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What it takes to be an industrial PhD supervisor at Elsevier

Noelle Gracy

Elsevier academic partners want to
stay at the cutting edge of research and ensure that their work is impactful in
society. One way they do it is to have their PhD students work on real-life
projects in industrial settings. Elsevier has embarked on a program to support
the PhD researchers doing work in applied data science. It gives researchers a
chance to work with Elsevier
staff and data that are relevant to real life challenges. It also
gives Elsevier teams an opportunity to tap into young talent, keep a finger on
the pulse of academic research and exercise research and mentoring skills.

This year Elsevier
is supporting 12 PhD students at six centres, with the staff acting as
industrial sponsors:   

But what does it mean to be an industrial PhD supervisor?

We asked George Tsatsaronis, VP Data Science, Research Content Operations, in Amsterdam.  George has a PhD in Text Mining and has acted
as both an academic and industrial PhD supervisor in his professional roles at TU Dresden, Transinsight GmbH, and
Elsevier.    

  • Why did you take on the role of an industrial PhD supervisor?

Personally, I enjoy
doing research and thinking as a researcher. The idea of being able to
participate in developing a study from scratch is really satisfying.  I love when I’m the first to take something
experimental and apply it in a real system. 
It’s like someone makes a new prototype car and you’re the first to
drive it.

  • How does the role as an industrial supervisor
    differ from an academic PhD supervisor?

They’re complimentary
roles.  The PhD supervisor has the
responsibility to ensure that a PhD project is designed correctly.  They need to ensure that a student does
research in the area that hasn’t already been explored.  It needs to be substantial enough for a PhD project,
but it also needs borders. 

The industrial
supervisor has the responsibility of coming up with the best possible
application of the outcome of the student’s research. It is a big responsibility.
If the application we choose for the algorithm isn’t appropriate, it might show
no impact. But that’s because it’s not applied properly, not because of the
algorithm . Normally a person who takes a role of an industrial
supervisor is experienced enough to pick which application of the research
project will have demonstrable impact.

  • What kind of skills/experience do you need as
    a PhD supervisor? Do you have to have a PhD yourself? 

It’s not
necessary, but it’s helpful.  It’s
helpful because you understand the process and structure of a PhD program.  Your role as an industrial supervisor is to
help students showcase their research and help them graduate. They need publications, generally, so it’s helpful if an industrial
supervisor understands what research makes a publishable unit, what quality of
work is publishable, what’s been done already in the academic field, what’s
considered cutting edge – all of that is useful. 

Industrial
supervisors also need to be good people managers with a willingness to dive
deeply into the latest published work.  They
need to help students position their research in comparison to other current
work and help them shape the project to fill in what is missing.  Students can’t graduate just because their
work is applicable.  It needs to be novel
and contribute to the field, so the industrial supervisor needs to be
up-to-date with the field.

  • What projects are the most appropriate for
    industrial PhDs?

Ideal projects
are the ones on the roadmap for products 2-3 years down the line (Horizon 3).  I would not attempt to bring PhD students
onboard and co-supervise them to contribute to something I need to deliver this
year. With a Horizon 3 timeframe, students can work at their own pace without
stress and they can validate their work appropriately and publish it as
required.

Projects should
also have specific requirements in terms of objectives and success
criteria.  We can’t ask a PhD student to “improve
the Scopus set.” They need a project that is well-defined and specific. 

  • What value does it bring to Elsevier?

It clearly brings
us talent. My team has supervised several PhD students in the recent past. We were responsible for shaping their work in a way
that the outcome would be helpful for our industrial applications.  Three of those students came to work at
Elsevier as NLP scientists.  And now we
see that they’re engaged into research collaboration and act as industrial
supervisors to new PhD students.

If we really do
our best to become a global leader in information analysis and data science applications,
we need to nurture an environment in Elsevier that is attractive to the best
talent. For that reputation to be built and that environment to be created, we
need to prove to the top universities and departments that we have interesting
content, people and applications.  We
need to show that our products and services make difference to society.  PhD programs are a unique opportunity to do
that.  Universities have the raw
materials and turn to industry partners, like us, to show impact.  This allows us to be a leader in transforming
and producing the best work out there in applications that are genuinely important
to our society. 

Noelle Gracy, PhD, is the Head of the Research Collaboration office at Elsevier. The Research Collaboration Unit handles about 70 research collaborations annually, primarily in areas of machine learning, clinical decision support, research metrics and research integrity.

Image credit: Headway from Unsplash

Picture credit: Elsevier

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