Hi and welcome to the Neil Ashton podcast.
In each episode,
we explained some of the fascinating ways that science and engineering
are changing the world around us.
We talk to leading engineers from elite level sports like cycling and Formula One
to some of the world's top academics to understand how fluid dynamics,
machine learning and supercomputing
are bringing in a new era of discovery.
We also hear some of their life stories, their career advice
and lessons they've learned on the way that I hope will be helpful to you too.
So sit back and enjoy this episode.
Welcome back to the Neil Ashton podcast.
Well, today is actually the last episode of this first season.
So it's gonna be a short one
really just to
say, thank you, I suppose to the people who have listened to this first season,
which has gone on a journey, I guess,
certainly myself, I wasn't sure whether any would even listen to these episodes
and uh purposely picked a range of guests and topics that
were quite broad and I really wanted to see what people liked the most,
what was interesting to them.
And it also reflects, I guess the
the,
the broad
taste and interests that, that I also have.
Um, so
I also wanted them to go into um, a reasonable amount of detail.
And so probably as you've seen, they have been quite long. Um
I think the,
the longest one was with Professor Tony Purnell and that was over two hours, I think,
for some of them, I managed to keep it a little bit shorter.
So just, uh you know, a couple of, uh just an hour or so,
but I was a fan of the long form podcast because really to hear people's stories, you,
you kind of have to,
you know,
do it over time and it's difficult to edit and condense it down to just, you know,
seven minutes.
So
I know that YouTube says you should never make videos that long. Um But
yeah,
I wasn't as concerned about that and uh
I'd be interested actually to know whether people tend to listen
to this on Spotify or Apple or tend to watch it.
Uh When I created these being an hour or two hours,
my assumption was that people would listen to it.
So, if you are watching this on YouTube, I appreciate that.
A one or two hours on YouTube is quite long.
So if you are watching it,
I would recommend that you instead perhaps search on Spotify or
Apple for this podcast because I personally think listening to it actually
is um
it's better.
So, yeah, I just really briefly sort of recap and maybe even remind people what,
you know, who I've spoken to, what I've covered and a little bit about the
future. So,
yeah, this is the last episode I've done 13 episodes for this season.
So I'm gonna take a bit of a break, um, for a month or so over the over August,
um, got some really exciting
people to speak to already for the next season.
But I thought I'd give it a little bit of break.
It gives me chance to focus on some other stuff a little bit and,
and do a better job at taking a step up
trying to make these maybe a bit more professional,
uh get even, you know, broader range of, of, of speakers
and also take some feedback on what people liked or didn't like.
So I'm hoping it will come back in the second season with a even better
podcast that can be even more enjoyable and worthwhile listening to.
But
yeah, I,
who did we speak to the beginning? So, uh Dr Florian Menter, that was
essentially the, the first episode.
But technically, it's episode two because the first was a podcast intro.
And I, I really enjoyed that conversation.
I think it was actually one of the most popular ones because
uh Dr Florian Menter has been one of these pioneers of CFD.
And as, as I mentioned in the episode with him, you know,
has stayed reasonably quiet.
In a way, even though he's very well known,
um he, he's kind of, you know, not
attracted the light and not wanted the limelight.
So I think a lot of people liked that episode because
it really gave them a better understanding of who he was,
uh what he's done and some of the history around those, you know,
turbulence models and CFD.
So that was really
an episode focused on the CFD and even the turbulence models
sort of community.
And I was pleasantly surprised how many people
actually uh were interested in that one.
So if you haven't listened to it already,
I would um I would go back and,
and maybe take a look because it might be interesting for some,
the, the third episode
was with Professor Professor Tony Purnell and
I,
that was like I said,
one of the longest ones and I would really encourage people to listen to it,
listen to it, maybe not watch it.
Um because Tony had done so many things, I mean,
it's hard for me to describe, for someone to have run a Formula One team
then being at the top of the FIA
then being in charge of the British cycling project for, you know,
more than four years
through the whole Olympic cycle
and being a professor at Cambridge University
and having had a very successful business that he sold, you know,
for quite a lot of money,
he's done so many things and he's a game on these people that
if he wanted to have done, he probably could have, you know, spoken louder and,
and got more media attention and,
but he stayed a bit, you know,
quieter and so he's probably not as well known to people,
but I would argue that what he's actually done
puts him up there with, you know, some of these really top,
uh, engineers. So, yeah,
that, that was, um, a great one for me and, and
actually Tony is someone I really looked up to
because we actually worked together on the British cycling.
And maybe what I didn't say is that, you know,
that really was an important point in my career because it gave me
a
real insight into one sport. I'd really like, you know, I enjoy cycling myself,
but I learned so much from it and it helped me then when I
went on to do things uh with Formula One or other things later,
um
I've really learned a lot from that and it,
and it opened the doors for many things and, and, and, and
expanded my network. So I was really appreciate to Tony who gave me the chance,
you know, to, um
to work on that project.
And it, and it was also, it was Tony,
but it was also other people within the British cycling.
Um you know, realm.
Um some people who still work for British cycling now and still do other things like,
like Chris.
Um
but yeah, that was an episode and I,
and I hope you may go back and listen to that because I thought that was a good one.
The fourth one was my attempt to do something a little bit different.
And the title was Academia or Industry PhD or No PhD.
And I
wanted to give an experiment
to do something a bit different.
So, rather than interviewing somebody,
I thought I'd do it on my own this episode and,
and discuss something that's quite close to my heart,
which is this decision that many people go through of, you know,
do I try and progress through academia to become a professor?
Do I go into industry?
I get a lot of questions, you know, is it worth doing a PhD
financially and, you know, for life reasons?
So I really went into some detail and I was pleasantly surprised how many people
um
valued having some of that feedback.
And I think I'll try and do a few more for the next season.
So I'd be interested to know, reach out to me if you have ideas of
topics to cover.
But that was one that really tried to um
pass some of the knowledge that I've gained, which is not
complete by any means.
Uh But yeah, it was a bit of a solo one
in the description of the podcast. I often talk about um
you know, Formula One, cycling, how it links to machine learning and engineering.
And so episode five was with um Dimitris Katsanis who is one of the world's top bike designers.
And so it was a chance for me
to revisit that cycling theme,
something that we will also be um doing more of in the next season.
Something I've got a passion for this and there's lots of more
areas we can explore. So we're going to focus a lot more
and some of the aerodynamics
in the next season and talk to some people from some of the cycling teams.
Uh But Dimitris Katsanis's conversation was really interesting
if you're into bikes and the way
that they operate and the mentality of how you design a race winning bike.
So that was episode six was with Juan Alonso.
So this was a bit like the Florian Menter. One was again a CFD focused episode.
Uh Juan Alonso has created this startup, Luminary Cloud.
And I think it's a good opportunity to hear from someone who is
um taken
his findings from academia and, and tried to create a start up out of it.
And I personally find that interesting because there's quite a
few start ups around now in the CFD space.
So if you listen to episode, you'll get a bit of a sense of what drove him to do it and,
and why he's doing it.
And he's also a really nice
guy.
And I think a lot of the lessons and things that he spoke about are useful for,
for many, for many people.
I keep switching back and forth between more deep dive in
sort of academia or CFD and then
more the applications of it.
So the seventh episode was with Pat Symonds.
Um
he was kind enough to invite me to his house where we did the interview.
And um
uh
annoyingly the audio didn't work out that well for me.
So it was the first time doing an actual in person interview.
But, you know, hearing his story, his way of rising up through the ranks to arguably,
you know, one of the top positions in Formula One.
He's another person that was really crucial for my
career because he,
he took a bit of a gamble.
I think in having me to be a consultant for Formula One, a bit like Professor Tony Purnell,
these people who I'd like to think were
um had an eye on trying to help the next generation come through.
And so,
you know, Pat didn't have to do it.
He could have, you know, gone with someone far more experienced or older,
but I really appreciate the trust he put in me. And I, I'd like to think that I
gave them some insights from, you know,
what's going on in the world and cutting edge CFD.
And, and since then, and still now I really value
Pat and all that he's done for my um career. And so I was
always wanted to speak to him more about what he's done in his life.
And so this podcast was a great opportunity to, to go and speak to him.
So again that if you're really into Formula One,
that's a good one to listen to.
Um
I, I pivoted
from the Formula One then to speak to Jack
Dongarra,
which was all about high-performance computing.
Um Actually, high-performance computing is something that I've
more and more focused on, particularly my day job at, at Amazon.
And it's one of those topics that I feel I didn't know as much about before I joined AWS
and, and it's become something I've incredibly,
I find very interesting because it touches many fields.
It's not just CFD, it's, you know, weather forecasting, drug discovery,
machine learning
and, and so it's, it's something I think is useful for people to know about because
the computing side of it is actually quite important in almost any area of
simulation or engineering.
It's really what is the, the driving force buying so many of our things today.
If you look at generative AI or these large language models,
they wouldn't exist if you didn't have a supercomputer to train these models.
So that was a good episode with him.
And he was kind enough to speak to me and,
and we try to look a little bit towards the future.
It may go into a little bit of technical detail, you know, into the HPC world. But I, I,
uh, I think it's something good to know more about.
The next episode,
episode nine was with Chris Rumsey and he's uh
another person who I personally looked up to and,
uh, again has
helped me a lot, you know, inviting me um
to or welcomed me into some of these
aerospace activities. You know, a lot of my background has been an automotive,
but I've always loved aerospace and space
and, um
I didn't come from that world per se.
You know, I, I, although I did work for NASA a little bit,
you know, nothing like people like Chris and others who are, you know,
full time employees at NASA.
And Chris has always been very good to me
and I've learned a lot from him and I really, you know, admire him as a person.
Um We work quite closely over the past few years in some of
these things called the High Lift Prediction Workshops
with NASA and Boeing and others.
And again, I, I was always intrigued to know a little bit more about Chris.
And so I this episode was
like many episodes. It's,
you know, the secret here is,
it's an opportunity for me to speak to people I like to hear from as well.
So that was a good episode. A bit similar to Florian Menter and
um Juan Alonso, a bit of a CFD focus.
But if you haven't listened to it already, hopefully you'll, you'll enjoy it.
Now,
the past, um, three or technically four episodes
have dived into this. AI for Science and this
really is a sort of mini series.
I started off with me giving some of my thoughts.
It was a bit of a quick episode really.
It was more just to sort of frame some of the discussions. Um,
and it's all about trying to look at
if machine learning could be a revolutionary thing.
It's certainly so it's a bit of a hot topic
and one that divides people, some people think, oh, it's a load of rubbish,
this machine learning, you know, we've been doing it for 2030 years.
It's just got a different name
and there are others who are far more bullish.
So, what I thought I would do is I would,
you know, try and speak to
some of the top people in the world and instead of me giving my opinion,
how about trying to, 00. And also instead of just one person giving their opinion,
if I speak to three different people, hopefully we can get a broader sense of
what the community is thinking.
So I purposely
tried to speak to them.
So, Max Welling is actually a very well known person, machine learning world.
I don't come from the machine learning world.
Um But if you even start to look into it, you'll see.
Max Welling is like one of the top guys.
I mean,
he was a VP at Microsoft one of the top people were in their research department.
He was pioneering with things like autoencoders, which if again,
if you're into machine learning,
you'll realize is a building block of so many things.
And he's turned his hand to the AI for science realm
and he's a very smart individual. He's in many of these workshops and committees and
uh reviewers for, you know, journals. So he's
literally one of the top people.
I was kind of amazed that he even said yes, I was so pleased that he said yes.
And I like that episode cos I
genuinely learnt things from that
and um
it was good to get his opinion on, on, on the potential of AI for science
the next one.
But, but Max Welling is not a fluid dynamics specialist.
That's not actually his background,
he comes more from the pure ML world.
Whereas um then I spoke
to Karthik Duraisamy in episode 12
who comes from a more similar background to me, more of a sort of CFD or
turbomachinery background, but has turned himself to
uh really focus on machine learning applications.
And that was a good one because I felt that gave an even more
useful insight into how it could be used for fluid dynamics.
Um and, and CFD and aerodynamics,
which is obviously kind of the theme of this podcast.
You know, I
I do try and cover other topics but as you probably noticed it's
aerodynamics computational fluid dynamics,
anything to do with engineering essentially.
And so
Karthik Duraisamy gave a very insight. He was,
um I would say
a realist almost or,
or someone who realized that AI has its
place but he wasn't trying to claim it could,
you know, do everything.
But he's been focusing a lot on this thing called foundational models,
which is very interesting.
The um the final episode was with Anima who is
again very well established in the, the field is
highly influential.
And as I said in the episode, has held very senior positions at companies
who are really putting the money behind a lot of stuff like AWS and NVIDIA
and of course, being, you know, a full professor at Caltech
and
she um has really been the one pushing some of the debate.
And so I wanted to hear her as well.
Now, as I alluded to, she's a bit more bullish,
she's more confident of what we can achieve probably more so than Max and
Karthik Duraisamy.
But I wanted to have, you know, that, that blend of it.
So that's where we are. We're at today. Those 13 episodes have, have, have come out.
Um
What would I like to do in the next season? What's to come?
Well,
I would like,
and I'm going to expand
and try it some more into the engineering side.
So some of the topics that we're going to cover in the next season will be things like
hypersonics.
What are the challenges? Why is hypersonic so important to the world?
What are the, you know, science challenges with
hypersonics being more than mach
five? So very, very fast,
want to get into um supersonic planes.
So things like Concorde, you know, could something come back again,
gonna do, gonna um double down on some of the um CFD. So we've got some really
uh amazing legends of the CFD world to speak to
uh formula.
One, of course,
some really big figures in Formula One who will
be um recording with in the next few weeks.
Uh and cycling, I want to um to, to get into, in a, in a more deepening way.
So it's not gonna be that different than the first episode uh than the first season.
Um But I do want to uh tackle some what I think are very interesting engineering,
engineering challenges.
And one of the final things we're gonna cover is
some of the things to do with space, which has always been something I've
loved to, you know, getting to Mars,
what are some of the challenges and things like that?
So,
but
I'm flexible
and I'm trying to be guided by what people listening to this one, not just myself.
So if you have some ideas, please go to the YouTube, it's probably the easiest way
and put a comment until let's say this video or any
of the videos on what you may like or what you dislike
or um on LinkedIn.
If you follow me on LinkedIn and send me a message um with feedback or ideas,
then I'll definitely take it into account.
So,
yeah, thanks again.
Really appreciate people um getting involved and listening to this.
I'm, I'm, I'm pleased that it's resonated with some of you very happy about that.
And, uh,
yeah, I hope I can do it again
for season two, but for now
that's it
gonna have a bit of a break and I'll probably see you again in, uh, September.
So thank you for listening
and watching.
Ok.