个人简介

原发杰，2018年毕业于英国格拉斯哥大学，获计算机博士学位，博士期间曾在西班牙Telefonic Research与新加坡国立大学从事研究工作。2019-2021就职于腾讯，任职机器学习高级研究员。2021年4月加入西湖大学，任工学院特聘研究员，从事AI大模型与计算生物学相关研究。

学术成果

原发杰博士及工作期间主要从事传统机器学习及交叉学科相关的应用科学研究。 其中以一作、共一、通讯作者身份在机器学习与人工智能相关顶会顶刊 （NeurIPS， ICLR，UAI，SIGIR，WWW，WSDM，CIKM，ACL，IJCAI，ICDE，TKDE，TPAMI 以及Molecular Cell）发表学术论文30余篇，曾获ICTAI2016最佳学生论文，腾讯工作期间授权AI专利十余项，多项科研成果及专利等核心技术，如LambdaFM（CIKM2016），NextItNet（WSDM2019），PeterRec（SIGIR2020）被应用于大规模工业检索和推荐系统，具有较好的业界影响力。

在西湖大学致力于AI大模型研究，实验室发表了多项AI大模型相关研究成果，包括蛋白质大语言模型发表在ICLR，NeurIPS以及Molecular Cell，以及推荐系统相关大模型基准建设，发表在SIGIR，WSDM，ICDE，NeurIPS，SDM，TPAMI等。

代表论文（*相同贡献，^#通讯）

1. J. Su, C. Han, Y. Zhou, J. Shan, X. Zhou, F. Yuan^#.SaProt: Protein Language Modeling with Structure-aware Vocabulary. ICLR2024.

2. Y. He, X. Zhou, C. Chang, G. Chen, W. Liu, G. Li, X. Fan, Y. Ma, F. Yuan^#, X. Chang^#. Protein language models-assisted engineering of Uracil-N glycosylase enables programmable T-to-G and T-to-C base editing. Molecular Cell2024.

3. J. Zhang, Y. Cheng, Y. Ni, Y. Pan, F. Yuan^#. NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation. TPAMI2024.

4. Y. Cheng, Y. Pan, J. Zhang, Y. Ni, F. Yuan^#. An Image Dataset for Benchmarking Recommender Systems with Raw Pixels. SDM2024.

5. Y. Li, H. Du, Y. Ni, P. Zhao, Q. Guo^#, F. Yuan^#, X. Zhou, etc. Multi-Modality is All You Need for Transferable Recommender Systems. ICDE2024.

6. J. Fu, F. Yuan^#, Y. Song, Z. Yuan, M. Cheng, etc. Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights. WSDM2024.

7. Z. Yuan*, F. Yuan*^#, Y. Song, etc. Where to Go Next for Recommender Systems? ID- vs.Modality-based recommender models revisited. SIGIR2023.

8. G. Yuan*, F. Yuan*^#, B. Kong, etc. Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems. NeurIPS2022.

9. M. Hu* ，F. Yuan*^#, K. Yang, etc. Exploring evolution-based & -free protein language models as protein function predictors. NeurIPS2022.

10. F. Yuan, G. Zhang, A. Karatzoglou, X. He, J. Jose, B. Kong, Y. Li.  One Person, One Model, One World: Learning Continual User Representation without Forgetting. SIGIR, 2021.

11. J. Wang*, F. Yuan*, J. Chen, Q. Wu, C. Li, M. Yang, Y. Sun, G. Zhang. StackRec: Efficient Training of Very Deep Sequential Recommender Models by Layer Stacking. SIGIR, 2021.

12. M. Chen*, F. Yuan*, Q. Liu, S. Ge, Z. Li, R. Yu, D. Lian, S. Yuan, En, Chen.Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement. SIGIR, 2021.

13. L. Chen*, F. Yuan*, J. Yang, X. Ao, C. L, M, Yang. SkipRec: A User-Adaptive Layer Selection Framework for Very Deep Sequential Recommender Models. AAAI2021.

14. F. Yuan, X. He, A. Karatzoglou, L. Zhang. Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation. SIGIR 2020.

15. Y. Sun*, F. Yuan*, M. Yang, G. Wei, Z. Zhao, D, Liu. A Generic Network Compression Framework for Sequential Recommender Systems. SIGIR2020.

16. F. Yuan, X. He, H.Jiang, G. Guo, J. Xiong, Z. Xu, Y. Xiong. Future Data Helps Training: Modelling Future Contexts for Session-based Recommendation. WWW2020.

17. F. Yuan, A. Karatzoglou, I. Arapakis, J. Jose, X. He.  A Simple Convolutional Generative Network for Next Item Recommendation. WSDM2019.

18. F. Yuan,X. Xin, X. He, G. Guo, W.Zhang, T. Chua, J. Jose.  fBGD: Learning Embeddings From Positive Unlabeled Data with BGD. UAI2018

19. X. Xin*, F. Yuan*, X. He, J. Jose. Batch IS NOT Heavy: Learning Word Representations From All Samples. ACL2018

20. G. Guo*, SC.Ouyang*,F. Yuan*. Approximating Word Ranking and Negative Sampling for Word Embedding. IJCAI2019

21. F. Yuan, G. Guo, J. Jose, L. Chen, H. Yu, W.Zhang.  BoostFM: Boosted Factorization Machines for top-N Feature-based Recommendation. ACM IUI2017

22. F. Yuan, G. Guo, J. Jose, L. Chen, H. Yu, W.Zhang. LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates. CIKM2016

23. F. Yuan, J. Jose, G. Guo, L. Chen, H. Yu, R. Alkhawaldeh. Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation. ICTAI 2016 (Best Student Paper)

实验室岗位招聘

    本课题组拟长期开展机器学习、数据挖掘和AI+生物信息方向的研究。欢迎申请课题组博士生 ，科研助理，博士后，研究员系列职位，在校生欢迎来实验室访问实习。请有意向者发简历到yuanfajie@westlake.edu.cn。

联系方式

    电子邮箱：yuanfajie@westlake.edu.cn

    个人网站：https://fajieyuan.github.io