Thoughts on PhD


This post serves as a record of thoughts regarding PhD. This post is from the person who is about to embark on the journey of getting a PhD. The thoughts from this post may look stupid or naive for someone who has already gone through the phase. However, based upon my past experience, if you don’t have some baseline for something that you decide to begin fighting for, you can barely have a measure on how much you have progressed when you actually start to fight, and highly likely, you may fall into the same trap over and over again for future similar situations.

Let’s dive into …

I have been considering getting a PhD since my sophomore year at University. This page summarizes some commonly-seen motivation for people getting a PhD. I think mine can be partly described as “Dr. Hu — sounds cool!” and “Eternal quest for knowledge (yeah right!)”. Another part, I guess, probably be the childhood dream of becoming a scientist. However, now, after two years working in industry, I realize that the most important motivation for me to get a PhD is that I want to have the ability to solve the problem that nobody explores before. This is different from quick learner because quick learner means grasping the material that has already studied before quickly. But, that doesn’t mean he can handle the unexplored area very well. I like to ask “why” when I face a problem but gradually I realize that I don’t have enough knowledge and more importantly, the confidence to solve some of crazy ideas in my mind. So, getting a PhD means I build some good knowledge foundation in some specific area and process the ability to solve any open question, even it is from the area that I haven’t explored before.

However, getting a PhD is a non-trivial task and itself demands lots of commitment. I personally view PhD and marriage are two the most serious commitment a person can ever give in his entire life. I try to play “rational” card here by doing some evaluation beforehand because I’m type of do-something-that-can-be-successful person and several years of study in economics make me become more and more like a “risk-averse” kind of person. So, I do various RA jobs in various departments (i.e. Math, Psychology, Biostatistics) to get a sense of what PhD life might look like.  The result is not good for me because I find out that working on some topics that you have the least interest in can be a lot like being in jail. However, those undergraduate research experience also has its positive side.  Imagine if I jump into the graduate school directly and choose some direction that I have no interest in (i.e. medical imaging),  I am sure that I cannot survive till the end. Sometimes I feel that the process of making decision is a two-way street: one way to do is to pick something that fits you from the pool; the other way is that you get rid of the choices that definitely do not work for you and then see what is left inside the pool. Apparently, for me, the latter strategy works slightly better.

So, I choose to work in industry for two years to find the things that I have passion about.
There isn’t much left in my choice pool by that time: I either pick from AI or from system.  For AI,  my focus is majorly on the application of ML techniques, such as CV, NLP. For system, my choice is distributed system (i.e. distributed database, distributed file system).  So, I need to carefully think about the pro and cons for which track I decide to pursue. Like I said in my offer choice post, there can hardly be a perfect choice that meets your need by any measure. Preference ranking in economics may be too ideal.  There is always trade-off. After spending two years working in database, I realize that I’m not really a hardcore system guy.  The most attractive feature from system is that I can do lots of coding. The coding here is naturally different from coding in, say, ML. In system, most of coding is done involves implementing data structures, data process models, and so on. However, for ML, coding is more like a direct translation of some mathematical formulas. However, the problem I find out about system research is that it is hard to propose problems that can directly link to the industry level production. This problem becomes clear to me after I attend DTCC 2017 last week.  The key success element for building a system is the production painpoints or user scenario. Alibaba and Tencent build system just to cater their specific business scenarios. In my view, the system has value once it can solve some specific problems that are not formed from someone’s imaginary. This can be very hard for newcomers who just join inside academic circle. In this case, advisor may work like a offer manager who regular visits companies to see what kind of problems they try to solve and bring those practical problems back to the research group and hopefully these issues can be resolved by his students. Research is all about solving problems and great research comes from the problems that have or potentially have great impact in the industry or people’s daily life.

In addition, if I recall the fun course experience from my undergraduate, I realize that I have much more fun with manipulating formulas and work out the problem that has strong connection with people’s daily life. The biggest trend right now is on big data. However, to be honest, for database system developer like me, I can barely get in touch with actual big data in my daily work. So, whether the system I build is robust enough to handle the actual big data, I don’t know. The only thing I can say is that I implement the design correctly. So, I feel like it is really hard to work out some good system by spending most of the time in school. This idea is partially confirmed by the trend that people jump out of academia and head to the industry like this.  However, even I have spent almost all the space so far talking about the “problems” I have observed about system research. I do enjoy the “traditional” programming scheme that system research possess. Rather than taking some data and train a somewhat blackbox network to achieve outcome, traditional if-else programming feels more rewarding for a hardcore programmer.

For AI, things can be radically different from system. Specifically for ML, one thing I learned is that ML is used to solve for the task that can hardly solvable by traditional programming, like autonomous driving, pattern recognition. Those stuff has strong connection to people’s daily life, which means can make a lot more impact. This is some historical pattern that can be easily observed: serving individual people is lot more profitable than serving big companies. Doing research on system is a lot like serving big companies if we consider the problem: who needs to build infrastructure from scratch? However, working on AI is a lot like serving people by making iPhones. If we observe the trend of companies like IBM and Apple, this analogy can easily work. So, even programming in ML is less satisfying in my sense, we just need to embrace the future to better maximize our utility.  Of course, mathematics are quite bit involved in the field of AI, and tweaking parameters of learning models can feel quite subjective. However, I guess that’s some obstacles I need to face. The rationale is same as before: there is no perfect choice and we just need to try even if we have only 10% confidence about success.

Last word …

The motivation for considering this issue right now is that I need to start planning my course schedule for the upcoming semester. The course schedule can be balanced between system and AI. But, it can also be AI focused. So, I really need to evaluate myself to see which direction I want to go.  There is a famous quote in China: “Choice matters!”

Takeaway from DTCC 2017

由于同事出差,我有幸参加了在北京国际会议中心举办的第八届中国数据库技术大会(Database Technology Conference China 2017)。这是我第一次参加业界交流大会,内心还是格外兴奋的。这次大会确实有很多的收获,我想用这篇博客记录下来。本来我想用英文记录的,毕竟对于计算机领域,英文是我的“母语”,但是介于分享主要以中文为主,所以我就还是以中文来记录了。



Get some sense from the peers

Focus on your own product is quite important. However, it’s even more important to see how your peers doing. I’m not an architect yet but I feel it’s helpful to begin thinking like an architect and see what the problems that your peers are facing and how they try to solve them. In addition, by knowing how’s the going with your peers, you may get a measure of yourself: is the work you are doing on the same level as your peers? Are you in a good shape in the job market? What’s the gap you need to fulfill skill-wise?

Deepen the understanding of the field

Even almost two years working on the database field, I still think myself as a newbie. This is mainly because database is arguably the most complex software that people can ever make and there are tons of stuff I don’t know. So, I want to see in a high level that what’s the trend of the field and what kind of reflection that people derive from their day-to-day engineering practice. I think this may help me to catch-up with the masters.

AI or System?

As I disclosed in my last post, I decide to head back to school and get a master degree. To be honest, my ultimate goal is to acquire a PhD in Computer Science and currently I’m actively preparing for it. The most important question is that which field I want to study?  I have two options and I have some interests in both fields: AI and System. Why these two options and not others is worth a whole new post and I don’t want to discuss here. So, my task for now is to gather as much information as possible about these two fields and see which one looks more attractive to me. This event is extremely helpful because it has sharing on System as well as on AI.

Day 1


年度主题解读 (曹鹏 – 京东金融副总裁)


  1. Finance领域受到了机器学习的冲击,最近几年有越来越多的FinTech公司出现。机器学习在这种公司的主要应用从这个分享来看是对客户群体更加精确的定位和分析。相应的,对于量化交易策略的作用,这个分享没有涉及。我最近一直比较关心机器学习在金融领域的应用,但是从这个分享上,我没有找到我想要找到的答案。因为,在我看来,对客户群体的精确定位是一种机器学习的通用应用,并不具备金融行业的独特性。
  2. 数据公司在我看来是一个不错的创业想法。分享中提到数据对于京东金融的重要性。他们不仅要求数据的广度,也要求数据的厚度。一个重要问题是数据是具有很强的时效性和冷热变化的。一年前顾客的消费记录对于现在来说并不具备非常强的指导意义。因此,京东金融每天都要收集大量的数据(~6TB)来保证整个分析的准确性。同时,演讲者透露出即便在这种情况下,他们觉得数据还是远远无法满足他们的需求的。这个就能解释为什么IBM最近收购了The Weather Company和医疗影像公司Merge Healthcare:无非就是看上了这两家公司的数据。这让我想做数据贩卖商会不会是一个不错的创业点子呢?

数据库发展概览 (吴承杨 – 甲骨文)


  1.  在去IOE喊了那么多年的今天,Oracle的市场占有率依然有56%之多
  2. 数据库的未来是云:这里演讲者用一个case讲述hybrid cloud的重要性。企业现在面临的问题是如何将公有云的数据和本地服务器上的数据有效的对接在一起以及如何将公有云私有化等。整场演讲更像是Oracle解决方案介绍会,技术方面很少涉及,但是指出了未来数据库发展的方向:上云。
  3. 演讲者台风不错,是一个不错的演讲者。

数据技术的下一站 – 数据应用 (王桐 – 永洪科技)



达梦如何冲击核心业务系统 – 国产数据库的产品发展之路 (韩朱忠 – 达梦数据)

我觉得这个分享可能是今天最励志的分享了。整个分享讲的就是一个国产小厂商是如何奋斗和外资数据库斗争,一点点争取市场份额,成长到今天这个样子的。这里边讲到的一个关于他们对这个用C写的数据库的SQL优化能力进行提升的例子。 他们曾经遇到过一条SQL, 长达3.9K行,换句话说就是粘到word文档里能粘350多页。里边包含着17个inner join, 557个子查询, 831个or筛选, 1000+个查询字段,2731个case when。他们通过不断优化将这个SQL语句从几百分钟降到不到1秒。另外一个故事是讲国产数据库生存的艰辛。因为大企业及银行电信等核心产业的数据库都是采用外资的, 国产根本进不去。国产只能在中小企业市场去竞争。但是,这家数据库通过自身的不断努力,终于拿下国家电网的单子以及西藏和东方航空的单子。这在我看来是非常了不起的成就。这就让我对IBM产生了反思。我不觉得我们DB2能在不经过针对性的优化的情况下就能处理这么复杂的SQL语句。这个例子也让我觉得要么我们是在用我们的名声和过去的积累在赢得客户,要么就是DB2售前的同事在做POC的时候super tryhard。我明显感受到我们和这些国产数据库在努力程度上的差距。也许有一天我和他们的地位会呼唤?我相信这是IBM高层不愿意看到的事情。我们确实该努力了。

SSD的IO Determination特性在数据库业务优化中的应用与拓展 (阳学仕 – 宝存科技)

这个是从storage上出发来讲如何用软件模拟硬件来提升读写速度。换句话说,这个分享带给我的思考就是数据库怎样才能利用IO determination提升读写速度。这里讲的IO determination我粗浅理解看来就是让硬盘上的应用能更加和谐共处,并通过提升应用优先级,IO资源上下限,以及时间上对读写顺序进行优化等方式来使应用获得所需要的资源。另外SSD对于网络发展的匹配也有涉及:通过硬件的提升,我们现在基本可以做到本地写入和通过网络写入远程只有10几微秒的差距。这些在我看来是属于OS的领域。硬件对DB的加成这个方向让我感到耳目一新。

面向未来的数据库体系架构的思考 (张瑞 – 阿里巴巴)


  1. 国内厂商和IBM在对待数据库上有本质上的区别。国内厂商如阿里巴巴,腾讯,以及百度都是以自身业务痛点作为出发点对自家的数据库进行开发和改造。所以相应的,这些家的数据库改造,提升都是带有极强的针对性的。他们的数据库架构可能并不具备非常强的通用性。相反,IBM是把数据库作为产品来销售的,因此在数据库本身设计上考虑到的更多是面面俱到,大而全的尽可能满足所有用户类型的需求。这就导致在某些场景下,IBM的DB2做不到像AliSQL, OceanDB, TDB那样强劲。因此,在超大型公司做数据库,最终方向可能都是“私人订制”。
  2. 机器学习与系统结合的越来越紧密。这里演讲者提到他们想在未来把自动运维转换到智能运维上面来。SQL不再是DBA来手动看,而是通过ML的某种方式来进行优化。这些阿里的人还没有想好但是他们觉得这是未来的方向。


下午听的有”百度NewSQL数据库系统”, “Tencent MySQL内核优化解析”, “滴滴大数据应用”,“自然语言技术在文智趋势分析产品上的应用”。百度上最大收获是说现在分布式事物数据库非常的热,如果研究透,就没有在国内趟不过去的问题。另外一点收获就是不要过分崇拜Google系统。虽然细节我没有听的特别懂,但是从演讲者言语间我感受到,黑猫白猫抓到耗子就是好猫。有的时候不能太学究。而且系统之间即使是理念一模一样,但是由于implementation不同,也会导致巨大的性能差异。

腾讯的讲的非常Technical, 加上演讲者是技术出身,整个session非常的煎熬,感觉就是内核优化是个大坑,需要很扎实的DB知识。最后两场我选得是和机器学习相关的。不得不说没有达到我心中的理想。滴滴介绍的是他们一些数学模型应用的场景。我感觉演讲者应该是加入滴滴时间不长,并没有从一些模型上讲出个所以然来,反倒是应用场景上更让我感受到经济学家也是有用武之地的:比如说如何运用高峰涨价来调控司机和打车人之间的供求关系,以及如何收取取消订单等行为给平台所带来的损失。也许是民怨太重,整个滴滴分享感觉像是个新闻发布会。最后的自然语言技术应用是非常无聊的。演讲者是产品经理出身,主要介绍了下腾讯是如何针把NLP技术应用在新闻上的。非常泛泛,没有提及一些NLP上的技术难点,非常失望。

Day 2


  • Informix现在是和物联网IOT紧密的捆绑在了一起

在IBM我的邻居就是Informix Technical Support组。他们组的老大之前也分享过Informix在物联网领域的应用。这在我看来是为Informix这个昔日的巨人在找新的发力点以获得新生。这点也在今天题为“万物互联时代的数据库支撑平台–SinoDB”上获得了印证。SinoDB可以理解为Informix的fork因为这个公司从IBM这里获得了Informix的源代码的授权。不得不说的是IBM在这里变成了吐槽的对象,这些以Informix元老员工成立的公司认为IBM并没有善待Informix这个继子。他们认为是时候把自己的“孩子”重新领回来让他茁壮成长了。这也让我不得不思考当初IBM收购Informix到底是为了什么?问了问和我一同参会的同事,Informix的代码是否已经和DB2的有机的融合在一起现在还是个未知数。这也让我明白为什么在Oracle收购MySQL之后会出现这么多MySQL的fork:毕竟不是亲儿子。

  • 问题的多重性和domain knowledge的重要性

下午场我就是盯着机器学习专场在听。其中我觉得来自连家的“机器学习技术在房屋估价中的应用”的分享最为有意思。分享的内容其实从标题就可以猜出个八九不离十。这个分享一个重要的信息就是机器学习并不是以算法为核心的而是以建立在以domain knowledge为支撑的加工过的data的基础上的。对于链家的问题就是他们的数据量是十万级的,远不及一些图像处理或者文本处理的亿级别的数据。另外他们的数据是类别变量和连续变量混合,连续变量有数量级差异;以及不可避免的脏数据。这些都很大程度上决定了要基于domain knowledge的feature engineering和针对数据特点的算法确定。现在想想也就不难理解为什么从在本科上统计课到现在看的Prof. Andrew Ng’s ML课程,大家拿到数据的第一步都是plotting:就是为了能更好的结合自己的domain knowledge来观察数据特点及预处理。另外说一句就是,在我看来从昨天的滴滴大数据应用到今天这场链家的机器学习应用,他们本质上处理的问题都是属于经济学范畴。与经济学中计量经济所不同的是,机器学习的方法更加暴力:分析数据就是分析数据,而不是先要把问题归类到经济,然后按照经济的科班套路先建模再通过数据验证模型的套路来解决问题。我这里不想说也不够资格说哪个解决问题的方式方法更好。我想说的是一个问题放在不同角度来解决套路真的是完全不一样。站在不同位置上看待同一个问题也许能会擦出更加明亮的火花?

Day 3

最后一天就是全天的专场了。前两天听下来基本上对System, ML方向有了个粗略的sense。到了第三天我就把重点放在了其他一些领域比如说区块链。这里我觉得讲的比较好的就是“区块链与大数据技术结合的商业应用”这场。可以看出的是区块链作为一个新兴技术,由于账本本身是公开的,可以把 这个想象成一个巨大的只支持insert和select的数据库,那么对于这个数据库里的数据挖掘和针对这个数据库所能做的一些优化就成为了现在区块链届关注的重点。据介绍现在这个账本已经有3,400G这么大。我另外了解到,分布式账本这种技术应用场景还是非常广泛的。比如说红十字会接受捐赠就可以利用区块链技术使得所有捐款信息完全透明公开。说句题外话,现在任何一个项目都需要不同类型的人才。系统,AI都有自己施展拳脚的空间。