2019 in review


Much of this year’s activities have been covered in my last two progress reports: the MT 2018 one covered July-Dec 2018, and the HT 2019 one covered Jan-Apr 2019. Therefore I’ll only report the new, notable things that have happened since my last post (new stuff in bold):

What I did this year


Month What I did
Jul 18 IMDA internship, Algorithms self-study
Aug 18 IMDA internship, Algorithms self-study
Sep 18 IMDA internship, Algorithms self-study
Oct 18 MT 2018: Micro, PolSoc, applied for internships
Nov 18 MT 2018
Dec 18 Trip to the US: Boston, Colby, LA, Roseville, SF
Jan 19 HT 2019: Macro, Theopol
Feb 19 HT 2019, cooking for OUCS, OUAPS
Mar 19 Finished HT2019, went to Finland and Romania
Apr 19 Back to SG, failed technical interview, broke up, found internship
May 19 TT 2019: QE, Thesis
Jun 19 TT 2019, started internship at Inzura

Things that happened

Started TT 2019

I remember being eager to start TT 2019, as I only had QE. I was also looking forward to working on my thesis. The weather was pretty nice—although not as nice as last year—and I enjoyed myself greatly.

Learned QE

QE was fun and challenging. I like probability a lot, and I really want to know it very well. I liked how James Duffy taught QE in a rigorous way: everything from first principles, mathematically derived, good stuff.

The things I learned in PolSoc were given a coat of mathematical rigour: omitted variable bias, heterogeneity, endogeneity, etc.

I actually quite like time-series as well, even though we didn’t really get enough time to learn it.

It’s five weeks into my summer vacation and I haven’t started revising it yet, in part because I’ve been busy with other things.

Started planning my thesis and coming up with many ideas


Lots of fits and starts, wrong avenues, and interesting ideas I had to throw away—in other words, par for the course for any research project.

My thesis started off with nebulous preferences I had. I had one main goal to do a topic as quantitative and algorithmic and “codey” as possible (playing to my comparative advantage + makes me look smart). I had three initial ideas:

  1. “Methods” paper: apply a new technique to political science, like use deep learning to predict voter turnout
  2. “Takedown” paper: show that a published paper suffers from some flaws
  3. “Proper” paper: what one’s “supposed” to do: generate a hypothesis, gather some data, test the hypothesis and present one’s findings.

There are pros and cons to each approach. A ‘takedown’ paper is relatively easy to do , but unfriendly and makes little contribution to the literature. A “proper” paper is how you’re supposed to do it, but I didn’t like the idea of having to come up with an interesting question and gathering data and so on and so forth… Lastly, a “methods” paper is the best of both worlds but is difficult to come up with.

My first port of call was Tak Huen, who reads political science journals for fun. He gave me four copies of Political Analysis. There was one paper that caught my eye: A New Approach for Developing Neutral Redistricting Plans by Magleby and Mosesson (2018), which opened my eyes to algorithmic redistricting.

My initial thought was to think about a better algorithm for algorithmic districting, but after a more comprehensive lit review I realised that this had already been done to death, and there would be little chance of me coming up with anything novel.

But algorithmic redistricting gave me some ideas.

I spoke with Jarel, who did a thesis on contestation in Singapore politics, and he mentioned that it would be worth looking at algorithmic districting in the Singaporean context. Singapore is rather unique in the sense that it has GRCS, multi-member constituencies. Jarel pointed out that electoral disproportionality actually lies on a spectrum. On the one hand, we have perfect PR systems. Then we have regular FPtP systems, with single-member constituencies. At the extreme we have a presidential vote, where there is only one seat.

Where we have perfect PR systems, seat share is exactly vote share. Where we have a presidential system, whoever wins 51% of the vote controls 100% of the government. FPtP systems are in the middle of the road, because while it’s theoretically possible for a party to win 51% in every district and control 100% of the government, this will be a statistically unlikely occurrence.

Singapore’s unique GRC system moves it towards greater district magnitude: obviously, by having a winner-takes-all GRC, there are less districts overall.

Now imagine you are the PAP, an incumbent government with > 50% of the popular vote. In order to maximise your seats, your strategy is clear. Increase district magnitude as much as possible!

Therefore I wanted to ask the question: how has the GRC system benefited the PAP’s electoral fortunes? The only way to answer this with any degree of certainty is to run simulations with alternative districting plans, and find out how electoral outcomes might differ. It would be a very spicy paper if I could show that under a set of fair districting plans, the PAP would have won X less seats. Unfortunately, political concerns and the Singapore government’s reticience to release election data mean that I had to abandon the project.

But this led me to consider a more general question: how do number of districts, district magnitude, voter homophily and malapportionment affect seat share, holding vote share constant?

On 10th May 2019 (Trinity Week 3), I wrote the following:

Holding constant voters’ preferences, I use a simulation approach to find out how varying the number of districts and the way in which we draw districts affects the seat share of the incumbent in a 2-party, first-past-the-post (FPtP) system.

Motivation: a government can redraw electoral boundaries in many (not just “cracking and packing”, but also the number of districts, how misapproportioned a district is), and even (through for instance the HDB racial quotas) affect the geographic distribution of citizens. To what degree does this help incumbents like the PAP maintain their hegemony?

and brought it to Sergi. Sergi liked it, but said before embarking on a thesis, to do the following:

Please have a look at the following readings and come back to me with a very succinct plan with 1- question (identifying a gap), 2- theory, 3- hypothesis, 4- design. If you find an interesting reading, feel free to have a look at their bibliography and pull interesting references to read as well.

Bassel has been absolutely the best person I’ve had in my life so far. He got really interested in my problem, sending me tons of emails, sitting me down and telling me how I should proceed. I really owe him a huge debt, and I wouldn’t even know how to begin repaying him.

Bassel and I spent three hours at his house, and we talked through the problem. He gave very good advice — simplify, simplify, simplify. Pare the research question into one very focused question, and make as many simplifying assumptions as possible. In my case, the most interesting question we identified was: How does voter homophily affect the ratio of vote share to seat share?

Then began over a month of false starts, dead ends, and hard thinking.

I started off

On May 20, Sergi said

Have a look at Jonathan Rodden’s publications under the section ‘Political and Economic Geography’. Please skim/read all of those, starting with ‘Cutting through the Thicket: redistricting simulations and the detection of partisan gerrymanders’.

Then I looked at Jonathan Rodden’s work, and realised

Talked to Andy with the two ideas…

19th July

Current status of my thesis:

Andy sent an email to Rodden and his co-authors, and I

“Deep thinking” is incredibly beneficial. I’ll elaborate on this later

  1. K-nearest neighbours algorithm doesn’t take into account geographical features/state boundaries, and may also overstate partisan dislocation
  2. PD is sensitive to voter clustering and can give false positives of gerrymandering if voters are sufficiently clustered.

Currently waiting on their reply. But have strategic concerns.

Is it a good idea to do my thesis on a specific part of a specific measure of someone’s research paper, even if that person is Jonathan Rodden?

Now in active contact with Eubank and co.

Started playing a bit of frisbee

After two years in Oxford, I’d settled into a routine. I’d wake up, faff around, go for lunch, study a bit, go to the gym, cook dinner with Martin, study a bit more, then go to sleep.

There are good things about having a routine. First of all, it’s worked so far: I’m reasonably productive. I am doing well (but not 100%) on my work. I enjoy the food that I cook. I have a little bit of social interaction every day (mainly with Martin lmao). I lead a very easy, stressless life.

However, the thing about having a routine is that it’s very routine. It is at odds with serendipity and doing new things and meeting new people. After I broke up with Judy I was happy to be alone focusing on self-improvement (“monk mode”), and so the routine fit me at the moment. But I knew that I would not find anyone new in university if I continued this routine.

So I told myself that I would not allow myself to say no to anything. And thus when Edelweiss asked me if I wanted to play frisbee with OUMSSA, I readily agreed despite my initial hesitation.

This is why I have been playing some frisbee. I quite enjoy it so far, but I’m incredibly unfit and will have to improve my cardiovascular fitness.

Sent Sergi off

On the 9th of June, Sergi very abruptly sent us this email:

I’m writing with some news: I’ve been recently offered a full professorship in comparative politics at the University of Glasgow, and this suddenly became my last term at Oxford. My wife has been offered the same position at the same department. This is a big professional promotion for both of us, and an opportunity to bring many years of commute to an end.

Sergi’s not kidding—this is an incredible promotion for both him and his wife. Full professor so young is seriously impressive! His teaching load will go down significantly as well.

Sergi absolutely deserved it. He was so good as a tutor: he really, really cared for me.

Here’s an example. How other Oxford tutors usually mark our work: maybe few ticks here and there and one paragraph at the end considered very good already. On the other hand we had Sergi — - my weekly essays were ~2000 words long, and he’d always give feedback of 500-600 words at least, sometimes even 1000, which is half the length of my essay! I always looked forward to his comments on my work — it was like receiving a Christmas present. And it was his comments that spurred me to work harder and put more effort into my work, and aim to improve every essay.

I know that he fought very hard for me last year when I was applying for special dispensation to do a Thesis in Politics without doing three other politics subjects.

And I wouldn’t be surprised either if he was the one who recommended an Exhibitionship to be given to me, even though I did not get a Distinction in Prelims.

A poem came unbidden to my mind, and it struck me how apt it was for the occasion. In this poem the poet and his close friend are both scholar-officials posted far from their homes. The poet’s close friend has received a new, prestigious posting hundreds of miles away.

I translated it for Sergi’s benefit, but I am of course no poet:






Sending Off Vice Prefect Du on His Way to His Post in Suzhou

O’er the spires and walls of the Three Qins, our land,

There in wind and white mist, the Five Rivers descend. We must say our farewells, leave each other behind

For the faraway posts that our liege lord’s assigned.

While the vast seas bind us, we remain bosom friends;

We are neighbours in heart, though apart at sky’s ends.

Though our paths must now part, and I hold you most dear,

Lest like children we weep, let us hold back our tears!

Sergi was a truly, truly exemplary tutor, and I am so sad to have to bid him goodbye. I did not just lose a thesis advisor; I also lost a beloved teacher, mentor and friend. It was truly my good fortune, and an absolute honour, to have been his student these past two years.

See you soon, Sergi!

Said goodbye to finalist friends

I consider the following people my (especially) good friends, and am incredibly saddened to part with them. At least I will see the Singaporeans again.

From last year’s year in review post:

I must thank Jarel for speaking with me about my essay and providing me with my key theoretical insight, which came serendipitously two days before the essay deadline! I had ran the regressions earlier and were very frustrated that I couldn’t replicate Lijphart’s results. But Jarel said that in and of itself is a very significant finding!

Jarel: let me get this right: when you control for fixed effects, effects on gender become insignificant — right?

Me: yes

Jarel: ok that’s a very strong result

Jarel: i think i might have to rewrite the analysis bit as well

Me: im very excited now actually because of what you pointed out

Me: haha

Got a place in Guildford, Surrey

Now that I’ve started my internship, I really appreciate this place. The office is a 6 minute bike ride from my house and Tesco’s is also very close by.

Guildford is very quiet. I am lonely and have no friends here. There is nothing else to do but sleep, work, gym, and eat….

Started my internship at Inzura

Thanks to Mrs Hauw, I was able to intern at Inzura.

I love my work. I’m currently working on two main projects, with a third KIV if time permits. They are:

  1. Use deep learning to imitate the outputs of an existing (highly-complicated) rule-based system—the Driver Profiler—without doing slow and expensive database lookups.
  2. An intervention plan to triple the active users of a client company: Build a pipeline to send reminder SMSes to customers who have not installed the app, track who has clicked and who hasn’t, and use Thompson sampling to converge optimally onto the most effective SMS.
  3. (KIV) Program the cluster of 20 Raspberry Pis to perform distributed computing: MapReduce analysis on 2 million trips, running parallel copies of the Driver Profiler…etc.

Life in review

Now that I have talked about the things that have happened, let’s start the review. I want to review every facet of my life this year, and also set a course for the next.

Values, purpose, character and identity

What do I value? What don’t I value? What do I believe in? What sort of person am I? What sort of person do I want to be? Why am I here? What do I want to achieve?

I value the following traits: rationality, openness and honesty, intelligence, intellectual curiosity, competence, diligence, ambition, and above all, constant, relentless introspection and self-improvement,

I value the following things: knowledge (in particular the use of knowledge to make good decisions), free time (to spend with the people I love, and to pursue my interests), having a healthy and aesthetic body, and eating and sleeping well.

I hold the following beliefs (some more strongly than others): broad-strokes consequentialism, paternalism, atheism, rationalism.

I don’t value these things: money (and its trappings), power, status. (But see below for musings on money and power).

What sort of person am I?

If you asked me this question a few years ago I would have said that I was a smart but lazy person.

Now I don’t think I’m lazy anymore. This is because I can and do work very hard on things that are important to me, often going above and beyond what a normal “hardworking” person would do. For instance, I’ve put in a lot of effort into my two theses and my internship.

I think I have a very one-track, binge-type personality. When I get interested in something, it will consume my thoughts to a great degree, and I find it difficult to be interested in anything else. It also manifests itself in my addictions: occasionally I find myself bingeing clips of the Office or House and being unable to stop until 6am in the morning. In contrast, if I am not interested in something, then I will keep putting it off and it is very difficult to get myself to do it.

I think I still have a very lazy 本性, possibly very poor self-discipline. It is incredibly difficult for me to do the things I don’t like to do or am not interested in like laundry, or admin work.

I’m a person who’s very interested in knowledge and truth. I am a natural skeptic. This often manifests itself as argumentativeness and disagreeableness, which many find unpalatable. I understand (and have had first-hand experience from Mark and Filip) that it is disheartening to have one’s ideas shot down, but this is how our brains usually work — we focus on the negatives.

What sort of person do I want to be?

I want to be a more open and honest person. I think it’s Good to be open and honest, and hopefully other people reciprocate. That doesn’t mean saying mean things for the sake of saying them, but it does mean saying mean things if there is a good reason to do so.

I want to be a more generous person. To this end, I have started being more open with my money.

On myself: I know I have a frugal mindset and will not anyhowly buy things. I’ve tried to be less price-conscious when buying important things (like e.g. my trip to Finland and my trip to Romania—don’t care just buy the ticket only). I’ve tried to be less concerned about money in general e.g. not asking to split groceries cost when inviting people over for dinner, which is again something Judy mentioned.

I’ve been trying to treat my friends, like buying them dinner to show my appreciation for them. I want to keep this up and improve upon this next year.

As a generalisation of that second fact, I want to be a more kind and selfless person. I have a laser focus on my own self-improvement, but what about for others? When I was with Judy I prioritised my self development over spending time with her and making her feel loved.

How do I be a more kind and selfless person? There are not really any metrics to measure this, are there?

One metric — giving to charity.

It’s important not to virtue signal. If I do give, I should tell no one.

What am I here for? What do I want to achieve?

What’s the point of life, anyway?

Broadly consequentially speaking, it’s to maximise happiness (not the sort of electrodes-in-brain happiness, but a notion of “higher-order” happiness, as ill-defined as that might be).

I want my life on this earth to increase the happiness of those around me in greater and greater concentric circles. First increase my own happiness, then my family and friends, my fraternal organisations, and the wider world.

修身 齊家 治國 平天下

It may seem selfish to focus on myself and my immediate family first. Actually I have been thinking quite hard about this: given my intelligence, interests, personality and work ethic, I may have a chance of making a big impact on the world. At the very least, were I to try and optimise for income, I could make a lot of money and earn to give. The reason why I am trying to pursue early retirement is because I believe it will me and my family members happier. But maybe this is a selfish thing to do—maybe I have a moral obligation to not retire early and instead make more money to give, or forget my family to start a startup that changes the world.

But this is somewhat of a false dichotomy. Once I achieve financial independence and retire, I don’t plan to do nothing. In fact I probably still plan to work very hard, because I enjoy working hard solving difficult, interesting and impactful problems.

I’m not entirely sure what to think. I plan to have children, and I plan to spend a lot of time with them. What is the right amount of time to spend with one’s children? If I spend too much time with them, I necessarily will neglect my solving difficult, interesting and impactful problems. But if I spend too little time with them, then I haven’t really retired in the first place, have I?

They say it takes a village to raise a child. So I had some thoughts about the utopian village: a community of financially-independent parents with different skills and backgrounds, and we’d get together to raise children together. The children would be home-schooled in the best sense: highly personalised instruction, yet without the social isolation that makes many home-schooled kids a little weird. Can you imagine the fount of knowledge that would be available to the children, and the amazing projects that could happen?

The advantages of doing this is that it would scale by division of labour — instead of pouring my heart and soul into my one or two children, I could very easily teach more, and my burden would also be lessened because the workload can be shared. There are also knowledge complementarities.

Of course this is utopian — nobody is going to buy into this idea apart from me. But even if this never materialises I do want to give my children opportunities to learn from the best — for instance, if Tak Huen is in the US, maybe I can fly over and let my kid learn from him for a week about political science. Or Oskar or Rayhan could spark my kid’s interest in quantum mechanics. There are so many smart and passionate friends I have around me. Imagine being a kid again, with all the time in the world, and neuroplasticity — how amazing that would be if the kid could be surrounded by all my smart and passionate friends.

Contribution and impact

In my not-very educated opinion, the three biggest priority problems are global warming, poverty, and AI risk.

These problems are somewhat biased by saliency (saliency of my particular news bubble), but I’ve tried to update my knowledge with reports from EA.

global warming: huge problem, but my marginal impact is low

what are the biggest things I can do in my life to reduce my carbon emissions?

Location and tangibles

Currently in Guildford, Surrey. Quiet place, no friends, a lonely existence.

But good place to hunker down and “monk mode”.

Had to purchase some kitchen supplies to make myself able to withstand cooking. One of the best purchases was a big pan.

Tangibles: in Oxford, I pretty much have everything. Huge stock of kitchen equipment and supplies; have been slowly accumulating them over the years.

I can cook basically anything in any style apart from sous vide: I can steam, blanch, roast, bake, pan sear, pan-fry, stir-fry, deep-fry…

I want to get rid of some of my old clothes that are too small or have become discoloured.

I have too many files and books, but I like files, I like paper stuff.

Next year: I was originally worried about having too much stuff, but I realised that Martin will happily take all my cooking stuff when I leave.

Money and finances

I’ve done really well in this domain. Both spending and investing are on autopilot, and I haven’t had to worry.

I no longer keep a day-to-day budgeting log, but I’ll have to go through this year’s spending, and see how much I can take out of my allowance to invest.

OK, I’ve just looked through my spending. So scholarship has given me a total of 55k. I’ve spent 29k over a period of 6 terms and 5 holidays (MT 17/18, HT 18/19, TT 18/19), and invested the surplus (25k).

I spend about 3.5k every term or 2k pounds, that’s 21k over 6 terms. I have spent 8k on holidays, which is around 1.5k per holiday on average. (sounds about right: 2.3k for MT2018 and 1.5k for HT2019)

I have 3 more terms to go: that’s 6k more pounds, or 10.5k SGD. Including the holidays, it should be around 15k SGD. That means I should have 9,000 GBP in my accounts, and the rest can go to investment.

Career and work

I’m very pleased with my internship, and very thankful to Mrs Hauw and Richard for giving me this opportunity.

I love my work.


Novel: I’m learning new technologies and touching new stuff every day. In the span of two weeks I had to learn relational algebra, how to write a deep learning pipeline, how to use Keras to build a deep learning model, multi-armed bandit algorithms, etc.

Challenging and interesting: The projects are very interesting and challenging, and the faster I learn, the faster I go. There’s thus this huge internal drive to push myself to learn as much as I can. The projects also dovetail well with my schoolwork: I was able to connect the SMS intervention program to loss aversion in BEE, and it could be one of the experiments in my thesis.

Open-ended, no blockers: Richard tells me what projects to work on, but the projects are very open-ended. This suits someone like me, who learns quickly: if I put modesty to the side for a bit, I feel like any one of the projects could have taken a lesser mortal (kek) 8 weeks. But part of it is that I work independently—there are few if any blockers, I can go as fast as I want, and I have enormous latitude to tackle the problems the way I see fit. Because the projects are very open-ended, I have to plan the “grand strategy” or “grand plan” of how everything will work and how everything fits together, which I find very rewarding. Talk to Anthony Masih about this — he’s a systems engineer.

No bullshit work: I haven’t been given any admin or accounting or intern “get coffee for us” type of jobs. I’ve been treated very much like a full member of staff. In fact I feel like I get preferential treatment because as the “data scientist”, Richard makes the SWE team bend over backwards to give me data.

Very short commute: It takes me literally 10 minutes by cycle to get to my work. I was very lucky to have found a room very near to work.

Of course, no job is perfect. There are a few things that are non ideal:

There’s not really anyone to learn from: I’m the only “data scientist” on the team, and I really wish there was a senior or Chief Data Scientist I could learn from. As it stands, nobody really checks my data science or deep learning work, and when I get stuck on statistics or deep learning there’s only the Internet to learn from. (On the other hand, my colleagues are a great help if I have programming or software engineering questions.)

Not much welfare: Celine’s internship has “tech days” every Friday, and she started her internship off with a stay at a 5-star hotel in the Alps. I think she recently went for a brewery trip and free dinner as well. On the other hand, my workplace orders in three pizzas every Friday. (This complaint of mine is half-facetious.)

After only two weeks, I really like my work, and am seriously considering applying to do data science as a Master’s. I was thinking that I may make a stronger application to data science compared to computer science, which is a field where I have no comparative advantage whatsoever.

Health and fitness

I haven’t been nearly as consistent as I would’ve liked in going to the gym. However I think I’ve picked up several habits that are making me healthier:

Sleeping and waking up early every day (going to lectures/work without having to set an alarm).

This is the biggest change: not sure what exactly sparked it,

At the start of Trinity I realised that I have very bad mobility, and horrendous internal shoulder rotation. This is without a doubt caused by my penchant for benching a lot and my aversion towards any sort of horizontal pulling exercise (rows and deadlifts). I still don’t like deadlifts but I’ve been trying to row three times a week.

I actually got an injury! Very interestingly I was at OXCAR practice, and I tried to do two-finger pull ups on a door frame. All of a sudden I heard a loud “snap”, I fell to the floor, and felt a throbbing pain in my left ring finger.

Like an idiot, I actually jumped up and tried to do the same thing again. It hurt (even more), so I stopped.

Two weeks and several clinic visits later, I found out that I had actually partially ruptured my flexor digitorum profundus (“deep bender of the fingers”) tendon, on the distal phalange (last joint of the finger).

I was advised not to do any more finger-only pull-ups on a door frame, which I suppose is prudent advice. Nearly three months later, it still hasn’t recovered fully; so I’ve laid off climbing in the meantime.

As mentioned, I’ve also been playing frisbee, trying to get my resting HR down because bradycardia == fit, right?

Education and skill development

This year I did:

  1. Micro
  2. Polsoc
  3. Macro
  4. Theory of Politics
  5. QE
  6. Thesis in Politics
  7. Behavioural and Experimental Economics

I didn’t work very hard for Micro or Macro, which is nonideal. In Hilary Term I really slacked off a lot; somehow that term I wasn’t able to be productive and motivated.

I worked very hard on PolSoc, thinking that I would do some PolSoc for my thesis, but it turns out that my thesis will be on something completely different. (I don’t really find PolSoc interesting; this is despite it being taught incredibly well by Sergi).

I have the summer to work on QE and my two theses. I need to grok probability very, very well—firstly, because it’s difficult and cool; secondly, it’s the foundation of QE, and I believe that once I understand probability deep in my bones I’ll be able to do the rest of QE easily; thirdly, I have very recently become interested in Bayesian inference, and a firm grounding in probability – in particular the maths behind conditional probability — will be necessary.

I am proceeding at a good pace for my two theses. However, I am being blocked for both of them.

For my thesis in Politics, I recently messaged Jonathan Rodden…

I am proceeding at a good clip for Behavioural Economics.

I am not sure how much Econometrics will help in my application compared to Game Theory. But I’m thinking that having fun is more important. So still more keen on Game Theory for now.

Do I need a Master’s?

Pros and cons of doing a Master’s:

Things I think I have a reasonable grasp on (comparable to a decent but not top-tier undergrad who’s done a course on it):

Things I have some exposure to, but I don’t think I am competitive at:

Things I lack in my education:

The big question is:

How can I make the most competitive application to a top-tier Master’s program in three months’ time?

I should start to

GRE — find test dates ASAP, and have to mug for it.

Looks like this summer will be incredibly busy!

All the Master’s programs have one thing in common: they all say that understanding of probability/statistics, linear algebra and calculus is critical.

So I need to have worked though courses in linear algebra and calculus. There is a Coursera specialisation on it and I think I will apply for IMDA funding.

From the CMU Master’s in Computational Data Science:

The application requires a statement of purpose. What makes a good essay?

We are looking for strong, experience-based evidence that you can do well in our degree program and that you “fit” based on our areas of focus. For example, a description of a large software or research project, your involvement in the project, and the impact of the research is good evidence. An explanation of what drew your interest to the MCDS program and how it relates to your professional goals is also useful. You may also take this opportunity to explain any apparent weaknesses in your application.

In conclusion, I think that following this game plan will best increase my chances of a successful application:

  1. Start practicing for GRE, book test date, possible retest
  2. Start learning linear algebra and calculus (from Coursera specialisation, Strang’s lectures, and Coding the Matrix
  3. Demonstrate competence in linear algebra, possibly by doing a project (‘de-perspectivising’ videos?)
  4. Demonstrate competence in multivariate calclus — maybe by writing a primer on Bayesian
  5. Make use of my internship to kill two birds with one stone: do BEE AND make sure internship is related to data science AND learn as much as I can as possible (the equivalent of an undergraduate course: networking, databases, distributed computing)
  6. Document, document, document—I do a lot of good work, but I have to write down exhaustively what I did, and why it’s impressive

Social life and relationships



Finalist friends


Emotions and well-being


Productivity and organisation


Although I haven’t really been very productive throughout the year,

Discovered Complice

Discovered the awesome power of deep thought

By this I mean if I lie down and close my eyes and do nothing except think really hard for half an hour, I can solve difficult problems.

Something that also helps me is talking to others about it, although I’m not sure if this is me solving the problem or them solving the problem.

One of the things I really like to do is to have college lunch and then retire to OWL’s reading room. I’ll lie down on the couch, put a book over my face, close my eyes and think about a problem that I’ve been having (usually thesis), and drift off to sleep. When I wake up, I’ve usually thought of a solution (or a possible approach) to the problem.

Now I didn’t really think much of this until I was able to harness it more consciously recently.

Two breakthroughs:

As mentioned, one of my initial ideas was to use simulation to compare an observed PD value under a specific districting plan with a reference distribution of PD values under different districting algorithms. It turns out that Rodden had already

I was thinking of crafting an email to Rodden and co-authors to think about how I could build upon their research.

I had a very sketchy outline in my head/very weak intuition about criticisms with the PD informed by my previous exploration with it.

It makes me feel like I have a superpower

Things I need to keep thinking about:

I need to allocate time every so often for deep thought:

Adventure and creativity



This year has been wonderfully kind to me. I continue thriving at Oxford— I am enjoying the company of my close friends, immersing myself deeply in interesting, challenging academic work, and having the spare time to live life in an unharried, serendipitous manner.

I grew as a person dating Judy. She precipitated a sea change in my attitude towards money. The relationship has given me much more clarity about the kind of person I would best click with.

My scholarship continues to give me money, which has allowed me to be blissfully insulated from pecuniary worries. I have become financially secure and I now want to make a deliberate attempt to be generous towards my friends and family.

My internship is very interesting and challenging, and has just the right balance of structure and open-endedness to hook me. The CEO, Richard, is gregarious and really knows his stuff. Having done a bit of data science at my internship, I think I’m happy to pursue it as a Master’s degree.

I picked up a new productivity habit (Complice), and it helps that my thesis and BEE mini-thesis are chugging along nicely.

Overall, I’m in a good place. Self-esteem is high because of my social relationships, academic success. No troubles due to financial stability. Good internship thanks to my eternal benefactor Mrs Hauw.

I made a comment two years ago:

The grind never ends—not after my As, not during army, not even after I have gotten my scholarship. But I am beginning to realise that I may like and even need this grind in my life more than I think I do.

If only I knew how true this comment would prove to be.

Post list