Author Archives: Henry (James) Broster

Work Hard, Play Hard: Balancing Sport and Studies at Oxford

I have always been involved in sport, having played football for Bristol Rovers and at county level when I was younger. That continued after arriving at Oxford, although in several different forms. In my first year, I ran the London Marathon for Alzheimer’s Research UK. In my second, I rowed with my college, and more recently, I took up boxing, competing against Cambridge to pick up the illusive ‘Blue’ and accompanying blazer.

Trying to pursue sport alongside a PhD has not always been easy, but it has been one of the most enjoyable parts of my time at Oxford. It has helped me step away from work, manage stress and maintain a competitive outlet outside academia. At the same time, Oxford sport can become extremely demanding, so finding a balance is sometimes challenging!

There is always more work to do

One of the difficulties of doing a PhD is that the work never feels completely finished. There is always another paper to read, experiment to run, result to analyse or paragraph to improve. If you wait until everything is done before exercising, you may never leave your desk (which is sometimes the case).

Sport creates boundaries that research rarely creates for itself. Training begins at a fixed time, teammates expect you to be there and competitions cannot be rearranged around your workload. I have often gone to training feeling that I should have stayed at my desk, only to return less stressed and able to concentrate more clearly. Taking a break can feel unproductive, but spending another hour staring at the same problem is not always the best way to solve it.

What sport adds to academic life

Sport develops many of the same qualities that academic work demands. Training consistently requires discipline, progress is rarely linear and poor performances teach you to keep going when things are not working as you hoped. It also provides a competitive outlet and a clearer sense of progress than research often does, which can be valuable when your PhD feels slow or uncertain.

Just as importantly, sport introduces you to people beyond your department and gives you something meaningful outside your work. During a PhD, it is easy to let academic progress determine how you feel about yourself. Having another community and another part of your life that matters helps keep a failed experiment or unproductive week in perspective.

Of course, sport does not need to be justified entirely by the lessons it teaches. At the end of the day, it is something I enjoy doing.

When play becomes more work

Although having another ambitious goal alongside your studies can also be extremely rewarding, the difficulty is that sport at Oxford can quickly become a serious commitment. Chasing a varsity place can demand a great deal of training, recovery and focus. Sometimes sport may genuinely matter more than your studies (in the lead-up to a competition, for instance), and that is fine, provided it is a deliberate choice and that things are still kept in perspective, rather than something you have been swept into without considering the trade-offs.

Finding the balance

Balance does not mean dividing every day equally between work and sport, nor does it mean always putting your studies first. There will be periods when training deserves more attention and others when academic work must take priority.

Moderation does not mean lacking ambition. You can train seriously, chase difficult goals and care about winning while still maintaining perspective. Equally, working hard does not require allowing your PhD to consume every other part of your life.

Sport has made my time at Oxford busier and occasionally harder to manage, but it has also made it far more enjoyable. It has given me friendships, challenges and experiences that I would never have found through academic work alone. It may even have made me better at my PhD, but that is not the only reason it was worth doing.

An Introduction to the Basics of Reinforcement Learning

Reinforcement learning (RL) is pretty simple in theory – “take actions, get rewards, increase likelihood of high reward actions”. However, we can quickly runs into subtle problems that don’t show up in standard supervised learning. The aim of this post is to give a gentle, concrete introduction to what RL actually is, why we might want to use it instead of (or alongside) supervised learning, and some of the headaches (figure 1) that come with it: sparse rewards, credit assignment, and reward shaping.

Figure 1: I’d like to help take you from confusion/headache 🙁 (left) to having a least some clarity 🙂 (right) with regard to what reinforcement learning is and where its useful

Rather than starting with Atari or robot arms, we’ll work through a small toy environment: a paddle catching falling balls. It’s simple enough to understand visually, but rich enough to show how different reward designs can lead to completely different behaviours, even when the underlying environment and objective are the same. Along the way, we’ll connect the code to the standard RL formalism (MDPs, returns, policy gradients), so you can see how the equations map onto something you can actually run.

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Reflections on GRC CADD 2025: A Week of Insight, Innovation, and Baseball

Henry

Back in July, some very lucky OPIGlets ventured across the pond to discover life in Southern Maine (and Boston!). For someone visiting Boston for the first time, no trip would be complete without a Red Sox game—a thoroughly enjoyable highlight (see Figure 1). While we were there, we also went to Gordon Research Conference (GRC) on Computer Aided Drug Design (CADD).

A flock of OPIGlets taking in the Fenway Park experience at a Red Sox game.
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Featurisation is Key: One Version Change that Halved DiffDock’s Performance

1. Introduction 

Molecular docking with graph neural networks works by representing the molecules as featurized graphs. In DiffDock, each ligand becomes a graph of atoms (nodes) and bonds (edges), with features assigned to every atom using chemical properties such as atom type, implicit valence and formal charge. 
 
We recently discovered that a change in RDKit versions significantly reduces performance on the PoseBusters benchmark, due to changes in the “implicit valence” feauture. This post walks through: 

  • How DiffDock featurises ligands 
  • What happened when we upgraded RDKit 2022.03.3 → 2025.03.1 
  • Why training with zero-only features and testing on non-zero features is so bad 

TL:DR: Use the dependencies listed in the environment.yml file, especially in the case of DiffDock, or your performance could half!  

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My CCDC Science Day Experience

In June, I had the opportunity to visit the Cambridge Crystallographic Data Centre (CCDC) for Science Day to give a lightning talk on my rotation project with OPIG. The day was packed with presentations from researchers and PhD students collaborating with the CCDC, offering a great opportunity to hear about some of the fascinating work happening there in the fields of Structural and Computational Chemistry.

We kicked off with a dinner at the University Arms in Cambridge. This was a great opportunity to meet people who were attending Science Day in a relaxed environment, complemented by the lovely food and drink.

The next day was all about the talks. The first part of the day was filled with longer talks by more senior PhD students and CCDC researchers, followed by lightning talks from first-year PhD or master’s students. These shorter presentations provided a fast-paced overview of each project.

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