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Hi and welcome to the Neil Ashton podcast.

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In each episode,

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we explained some of the fascinating ways that science and engineering

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are changing the world around us.

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We talk to leading engineers from elite level sports like cycling and Formula One

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to some of the world's top academics to understand how fluid dynamics,

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machine learning and supercomputing

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are bringing in a new era of discovery.

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We also hear some of their life stories, their career advice

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and lessons they've learned on the way that I hope will be helpful to you too.

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So sit back and enjoy this episode.

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Welcome back to the Neil Ashton podcast.

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Well, today is actually the last episode of this first season.

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So it's gonna be a short one

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really just to

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say, thank you, I suppose to the people who have listened to this first season,

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which has gone on a journey, I guess,

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certainly myself, I wasn't sure whether any would even listen to these episodes

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and uh purposely picked a range of guests and topics that

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were quite broad and I really wanted to see what people liked the most,

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what was interesting to them.

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And it also reflects, I guess the

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the,

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the broad

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taste and interests that, that I also have.

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Um, so

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I also wanted them to go into um, a reasonable amount of detail.

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And so probably as you've seen, they have been quite long. Um

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I think the,

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the longest one was with Professor Tony Purnell and that was over two hours, I think,

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for some of them, I managed to keep it a little bit shorter.

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So just, uh you know, a couple of, uh just an hour or so,

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but I was a fan of the long form podcast because really to hear people's stories, you,

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you kind of have to,

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you know,

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do it over time and it's difficult to edit and condense it down to just, you know,

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seven minutes.

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So

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I know that YouTube says you should never make videos that long. Um But

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yeah,

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I wasn't as concerned about that and uh

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I'd be interested actually to know whether people tend to listen

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to this on Spotify or Apple or tend to watch it.

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Uh When I created these being an hour or two hours,

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my assumption was that people would listen to it.

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So, if you are watching this on YouTube, I appreciate that.

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A one or two hours on YouTube is quite long.

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So if you are watching it,

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I would recommend that you instead perhaps search on Spotify or

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Apple for this podcast because I personally think listening to it actually

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is um

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it's better.

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So, yeah, I just really briefly sort of recap and maybe even remind people what,

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you know, who I've spoken to, what I've covered and a little bit about the

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future. So,

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yeah, this is the last episode I've done 13 episodes for this season.

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So I'm gonna take a bit of a break, um, for a month or so over the over August,

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um, got some really exciting

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people to speak to already for the next season.

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But I thought I'd give it a little bit of break.

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It gives me chance to focus on some other stuff a little bit and,

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and do a better job at taking a step up

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trying to make these maybe a bit more professional,

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uh get even, you know, broader range of, of, of speakers

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and also take some feedback on what people liked or didn't like.

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So I'm hoping it will come back in the second season with a even better

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podcast that can be even more enjoyable and worthwhile listening to.

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But

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yeah, I,

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who did we speak to the beginning? So, uh Dr Florian Menter, that was

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essentially the, the first episode.

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But technically, it's episode two because the first was a podcast intro.

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And I, I really enjoyed that conversation.

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I think it was actually one of the most popular ones because

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uh Dr Florian Menter has been one of these pioneers of CFD.

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And as, as I mentioned in the episode with him, you know,

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has stayed reasonably quiet.

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In a way, even though he's very well known,

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um he, he's kind of, you know, not

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attracted the light and not wanted the limelight.

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So I think a lot of people liked that episode because

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it really gave them a better understanding of who he was,

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uh what he's done and some of the history around those, you know,

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turbulence models and CFD.

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So that was really

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an episode focused on the CFD and even the turbulence models

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sort of community.

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And I was pleasantly surprised how many people

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actually uh were interested in that one.

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So if you haven't listened to it already,

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I would um I would go back and,

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and maybe take a look because it might be interesting for some,

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the, the third episode

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was with Professor Professor Tony Purnell and

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I,

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that was like I said,

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one of the longest ones and I would really encourage people to listen to it,

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listen to it, maybe not watch it.

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Um because Tony had done so many things, I mean,

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it's hard for me to describe, for someone to have run a Formula One team

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then being at the top of the FIA

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then being in charge of the British cycling project for, you know,

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more than four years

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through the whole Olympic cycle

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and being a professor at Cambridge University

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and having had a very successful business that he sold, you know,

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for quite a lot of money,

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he's done so many things and he's a game on these people that

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if he wanted to have done, he probably could have, you know, spoken louder and,

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and got more media attention and,

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but he stayed a bit, you know,

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quieter and so he's probably not as well known to people,

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but I would argue that what he's actually done

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puts him up there with, you know, some of these really top,

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uh, engineers. So, yeah,

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that, that was, um, a great one for me and, and

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actually Tony is someone I really looked up to

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because we actually worked together on the British cycling.

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And maybe what I didn't say is that, you know,

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that really was an important point in my career because it gave me

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a

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real insight into one sport. I'd really like, you know, I enjoy cycling myself,

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but I learned so much from it and it helped me then when I

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went on to do things uh with Formula One or other things later,

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um

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I've really learned a lot from that and it,

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and it opened the doors for many things and, and, and, and

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expanded my network. So I was really appreciate to Tony who gave me the chance,

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you know, to, um

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to work on that project.

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And it, and it was also, it was Tony,

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but it was also other people within the British cycling.

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Um you know, realm.

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Um some people who still work for British cycling now and still do other things like,

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like Chris.

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Um

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but yeah, that was an episode and I,

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and I hope you may go back and listen to that because I thought that was a good one.

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The fourth one was my attempt to do something a little bit different.

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And the title was Academia or Industry PhD or No PhD.

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And I

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wanted to give an experiment

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to do something a bit different.

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So, rather than interviewing somebody,

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I thought I'd do it on my own this episode and,

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and discuss something that's quite close to my heart,

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which is this decision that many people go through of, you know,

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do I try and progress through academia to become a professor?

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Do I go into industry?

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I get a lot of questions, you know, is it worth doing a PhD

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financially and, you know, for life reasons?

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So I really went into some detail and I was pleasantly surprised how many people

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um

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valued having some of that feedback.

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And I think I'll try and do a few more for the next season.

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So I'd be interested to know, reach out to me if you have ideas of

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topics to cover.

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But that was one that really tried to um

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pass some of the knowledge that I've gained, which is not

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complete by any means.

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Uh But yeah, it was a bit of a solo one

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in the description of the podcast. I often talk about um

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you know, Formula One, cycling, how it links to machine learning and engineering.

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And so episode five was with um Dimitris Katsanis who is one of the world's top bike designers.

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And so it was a chance for me

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to revisit that cycling theme,

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something that we will also be um doing more of in the next season.

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Something I've got a passion for this and there's lots of more

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areas we can explore. So we're going to focus a lot more

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and some of the aerodynamics

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in the next season and talk to some people from some of the cycling teams.

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Uh But Dimitris Katsanis's conversation was really interesting

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if you're into bikes and the way

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that they operate and the mentality of how you design a race winning bike.

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So that was episode six was with Juan Alonso.

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So this was a bit like the Florian Menter. One was again a CFD focused episode.

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Uh Juan Alonso has created this startup, Luminary Cloud.

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And I think it's a good opportunity to hear from someone who is

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um taken

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his findings from academia and, and tried to create a start up out of it.

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And I personally find that interesting because there's quite a

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few start ups around now in the CFD space.

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So if you listen to episode, you'll get a bit of a sense of what drove him to do it and,

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and why he's doing it.

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And he's also a really nice

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guy.

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And I think a lot of the lessons and things that he spoke about are useful for,

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for many, for many people.

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I keep switching back and forth between more deep dive in

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sort of academia or CFD and then

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more the applications of it.

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So the seventh episode was with Pat Symonds.

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Um

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he was kind enough to invite me to his house where we did the interview.

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And um

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uh

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annoyingly the audio didn't work out that well for me.

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So it was the first time doing an actual in person interview.

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But, you know, hearing his story, his way of rising up through the ranks to arguably,

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you know, one of the top positions in Formula One.

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He's another person that was really crucial for my

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career because he,

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he took a bit of a gamble.

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I think in having me to be a consultant for Formula One, a bit like Professor Tony Purnell,

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these people who I'd like to think were

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um had an eye on trying to help the next generation come through.

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And so,

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you know, Pat didn't have to do it.

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He could have, you know, gone with someone far more experienced or older,

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but I really appreciate the trust he put in me. And I, I'd like to think that I

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gave them some insights from, you know,

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what's going on in the world and cutting edge CFD.

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And, and since then, and still now I really value

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Pat and all that he's done for my um career. And so I was

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always wanted to speak to him more about what he's done in his life.

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And so this podcast was a great opportunity to, to go and speak to him.

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So again that if you're really into Formula One,

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that's a good one to listen to.

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Um

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I, I pivoted

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from the Formula One then to speak to Jack

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Dongarra,

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which was all about high-performance computing.

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Um Actually, high-performance computing is something that I've

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more and more focused on, particularly my day job at, at Amazon.

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And it's one of those topics that I feel I didn't know as much about before I joined AWS

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and, and it's become something I've incredibly,

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I find very interesting because it touches many fields.

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It's not just CFD, it's, you know, weather forecasting, drug discovery,

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machine learning

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and, and so it's, it's something I think is useful for people to know about because

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the computing side of it is actually quite important in almost any area of

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simulation or engineering.

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It's really what is the, the driving force buying so many of our things today.

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If you look at generative AI or these large language models,

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they wouldn't exist if you didn't have a supercomputer to train these models.

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So that was a good episode with him.

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And he was kind enough to speak to me and,

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and we try to look a little bit towards the future.

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It may go into a little bit of technical detail, you know, into the HPC world. But I, I,

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uh, I think it's something good to know more about.

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The next episode,

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episode nine was with Chris Rumsey and he's uh

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another person who I personally looked up to and,

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uh, again has

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helped me a lot, you know, inviting me um

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to or welcomed me into some of these

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aerospace activities. You know, a lot of my background has been an automotive,

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but I've always loved aerospace and space

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and, um

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I didn't come from that world per se.

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You know, I, I, although I did work for NASA a little bit,

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you know, nothing like people like Chris and others who are, you know,

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full time employees at NASA.

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And Chris has always been very good to me

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and I've learned a lot from him and I really, you know, admire him as a person.

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Um We work quite closely over the past few years in some of

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these things called the High Lift Prediction Workshops

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with NASA and Boeing and others.

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And again, I, I was always intrigued to know a little bit more about Chris.

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And so I this episode was

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like many episodes. It's,

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you know, the secret here is,

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it's an opportunity for me to speak to people I like to hear from as well.

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So that was a good episode. A bit similar to Florian Menter and

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um Juan Alonso, a bit of a CFD focus.

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But if you haven't listened to it already, hopefully you'll, you'll enjoy it.

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Now,

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the past, um, three or technically four episodes

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have dived into this. AI for Science and this

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really is a sort of mini series.

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I started off with me giving some of my thoughts.

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It was a bit of a quick episode really.

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It was more just to sort of frame some of the discussions. Um,

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and it's all about trying to look at

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if machine learning could be a revolutionary thing.

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It's certainly so it's a bit of a hot topic

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and one that divides people, some people think, oh, it's a load of rubbish,

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this machine learning, you know, we've been doing it for 2030 years.

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It's just got a different name

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and there are others who are far more bullish.

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So, what I thought I would do is I would,

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you know, try and speak to

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some of the top people in the world and instead of me giving my opinion,

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how about trying to, 00. And also instead of just one person giving their opinion,

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if I speak to three different people, hopefully we can get a broader sense of

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what the community is thinking.

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So I purposely

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tried to speak to them.

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So, Max Welling is actually a very well known person, machine learning world.

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I don't come from the machine learning world.

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Um But if you even start to look into it, you'll see.

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Max Welling is like one of the top guys.

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I mean,

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he was a VP at Microsoft one of the top people were in their research department.

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He was pioneering with things like autoencoders, which if again,

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if you're into machine learning,

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you'll realize is a building block of so many things.

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And he's turned his hand to the AI for science realm

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and he's a very smart individual. He's in many of these workshops and committees and

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uh reviewers for, you know, journals. So he's

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literally one of the top people.

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I was kind of amazed that he even said yes, I was so pleased that he said yes.

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And I like that episode cos I

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genuinely learnt things from that

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and um

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it was good to get his opinion on, on, on the potential of AI for science

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the next one.

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But, but Max Welling is not a fluid dynamics specialist.

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That's not actually his background,

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he comes more from the pure ML world.

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Whereas um then I spoke

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to Karthik Duraisamy in episode 12

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who comes from a more similar background to me, more of a sort of CFD or

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turbomachinery background, but has turned himself to

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uh really focus on machine learning applications.

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And that was a good one because I felt that gave an even more

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useful insight into how it could be used for fluid dynamics.

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Um and, and CFD and aerodynamics,

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which is obviously kind of the theme of this podcast.

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You know, I

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I do try and cover other topics but as you probably noticed it's

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aerodynamics computational fluid dynamics,

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anything to do with engineering essentially.

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And so

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Karthik Duraisamy gave a very insight. He was,

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um I would say

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a realist almost or,

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or someone who realized that AI has its

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place but he wasn't trying to claim it could,

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you know, do everything.

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But he's been focusing a lot on this thing called foundational models,

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which is very interesting.

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The um the final episode was with Anima who is

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again very well established in the, the field is

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highly influential.

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And as I said in the episode, has held very senior positions at companies

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who are really putting the money behind a lot of stuff like AWS and NVIDIA

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and of course, being, you know, a full professor at Caltech

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and

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she um has really been the one pushing some of the debate.

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And so I wanted to hear her as well.

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Now, as I alluded to, she's a bit more bullish,

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she's more confident of what we can achieve probably more so than Max and

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Karthik Duraisamy.

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But I wanted to have, you know, that, that blend of it.

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So that's where we are. We're at today. Those 13 episodes have, have, have come out.

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Um

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What would I like to do in the next season? What's to come?

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Well,

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I would like,

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and I'm going to expand

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and try it some more into the engineering side.

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So some of the topics that we're going to cover in the next season will be things like

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hypersonics.

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What are the challenges? Why is hypersonic so important to the world?

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What are the, you know, science challenges with

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hypersonics being more than mach

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five? So very, very fast,

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want to get into um supersonic planes.

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So things like Concorde, you know, could something come back again,

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gonna do, gonna um double down on some of the um CFD. So we've got some really

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uh amazing legends of the CFD world to speak to

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uh formula.

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One, of course,

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some really big figures in Formula One who will

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be um recording with in the next few weeks.

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Uh and cycling, I want to um to, to get into, in a, in a more deepening way.

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So it's not gonna be that different than the first episode uh than the first season.

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Um But I do want to uh tackle some what I think are very interesting engineering,

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engineering challenges.

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And one of the final things we're gonna cover is

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some of the things to do with space, which has always been something I've

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loved to, you know, getting to Mars,

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what are some of the challenges and things like that?

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So,

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but

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I'm flexible

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and I'm trying to be guided by what people listening to this one, not just myself.

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So if you have some ideas, please go to the YouTube, it's probably the easiest way

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and put a comment until let's say this video or any

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of the videos on what you may like or what you dislike

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00:19:09,819 --> 00:19:11,660
or um on LinkedIn.

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If you follow me on LinkedIn and send me a message um with feedback or ideas,

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00:19:16,380 --> 00:19:18,550
then I'll definitely take it into account.

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So,

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00:19:19,619 --> 00:19:20,800
yeah, thanks again.

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00:19:20,810 --> 00:19:24,939
Really appreciate people um getting involved and listening to this.

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I'm, I'm, I'm pleased that it's resonated with some of you very happy about that.

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00:19:29,540 --> 00:19:30,459
And, uh,

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00:19:30,660 --> 00:19:32,199
yeah, I hope I can do it again

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for season two, but for now

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00:19:34,729 --> 00:19:35,400
that's it

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gonna have a bit of a break and I'll probably see you again in, uh, September.

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So thank you for listening

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and watching.

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Ok.