to construct a fine-tuning dataset MathVL, and develop a series of specialized mathematical MLLMs MathGLM-Vision with various parameter-scale backbones.

We meticulously construct a comprehensive benchmark, named VisScience, which is utilized to assess the multi-modal scientific reasoning across the three disciplines of mathematics, physics, and chemistry.

We explore the history of negative sampling, categorize the strategies used to select negative samples, and examine their practical applications.

We design the quasi-triangular principle and introduce TriSampler to selectively sample more informative negatives within a prescribed constrained region.

We propose a 2 billion-parameter language model MathGLM that can accurately perform multi-digit arithmetic operations with almost 100% accuracy without data leakage.

We present BatchSampler to sample mini-batches of hard-to-distinguish (i.e., hard and true negatives to each other) instances by leveraging the proximity graph and a random walk with restart.

We propose a novel method ViLTA that utilize a cross-distillation method to generate soft labels for enhancing the robustness of model.

We propose a spatiotemporal aggregation method STAM to efficiently incorporate temporal information into neighbor embedding learning.

We design three region principle to select negative candidate and propose RecNS method to sythesize hard negatives.

We present MixGCF that can study negative sampling by leveraging both the user-item graph structure and GNNs’ aggregation process to design the hop mixing technique to synthesize hard negatives.

We develop a theory and quantify that a nice negative sampling distribution is \( p_n(u|v) \propto p_d(u|v)^\alpha \), \( 0 < \alpha < 1 \). Additionly, we propose Markov chain Monte Carlo Negative Sampling (MCNS), an effective and scalable negative sampling strategy for various tasks in graph representation learning.

• Huawei Scholarship, 2023.
• Excellent Graduate of Beijing, 2019.
• Outstanding Academic Paper Award, Tsinghua University, 2019.
• 129 Scholarship of Tsinghua University, 2018.
• National Scholarship by Ministry of Education of China, 2018.
• National Scholarship by Ministry of Education of China, 2014.

Thanks to Jon Barron