More advice over breakfast

We had a breakfast talk this morning, and I was one of the two organizers. The topic that I planned was “How to cope with your first PhD semester?” but it the talk soon widened up to coping with a PhD in general. There were interesting ideas around, most of which came from SL, our guest-of-honor for the day.

  • Many useful pieces of advice is contained in this blog post on the SUTD Brain Lab website
    • Have a notebook to write down ideas (still experimenting with different ways to take notes)
    • Have a personal website (I’ve already done this, yay!)
    • Have regular meetings with your advisor (thankfully, I don’t have to push for this as my advisor is super “on”)
    • Write down minutes of the meetings (need to start doing this)
    • LaTeX (I taught myself LaTeX—with helps from Google, of course—and I’ve been submitting homework and reports in LaTeX since the middle of my first semester).
    • GitHub (Sean’s been telling me to do this, and then Zunction wrote a nice tutorial and asked me to try out. I’m gonna start tomorrow).
    • Python and other Python related stuff (during the summer I started learning Python, but I was also learning R at the same time. And I found learning two languages at the same time difficult. So right now, Python’s status is “in the queue”).
  • Every 3 months, sit down in a quiet corner and have a mind map to connect all the research ideas into a big picture. Eventually, you’re gonna have a giant mind map that is the gist of your thesis.
  • Have a “research summary” every month to consolidate all the things you’ve come across in that month. Well, I actually did this in my summer project—I wrote 3 reflections in those 3 months. But I have to admit those reflections were more on feelings than on science.
  • It is the act of sorting and organizing ideas and references that is the most important. It is a mental exercise that helps you have a “helicopter” view of your work. The actual end results of the sorting is less important.
  • Sometimes it is more important to let people experience things and come to the conclusion on their own than telling them everything.

Now, those are practical and specific tips, but here’s the coolest one: in your PhD, it is super important to work with other students. SL has seen very smart PhD students working alone and ending up nowhere while less smart PhD students succeeding by drawing supports from a group.

I have to say there are many cool people around here. It’s great!

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A piece of advice, and two stories

Here is the advice that corrected my course: the physical system is important and should never be neglected.

I am currently doing some computer experiments on a small river basin. I ran a statistical model on the data last week, and yesterday reported how the model performed. I then discussed some ideas, but was still focusing on modelling. Interestingly (to me), before even looking at my graphs, my advisor picked up his calculator and converted the monthly flow (in million litters per month) to instantaneous flow rate (m³/s) and told me “It’s a creek” – something I never thought of. As we went on, he told me “We are not just crunching numbers”, but modelling a physical system, and knowledge of such system is important in understanding the model results.

Here are two stories I heard on a pleasant bus ride that broadened my perspective:

Story 1: a young and rising computer scientist was presenting deep learning to a group of senior experts. This was in 2011, a year before the sweeping prominence of the algorithm in literature. As the presentation was about how superior deep learning was compared to other known algorithms, it offended the elders and they criticized him for not paying attention to the real phenomena underlying computer vision. The young scientist’s point was to forget about all the sciences and just let the neural networks crunch the numbers. One year later, the machine learning community showed that he was right.

Story 2: a machine learning guy, let’s call him Tom, was interviewing for an attractive job. To make sure that this guy could actually program, the panel gave him the fizz buzz problem. In a nutshell, Tom had to write a programme that runs through a series of numbers between 1 and 1000 (the range varies depending on the version you hear), for each of which, print “Fizz” if said number is divisible by 3, “Buzz” if it’s divisible by 5, “Fizz Buzz” if both, and just the number itself if neither. Tom thought very hard and proposed the following algorithm:

  • First, create a training set of 200 random numbers between 1 and 1000
  • Label them (“How do you label them?” – asked the interviewers. “Well, I will use if…else… commands like this…” – said Tom, at which point the interviewers thought it was fine to stop, but Tom went on)
  • Run a neural network to learn the training set (and describe the network sophistically Tom did)
  • Now run the trained network on the test set (“The network will perform with at least 97% accuracy” – said Tom proudly).

The first story is anecdotal while the second one is sort of a parable (I wanted to use “parablic” but apparently that’s not a word).

The moral of the stories is: don’t underestimate the power of machine learning, but don’t overestimate it either.

I am really enjoying my PhD. There are many good people around.

Just one last note. In my school, we don’t have “PhD supervisor” but “advisor”, and I love the term. No “supervisor” means I am independent, while having an “advisor” means I will not be wandering alone in a “random forest”.