Up and down, but always forward

How much of an academic career is down to planning, or simply luck? Does taking risk pay off? These are recurrent questions, and it is always nice to get the perspective of a more senior colleague. Jenny Martin's latest blog post is particularly interesting, as she has just accepted the role of Director of the Eskitis Drug Discovery Institute at Griffith University. Many of us could do a lot worse than having the same career.

Jenny suggested on Twitter that I blog about the topic, so let's have a look at how I got my current Lecturer position (equivalent to Assistant Professor if you are reading from North America), which is not a bad position to be in if you consider the overall numbers. A lot has happened since my 'tightrope' analogy for postdoctoral life, but let's start even further than that.

As a kid, I was fascinated by weather and climate. I would collect and analyse my own data, read any book I could find on the topic, etc. I even spent a week as a 16 year old intern at my local weather station in 1998. Despite coming from a relatively low SES background, I was attending an excellent high school, thanks to a number of factors (family commitment, partial fee waiving, etc.). I was having good marks, so nothing seemed impossible. I looked up how to become an engineer at the French national meteorological service. Less than a handful of positions every year? No problem! Very difficult entrance exam for their school? Not afraid! After all, I was already reading the book used in the classroom I would hopefully attend 3-4 years later, so I knew I would enjoy it.

Classes préparatoires being extremely competitive (worse than some grants I am applying for these days), things did not turn out as planned. I ended up going for my second choice of "grande école", an engineering school specialising in Computer Science and Mathematics. Weather forecasts rely on computational models, so the idea was still to eventually work in that field. The French system is quite complex, but because these schools recruit after 2-3 years of classes préparatoires, you spend 3 years studying there and graduate with the equivalent of a 5-year M.Eng. degree elsewhere. In each of the last two years, students undertake a 6-month work placement, and are encouraged to consider gaining some international experience. It is not easy to convince a foreign company that having a French "stagiaire" has many benefits, so I mostly targeted universities (which seemed more likely to have interesting, short-term projects I could really take ownership of). I focused on Ireland (including Northern Ireland), as I was hoping to spend some time there at some point anyway.

Only one university replied with real interest (and some funding!), so in April 2004 I started my six-month project with Martin Crane at Dublin City University, working on computational fluid dynamics. It went very well. As expected from a previous trip, I loved the country, and research was great too. The project led to my first conference presentation, and some time later we even got a paper out of it. Becoming an academic was my new goal. The plan was to come back to this group for my second placement, work on a new project, get some preliminary results, and apply for a PhD scholarship to work with Heather Ruskin and Martin. In my head, it was fairly straight-forward. Later on, I realised it involved a fair amount of luck as well: the success rate for the scholarship I was awarded was about 20%.

As I was starting my PhD, Microsoft Research had a workshop, and later a report, called Towards 2020 Science. It highlighted the need to "produce ‘new kinds’ of scientists now urgently needed (computationally and mathematically highly literate)". I thought that being at the interface between computing and biomedical sciences would be really fitting for me. A great match for my research goals and my interests in data analysis, modelling, etc. from the early days. My career goal became a little more refined.

My PhD was fairly uneventful, with a few conferences and papers, except for one detail. At a conference in Italy, I met a Japanese researcher, Hiroyuki Ohsaki, who at the time was working at Osaka University. His focus on communication networks was quite far from my work on the immune response to HIV, but we realised there was a nice overlap in the modelling approaches we were using, and decided to keep in touch.

This, and a new project on epigenetics, brought a focus on Japan. I started to collaborate with a few labs there, and it looked like I would spend some time either there or in the USA (I even interviewed for a postdoctoral position in Chicago). At that point, just under two years after my PhD, I was awarded an IRCSET Marie Curie fellowship to spend 18 months in Hiroyuki's group in Osaka (followed by a year back in Ireland for the "return phase"). It was great news. The move was supposed to bring me one step closer to a faculty position: a competitive and well-regarded fellowship, some additional international experience, and hopefully a couple of nice papers. The Global Financial Crisis changed all that. The situation degraded fairly quickly in Ireland, and the government imposed a recruitment ban. Some positions I applied for were withdrawn before any interview took place. For the single position that came through, I was shortlisted for interview but not offered the job.

It was time to take a big risk. I applied for a 3-year fellowship to work with Hiroki Ueda at RIKEN. It was a risk on multiple levels. The success rate is typically about 10-15%, but more importantly, I would be working directly in a biology lab, rather than in collaboration with one. This could be a great career boost, or a dead end, depending on how it went.

Thankfully, it paid off. I got the fellowship, moved back to Japan, and worked on great projects. We made a brain transparent, and later even a whole mouse. We also had a nice study using CRIPSR, and a number of other projects that are not published yet but have produced some very interesting results as well.

Soon enough, it was time to prepare applications again, especially as recruitments can start months before the scheduled start date (due to visas, etc.), and because not all countries operate on the same university calendar. At that point, I had decided to only apply for faculty positions. I was not interested in being a perpetual postdoc so, with a few good results behind me, it was make-or-break time. My wife and I prepared a shortlist of countries where we could see ourselves live. In no particular order: Japan, Canada, USA, Australia, and a few European countries (not including France, ironically). I quickly began to find a few suitable positions advertised in these countries. The number of PhD graduates means that there is fierce competition for each position, though.

I received a few rejection letters, and some universities never bothered to share an outcome (isn't that annoying!), but I also got good news. Within five months of starting that process, I called the search off and accepted a position at QUT in Brisbane, Australia. New country, new challenges, but it is nice to have the ability to establish my own line of research.

Looking back to 2004-2005, it has not been a straight path, but I managed to bounce back when needed. I am a Lecturer in Data Science, and I work on biomedical systems. Of course, rather than an end goal, this is only the beginning. There are more challenges ahead (more funding please!), but I can also see a number of very exciting opportunities.

Back!

Last year I decided that, in the interest of time, it was better to put all my online efforts into a single place.

As it made absolutely no difference, I am working on reverting that change and will use this blog again (as it is better referenced that the other one). I have also started to transfer the content here. Do not be surprised if you see old posts popping up.

Going to Japan as a researcher (1)

[note: this is a repost, on 15/05/2015 of an article I had prepared for another version of my blog(s). It was originally posted in April 2014, so some of the information may have changed. Leave a comment below if you have a question]

There are quite a few people interested in coming to work in Japan, so I suppose some of them might be researchers looking for opportunities here [back in 2014, I was still working in Japan].

Depending on your career stage, the opportunities will be different, so I will consider three stages: student (MSc or PhD), young researcher (defined as within five years of completing your PhD) and experienced researcher (at least five years of experience post-PhD). Today, I will focus on options for young researchers.

The most obvious option, at any stage of your career, is direct hire. The main difficulty is to identify suitable positions. As always, networking is a valuable tool. If you are hoping to move to Japan, present your work at conferences in your field and try to meet Japanese attendees. They may be able to direct you to some of their contacts looking for candidates, or might even have a position in their own laboratory. My first position in Japan was the result, in part, of a discussion at a conference in Italy many years before I even considered Japan an option.

Another route to find research positions is the Internet. Websites such as Nature Jobs, Science Careers and Times Higher Education all have the options to search by geographical regions, and to create email alerts tailored to your needs. Another great resource, specific to jobs in Japan, is JREC-IN (Japan REsearch Career Information Network), maintained by the Japan Science and Technology Agency. You can filter results by region, prefecture, institution type, research field, etc., and can also save the corresponding email alerts. Very useful!

As a young researcher, another option is to find your own funding, and there are a few fellowship programs worth mentioning. The best known probably is fellowship program of the JSPS (Japanese Society for the Promotion of Science). Their website has all the information you will need. Interestingly for people who want to come to Japan long-term, the program also has a specific stream called "Pathway to University Positions in Japan". There is also a funding stream for short visits to Japan, but be careful about the impact on your eligibility if you plan to apply for a standard fellowship later on.

Many research institutes also have their own postdoctoral fellowship program for foreigners, such as RIKEN with the aptly named FPR (Foreign Postdoctoral Researcher program). If your research field has anything to do with Physics, Chemistry, Biology, Medicine or Engineering, it is definitely worth looking into. The FPR program is funding my current position, and I can recommend it. The website has all the details you need to know more about the program, check your eligibility and prepare an application.

International programs may also be an opportunity to relocate to Japan. Two such programs come to mind. The first one is the Human Frontiers Science Program (HFSP). The program is open to applicants with a Ph.D. in a biological discipline (Long-Term Fellowships) as well as those from outside the life sciences (Cross-Disciplinary Fellowships). Interestingly, HFSP fellowships offer the option to stay the full three years of the fellowship in the host country or to use the last year of the fellowship to return to your home country or to move to another HFSPO member country. I think this "return phase" option can be really useful, and it is also a feature of the other scheme I wanted to mention, the International Outgoing Fellowships for career development (IOF) offered by the European Commission through the Marie Curie Actions.

 With that program, the return phase has to take place in Europe, but anyone can apply, irrespective of nationality and current location. Several national funding agencies across Europe have similar schemes co-funded with the European Commission. In such cases, the return phase obviously takes place in the corresponding country. My first position in Japan, mentioned earlier in this post, was funded by such a program.

As you can see, there are many options if you want to come to Japan as a young researcher, and this post is not meant to be exhaustive. These positions are generally quite competitive, but this does not mean you should not try. If you know any other funding program worth mentioning, or maybe some useful online resources, please leave a comment below.

The postdoctoral tightrope walk

(I wrote this piece for a column context a year and a half ago. It made it all the way to the final round of reviews but did not win anything, so I have decided to publish it here, unedited.)


Doing a PhD is often compared to running a marathon, perhaps rightly so. Both are lengthy efforts, and sometimes have an unexplainable aura to outsiders. For every runner who hit the invisible wall a few miles from the line, there is at least one PhD student who remained stuck at ABD (All But Dissertation) for an unhealthy length of time. Yet, even though there were occasions for deep digging and teeth gritting, the finish line has been crossed, and the PhD awarded. It was then time to get ready for something entirely different.

If a PhD is a marathon, a postdoctoral position is closer to tightrope walking, and requires a supplementary set of skills. Balance is essential.

One of the first challenges is to efficiently divide time between research and the additional tasks that are handed over. Postdoctoral researchers are for instance more directly involved in preparing proposals. If you are affiliated to a university, teaching duties and supervision of research students will also be more present than during postgraduate times. While these tasks are interesting, as well as useful in terms of career development, they can also be time consuming. Postdoctoral contracts are typically quite short, and research outcomes will obviously be an essential criteria when applying for the next position. Falling behind schedule is a clear risk, and planning well ahead is required.

Striking the right balance between old and new projects has also proved to be an important question. It is only natural that research interests evolve once the final touches have been put to the PhD project. After all, this took several years to complete, and the start of postdoc life may be time to let some fresh air in, and to get involved in some of the hot topics that have recently emerged in your field.

Turning to former supervisors and senior colleagues when I was considering these options, I received a very sound advice not to start again from scratch. Years of working on a specific project represent an expertise that should not be wasted. The key, here, was the increased emphasis on multidisciplinary research. It gave me a chance to make the most of the techniques I had learned and developed, while still enjoying the excitement of working in new areas. This is typically prevalent in my research area (computational modelling), but has also been occurring more and more frequently across all fields.

The biggest challenge, however, has to be the work-life balance. The problem is not new, of course. Being a PhD student obviously was a time-consuming, and on occasions self-absorbing, experience. The main difference is that, from the start, my then partner (and now wife) knew that my PhD would not last more than a few years. It did not make me less busy, but it certainly helped accepting times when I got the balance wrong. Would I be able to give a similar timeline now? I am currently two and a half years into my postdoctoral life, and my present funding runs for another 20 months, but I have no guarantee on what happens next.

We are in our third country (Japan following France and Ireland). Our objective was to settle somewhere, and to consider raising a family.  Unless the global economy rapidly recovers from the recent meltdown, permanent positions will likely remain sparse, and I may have to remain a postdoc a little longer. This will work only if I preserve a sanctuary for life outside research. There is no magic formula for this, but walking on a tightrope is not meant to be easy.

Electricity consumption in Japan

Most people are probably aware that, following the March-11 earthquake and tsunami, Japan had to reduce its electricity consumption. As one of the measures was to shift activity away from the peak hours (by starting work earlier, or working on week-ends), there has been some discussion as to whether this only resulted in evening out consumption and reducing peak-hour demand, or if this really had an impact on the total energy consumption.

Fortunately enough, as part of their efforts to promote energy savings, TEPCO is publishing the energy consumption hour by hour, all the way back to January 2008, (see here). As the company is responsible for providing electricity to the Kanto region (including Tokyo), Fukushima prefecture and parts of Shizuoka prefecture, this is where most of the effects of energy savings should be seen. (If anyone wants to look for, and compile, the data for the whole of Japan, please feel free to do so. And if you do, leave a link in the comment section.)

As could be expected, there is quite a drop in the peak demand, (see Table 1 below for details). In July, it was reduced by 22.7% compared to 2010 (or 20.3% if you average the peaks observed in 2008, 2009 and 2010). In August, it was reduced by 16.4% compared to 2010, or 14.5% compared to the average peak.

As you have experienced first-hand if you are in Japan, a number of other measures have been implemented. All over the country, the A/C is set higher, some lights have been turned off (especially in Tokyo), and even the drinks in the vending machines are not as cold as they used to. These additional measures do not simply even out the consumption; they reduce it. The results is that the average consumption (Table 2) is also reduced: -14.3% in July and -18% in August if you compare only to 2010 (we had a very hot summer last year), or -11.9% and -12.3% if you compare to the average of the last three years.

This is confirmed by looking at the minimum hourly consumption (data not shown but available upon request), down this summer by about 10% compared to 2010, or approximately 6% compared to 2008-2010.

Of course, these figures do not tell us whether this is enough of a reduction, or anything about how the electricity is (or should be) produced. But at least it should put to rest any doubts about the impact of these measures.

(I had to process the hourly data to get the tables below, but I do not think I made any error. If you notice any, let me know.)

	2008	2009	2010	2011
January	5502	5029	5240	5091
February	5407	4861	5199	5150
March	4775	4854	5171	5023
April	4462	4115	4734	3575
May	4445	4155	4205	3544
June	4525	4652	5132	4571
July	6008	5450	5999	4638
August	6089	5292	5888	4922
September	5496	4718	5828
October	4313	4144	4415
November	4753	4731	4599
December	4830	4955	4879

Table 1: Maximum hourly consumption

	2008	2009	2010	2011
January	3946	3699	3748	3907
February	4089	3665	3918	3897
March	3590	3447	3636	3274
April	3390	3137	3433	2888
May	3295	3080	3138	2862
June	3464	3339	3544	3115
July	4051	3725	4057	3476
August	3959	3705	4246	3481
September	3733	3353	3829
October	3345	3220	3271
November	3447	3397	3387
December	3599	3636	3596

Table 2: Average hourly consumption

(This post may or may not signal the revival of this blog. Time will tell, but you know that it will eventually happen at some point in the near future.)

Can the future be modelled?

[This post is based on an earlier one posted, in French, about a month ago]

My principal research area is complex systems modelling. Without getting too technical, the main purpose is to develop computer-based representation of some interesting phenomena.
There is no definite consensus in the scientific community as to when a system becomes complex, but you will probably agree that genetics, the climate, the immune system, or even financial interactions between stock markets, are "complex".

In future posts, I will probably try to detail some of the techniques that can be used, (as many are quite interesting!), but today's focus is on the concept of future.
The main goal of our work is to better understand these systems. In this process, proposing predictive tools, (and validating them), is often essential.

The main limitation is that, by nature, any model corresponds to a choice: it does not contain all the components of the real system. Otherwise, this would imply a complete understanding of that system, which would contradict the original motivation for this work.
Any model is, therefore, an imperfect representation of the corresponding system, and is generally valid only under certain conditions.

As a consequence, a model does not predict *the* future, but provides a set of possible scenarios, (these being more or less probable, depending on model realism).
On one of my projects, I am working on models of epidemic spreads. These models, (mine as well as those developed by colleagues elsewhere), will never "predict" the exact evolution of an ongoing infectious outbreak. Even a perfect model would be unable to provide this.

Let us assume, for the sake of the argument, that such a model exists, and that a simulation is run, based on the infection of an avatar for Mr Smith, who was just diagnosed with an emerging disease. The "perfect model" returns terrible news: according to the latest simulation, Mrs Smith probably is already infected, and the disease will rapidly spread through the entire population.
Yet, a week later, no further case was diagnosed. After extensive investigations, it becomes apparent that, at the very moment Mr Smith was put in quarantine, his wife, (who was very worried about these developments), suffered from a heart attack and passed away. She never had a chance to infect others, and the outbreak never occurred.

This is a made-up example, but the conclusion is nonetheless very clear: the future does not exist. There is a set of possible evolutions, and the present is the result of one of these taking shape. A predictive model "only" reduces the set and identifies the most likely outcomes, (which is already very useful!).

Another crucial aspect is the time scale used for the model. For instance, in the context of climate change, a long-term temperature increase does not mean that that every day will be warmer than the previous one, or even that every year will be warmer than the previous one.

Let us consider an artificial system, in which we introduced a forced increase, and some random variations. We could for instance generate a series of values where the i-th element is given by the equation below, (based on a normal distribution). In short, if Mathematics are not really your thing, the first value is obtained from small variations around 995, the second from similar variations around 996, the third around 998, the fourth 1001, and so on.

This results in a typical evolution, shown in the figure below. On the one hand, from one value to the next, the difference can be quite large, and can be positive or negative, (dotted line). On the other hand, the average value over the last ten values, (red line), is more stable, and largely on the increase.
In particular, it is interesting to look at values 40 to 60: variations are significant, and include severe dips, but the average value keeps increasing.
With a little imagination, it is easy to understand why denying global warming on the basis of a poor summer or a very cold winter does not make any sense, (nor, conversely, does it make any sense to point to a single warm summer or mild winter to "prove" global warming).

In some systems, there might be a positive feedback: the higher the current value, the more likely its increase becomes, (and the lower it is, the more likely it is to decrease further). This can for instance be taken into account using the following equation. Instead of using 995+i for the i-th value, we take the previous value as a reference point.

The overall behaviour is more stable, but periods of "negative growth" also tend to last longer, (see values 25-30 below). This model is very crude of course, but does not seem too far from some phenomena observed with stock values, (Finance is not my strong point, so correct me if I am wrong).

The time scale is, therefore, crucial. A trader will monitor the stock values by the minute, while a pensioner will be worried about the long-term evolution, over several years. Similarly, climate science will investigate changes over decades and centuries, while meteorology focuses on the hours and days immediately ahead.

This implies the development of specific models, which are developed to answer precise questions about the system. As a result, you should always be careful when someone uses the fact that weather forecasts are not accurate for more than a few days ahead as an argument to doubt the validity of climate model.

Economics of Ecology

Ecology is often said to be detrimental to our modern economies. Fighting to protect biodiversity, or to tackle global warming, should apparently not be priorities, because it would have a negative impact on growth. Nature recently featured an interesting reply to these views, in particular through an interview with Pavan Sukhdev, who leads a study on The Economics of Ecosystems and Biodiversity, (TEEB study).

One of the main arguments is that GDP (Gross Domestic Product) does not account for factors such as well-being and education levels, or use of natural resources, pollution levels, etc.

A striking example of this limitation is given at the start of the interview. A costly divorce involving an expensive lawyer, or a car crash resulting in repairs and medical bills, both correspond to an increase in GDP, but are not associated with any improvement of well-being.
Progress is not limited to GDP growth.

Similarly, a protected patch of forest has a number of benefits, most of these without any measured commercial value, such as pure air, natural habitat for animal species, formation of soil, potential for discovery of new medical cures, etc.

Obviously, putting a price on everything is not, in itself, a solution, (and poor estimates may lead to damaging decisions), but the recent actions on CO2 emissions show that "you cannot manage what you do not measure" (as Pavan Sukhdev puts it).

The TEEB report also highlights the need for a long-term view on these issues. Marine Protected Areas can, for instance, with short-term local costs for fishermen, if a no take zone is implemented. However, once fish populations have recovered, increasing catches have been observed: almost 75% of the US haddock catch are taken within 5 km of a fishery closed area, off the New England Coast (Fogarty and Botsford, 2007).

The report has a lot more content, and is worth reading, but as discussions start in Copenhagen, keeping at least these few facts in mind would do no harm.


For more details :

• Nature article, and interview with Pavan Sukhdev..


• "TEEB for Policy Makers report", (available on the project website).


• Newsweek article.


• M.J. Fogarty, L.W. Botsford, "Population connectivity and spatial management of marine fisheries". Oceanography 20(3):112-123, 2007, (available online).

Stand-by

As you have probably noticed, it has been quite a while since my last post here...

This blog is currently on stand-by, as I have very little time to write decent posts for this page. In the meantime, if you have a subject you would like me to cover, please feel free to leave a comment below this post, and I will do my best to help.

On the same page with WHO

This post comes third in a series focusing on the influenza outbreak. Earlier posts are available here and here.

The most crucial point in these texts was that the WHO pandemic scale only takes virus spread into account, and does not include any considerations on its virulence. This was a concern, because of the subsequent media coverage and the long-term risk of complacency.

It is, therefore, quite reassuring to see this fact being increasingly picked up in news reports.
French newspaper Le Monde even quotes a WHO spokesperson stating: "It is true WHO only considers virus spread to decide on its alert level. There is no alternative, as virulence is a subjective criterion, which varies between countries." (this is my own translation; the whole article is available here)

This change arrives quite late, but still in time for level 6.
This level, the highest on the WHO pandemic alert scale, will probably be announced this week.

As explained earlier, the only difference between levels 5 and 6 is the detection of a new epidemic outbreak on a different continent.
The infection started in North America, and quickly spread there, leading to level 5. Since then, countries such as Spain have been under close scrutiny, but it now turns out that Japan might be the country officially leading us into a global pandemic.

You can expect a return of influenza to the main headlines, but let's hope that, this time, media coverage will not neglect any useful information.

Complacency, or overreaction?

The influenza outbreaks has almost disappeared from mainstream news, but remains an important focus in high-profile scientific publications such as Nature.

Its latest issue (May 7th) features an interesting editorial: Between a virus and a hard place. In brief, the main message of this editorial is that "complacency, not overreaction, is the greatest danger posed by the flu pandemic".

In a way, this is very true: the virus was (and still is) spreading rapidly, and it took some time to determine how virulent it was.

However, as I tried to explain earlier, viruses can now spread more rapidly and are identified more readily, independent of their virulence (which takes some time to assess).

Repeated overreactions can only lead, in the long term, to complacency. This, as suggested, would be dangerous.
The only valid long-term solution is, therefore, to promote reasonable reactions.

Pandemics: more but less?

One of the advantages of having a fairly recent blog is that there is little readers to complain about the lack of update of the recent weeks.
That being said, this silence was not exactly planned, and I will try to post more often in the future.

The recent flu outbreak has captured the headlines, and I have wondering about the way scientific information is broadcast to the general population.
In particular, is it good practice to use, without further explanations, the notion of pandemic.

WHO scientists use technical terms, and rightly so, of course. It is easy to imagine the anger, were WHO (or any official organisation) to use ambiguous terms.

However, it must be noted that recent changes in our societies modified the implications of pandemics.
Up to a couple of decades ago, a pandemic really was bad news: you would need a large pool of infected individuals for the disease to spread globally, (in order to compensate for the limited exchanges between continents), and you would have to wait for a large number of casualties before a link could be made between these, (to compensate for the lack of efficient sequencing and monitoring resources).
In that sense, there was a strong correlation between what was identified as a pandemic (a disease outbreak spanning worldwide) and what corresponded to a very virulent infection.

The situation is quite different today. Population mobility implies a very rapid spread of any new disease (or strain thereof), as was observed in recent days. Similarly, a few serious cases are enough to draw very high attention and close monitoring of the outbreak.

A pandemic used to be identified a posteriori, and only in instances when the involved strain was virulent enough. In the general population pandemics of the past are, therefore, reminded by the thousands (or millions) of casualties they never failed to induce.
This is no longer the case, and an outbreak can reach level 5 (out of 6) in the WHO phases of pandemic alert before a thousand people are infected.

It would be very useful if media reports could explain that crucial change, so that we can prevent ourselves from excessive reactions.
In the current world, it is likely that we will regularly have outbreaks reaching high levels of alert, with only a fraction of them turning out to be as deadly as the ones we remember from the past.

This is the whole point of having an alert system, after all!

Welcome here!

Welcome on this blog!
If you allow me, I will take a few minutes to explain the rationale behind the creation of this blog.

As you may know, a researcher spends a fair amount of his/her time trying to efficiently disseminate the results he/she obtained. If we look at it in a very crude way, there are two levels of dissemination.

Obviously, the primary "target" of this effort is the scientific community: colleagues from the same area, but also the community at large, because a breakthrough rarely has implications in a unique domain. And with the increasing prevalence of multidisciplinary research, this is even more crucial.

This level of dissemination is paramount, and a researcher's efficiency in this task can largely determine how successful a career may be. On the other hand, it would be unfortunate to neglect another crucial level: knowledge transmission to the society as a whole.

Research is not possible without public funding, and it is therefore of chief importance that citizens are given the opportunity to access the results and benefits of this investment. Expertise levels are of course different, and so are expectations and questions, but neglecting this aspect would ultimately compromise the future of research.

The current economic climate makes this even more important: funding will decrease if scientists can not show they are worth being invested on, but at the same time, large-scale dissemination is becoming more difficult. Mass media are suffering, and cutting expenses often implies reducing the coverage of topics such as Arts and Science.

Obviously, this blog is not a solution in itself, but if it can help a few people get a clearer picture of the questions and challenges I am currently working on, then it will be worth the time taken to write these lines.
I guess it will at least be useful for family and friends... :)

Updates will be written during my spare time, so I apologize in advance for the potentially long time in between these.
If you can read French or Japanese, please feel free to have a look at what I wrote using those languages (for the latter, you will just have to wait until my level has sufficiently improved). The content is not necessarily identical.
And if you have any specific question, or if there is a theme you would like me to cover, please leave a comment!