0:04

You're listening to NeuroDivergent Mates.

0:07

Hello and welcome to another episode of NeuroDivergent Mates.

0:24

My name's Will Wheeler, joined with my main man, photon John Kev.

0:28

What's going on, brother?

0:30

I'm burning the midnight oil. I'm still working.

0:32

Burning the midnight oil. You know what?

0:37

I was actually a bit the same before I got on.

0:39

So I'm trying to find people for a role I'm actually working in type of thing.

0:49

So I've just been trying to reach out to a lot of like DES providers, stuff like that.

0:54

So just try. And a lot of the time when you're filling things out, it asks for all these little details, and a lot of the little details are things I don't know, but I've got to ask people and I can't do it until like the next day.

1:07

So I'm here filling things out all the way up to the podcast here and then I'm like, ah crap, but anyway, man, that's life.

1:16

Yeah, how's the moving going? How's the new house?

1:20

Yeah, pretty much done. I'm just unpacking the fun room right now.

1:23

I got like every Nintendo console ever made.

1:26

Yeah, great man Setting all that up, seeing which ones work after a couple of years in storage.

1:30

They're probably worth money, man.

1:33

Yeah, yeah, probably.

1:35

You never know. You should like get them framed.

1:37

I see some people have them in nice frames and stuff like that.

1:40

I want to use them. Okay stuff like that.

1:46

Yeah, I want to use them. Uh, okay, fair enough, just an idea, just an idea, but anyway. But what we should do, we should get our guest up today.

1:50

So today, what we're going to be covering is data science and dyslexia.

1:54

Join with our guest all the way from melbourne, nicholas lynchwood.

1:59

Did I pronounce that right, nicholas, or?

2:02

Nick sorry, yeah, it's just.

2:06

There's a dyslexic person, and I'm sure you would get this as well.

2:10

Pronouncing people's names is not the most easiest thing for us?

2:15

No, it's certainly not.

2:17

No, it's perfectly fine, nice man, nice Well.

2:20

Thank you so much for coming on the podcast today.

2:23

It was actually interesting.

2:25

I reached out to you a while ago.

2:28

I believe I've spoken with your mother a few times as well.

2:33

Did you want to give a shout-out to your mum?

2:35

Yeah, g'day Mum.

2:39

Yeah, I might give a shout-out to my mum as well.

2:42

Hey, hey, Mum.

2:45

How's it going? Kev, Do you want?

2:45

to do a shout out to my mom as well. Hey, hey mom, how's it going? Do you want to do a shout out to your mom?

2:48

I do, actually think mine's listening.

2:48

So, hey, mom, well, yeah, well, you're that close to your mom. Like your mom only lives downstairs, so you literally could shout out to your mom.

2:55

Yeah, do you know what I mean? But anyway, what we'll do, before we get stuck into this, let's just do a short shout out to any listeners out there.

3:03

If you haven't already done so. Please subscribe, like and follow to all of our social media pages.

3:09

We're available on tiktok, facebook, instagram, x used to be called twitter twitch, youtube oh, bloody, linkedin um and also, too, if you um want to listen to the podcast as a podcast, you can find us on all podcasting platforms.

3:28

Let's get this show on the road.

3:30

Also, too, if you've got any questions you'd like to ask us during the podcast obviously when you're live, not once we are have gone to the podcasting platforms please feel free to write in the comments there, and we'd be more than happy to get this going.

3:48

Fodon John, you ready, my man, I'm always ready.

3:51

You're born ready, right?

3:53

So, nick man, you know you've done some amazing stuff.

3:57

We know about that.

4:00

But, man, let our listeners know a little bit about yourself.

4:05

Yeah, so I'm an independent consultant.

4:08

I've been independent since September of last year, so it's about eight months or so, which is scary to think that it's been that long.

4:22

But I've worked with a few different organisations, both within Australia and internationally, and on things like my main sort of focus is doing mathematical and quantitative research, as well as data processing and analysis, for various organisations.

4:52

For that I was a data scientist with the Australian Institute for Health and Welfare and then for that I was also working for another independent consultant for the World Health Organisation based in Egypt.

5:14

Yeah, so I've had a few different career changes, but I also have a DPhil in mathematics and a Master's in health economics.

5:26

I feel like my main background is in mathematical modelling and statistics, quantitative analysis, data science odd jobs here and there.

5:40

Man, that sounds pretty fancy man, eh.

5:44

Not really. When you break it down, it's all pretty basic and simple, but it's a lot of complicated words for fairly simple things.

5:54

Maybe for yourself. I bet you, if I went into your job and went, what the hell am I doing?

6:00

You'd probably laugh at me, you know, but you know it does sound pretty interesting.

6:04

So you know, tell us a little bit about that work.

6:07

Like, what is it that you actually do?

6:10

Like you were talking about sorry, you were talking about doing some stuff with, like, the World Health Organisation.

6:17

I don't know if you know if I pronounced that correctly, but like, why is that needed?

6:23

Why is a lot of the work that you do needed?

6:26

yeah, that's a good question, um.

6:29

So really like no two projects I do really the same.

6:35

They're all individual and unique um and they all have uh um, they're all very, very different projects.

6:44

I've worked with the WHO twice.

6:47

Most recently I'm working with the European office and in that role, in this one, I'm effectively updating a series of their software that they're using to process the data that they have and that they collect.

7:08

The reason why this is important is because they process a lot of software.

7:16

They have a data portal that you can go into now and load a bunch of data and collect it.

7:23

Go into now and load a bunch of data and collect it, but the software that's used to update that particular um code and and to upload new data to it is quite old and I have been brought on to update it and and improve it.

7:41

That's one thing with them. I could tell you about some others and previous works that I've done as well.

7:48

Yeah, yeah, yeah, look, I think it's just interesting to hear you know you hear data science and it's sort of like you know, whenever I hear the word science, I think of like a person with a lab coat on.

8:03

You know, like in a lab or something like that.

8:08

Do you know what I mean? But I'm assuming what you're doing is completely different to that.

8:12

No, I do everything on a computer nowadays.

8:15

My background is in mathematics, so I've worked entirely on a computer my whole life.

8:26

I used to go into the lab when I was an undergraduate, but that was 15 years ago.

8:34

So no, no, no, it's a lot more common now to do this sort of stuff.

8:42

That particular project I mentioned wasn't so data science-y, it was more data processing, Other sort of data science stuff.

8:50

I feel like really that could mean the word.

8:53

Those words data science and machine learning.

8:56

they sort of get thrown around these days to mean almost anything, but they're pretty broad terms, pretty pretty generous terms at the moment would you reckon that like, so you know, I'm assuming that you know data science and all that type of stuff would be even things like you know, like recording numbers from earthquakes or you know?

9:21

Is it a revolve around that type of stuff, or would that be where you would fit in, or how does it normally work?

9:31

So I've specialised in the health field, yeah, but within that there are whole different types of areas where data science can be applied.

9:44

Sorry, I just wanting to clarify your question.

9:47

Is that what you're sort of asking where data science is used and oh, yeah, yeah, yeah, yeah, pretty much.

9:52

Yeah, like I'm assuming that you know you said you do stuff in health, but I'm assuming that it's quite broad as well yeah, yeah, it certainly is um um.

10:06

It could be used in predicting.

10:10

I think good a common case is is for it to be um used in predicting certain um examples or certain um phenomenon um, could you give us an example of that Like?

10:26

so, for example, say, with like COVID, which has just passed, would you I'm only using that because you're from the health, you know this is your background Were you able to look at certain things based on, I don't know, some of the records or data that you were seeing?

10:43

Or does it work around that way, or I'm not sure?

10:48

In the health field. Yeah, yeah, one way that it could do is like developing a short-term prediction for the number of cases in a given location.

11:11

Here's one example Like if you had the sufficient data and it was of a decent enough quality, or you had enough confidence in the data you were collecting.

11:22

One example you could use is developing an algorithm to predict very short um, like like a week ahead, how many cases you might expect.

11:33

Um, that could be one example you'd do is it?

11:37

is it true? I've heard a lot of people say these days that data is starting to get to the point where it's almost can be, is able to be prophetic, like it's getting better, like, better and better at predicting potential futures.

11:51

Is that true?

11:53

oh well, I think it depends on what sort of future you're talking about.

11:56

Um, I think earthquakes, for instance, are notoriously difficult to predict because they sort of just happen and then they're gone.

12:07

But you know, I'm not a geologist, so I'm not really an expert to speak in this field, but there are improvements that are being made.

12:16

In that it also depends on the context you're talking about.

12:20

I work in a lot of settings where there's very poor data quality or low data coverage.

12:30

Make it better and where you couldn't not, where there's necessarily low data coverage but where you shouldn't necessarily assume that there will be data provided, and that's certainly a challenge.

13:00

I know like when I was in COVID, one of the things that we did was I used, I developed epidemiological models to analyse the transmission of COVID-19 within a given population and, although I wouldn't call that specifically data science, several of the parameters we estimated were generated from an epidemiological if were generated from what was called the google mobility scores, which effectively they google took a bunch of phones that were using google software and they tracked their location over time and then they were able to publish this report, publish these as publicly available information, and that was categorised by countries and in some cases subnational estimates were also provided so that allowed us to generate sort of some estimates on parameters like the social distancing.

14:04

Parameters in the model we used were derived from that which would have been certainly an application of machine learning technologies.

14:17

Yeah, it's interesting. It's definitely interesting because you know for us where this is something completely new, and sort of hearing it from that perspective, because it is like, well, how do you get the?

14:23

You know for us where this is something completely new, and sort of hearing it from that perspective, because it is like, well, how do you get the social distancing measurements and how do they get this?

14:32

And hearing it from that perspective, it's like, yeah, that makes so much sense.

14:37

It's like technology is, you know, like, say, for example and this is something completely different with technology now like people are able to be tracked in certain areas at certain times and stuff like that, hence why they can sometimes find missing people or whatever that might be.

14:58

But anyway, we'll move on from that.

15:01

So you know, talking about all this interesting stuff that you do, how has dyslexia influenced your journey into data science?

15:11

yeah, that's uh, that's a tough question.

15:14

I guess it's kind of like asking what would have happened if the, if the asteroid that destroyed the dinosaurs, didn't come along.

15:23

What would have happened if COVID never happened?

15:27

How would the world, how would I be if I didn't have this part of me in my life?

15:39

There's no real counterfactual to compare that to.

15:42

There's no real counterfactual to compare that to.

15:51

But I would say that my dyslexia has influenced my journey into this field by how I can visualise problems.

15:56

I find, as a mathematician, if you can visualise the solution to a problem and the problem itself, well that's already half of the work done and that for the longest time, that was something that I was, I feel like I generally thought through images and thought through pictures.

16:23

I have various, I guess, a visual sort of memory or way of thinking and, yeah, that aspect has really helped me develop and I'll see problems and solutions in different ways and I think that's probably been the biggest way.

16:44

It's certainly helped me. Yeah, I'd say it's definitely had its challenges in that, without thinking with it, by thinking in pictures, I think, particularly when I was very young.

16:57

I don't know if I do that so much anymore, but thinking in pictures really helped me sort of visualise problems, but it also helped Made things like communicating and language very difficult.

17:10

Conversely as well.

17:13

There's always two sides of the coin.

17:15

Would you find that, like you would get really extremely frustrated at times because you could see what you were thinking in your head visually but trying to explain that was very difficult to say your colleagues or I don't know other, especially probably when you were studying and all that type of stuff.

17:37

That would have been quite a big challenge as well yes, pretty much.

17:42

You hit the nail on the head there, that was from one dyslexic to another right it's, it's, it's very frustrating.

17:50

It's like I have this thing in my head. I want to say this um, but it that's not.

17:58

Yeah, it's not necessarily how it works so when it comes to sorry, you're finished.

18:03

No, no no, I was saying enough. But my wife is very good she used to be a lawyer and very good with words and everything and she's almost the total opposite in some regards, and so she gets me saying like I don't know what's going on inside your head, I can't.

18:23

You do have to speak with words. So she keeps me on a good track.

18:27

Yeah, did you develop, particularly with regards to work?

18:31

You know tools and methodologies to deal with that and better communicate?

18:38

Yeah particularly after it was a couple of years.

18:42

It took me several years after my DPhil and my PhD to do that.

18:49

It was probably only when I was working as a consultant in the UK and with the WHO, that I got a lot better at what I feel, that I got a lot better at that, I feel, and I developed I think I developed some methods of dealing with that communication.

19:12

I think I had to really slow down and think about how I can structure things.

19:21

In a certain way. I'm a lot better when I write things down.

19:25

I find If I can write things down and I have the time to sort of process does this sentence make sense that's a lot easier for me.

19:36

If I'm writing a technical report or something that can be interpreted by like a technical document, that's a lot easier for me as well, where I just have like it's.

19:51

You know, in science everything's fairly standardised.

19:54

You have the introduction, methods, results, discussion, conclusion and it's all very neatly structured.

20:01

I think I don't know if that's a component to dyslexia, but I've heard a lot of similar people feel like they really like structure and having that reflected in a document is something I always like.

20:18

Writing like a more general piece for like a public or like something that's not like based on technical research is something I'd really struggle with and I still do.

20:35

Yeah.

20:41

So when you say like, so could it be, like, I don't know, writing a story about something?

20:47

Would that be difficult for you? But say, your technical research is where you really can shine, because you're like, yeah, man, I know this, like it's no tomorrow type of thing.

20:57

Yeah, I guess, like creative writing is something I've generally struggled with.

21:00

I think I told my wife this and she gets a good laugh out of it that I remember when I was in about sixth grade in the US, I was told to write a story or something as an assignment and it was only a short couple of pages story or something.

21:26

But I was marked down because I had no dialogue in my story.

21:31

I was like don't speak because I don't speak much.

21:38

So, yeah, why would they need to speak?

21:41

yeah, yeah so um, yeah, pretty much yeah, yeah, see, that's interesting because you know, and I think it's just such a good uh point that you know all dyslexic people.

21:55

You've met one dyslexic person. You've met one dyslexic person, right, you've met one dyslexic person, right, because, see, for me, creative writing is where I thrive, because I just have this I don't know imaginative sort of crazy type of world where others that's not their strength, you know, and I think that's a really important thing to definitely, you know, I think, to see the differences between it as well.

22:22

Yeah, that's amazing to hear Wow.

22:24

Yeah, yeah, yeah, yeah. But that's just one thing.

22:26

One dyslexic person right Now.

22:29

You know you did say a little bit about this before, but what are some of the difficulties you've had to overcome as a dyslexic data science Like, are you leading teams?

22:40

Have you been, you know, like you said before, trying to explain certain things to people?

22:46

Has that been a challenge or what?

22:49

What's it been like for you? Um?

22:53

um, writing was a challenge.

22:57

There was certainly a. So writing was a challenge there was certainly.

23:02

That's something that I struggled to overcome for a time, but I'm getting better at it.

23:06

What has been a persistent challenge and still remains is, I guess, my communication with other people and that I find I have confidence in my writing now and that I can write something.

23:21

And I have confidence that I can. I have time to assess it and think, like, does this make sense?

23:27

But I find in a meeting or yeah, that's when I really struggle is communicating sort of on the spot.

23:43

Um, generally, I find that my, my brain just doesn't think that fast.

23:47

Um, it's, it has a slow turnover speed.

23:51

I guess some people might say, um, it just takes me a long time to process, um, what people are saying, and then it takes me a long time to sort of understand like uh, or to come up with a response um, um.

24:10

I use like a good analogy is that, um, or my wife often teases me about that how I, um, she's a lawyer and oh, she used to be a lawyer and she comes up with responses like in seconds, um, because she's used to that and she has to be in stuff.

24:29

But um me, I'd be like, okay, well, you've, if I'm just imagining myself in that same situation and be like, okay, well, what often happens is she'll make a point or make an argument and it'll take me a minimum of usually hours, if not days, to get back to her about something.

24:52

So what you're?

24:53

saying is if you were our lawyers, we'd be going to jail, right?

24:57

now eh, yes.

25:06

Yeah, it would not count on me doing yeah, do you? Do you feel at all that that might be part of what makes you such a good data scientist, because you so thoughtfully consider?

25:11

Yeah?

25:12

that's certainly. Um, it's one of my challenges, like it's one of the the benefits I have in that.

25:18

I guess some things like code.

25:22

It really just takes you a very slow sort of methodical, very careful process to get through and the moment I'm just, you know, feels like you're bashing your head against a wall sometimes, but sometimes it, yeah, that's what it requires.

25:42

Can I say, right, like I know when I've been in meetings and I'm assuming this might be the same for you as well yeah, meetings on the spot, like responses, are really difficult for me.

25:55

But then I'll get out of that meeting and then my brain starts to churn over what I've taken in and then I start refining the process and be like man man, I should have said that in the meeting, or I should have said this to this person when I was trying to explain it.

26:11

Or you know, even getting on podcasts like this as well, I would like get off the podcast and be like, oh, why didn't I share that you know or this, and that I'm assuming that's how you process stuff yes, pretty much.

26:26

It takes me a while to process things um, and that it just just takes me time.

26:33

But fortunately of the people I've worked with, they haven't necessarily expected like I haven't been.

26:43

I've been fortunate enough to not be in the situation where I've sort of had to come up with like the outcome of an entire project depends on what I say in the next minute.

26:55

Thank God that would be a very stressful situation for me if you don't mind me asking, asking about the same thing on a slightly more personal tone has that resulted I mean, clearly, you're the smartest person on this podcast right now um, has that ever resulted in misconceptions though?

27:15

About, about things like that? I've seen it happen to other neurodivergent people where, because they're not processing things in a way that everyone that a neurotypical person would understand, they make a whole lot of assumptions about the person's intelligence or what kind of person they are.

27:30

Has that been a challenge? Yeah, probably.

27:40

Thinking of a specific example that comes to mind.

27:42

Yeah, I was.

27:48

I can give you an example I was working with.

27:52

I was working on a project a few years ago now and I was actually talking with a fairly senior politician in the country about a policy they'm hoping to implement.

28:21

This is during COVID and it was on a COVID-related policy and it was quite difficult to communicate the nuances of this type of analysis that I was doing, especially to sort of non-technical audiences and I've presented to non-technical audiences before about stuff.

28:50

That's not something I really struggle with so much.

28:53

It's that I guess what I struggle with in that particular case was communicating the specific nuances about things you can do and things you can't do and why that's important, I guess, because a curve of cases going up and then peaking at a certain point and then eventually going down or something, and they would sort of interpret that as a prediction about what will happen in the future.

29:41

But and I feel like a lot of people will put a lot of non-technical people will put a lot of their faith into these predictions or what they think is a prediction.

29:56

But in reality those graphs are generated from a series of mathematical formula and that formula is based on a series of parameters that are imported into those equations and in all of those, in each of those steps, there is quite a significant degree of uncertainty.

30:19

Of those steps, there is quite a significant degree of uncertainty that is compounded at every single iteration, to the point where, if you were to consider every single uncertainty and if you were to, at every single time, step and in its full complexity, consider every single uncertainty in its full complexity, throughout its entire mathematical problem, you would come up with an uncertainty band that says tomorrow there would be anywhere between zero and 100 million deaths of COVID.

30:53

It's like okay, but that's not very helpful.

30:57

So in that case, as a mathematician, you have to consider like okay, but that's not very helpful.

31:03

So in that case, as a mathematician, you have to consider like okay, what are the most likely outcomes?

31:07

We don't want to know every single outcome, we want to know what are the most likely ones and communicate that this is not a prediction.

31:15

This is a scenario. We are modelling a specific scenario happening under specific circumstances and in reality these models are.

31:24

They don't represent reality to its completeness, in that a model is a representation of, of society, in this case, um and that, because it's a model, we have taken this complex, messy system of society and represented it as a series of very clean mathematical functions and in that process we've had to make these simplifications or these assumptions that are very questionable, that are very like anyone would point out is very incorrect.

32:03

But we've had to do this because, one, we're interested in time, we need to do this quickly and, two, we want to get an answer.

32:17

We want to get some sort of estimate, like an estimate for the number of deaths averted or something, and that means we have to make assumptions that aren't necessarily based on, that are realistic but are not necessarily completely.

32:37

Evidence-based type of thing yeah maybe, or they could be based on evidence, but they're not.

32:43

They only take the most core principles.

32:47

They don't consider, you know, the flapping of the butterfly wings as it disperses air throughout the room and how that will affect the COVID particles.

32:58

So, it's kind of like you guys can see the metrics and you need to present it to people who can't.

33:03

Yeah, oh now I get it.

33:10

And there's a lot of complexity there and I guess people will see that like, oh, but you're producing, you're saying the curve, the cases are going to go up for a bit but they'll eventually reach a maximum and then they'll go down Like, yeah, but only under that specific circumstance.

33:25

That doesn't mean that's what's going to happen.

33:28

That's only one very specific scenario, and making that sort of distinction to people who had like a 10th grade sort of education in mathematics at best was quite a challenge.

33:47

Extremely difficult.

33:49

And I guess we also had a vested interest in going out to the public and going see, it's going to pink here and then it'll go away.

33:57

Well, thankfully I didn't have such a public-facing role, so that was thankfully not my responsibility, but I have a lot of respect for the people who do, because they're facing even more questions from idiots, if I'm allowed to use that word.

34:14

Yeah, go for it, mate.

34:16

Why isn't this working out the way the models have said they would and stuff?

34:21

Well, it's because in these models, specifically in models that relate to health dynamics, like, we have all these really complicated models, we have all these really complicated technologies, but all of those technologies are predictable, are sensible, are like when you have an iPhone If you call up something, you have confidence that it will do that, but you can't make a model with 100% certainty about what human behavior will do, and so, yeah, there was always that a bit of like uncertainty you had to incorporate, or did you like?

35:11

did you ever like watch the news and see people like presenting information and be like?

35:16

Oh what are? What are you talking about? You sound like an idiot.

35:19

That's not right.

35:23

No, not from my respect, no, not from that.

35:32

I was living in the UK for most of COVID and they have, yeah, but and I didn't see and I wasn't I had every confidence in they have one of the best sort of health like infectious disease programs in the world.

35:55

Other countries I have worked with other countries and with much poorer circumstances and situations but I wasn't familiar with the, I wasn't reading up on the news on those particular settings.

36:14

I did hear of a report that got leaked through a government that I'd worked on and sadly got leaked through a government agency and they interpreted the results like I think I explicitly put in the report do not interpret these results as a prediction.

36:39

These results are meant to be interpreted as blah and they did the exact thing.

36:45

They did the opposite of what you told them not to do, right?

36:48

Yeah. So in those cases I have to say like well, no, yeah, yeah.

36:55

Crazy crazy. Wow, that's interesting.

36:57

But you know what Now you know you've spoken to us about, like, the struggle with communication and all that.

37:04

But you know there must be a lot of reading and stuff you're having to interpret.

37:09

So how do you navigate tasks such as reading and interpreting text-heavy documents?

37:14

Are you using assistive technology a lot now?

37:21

I'm not actually so much I probably should be.

37:25

I don't tend to read a huge amount of technical documents.

37:31

The most I'll probably read is maybe a discussion on, like I will find a document and I'll read up on the methods or discussion or something.

37:45

Um, most of the documents I do read do occasionally, and I find when I do read them, though, it takes me a huge amount of effort to go through a paper or any sort of document and understand exactly what it is they're trying to do.

38:15

I really have to sort of go through it with a fine-tooth comb and sort of understand big sections or um, because I'm just very, very poor at skim reading.

38:29

It's something that I really struggle with.

38:32

Um, and when I do have to skim read, I try and do it in a very sort of focused way.

38:50

Either I will sort of just do a search for a specific word and look through that, or I haven't gotten.

38:59

I haven't done so much reading on a particular project.

39:02

I haven't done so much reading technical reading for a project recently in the time of Chachi Biti and stuff, so I can't really speak to that, but I think if I did need to summarise a large piece of information, I'd probably use that need to summarize a large piece of information.

39:23

I'd probably use that, um, but it's like, yeah, um, that makes sense yeah, yeah, yeah, no, totally, totally, totally no.

39:35

I and I suppose everyone has their own methods, like, say, for example, not everything I read, I will use assistive technology.

39:45

Sometimes, if I've got a lot of reading to do, I might use, you know, text-to-speech.

39:53

But that just depends on what it is.

39:55

If it's just a small little like blurb or something like that, I can sort of go with that.

40:02

But when it's more longer, like, say, for example, I read a um, it was a.

40:09

It was like a uh, some evidence, research on neurodiversity, and in like, uh, better education, right.

40:17

And um, yeah, I, there's no way I could sit there and I would just be like, no, this is crap, you know not that, not, the research is crap.

40:27

I'd just be like, no, I can't tolerate this.

40:29

But when I went through it with the assistive technology, I sort of felt I could sit back and be like, yeah, cool, but that's not a ongoing thing.

40:39

I think I still rely on a lot of still old-school methods, so having to read but you know that doesn't mean that I'd like it, but I can take it in and it really depends on what it is.

40:53

Usually I'll read like the first little bit and be like, oh, okay, now I'm getting what's going on.

40:59

Yeah, yeah, okay, now I'm getting what's going on.

41:03

Yeah, yeah, reading is one interesting you know what?

41:08

I saw someone post something up online recently and it was a parent, and I understand where they're coming from and they're like oh you know, I really just want to.

41:19

You know, they're talking about their dyslexic son and they're like you know, I just really want my son to be able to love reading and I thought to myself, yeah, that could be something that he never will want to do?

41:34

I just want my son to be able to be something that he's not.

41:36

Yeah, yeah, yeah.

41:38

Well, you know, I never sit there and go oh, now I can, because I can read a lot better than what I did when I was a kid.

41:45

But I don't sit there and go oh man.

41:47

I love reading now, that's just something I don't enjoy doing, if that makes sense.

41:55

Yeah, yeah, absolutely.

41:56

And I'm sure you're probably the same.

41:59

There are. I find that when I do read a book I enjoy, like I find I'm very picky about the books I read and then the information I read, and that if I don't like a book, I will really struggle to do it or it's almost like a deterrent for me to keep reading other books.

42:23

So I'm very I have to be very careful.

42:28

Yeah, it is something I struggle with, but then there are some books when I really like it and it's like wow, that's amazing, I really love that sort of stuff and um, so it's, it's a struggle, but it's I've.

42:44

But I've generally found that recently, after reading, I've found myself better, I've enjoyed myself more and yeah, yeah interesting.

42:58

I know what you mean. It's difficult.

43:02

I just it's not something like like so back in the day, say, when they had a newspaper and I know that's like you don't see too many newspapers these days, but I would normally you know the headlines, you know that's where I'd be like oh wow, I really want to read this because this is about that.

43:21

But I'd normally just choose like little parts of it and get the gist of it and then be like oh cool, this day and age I might read something can be like oh my God, if this is big news, I could probably just go onto YouTube and Google it you know what I mean and get the information via like a news place or something like that.

43:44

So it just really depends on the situation to tell you the truth.

43:47

But I'm just trying to think hey, kev, do you reckon we've already sort of asked this question?

43:52

So I'll read it out.

43:55

Sorry, you go Kev, no, no.

43:57

Well, yeah, I mean I'm interested in crossover because you were speaking earlier and you don't mind me reading the question out, yeah, yeah, have you found any unique strengths or perspectives that dyslexia brings to your approach to data science?

44:11

And I know, before the podcast we spoke a little about pattern recognition and we didn't get too much into it, but, um, uh, I excel at that in my work because I have to pull, pull a lot of spreadsheets and I can almost read it in a way that no one else can, and so I can see problems and issues or patterns that are important to the project.

44:34

Is that something that you find as being dyslexic as well?

44:41

Yeah, like I can remember when I was really young, um, both of my parents helped me in ways that no one else could and at really critical moments in my life.

44:54

Um, I know that I was about eight and I, I couldn't, I didn't, I couldn't read.

45:03

I could read, as my mum said, ten words and the rest was like everything else on the page.

45:12

I had no idea what that meant, and I think she was able to come down to my level and understand the struggles that I was dealing with and recognise that I was able to sort of bridge that gap, I guess, between writing or reading and thinking visually in the pictures that I had.

45:39

I think she would.

45:42

I guess I don't know how she did it through her own amazingness and intellectual abilities to sort of bridge that gap.

45:51

I don't know how best to describe it, how better to describe that other than the amazing work she did.

45:57

And I know, later I feel like I've blocked my um, a large portion of my old early childhood out from my memory, because I don't know why I just I just don't remember it.

46:17

Um, and I think my mom and my wife say it's probably due to the trauma I faced in that period, but I struggled with things.

46:25

I said I was struggling at that time.

46:29

Yeah, yeah, and were there a series of people throughout your life obviously your parents, because I'm just imagining you know how this presents as a child?

46:38

Again, I don't have dyslexia, I have autism and ADHD, but I know that that presented in a way that led to a lot of misunderstandings about my level of intellect and that kind of thing.

46:47

Um, uh, were there sort of like a series of people who saw that in you throughout your life and recognize that you are really intelligent and nurtured your journey into where you're?

46:59

yeah, of course, I think it's definitely my mum, my dad, my grandmother and a few teachers.

47:09

At critical, important moments in my life I told you about my mum.

47:16

I think my grandmother is someone who said to mum that you've got to make learning fun for him.

47:24

You've got to find a way to make learning fun, and I think she was able to see that within me, that she was also a very curious and interested person.

47:36

My father, I think when I was an early teenager, a young teenager realised that I could put together blocks in something or images and generate sort of structural buildings out of a construction toy called K'nex, which is very popular in the US, and able to make these rather large structures out of off-limits.

48:05

Was that like a Ferris wheel?

48:07

Yeah, one of the Ferris wheels.

48:09

Yeah, I think I remember seeing.

48:11

Your mum posted up a picture of the huge-ass Ferris wheel you created.

48:15

Yeah, yeah, yeah, I like doing things.

48:18

I think that may have been from a I'm not sure if that was from an instruction manual or not but I generally preferred not to like sort of use instructions.

48:27

They were a bit limiting. I preferred to and I that's an aspect of my visualization that I'm I really attribute um, in that dad and I would spend most of the weekend um putting these things together, making structures and just playing around with that, and I think that also developed my visualisation abilities, which later led me to excelling in mathematics.

48:57

Being able to visualise this is the problem, this is what's needed, this is the image we need to create and this is the state that it's at now.

49:06

How do we sort of get from this state to this state using these tools?

49:10

Um, I think helped improve my my visualizations abilities a lot and incredibly, yeah.

49:19

So no, no, that's interesting.

49:21

That's interesting, but did you find you know and I would assume that that's those were the fun parts?

49:28

You know how you're talking about? Your mum really, um, spoke about making education fun and all that, and and I'm assuming those would have been the fun parts being able to go.

49:38

Okay, this is what I'm thinking in my head, this is what I need to make, but when you would have gone to university, there would have been this huge gap of, like, all of this hard stuff, like I'm talking about the reading and writing and studying that you have to go through to be able to get to the fun stuff.

49:57

If that makes sense. Was that what it was like for you?

50:01

Not so much at university Well, during my DPhil there was that and when I was a working professional afterwards, those were the big struggles.

50:11

Not so much at my undergraduate, it was more, I guess, the emotional communication and expectations that I struggled with at the time, just sort of understanding all these sort of unsaid things about workplaces that I struggled with.

50:35

I don't know if it's a dyslexic thing or an autistic thing or what, but I've since come to learn that in a workplace there's a lot of sort of unsaid expectations that no one really teaches you.

50:51

But you need to learn or you need to find a way to understand these things, and that's definitely something I've struggled with.

51:00

I felt like at university it was still very structured.

51:04

It was still like you give your homework in once a week, you do this, you attend lectures, and it's all very sort of structured.

51:12

But outside of that, I think I really struggled with that sort of yeah, emotional understanding and yeah.

51:25

Did you ever get to a point, especially when you were studying, that you were like no, I don't think I can do this, this is, this is just too full-on, or you know?

51:35

Did you ever get to that point? Because you know, I know I've been in certain situations and you're like man, this person's trying to show me this and I'm still not getting it.

51:44

You know, did you ever felt like you got to that point?

51:48

Yeah. I did, particularly during my DPhil.

51:53

I thought that was a particularly challenging time in that regard.

51:59

Yeah, yeah, crazy.

52:01

There were a few times mostly four years of thinking like that.

52:07

Probably when you graduated, it would have been a real relief, right.

52:11

Yes, just to have that, and absolutely yeah.

52:18

But did you look at it and sort of go you know what they could have done things differently, that would have helped me better.

52:25

Do you ever look at it that way and go why didn't I do it this way?

52:29

Or why did I have to do it this way?

52:33

Yeah, I do. I think that whole time for me personally was a bit complicated for me in that I feel that I have matured substantially from that time.

52:52

I have changed myself in that I'm not the same person as what I was then and it's a bit of a difficult thing for me to, because I feel like I was not when I 10 years ago, when I started, or 11 years ago when I started my PhD.

53:22

I don't think I was ready to do that. I was not sort of in the mental state to complete that and I somehow struggled through.

53:30

But those four years were quite difficult and now that I'm out of it I'm able to sort of recognise those bad habits and account for them and recognise that like I had developed some really bad habits over that time and that there were a lot of things I guess people could have done better.

54:07

There was probably even more things that I could have done, I feel.

54:11

So I feel like I wouldn't blame anyone too harshly of my supervisors, too harshly of my supervisors, but myself I wouldn't say blame, but I'd certainly, if I could, I would go back and do something different.

54:32

Yeah, as you thought right, yes, yeah.

54:38

But do you know what right Like in saying that and I'm a big believer in this sometimes education, these experiences and these bad experiences we have, when we can get through them, you know you like you spoke about you were, you felt like a completely different person to what you were once you completed.

54:56

These can be good type of things because you do develop and grow and then you learn new things and maybe you do have doubt at some point, but you then get through it and you're like, oh my God, that gives me more confidence.

55:11

You know, that's what I've sort of felt, especially with education is that there have been certain situations where it's been incredibly difficult and frustrating, but then I've gotten through it and it's taken me to that whole next level that maybe I never thought I could get to at one point in my earlier years of life.

55:32

If that makes sense, yeah and I would hope that there's, um, you know, there might be some sort of science or data science minded kids who would hear this episode.

55:41

Um, because you know, I think I'm just going to embarrass you for a second um, you, like I said, you're clearly one of the most intelligent people we've spoken to, despite the fact you you know, just your dyslexia might sometimes present in a way that could make people make assumptions and whatnot about you.

55:56

Um, and you found the right people who believed in you and you found ways to get through stuff and it's actually a super inspiring story and, yeah, I'm feeling really good about this.

56:09

Like this is a great episode. There you go.

56:12

There you go To tell you the truth.

56:15

Yeah, sorry you go. No, no, no.

56:17

I was just saying that I think probably my biggest struggle was not the technical details of a project or work and that I've always been able to grasp that fairly easily, but it's the emotional communication of other people and that's Expectation that other people have of me and these sort of unsaid things that we expect you to do this but we're not going to tell you and it's taken me a long time to sort of realise that, like these people, they like things in a certain way.

56:54

These other people, they like things in a certain way and you've just sort of got to like Exactly.

57:02

And I would say to any neurotypical people listening, you know, take note of all of this.

57:06

You know because clearly the way that you experience the world, which may seem odd to some other people, sometimes has zero effect on your intellect.

57:14

It's got nothing to do with that, um, and I know that from personally, the amount of times I've had people who realize there's something a bit different about me and start talking to me like I'm a two-year-old is very frustrating.

57:27

Yeah, crazy, crazy, crazy.

57:29

Do you know what?

57:30

Sorry, you go Nick.

57:32

No, no, no, no. It's really hard to sort of understand like these unsaid expectations because no one, I guess, really teaches you that.

57:41

But it's also I've heard of like looking back on my own life and my own sort of experiences and that it's quite tiring to be with.

57:54

I can certainly empathize with other people and, like my earlier suit, my supervisors and like I must have been a real hell of a student to deal with not knowing any of these things and being very frustrating to deal with.

58:10

So I can certainly empathize with that.

58:13

I know my teacher in a high school teacher in english who I always hated english because although this teacher was actually rather good, I thought, but I generally hated English in high school and he said I've got to read between the lines.

58:31

And I guess the teenage version of me was like I said like, but there weren't any words between the lines so I can't read them.

58:39

And the 35-year-old version of me, 20 odd years later, says like actually that's rather smart advice.

58:50

You've got to sort of read things, read the message in the room, um, and and amongst people, um.

58:57

And being able and having that skill is something that has taken me a lot of blood, sweat and tears to figure out, more than maybe what other people have done.

59:11

No, no, no, awesome stuff. Like we've got a few more questions but I think we've sort of run out of time.

59:16

But, nicholas, thank you so much for coming on the podcast today.

59:21

It's been really interesting to definitely hear about what you do.

59:26

You know, the stuff around what you did with COVID around that period was really interesting as well.

59:33

But if someone wants to connect with you, what would be the best place to find you?

59:39

LinkedIn is probably best.

59:42

Yeah just in case they want some data science consultation or something like that.

59:47

Yeah, yeah, yeah.

59:48

Totally.

59:49

That's probably easiest. I don't know if I can give my email address out, yeah.

59:56

Oh, if you want to mate, yeah, yeah, that's all right.

59:58

Oh, what if they connect to you through LinkedIn?

1:00:02

Yeah, I find. I'm a bit more responsive to email than LinkedIn, but yeah, either way.

1:00:07

Yeah, yeah, do you want to share your email?

1:00:09

Sure, why not how?

1:00:10

do I do that, oh well.

1:00:13

I'll just say it probably yeah.

1:00:17

I don't think we could I might be able to get it up.

1:00:19

Hang on, I'll create a banner. Hang on.

1:00:21

So what is it? Let's fit dot nicholas at gmailcom uh, so let so just keep in mind everyone.

1:00:31

I'm dyslexic and I'm crap at typing.

1:00:33

Let's fit dot Nicholas at gmailcom.

1:00:43

Did you say that's right, gmailcom?

1:00:49

All right, let's see if this is right.

1:00:51

Okay, add banner, is that it?

1:00:56

Yeah, that looks right.

1:01:00

Yeah, good job, good job.

1:01:00

So, yeah, if you want to get a hold of Nicholas, definitely go to that email there or connect with you on LinkedIn there to be able to find some really cool stuff or connect with you if that's what you're looking to do.

1:01:14

So, ferdon John, did you have anything else you wanted to say before we close out the podcast today?

1:01:20

No, just thanks. So much for coming on, nick, I really appreciate it.

1:01:23

Oh, thanks for talking to me.

1:01:25

I really appreciate it, yeah man you're, you're, you're an interesting person, so thank you so much.

1:01:30

And, and please, anyone who wants to connect with nick, please go to either email him or connect with him on linkedin.

1:01:37

If you haven't already done so, please follow us, subscribe, like and follow on all of our social media platforms or check out the podcast.

1:01:46

Wherever you listen to your podcast, I'm your host.

1:01:48

Will wheeler join with my main man, photon john?

1:01:51

And this is neurodivergent mates.