新加坡有哪些AI的强组适合读MS(research)／PhD？

主要是nus和ntu啦

新加坡读cs博士不好找工作，比美国差太多。

转载：quora

I have never attended the national University of Singapore. I did attend the University of California Berkeley for both an undergraduate and graduate degrees .

Well I worked at some of the top US computer companies in Silicon Valley, I met many students and graduates of California, Stanford, Carnegie Mellon, and MIT. I only met one student/graduate of the national University of Singapore.

Just from my prevalence experience, I do not see NUS graduates working in many of the larger computer companies. Of course, they might have not mentioned that university.

In one of the companies, 35% of the engineering faculty at PhD degrees. And, they were required to publish one paper a year or to give one talk at a international conference. Now, things might be different since I last worked in the canoe company several years ago, but I believe the stands are extremely high.

Except for the four universities that you have mentioned, there are many universities that are good. It is extremely difficult to compare universities even in the computer science department. The implication, is that if you graduate from one of the top five schools United States, you're guaranteed a job. The other outcome implication is that if you are a graduate from NUS that you will never get a job. That's unlikely also. In my Pinyan, doing well in the current school or the best school that you can get in, will likely land you a job just like any other graduate.

Please understand that my experience is as a career coach counseling's graduates from all over the world in finding jobs. Therefore, my comments only related to getting a job or how they are in trying to get a job. I do not evaluate how smart they are or how smart they were in school.

/s/Richard Hom
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就看人胡说八道.....坡县的phd和香港一样很push。出来要不然是在高压下都混不下去，要不然就是一堆pub。跟美帝顶尖学校那种吃reputation的不太一样。

不过话说回来，因为坡县和香港这种玩法，太看pub导致文章的质量会比较有限(不够好，历史影响有限，只算影响因子当然不是问题)。

其实不要盯着所谓强组，要考虑人口论文比，要看这些论文多大层面是学生做的而不是老师对外合作输出。一堆要考虑的东西，你也不想老板手下几十号人，碰杯都轮不到你的情况吧

先从NUS说起，NUS AI方向最强的组莫过于Next++实验室（蔡达成（Chua Tat-Seng）组）了，不仅科研经费超多，博士博后待遇优厚，论文高产而且门生弟子遍布中国和新加坡各大高校，比如NTU的Hanwang Zhang，SMU的Qianru Sun，清华大学的高跃，中科大的何向南、查正军，山东大学的聂礼强、宋雪萌，湖南大学的曹达，南京理工的唐金辉，合肥工业大学的汪萌、洪日昌等（非常建议本科生到这些老师门下做RA，方便要推荐信），其中不乏杰青，“四青”，IEEE Fellow，阿里青橙奖获得者，TR35获得者以及院系的正副院长系主任等。

除此之外，CV领域的Jiashi Feng，DB领域的Xiaokui Xiao，NLP领域的Min-Yen Kan也是很强的。新来的老师如UC-Berkeley博士毕业的Yang You，Columbia University博士毕业的Zheng Shou也挺强的。

其次NTU来说，CS学院院长Chunyan Miao就不错，还有CV领域的Hanwang Zhang（前面提及了），DB/DM领域的Gao Cong，Cheng Long，Sourav S Bhowmick，ML领域的Sinno Jialin Pan，NLP领域的Aixin Sun，Shafiq Rayhan Joty。另外港中文MMlab在NTU的分部也挺不错的，主任是Chen Change Loy。

至于SMU和SUTD，只知道SMU的Steven C.H. Hoi（很早之前在NTU当AP的）前几年选上了IEEE Fellow并且在Salesforce Research Asia担任负责人。其他的了解不是很多，虽然名气上不如NUS和NTU，但是据说培养质量也挺不错的，还望知情者在评论区加以补充。

借楼发一下实验室的招生广告，24Spring的招生7月1号截止，24Fall的招生明年一月截止。请有兴趣的同学抓紧时间申请

NUS LV Lab (http://lv-nus.org/) 有多个PhD, Research Fellow, Research Assistant, visiting student/scholar 职位, 欢迎各位同学老师申请！

实验室目前研究方向包括efficient learning, backbone design, knowledge transfer, multimodal learning, interpretable and trustworthy learning, 以及vision application。科研倾向于curiosity-driven, 同学们可以自行自己感兴趣的提出research topic。实验室成员来自MIT, USCD, 清华(包括清华-伯克利), 浙大(包括竺可桢)等知名高校。

实验室计算资源丰富，氛围十分轻松, core value一直是enjoy research and be yourself.

在2022年一年中，实验室

● 在CVPR/ECCV/NeurIPS/ICML/ICLR/AAAI等会议发表~20余篇，包括多篇oral或spotlight.

● 实验室的同学以第一作者身份获 NeurIPS’22 best paper nomination，VCIP’22 best paper (sole recipient)。

● 实验室PhD和访问学者获得 中科大/四川大学/东方理工/AStar 等著名高校或政府研究所助理教授职位，北美字节/新加坡华为等大厂全职研究院职位, 北美Facebook/Microsoft等大厂intern职位。

● 实验室同学获Snapchat fellowship, 为亚洲唯一获奖人。

Welcome to LV-Lab – we look forward to sharing an exciting and memorable journey with you!

请发送邮件至: xinchao@nus.edu.sg

Note 1: 原则上，NUS研究生院需要GRE才能申请PhD。如果您profile很优秀，我们会尽全力帮您申请 GRE waiver，帮助您enroll.

Note 2: LV Lab 会有多名组员参加CVPR’23. 欢迎大家与LV Lab member当面交流！

欢迎来 Show Lab 一起做有意义的研究呀！

We aim to build AI Assistant on various platforms, including social media, metaverse/AR glass, robot, with the ability of understanding and creating video, audio, language collectively. This involves techniques like:

• Video Understanding e.g. action detection, video pre-training, tracking, segmentation in space and time.
• Multi-modal e.g. video+language, video+audio and AI-Human cooperation and interaction
• Generation and Digitization e.g. photorealistic avatar, talking head, text-driven video generation

实验室的研究都非常 solid，每一篇论文的代码都会附在 Show Lab 的官方 Github Repo 下：

最近大火的 Tune-A-Video 就出自 Show Lab （Github 3k+ star）

老师 @Mike Shou 也是知乎大V，年轻有为，指导水平超高！（Fellow of National Research Foundation (NRF) Singapore. He is on the Forbes 30 Under 30 Asia list）

而且更棒的，老师平易近人，跟学生打成一片，氛围超棒，比如组会的时候给同学过生日嘿嘿：