诚招2027年入学的研究生 Prospective Graduate Students (2027 Entry)

本研究组诚招2027年入学的研究生，欢迎保研和考研同学联系。计算机、信通、人工智能的学硕、专硕均可招收。请给我发邮件并附上成绩、研究陈述以及未来计划（如1-2年），邮箱地址 hwb13 [AT] tsinghua [DOT] org [DOT] cn。

不再接待23和24级的本科生进组学习。25级希望进组学习的本科生，请先参加26年春季机器学习读书会。

本研究组的研究领域是生成式、可信赖人工智能，具体包括多模态预训练大模型、人工智能对抗攻击与防御、人工智能不确定性预估。本研究组学术氛围好，给予学生充分探索的空间和前沿的指导，经费和机器资源充足，常年发表顶级学术会议、期刊论文。 本研究组对学生的要求是：1）有自驱力，2）数学理论或者编程水平高，3）成绩优秀。对本科访问学生还要求在研究组访问至少半年。

概率机器学习读书会 MLAPP Reading Group

强烈建议本科学生在参加科研访问之前参加MLAPP读书会，一般而言每年春季学期会面向本科生开放，具体辅导员会在班级群中通知。 请期待2026年春季学期第四届读书会！

媒体报道 Media Reports

• AAAI 2026 | Are Video Language Models Reliable? A Comprehensive Review of 23 Mainstream Models. 视频大语言模型到底可不可信？23款主流模型全面测评来了. Dec 2025. [Link]

• Hosted the 2025 Summit Forum on Frontier Technologies in Large Models, October 2025. [Link]

• Hosted the third “Hubing” Machine Learning Reading Group, July 2025, 2025年7月度第三节届斛兵机器学习读书会结束.[Link]

• Rui Sun, an undergraduate student at HFUT's Xuancheng Campus, had a paper accepted at ICMR 2025. 宣城校区本科生孙睿论文入选国际顶会ICMR 2025. [Link]

• Keynote speaker at the Graduate Academic Forum, December 2022, 2022年12月研究生学术论坛主旨报告 [Link]

• Hosted the inaugural “Hubing” Machine Learning Reading Group and presented awards, July 2023, 2023年7月度首届斛兵机器学习读书会并颁发优秀报告奖.[Link]

• Gave a keynote titled “Recent Advances and Challenges of AI” for HFUT graduate students in computer science and economics, November 2022, 2022年11月计算机学院与经济学院研究生学术论坛主旨报告“人工智能前沿进展与挑战” [Link]

• Gave a keynote titled “Recent Advances and Challenges of AI” in the “Face-to-Face with Experts” lecture series, December 2023, 2023年12月大咖面对面学术报告主讲“人工智能前沿进展与挑战”。[Link]

推荐信 Recommendation Letters

谢谢你认为我的推荐信会对你的下一步计划有帮助，但是我对撰写推荐信将有以下原则：

• 对于需要申请硕士、博士研究生的推荐信的学生，至少要与我一起努力工作6个月以上。

• 一般需要有较好的论文、项目等成果才能获得强烈推荐。

请使用我的工大邮箱进行联系： wenbohu [at] hfut [dot] edu [dot] cn

Thank you for considering me as a referee for your academic applications. Please note the following guidelines:

• To request a letter for a master's or Ph.D. application, you should have worked with me for at least six months.

• The content and strength of my recommendation depend on our research collaboration and your contributions. A strong recommendation requires solid research or project achievements.

Please contact me using my HFUT email address: wenbohu [at] hfut [dot] edu [dot] cn

Students and Interns

Current Students

• Shishen Gu (2023-). Master's student, HFUT. Research: multimodal model security.

• Zijun Chen (2023-). Master's student, HFUT. Research: reliability of large language models. One COLING paper.

• Ming Jin (2023-). Ph.D. student, HFUT. Research: cross-modal retrieval and security. Two TCSVT papers.

• Ying Liu (2023-). Ph.D. student, HFUT. Research: probabilistic machine learning and AI for science.

• Youze Wang (2022-). Ph.D. student, HFUT. Research: multimodal model security. Two TMM papers and one TCSVT paper.

I have worked with many talented students and interns and learned a great deal from them:

Former Students

• Yongle Li. Master's student, HFUT. Worked on large language models and tool use. One PRCV paper.

• Hao Ye. Undergraduate student, HFUT. Worked on large language models and knowledge bases, with one ICDM workshop paper. Moved to BUPT for master's studies.

• Ao Shen. Undergraduate student, HFUT. Worked on several course projects. Moved to UIUC for master's studies.

• Peng Cui. Master's student, Tsinghua University. Worked on uncertainty quantification for time series, with two papers, including one at NeurIPS. Continued with Ph.D. studies at Tsinghua University.

• Qingyi Pan. Master's student, Tsinghua University. Worked on model interpretation for time series forecasting, with one IJCAI paper. Continued with Ph.D. studies at Tsinghua University.

• Jiaxin Gao. Master's student, Université Paris-Saclay. Worked on time series analysis for predictive maintenance and industrial intelligence. Had one paper accepted by Water Power and won two data competitions. Moved to SJTU for Ph.D. studies.

• Yingtao Luo. Master's student, University of Washington. Worked on machine learning methods for PDE discovery, with one KDD paper. Moved to CMU for Ph.D. studies.

• Xin Sun. Undergraduate student, SJTU. Worked on uncertainty calibration for inverse propensity estimation and submitted one paper. Moved to the Institute of Automation, Chinese Academy of Sciences, for master's studies.

• Zhiheng Zhang. Undergraduate student, BUPT. Worked on dynamic Granger causality and submitted one paper. Moved to Tsinghua University for Ph.D. studies.

• Wenkai Li. Master's student, Tsinghua University. Worked on time series anomaly detection, with one journal paper. Continued his master's studies at Tsinghua University.

• Xianrui Zhang. Master's student, BUAA. Worked on time series toolkit development for RealSeries. Moved to City University of Hong Kong for Ph.D. studies.

Resources

Resources for students new to machine learning:

1. “Mastering Your PhD–Survival and Success in the Doctoral Years and Beyond”. [Link]

2. “Ph.D. Advice from H. T. Kung”. [Link]

3. “Probabilistic Machine Learning” – a book series by Kevin Murphy. [Link]

4. “Deep Learning” – a book by Goodfellow et al. [Link]

5. “Deep Learning Tuning Playbook” – a guide to tuning deep learning models, written by Google engineers. [Link]

6. “ML Visuals” – reusable figures and templates for scientific writing. [ML Visuals]

7. “Computer Science Self-Study Guide” [Link] (In Chinese)

8. LaTeX

   1. [An Overleaf tutorial]

   2. [A short math guide for LaTeX]

   3. A WYSIWYG mathematical editor: [MathCha]

   4. A table generator supporting LaTeX, HTML, and Markdown: [tablesgenerator]

9. Computer science conference and journal lists (for reference).

   1. [CCF list]

   2. [Tsinghua list]