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Wenbo Hu's Homepage – Students
诚招2024年研究生与本科访问学生 For perspective graduates in 2024
本研究组还有1个2024考研硕士生名额，欢迎联系！
本研究组目前招收2024年入学的硕士研究生，同时招收硕博连读学生和本科访问学生。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 wenbohu [at] hfut [dot] edu [dot] cn。
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。本研究组将支持优秀的推免硕士和博士研究生申请斛兵学子提升计划。
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。
2023年春季学期第一届机器学习读书会已结束，请看官网新闻，请期待2024年春季学期第二届读书会！
媒体报道 Media Reports
Keynote Speech at Graduates Academic Annual Pannel, Dec 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 Series and presented final 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]
Keynote Speech at with topic “Recent Advances and Challenges of AI” for HFUT graduate of CS and Economy, Nov 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]
Keynote Speech at Face-to-face with Big Shots Series, Dec 2023， with topic “Recent Advances and Challenges of AI”, 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 Letter
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：
请使用我的工大邮箱进行联系： wenbohu [at] hfut [dot] edu [dot] cn
Thanks for those who believes my recommendation letter would be helpful for your next plans. However, I have the following principles:
For a recommendation letter to support one's application for the Ph.D (M.S.) program, the student should work with me for at least 6 months.
The recommendation letter contents will be dependent on the our research process and achievements. One will receive a strong recommendation only if has solid achievements.
Please use my Affiliation Email for contact: wenbohu [at] hfut [dot] edu [dot] cn
Past students/interns
I had worked with multiple talented students/interns, from whom I learned a lot:
Peng Cui, Tsinghua Master. Worked on time series uncertainty quantification with two papers, one NeurIPS. Continue Ph.D study in Tsinghua.
Qingyi Pan. Tsinghua Master. Worked on model interpretations for time series forecasting with one IJCAI paper. Continue Ph.D study in Tsinghua.
Jiaxin Gao. Université Paris-Saclay, Master. Worked on time series analysis with respect to predictive maintenance and intelligent industry. One paper accepted to Water Power journal and won two data competitions. Move to SJTU for Ph.D study.
Yingtao Luo. University of Washington, Master. Worked on machine learning methods for PDE discovery with one KDD paper. Move to CMU for Ph.D study.
Xin Sun, SJTU Bacholar. Worked on uncertainty calibration for inverse propensities with one paper submitted. Move to the Institute of Automation of the Chinese Academy of Sciences as a master.
Zhiheng Zhang, BUPT Bacholar. Worked on dynamic Granger causalities with one paper submitted. Move to Tsinghua University for Ph.D study.
Wenkai Li, Tsinghua Master. Worked on time series anomaly detection with one paper journal paper. Continue master study in Tsinghua.
Xianrui Zhang, BUAA Master. Worked on time series toolkit development as part of RealSeries. Move to CityU of HK for Ph.D study.
Resources
Some resources which may be useful for the ML beginners:
“Mastering Your PhD–Survival and Success in the Doctoral Years and Beyond”. [link]
“Ph.D. Advice from H.T.Kung”. [link]
“Probabilistic Machine Learning” - a book series by Kevin Murphy. [Link]
“Deep Learning” – a book by Goodfellow et al. [Link]
“Deep Learning Tuning Playbook ” – a document by Google engineers for researchs of how to tune deep learning models. [Link]
“ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.” [ML Visuals]
LaTeX
[An overleaf tutorial]
[A short math guide for latex]
An online math LaTex editor, WYSIWYG: [MathCha]
An online table generator, which supports LaTeX, HTML and Markdown: [tablesgenerator]
CS conference and journal lists, for reference only.
[CCF list]
[Tsinghua list]
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