Training the PhD supervisors

I just completed another "training the PhD supervisors" course, in anticipation of my first Cambridge PhD students. I have a few thoughts on training supervisors, but first my credentials and context: 

1. Unlike most science professors, I took formal training in higher education, through a two year part-time Graduate Certificate program, and have published on PhD training.

2. 26 PhD students as supervisor (16) or co-supervisor (10). Of these, 18 graduations, 6 students still in progress and 2 drop-outs. Some easy experiences, where the students flew though. Some wonderful experiences, where I really got to help the student grow and flourish. Some steep learning curves, where the student and I took longer to get it together, but ultimately we both learned from the experience and the student suceeded. Some nightmares, that had me on the edge of quitting and occasionally still give me insomnia. I am a better supervisor today than I was 10 years ago, and hopefully I will be a better PhD supervisor in 10 years than I am today.

3. I see the PhD as a program where you create the environment that gives the student the opportunity to grow. This is difficult, since it involves understanding the student and pushing them just the right amount to stimulate them without intimidating them. The PhD for me is a highly versatile program, and I am happy for it to steer towards many different outcomes based on what the student is aiming for (academia, industry, etc).

So, my thoughts on training programs for PhD supervisors

First, they are necessary. The messages end up being fairly simple. Remember your PhD student is a person as well as a student. Learn that your student has different needs and expectations that you did as a PhD student. Learn to listen to their expectations, learn to be explicit in your expectations, be prepared to discuss and compromise. Document and revisit discussions. Learn the boundaries of reasonable expectations on both sides. Learn when to bring in extra help, learn where that help can come from. While these messages are simple, for many PhD supervisors it will be the first time they've explicitly heard them, and often new supervisors rely excessively on the lessons of their own n=1 PhD. 

This is the raison d'être of these training programs, and the central work is typically done well. There are several common failings, however:

1. Pedagogy has a teaching problem. Education is an advanced academic field, with a highly specialised language, just like other fields. Unfortunately, many education experts use this language when training PhD supervisors. It is a major turn-off, especially to STEM academics, where even common humanities terms can be opaque or even just mystifying. Most supervisors are going to get less than one undergrad credit worth of education training - the use of specialist language is unnecessary and a barrier to concept uptake. I fully acknowledge that STEM disciplines have the same language barrier. I hope that one day there is a concerted effort to bring knowledge from STEM into humanities - and at that point we will need to learn the language of humanities to effectively communicate. But during supervisor training the onus is clearly on the trainer to use discipline-neutral language.

2. Humanities and STEM are just too different. The PhD programs are so different, in style, outcome and supervision, that examples and advice end up being so generic it is of little value, or it jars completely with one of the fields. Just split up these training courses into humanities and STEM, replicate the common content and specialise the field-specific content. 

3. Supervisor training programs are too reactionary. A common mistake for new supervisors is to focus on correcting problems that they experienced during their own PhD. It can result in them being blindsided by different challenges. Ironically, the very classes that teach this are often guilty of the same problem. These courses are designed around the failings of current senior faculty. It is almost "what do we wish our senior lecturers had been taught 20 years ago?" in content and context. In STEM, the biggest failure in the senior supervisor population is the "sink or swim" mentality, which essentially assumes that any student who struggles is not cut out for a PhD (i.e., the failure is entirely in the student). This is demonstrably incorrect and propogates major problems of inequality. However, while this flaw is common in senior supervisors, it is becoming extremely rare in junior supervisors. When given problem examples, junior supervisors tend to first assume the failures are entirely in the supervisor. I have seen more issues arise from junior supervisors trying to be a friend to their students, or over-committing their time to a single student, then I have from junior supervisors neglecting their students. This is not to say that neglect is not a problem - it is, and needs to be addressed. However training courses for junior supervisors should better reflect the problems that are common in junior supervisors. 

4. Training programs are less valuable because they are siloed. This training is focused on the well-being of the student, and is essentially dedicated entirely to situations where the student has a problem that can be fixed by behaviour-change in the supervisor. We know, however, that junior faculty are under enormous stress, rife with anxiety. One of the biggest sources of stress can be the very rare cases of problem students. This situation, of a problem that requires behaviour-change in the student, is almost entirely neglected in supervisor training. We are trying to fix one side of the equation in this training, and the other side is often entirely neglected or dealt with in a generic "stress resilience" training course (which also assumes the flaw is in the faculty not being able to deal with the stress). What we need is integrated training. Pitch us the same problem scenario twice, but with different missing context. Walk through the problem scenario with missing context A, where you need to change. Walk through the problem scenario with missing context B, where the student needs to change. Discuss how to identify developing problems, how to reflect on whether you are dealing with a context A or context B issue, and what practical steps to take in each context. I really dislike the problem scenarios where we are expected to take a one paragraph description at face value - real lab problems are never that simple, and always involve looking at a problem from multiple perspectives. Real solutions always involve trade-offs. Let's not pretend to junior supervisors that they will be in a situation where they can just invest limitless time - there needs to be hard barriers to stop work-life imbalance on their side. Let's also not pretend that a supervisor-student relationship exists in isolation - it has impacts on the entire lab, and trade-offs are always required. Perhaps this comes from a STEM vs humanities divide, but I see the concept of the team/lab almost entirely neglected in problem scenarios and trouble-shooting.

Finally, a little self-reflection. I would give this particular training course a 9/10 - probably the best I've been through. And yet 90% of what I wrote is a criticism. Occupational hazard? I think in STEM we move very quickly on from the success to trying to fix the failures. I know that when I run evaluations I need to force myself to stop, and say "well done on X, Y and Z. These are important. Congratulations. Now let's talk about A, B and C, which need some improvement...... Again, well done on X, Y and Z."

Position open for a data scientist

We have an exciting opportunity for a post-doc or staff scientist to join the Liston lab at The Babraham Institute. The role will be responsible for leading the development of data analysis methods and bioinformatics pipelines for immunology projects. This is a perfect position for a computationally-orientated scientist who wishes to model real biological data and is willing to learn immunology.

Applicants will have a PhD in mathematics, computer science or bioinformatics. For those with additional postdoctoral experience, a more senior position with added responsibilities is available. Applicants from diverse academic backgrounds are encouraged to apply, immunology experience is not essential. Experience at being embedded in a wet-lab environment, prior work on flow cytometry or single-cell RNA-seq data, or experience in machine learning will all be considered strong assets.

This job is ideal for a strong mathematical candidate who wants to apply their knowledge to biological problems. The successful candidate will work as part of a wet/dry mixed lab, develop novel tools, analyse data, create mathematical models and aid in the design of experiments to test those models. The job has potential career development into a long-term staff scientist, or would suit candidates looking to develop skills for an independent position.

The Liston lab is a fun, international and multi-disciplinary environment, which welcomes diversity and supports the career growth of lab members. 

Learning all about the immune system

Happy readers of "Battle Robots of the Blood"!

Coronavirus is infectious before illness

Coronavirus science simplified: number 6. This article in Nature Medicine looked at the amount of virus present in patients before and after they got symptoms. The data is clear: you can spread COVID19 before you actually get sick, so wear a mask! Read the original paper, or see the illustrated abstract by Tenmai.

What we are doing during the COVID-19 pandemic

This is a strange time for any workplace. People suddenly working from home, large changes in job duties, some people left without much to do while others are expected to manage whole new realms of bureaucracy while also continuing their full-time job. For us, as an immunology lab, this pandemic has an added dimension of peculiarity: our work is directly relevant to the ongoing situation.

Looking back on how we dealt with the outbreak, we were ahead of the curve. We put in place strict social distancing and work-from-home measures well before our institutes / government did (and, I would argue as an immunologist, our lab rules were more science-based than those later imposed on us). We also started a public education program on COVID-19, with an interactive Virus Outbreak simulator, an illustrated series translating scientific  articles into lay language and even released a kid's book explaining Coronavirus (with special thanks to lab members Dr Teresa Prezzemolo, Julika Neumann and Dr Mathijs Willemsen for translating this into different languages).

We also had lab members head back to the clinic to help with the capacity issues created by COVID-19. Dr Frederik Staels and Dr Erika Van Nieuwenhove suspended research to increase their clinical duties, and Dr Stephanie Humblet-Baron and Dr Mathijs Willemsen were on-call in case the system was overwhelmed.

Silke Janssen, processing patient blood

Our lab never completely shut-down though - we had important work that needed to be done. I'd like to call out Dr Susan Schlenner, Dr James Dooley and Dr Lubna Kouser who led the unglamorous but key administration on securing the safety of team members who had to be in the lab. Our Leuven lab was central to the processing of clinical COVID-19 samples. We usually think of clinical trials being run by MDs, but the work does not end after the blood is collected. I really want to call out the key contributions of Silke Janssens and Dr Teresa Prezzemolo. Without them, coming in all day, every day to process blood samples, clinical research of COVID-19 would have been crippled.

Dr Teresa Prezzemolo in the L2 labOur team, lead by Dr Stephanie Humblet-Baron, also analysed the samples prepared. We performed an ultra-high parameter analysis (far beyond state-of-the-art hospital diagnostics) of the T cell phenotype of COVID-19 patients: months of work from Dr Teresa Prezzemolo, Silke Janssens, Julika Neumann and Dr Mathijs Willemsen. Data analysis by Julika Neumann, Dr Carlos Roca, Dr Oliver Burton and Dr Stephanie Humblet-Baron identified a novel link between IL-10-producing Tregs and COVID-19 severity. We are now following this up to see if the link is useful as a biomarker or even is mechanistic in disease program. We have made our data an open resource (link), allowing other groups around the work to analyse our work. We are continuing to follow these patients and will soon have more and more information about why some patients remain completely healthy and others develop severe, even fatal, disease.

Dr Dooley and Dr Kouser (pre-COVID-19)We are not just clinical immunologists - we are also basic research immunologists. Mysterious virus triggering immune-mediated destruction of the tissue? We can deal with that. The whole lab contributed to the design of a new potential therapeutic, but I would especially like to call out the contributions of Dr James Dooley, Dr Oliver Burton, Dr Lubna Kouser and Fran Naranjo. Manufacturing is now complete and we are moving to pre-clinical testing. Hopefully we have a vaccine for SARS2 before our treatment is complete, but it is designed to deal with an unknown SARS3 equally well.

Suffice it to say, we have been as busy as we've ever been, and we will likely remain just as busy well after COVID-19 stops making the headlines. Which brings me to my final plea. Don't forget about scientific research. Unsung heroes during the pandemic, our staff are putting in an enormous effort. And yet we face an incredibly uncertain funding situation. Universities and research institutes have taken an enormous financial blow with this pandemic, and unless governments step in with a large financial rescue package, those scientific research staff who got us through the pandemic are going to be laid off in huge numbers. Even if you don't care about the moral imperative of looking after the people who stepped up when we needed them, there will be a SARS3 or novel flu pandemic in the future. We need to secure the research infrastructure to combat them right now. Science is not a factory that can be switched on and off at will - we need to maintain research excellence, scientific equipment and most of all key staff contracts over the long-term.