SDS PODCAST

EPISODE 251:

TRANSFORMING

THE IDENTITY

AUTHENTICATION

SPACE

Kirill Eremenko: This is episode number 251 with CEO and Data

Scientist at TypingDNA, Raul Popa.

Kirill Eremenko: Welcome to the SuperDataScience Podcast. My name

is Kirill Eremenko, Data Science Coach and Lifestyle

Entrepreneur, and each week, we bring you inspiring

people and ideas to help you build your successful

career in data science. Thanks for being here today,

and now, let's make the complex, simple.

Kirill Eremenko: This episode is brought to you by our very own data

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Kirill Eremenko: Welcome back to the SuperDataScience Podcast, ladies

and gentlemen, super excited to have you on the show

today because we've got a very exciting guest, Raul

Popa, who is the CEO and Data Scientist at

TypingDNA. What you're about to experience on this

podcast is very, very different to probably anything

you've heard before because we're talking about a

brand new industry that is completely crushing it and

disrupting everything we know about security, and

this is the world of typing biometrics.

Kirill Eremenko: The idea behind typing biometrics is that based on

how you type, whether it's on your laptop or computer

or on your mobile phone, it can be established that

you are you. You can be identified just as that can be

done with your fingerprint or with facial recognition,

same thing can be done through the patterns that you

use for typing. As you can imagine, that can

completely revolutionize how we identify people, the

whole world of two-factor identification. It's also a very

passive process, non-intrusive. You don't have to get

an SMS and type in a code. It just happens in the

background.

Kirill Eremenko: Raul Popa is the CEO and the Data Scientist at a

company called TypingDNA that is spearheading this

whole industry, one of the leading companies in this

space. Today, we'll get to hear from him all about this

world. You'll learn about typing biometrics, what it is,

how it works, how machine learning and data science

enable and propel this industry forward. You'll also

hear about different applications ranging from making

sure students don't cheat on exams all the way to two-

factor authentication for banks and other financial

institutions.

Kirill Eremenko: The fact that Raul is both the CEO and the data

scientist at the same time makes this conversation

that much more interesting because we get to dive into

both worlds. From the perspective of data science, we

talked about pattern recognition, anomaly detection,

one-shot learning, binary classification, data sampling,

generative algorithms and more. From the world of

business, we talked about what it's like to run a data

science startup and going from idea to research to

making a business happen. As you can imagine, we've

got lots of interesting things to cover.

Kirill Eremenko: Before we dive in, I want to give a shout out to the fan

of the week. This one is from Andy who said, "The

FiveMinuteFriday episodes always feature an insightful

look into a unique topic, meditation, the importance of

enjoying the moment, how to maximize efficiency and

continuous inspiration to supercharge our own

careers. Thank you, Kirill." Thank you, Andy, so much.

For the rest of you guys out there, if you haven't left a

review yet, make sure to head on over there on your

podcast app or just go to iTunes, and you can leave a

review on the SuperDataScience podcast. It would

mean a ton to me. I would be very, very excited to read

your review. All right, guys, I'm super excited about

this one. Let's dive straight into it. You will learn

plenty about running data science startups and how

the world of typing biometrics works. Without further

ado, I bring to you Raul Popa, the CEO and Data

Scientist at TypingDNA.

Kirill Eremenko: Welcome back to the SuperDataScience Podcast, ladies

and gentlemen, super excited to have you back here on

the show. For today's episode, we've got Raul Popa

joining us from New York. Raul, how are you going

today?

Raul Popa: Hi, I'm fine, everything is fine, welcome to your

audience and nice to meet you.

Kirill Eremenko: That's awesome. That's awesome. Thank you for

coming on the show. I like how we were chatting before

the podcast that because you're based between

Romania and New York, we're talking about the

weather in New York, and it's like 17 degrees, and

usually, when I talk to somebody from the US, it's in

Fahrenheit. That's very cool. How do you find

adjusting to the weather in the US, when everything is

in Fahrenheit and you are used to Celsius?

Raul Popa: I just set it on Celsius on my phone, and everything is

fine. I don't really know it's Fahrenheit. I know that I

have to set temperature in the apartment around 70,

but I don't really know what that means in Celsius.

Kirill Eremenko: Yeah. Yeah, it's so interesting. It's a nonlinear

conversion. It's a trivial conversation from Fahrenheit

to Celsius. I personally get confused all the time, and I

just wish there was one system around the world.

Nevertheless, how are you enjoying New York? Have

you been there for a long time this time around?

Raul Popa: Yeah, only for two weeks. I used to spend more time.

Last year, I've been here about five months. The rest of

the time, I've been in, mostly, Europe and Romania.

Kirill Eremenko: Okay. Got you. You've done quite a few presentations

and pitches through the nature of your business.

You've even done a TEDx talk, but this is your first

podcast, so congratulations. I think our listeners are

going to be very excited to hear what you're about to

share. Are you excited about this?

Raul Popa: I'm really excited, yeah.

Kirill Eremenko: Awesome. Awesome.

Raul Popa: Maybe a bit nervous as well.

Kirill Eremenko: That's totally normal. That's very normal. All right, so

Raul, tell us a bit about yourself. You're running this

very innovative, different business that many people

haven't even heard of this type of technology before,

and by the looks of it, from what I've seen, what I've

read, you guys are really crushing it. To get our

listeners up to speed, please tell us what is TypingDNA

and how did you come up with this idea.

Raul Popa: Yeah, so I'm CEO and Data Scientist at TypingDNA,

and this is called typing biometrics, a behavioral

biometrics company, basically. We'll look at how

people type and build behavioral biometrics profiles

that we use for authentication and fraud prevention.

From an AI perspective, I'm more into pattern

recognition, anomaly detection, one-shot learning, and

binary classification. I know it sounds trivial, but

being able to give a yes or no answer in a fraud

problem is really tough, and the difference between 90

to 95% accuracy matters a lot.

Raul Popa: The techniques behind our technology are

exponentially more complex than what you would

typically think for solving a binary classification

problem or a one-class classification problem if you

want, so just to understand, from a machine learning

point of view, how it looks. I think what we're building

looks innovative from the outside. From the inside, it's

just pattern recognition. It's just applied to something

that machine learning was not applied before or not to

this extent.

Kirill Eremenko: Okay. Okay. Got you. That's very exciting. For us to

get a better understanding, like an intuitive

understanding of how this works, let's say you're

working ... You partner up with financial institutions.

You partner up with banks, other kind of companies

that ... Tell us a bit about that. What kind of

companies use TypingDNA services?

Raul Popa: Yeah, so we're at the beginning now. TypingDNA itself

is at the beginning, but basically anywhere where you

would need another factor for authentication or

another security layer other than a simple password or

anything like a push identification or one-day

passwords sent via text message, you will probably

want to use TypingDNA.

Kirill Eremenko: Okay.

Raul Popa: We started with proctoring companies or online

assessment companies, companies like ProctorU or

Mind Prov. Actually, they verify students when they

take exams, and before using our technology, they

were using real people. It's more expensive like that.

We helped them reduce people, and everybody is

happy. Students can take exams faster, and they

cannot cheat as much as they did before.

Kirill Eremenko: Okay. Got you. Very interesting, and so tell us a bit

about how this would work in the background. Let's

say a financial institution, a bank is using TypingDNA

to authenticate their users. I'm logging onto my online

account. I get to my computer. I type in the address of

the URL of the bank's online portal. I type in my name.

I type in my password. Then I click Log In, and this is

the point where I'd normally get an SMS to verify that

it's me. Where in that process does TypingDNA come

in? Where do your algorithms start to recognize me?

Raul Popa: You don't really have to get the SMS if you're using

TypingDNA. That's the point. Anywhere you type

something, like your email password, credit card,

anything that you previously typed, you can use ... An

application can use TypingDNA to record the timing

between the keys, how long you keep each key

pressed. These are the kind of information that we're

looking at. Based on that, we build a profile that I told

you about, and we can do authentication right then

and there and use that as a two-factor.

Kirill Eremenko: Got you.

Raul Popa: Also, financial institutions can use our technology for

employee-facing authentication. Nowadays, for 2FA,

they typically use a hard token or a push identification

system, you'd have to install something on your mobile

phone and use that. I think that's really not something

very user-friendly, so we offer more like a frictionless

solution. We just look at how people type. If that fails,

sometimes, it fails, if that fails, we can always go and

use the one-time password over SMS or the push

identification or anything other.

Kirill Eremenko: Okay. Got you. How much typing is required to

authenticate a user? If I just type in my email, is that

enough, or do you need me to type in maybe 20 words

or 50 words? What is the minimum amount of words

required to authenticate somebody?

Raul Popa: I think this is where things get really interesting. For

data scientists, I think this is where things get really,

really interesting because to build a good model, you

will need 15 samples at least, 15 previous typings, so a

person typing 15 types. You asked for the length of the

text, we can work on text of eight characters or 20

characters. We found that a lot of use cases have

around 28. This is the average of use cases that we

form, around 28 characters, like email plus password

combination, or credit card name, or other things like

that, but also username, password is a bit less, but

still, 20, 20-something plus usually work really well.

Raul Popa: We can do authentication even with just one or two

samples. I know this is something that machine

learning was not supposed to be used for, but we

actually do that. This field of one-shot learning, we're

using very few samples to do prediction, to predict

whether somebody is the same person or not with the

owner of the account. That's really interesting.

Kirill Eremenko: That's very cool. How unique are these typing

patterns? With fingerprints, they're pretty reliable.

With facial recognition, even more reliable. How about

typing patterns? What is the chances of two people

having the same typing pattern?

Raul Popa: This is a good question. I want to say it's as ... I think

the technology is at the beginning, and we will see

more improvements in this. Depending on how much

text you've got from the user, if the user typed a

sentence 10 times, then you can capture the

uniqueness of his typing. It will be really hard to

break. If you only want to do authentication after just

one or two samples, then the authentication, the

match will have some potential error, some potential

false positives or false negatives exist there.

Kirill Eremenko: Yeah. Okay. You would say it's almost as reliable as a

fingerprint when you have enough data to authenticate

the user, let's say, 10 samples we have or 15 samples.

Raul Popa: Yeah, I'd say that, but I'd say that fingerprints and

face recognition, for example, on the other side are so

public. I mean, you leave your fingerprints everywhere,

your faces everywhere, so somebody can snap a photo

of you at high resolution and use that to authenticate

pretty much anywhere other than face ID. With typing

biometrics, it's not the same. You can ask in your own

application for that user to type a specific name that

he will never type in other platforms, like random

combinations of words, his password, or anything like

that. To get how that person type that exact text in a

different environment is almost impossible. We're

looking at something that has more reliable character

like typing pattern. From a reliability point of view, I

think that it can be even more reliable than fingerprint

or face.

Kirill Eremenko: Wow, I'm still sitting here in amazement because I've

never considered this be an option for authenticating.

Well, in that case, if you ... You have somebody who

types in their password, but what about tools that

teach people how to type? There's the whole fast speed

typing learnings where you can use the QWERTY or

the Dvorak keyboard layout to type faster. Let's say

somebody is using the standard QWERTY keyboard

layout and they learned how to type faster, so they

learned those techniques for typing. Now, you have,

let's say you have a thousand people who learned

using the same typing program, and they're very

diligent. They got very good at it. Wouldn't they have

very similar typing patterns?

Raul Popa: I don't think they are similar because their size of

fingers and how they use the fingers and the muscles

differs. If people would take the same running classes,

would they run the same? Would their walking style be

the same? I don't think so. It could be very similar, but

still, there is a lot of character in watching somebody

typing or watching somebody walking or even

speaking. Even if you sing at the opera and other 10

singers, you would not maybe distinguish the opera

voice between those, but when you talk like normal

people, your voice will be singular. Right?

Kirill Eremenko: Mm-hmm (affirmative).

Raul Popa: This is only intuition. I'm just talking about what my

intuition says, but from our test, we adapt. For

example, it's like face recognition adapts when you

grow a beard, or-

Kirill Eremenko: Or you're wearing sunglasses.

Raul Popa: Yeah. Yeah. Yeah.

Kirill Eremenko: Okay. Got you.

Raul Popa: We adapt when people start typing faster or differently,

but if it's very different, then we have to fall back to a

different method for authentication, for example, but

not all use cases are about authentication. We can do

all sorts of other things, like making sure somebody is

not sharing accounts with other people, like all sorts of

things. We have about 20 different use cases that we

identify.

Kirill Eremenko: Got you. Okay. Well, that's very cool already, so I hope

our listeners are as excited as I am to dive into this. I

really love that you are both the CEO and the data

scientist, and you actually point that out in your

LinkedIn that you perform both roles in your company.

It's very cool. That means we can dive into the whole

notion of setting up a business about a really cool idea

and about some research, a data-driven business, and

on the other hand, the techniques and algorithms that

allow for all this to happen, for this technology to

work.

Kirill Eremenko: Probably, let's start on the data science side of things.

We already touched on a little bit on the pattern

recognition, anomaly detection, one-shot learning,

binary classification. What can you share? I know a lot

of this would be proprietary information and that you

can't share freely on this podcast. Nevertheless, what

can you share with our listeners that might be exciting

for them? What kind of machine-learning algorithms

are used in typing biometrics these days? What's new?

What's hot, and what can they look into if they ever

want to get into this field?

Raul Popa: It's a tough question. For TypingDNA, I cannot go into

details, unfortunately. I can say one of the most

important things, greatly underestimated, not just in

typing biometrics, but in any kind of machine-learning

applied technology is data sampling. We had a lot of

misfortunes at the beginning of building TypingDNA

because we didn't address that correctly. Whenever

you deal with fraud prevention, anomalie detection,

you'll always find extremely unbalanced sets of data,

also very noisy, so it's easy to throw away 90% of your

extra data, but it's not always an option.

Raul Popa: Generative algorithms also seem to work really well on,

mostly, all levels, I mean, regardless of what you're

building, I suggest people should try to generate data

as much as possible. Make sure you don't compromise

your testing and cross-validation sets, however, but

definitely generate data if you can. It can help you

improve your general accuracy no matter what you're

building.

Raul Popa: A big part of what we're doing is called one-shot

learning, being able to predict the class just by seeing

one single sample or very few ones. Techniques like

transfer learning might work well here, just there's not

enough information out there about what to do and

how to do that. I was researching a lot about how to do

one-shot learning because I really wanted to make the

technology work for just one previous sample or two

previous samples because, otherwise, it's not efficient.

Raul Popa: Also, since we're talking about security, so our

technology used to prevent fraud for security purpose.

A good technique is to use multiple algorithms

[inaudible 00:23:28], stacking generalization or

blending, but unlike for Kaggle competition, stacking

generalization work well for security algorithms

because it's harder to break multiple algorithms at the

same time. If you're a hacker, you want to break these

algorithms, really hard.

Raul Popa: You probably know about adversarial examples

samples used to trick traffic signs, probably use

[crosstalk 00:23:57], stop signs that you stick some

tape on it, and all of a sudden, self-driving cars will

recognize those as 45 miles limit, speed limit instead of

stop sign, and that's a huge thing. Right?

Kirill Eremenko: Yeah.

Raul Popa: Adversarial glasses used to trick face recognition

systems to believe you're somebody else. Using

completely different types of machine-learning

algorithm in your production will help reduce the

ability for a hacker to hack into your system with a,

sort of, master key if you want, but this is something

that I suggest people would do if they do anything

related to security or to fraud prevention or to verify

users in any way, anything related to authentication or

identity. Yeah, so unlike Kaggle which people do

blending and stacking to be at a better accuracy. Here,

you're not after the best accuracy. You're after better

chance of succeeding or smaller chance of succeeding

for hackers.

Kirill Eremenko: Got you. That's very interesting about the comparison

to data science competitions versus the real world and

different objectives. What would you say your

experience with research has been in data science? We

talked a little bit about, in competitions, you just want

many models to get the best accuracy. In the real

world, you want many models, and specifically in this

use case, to get the best security. What about

research? How was your process of researching this

technology using data science?

Raul Popa: I learned a lot. I did some of the main data science

online courses, tried to follow masters, Andrew Ng,

Geoff Hinton, Yann Lecun, Yoshua Bengio, Ian

Goodfellow, so forth. Basically, I read stuff that was

new to data science or to machine learning and trying

to understand what are all these things going, where

are all these things will ... Where it will connect

without a purpose in mind, like doing typing

biometrics or other pattern recognition problems. I just

wanted to learn more.

Raul Popa: A few years ago, there were not a lot of resources, not

a lot of frameworks and libraries, and you had to

basically code everything yourself, and understanding

the math, use the great apps. It was really important.

For example, I never did Kaggle competitions. I really

think people doing that are really crazy. I know a few

guys who really scored on top of that. I know building

hundreds and hundreds of model and stacking them

and getting rid of half of your work or 90% of your

work just to make a small minor improvement takes a

lot of time, a lot of ambition. I could have never done

that. Yeah, but one of the things that I really like is

you have the Winner's Interview on Kaggle blog, those I

really like to read, and I still read them, really cool.

Kirill Eremenko: Okay. Got you. Got you. From your research, first of

all, you mentioned before, back in the day, there

weren't so many frameworks available, and you had to

go into the math and that was very helpful. Now that

we have things like TensorFlow, PyTorch, and other

tools that make it easier to create, let's say, for

instance, in this case, deep-learning algorithms, AI,

would you say that learning the math is essential, or

people can get started faster without having to learn

the math?

Raul Popa: You don't need to know the math anymore. I mean,

you can if you really want or if you want to develop the

field. If you want to advance the field, yes, you

definitely have to understand the details to create that.

Other than that, just to use machine learning for your

standard problems like typical computer vision,

recognize objects, classify things, definitely, you don't

need to know the math.

Kirill Eremenko: Got you. Okay. Then going from research to building a

company, so tell us a bit about that process. How did

that researches you were doing, turned into the idea of

actually turning into a company and what are some of

the challenges that you face with building a startup

out of research?

Raul Popa: Yeah, I spoke to a few events about this, and they keep

inviting me to talk about this because it seems like a

lot of data scientists want to make the move to start a

business or do a startup, and it's not really easy

because they're very, very good at what they do. We all

know data scientists are paid really well, really hard to

break from that lifestyle and make a company where

you will work forever, 24/7. You will never see the light

at the end of the tunnel. That's really hard.

Kirill Eremenko: [Inaudible 00:29:49] Yeah.

Raul Popa: Yeah. Yeah.

Kirill Eremenko: Eat rice and beans, live on a friend's couch, very

difficult.

Raul Popa: Exactly, so it's really, really hard. I wouldn't advise a

lot of people to do this unless they're risk-takers.

There is a case where you research something and you

find that thing that nobody advanced it enough like I

found typing biometrics. There are so many fields

where the research didn't go deep enough, and you

just know that you have a ... You can say it's a

breakthrough, but maybe it's just something that you

saw in a different domain. You just apply that and

realize, "Hey, I can use this to advance this field." At

that point, you can actually use that to create a

company or to start a product and actually solve the

problem. I think that's like a calling. If you have that,

why not start a company? Right?

Kirill Eremenko: Okay. Do you think somebody can start a company

while they have a normal nine-to-five job and see how

it goes, test things out?

Raul Popa: I don't know a lot of people who managed to do that. I

think it's really hard sometimes, and if you have a safe

net to go back to like a normal job, you would just do

that. You would have to let go that job and live on

whatever you have saved or raise some money, find co-

founders and start a company. It takes a lot of

courage, but the good thing is that you can always go

back to work for Goldman Sachs or whatever company

pays you.

Kirill Eremenko: Yeah. Yeah, so you kind of have to burn the bridge to

force yourself. Let's just say if you want to take the

island, burn the boats, right, something like that, so

there's no way back.

Raul Popa: Yeah, definitely. Yeah. Yeah. Some of the challenges as

data scientists starting their company were around

creating a prediction-level software, so basically, you

start with the research, you have models. You have

stuff like that. You have to turn that ... We turned that

into an API that is completely scalable, can do millions

of transactions in a day. To do that is not complicated,

but it's not easy science. It's different type of science,

that data science. It takes a bit of ambition to learn it.

Raul Popa: Also, for what I did, I didn't start with a lot of data that

I got from university or something like that. Actually,

so gathering data, lots of data was really, really hard.

If you're a startup or you're building a startup, it's

almost impossible to do it with small data. For

example, I started with 200 friends. I sent the link to

type and ask them to ask some text and do a very

quick survey. After that, I went to my Mensa group,

the high IQ society?

Kirill Eremenko: Men-

Raul Popa: Those high IQ society called Mensa.

Kirill Eremenko: No, I haven't heard of them.

Raul Popa: Yeah. I am a member of that.

Kirill Eremenko: Okay.

Raul Popa: I asked them to help. I told them that I can build a

classifier based on the people typing patterns that will

try to differentiate between regular people and high IQ

ones. They loved it. I got about 400 people to take the

test and complete the survey with things like age,

gender, personality profile, stuff like that.

Kirill Eremenko: Yup.

Raul Popa: After that, I used to data to build a fun test. It wasn't

enough data to build authentication systems but like

400 people. I built a fun test titled Find Out What Your

Typing Says About You. I put it out there. In two days

or so, it got viral. People started sharing to their

friends and forums and personality forums, and it got

on Reddit at some point. One morning, I woke up with

about 20,000 samples in my database. The server was

down. I realized that "Hey, I have more data than

needed to start my research," but to get there, I had to

trick people to help me.

Kirill Eremenko: Well, trick, I wouldn't say trick. You just encouraged

them in different ways. You gave them back something

that they-

Raul Popa: Yeah, actually, I created some algorithms trying to

classify things like gender or age or IQ based on how

you type.

Kirill Eremenko: Yeah.

Raul Popa: There are some similar characteristics between people

that shared the same attributes like age, for example,

and you can see them typing in a different way. We got

60, 70% accuracy, and so not a lot, but for a fun test,

was really fun. People were really intrigued. A lot of

people like that, they didn't get the right MBTI profile

name or they've got different gender. They were

questioning their gender now. I mean, it's just a fun

thing.

Kirill Eremenko: Nice. Yeah. Got you. You link it up to that and Myers-

Briggs personality test, right?

Raul Popa: Yeah.

Kirill Eremenko: Yeah, that's smart. That's pretty cool. Basically, what

you're saying is that when somebody is moving from

being a data scientist and having an idea, maybe even

doing some preliminary research on that idea, seeing

that they can break into a field and revolutionize

something, and moving from there to actually building

a company, there's a lot of challenges along the way

from productization to gathering data, and probably

lots more other challenges. Maybe we'll talk about a

couple more.

Kirill Eremenko: You need to be prepared for them, and you need to

also think outside the box this whole [inaudible

00:36:01]. In this case, with the data situation, like the

fun test. I think that's a genius idea and will get you

data because data is value, right? Data is valuable.

You can go scrape the web for data. You can go buy

data. You can go do a fun test like that to get the data,

but you need to consider all of these things before you

dive into starting a data science business, right?

Raul Popa: Yeah, totally. Yeah. I think being able to think

creatively about how you gather your first data or to

think in steps, first, you do this, then you do that, but

then you have real data to do your research. I think

it's really important.

Kirill Eremenko: Got you.

Raul Popa: You can partner with some other companies or

universities. Sometimes, you can do that. Other times,

really, really hard.

Kirill Eremenko: Okay. What are some of the other challenges that you

face when starting your data science or typing

biometrics business?

Raul Popa: Well, there are business problems, stuff like you need

more people or you need people to help you with

marketing and research, like research on the

marketing side, market research, sales. You have to

meet investors, extremely hard. You need investors.

They want to see someone who knows everything,

who's able to sell, who's able to do research, who's

able to manage people and everything like that. I'm not

saying I'm not that person. I'm saying nobody is that

person. It's really hard to be a perfect dude to do a

machine learning algorithm to a business, basically.

Kirill Eremenko: Got you. Okay. Okay.

Raul Popa: You have to meet a lot of things about that. Internet

has plenty of links.

Kirill Eremenko: Yeah. Yeah. I can totally attest to that, that you ...

Running a startup is not just about an idea. You need

to also have a business mind or start learning to have

a business mind, and that's completely different, or

maybe you can partner up with somebody right? You

can stay the data scientist, and somebody else can be

your sales director or your business development

director, something like that, chief operations officer,

somebody who's going to help you grow the business.

That's also another thing to consider.

Raul Popa: That's what I actually did. I asked two friends of mine

that were helping me from the side to join a project,

Christian and Adrian. One of the things that we did is

apply to accelerators. We got to Techstars in New York

City last year, and that really helped us with investors,

and helping cover the gaps that we had at that point,

really, really valuable for us. Also, we're from Romania.

We started in Romania, Eastern Europe, really hard to

build something from that side of the world and get

global exposure so we had to move to US to do that.

Kirill Eremenko: Yeah. Yeah. Got you. Now, you're half-half, US,

Romania, right?

Raul Popa: Yeah. Yeah. Yeah. I'm half-half. Right.

Kirill Eremenko: The team as well, like you got some people in the US,

some people in Romania.

Raul Popa: Yeah, but we do R&D is still Romania for different

reasons. I really think that Romania has a lot of talent.

Kirill Eremenko: Yeah, definitely, Romania has a lot of talent. I wanted

to ask you though, how do you find combining your

role as a data scientist and as a CEO because,

previously, from our discussion, I think everybody got

the gist that you are actually very involved through the

algorithm and you do quite a lot of research. You're

very up to date with the technological part of things.

That requires a lot of time from how I can imagine it.

At the same time, running a business requires a lot of

time as well, meeting clients, doing marketing, making

business decisions, scaling, growing, things like that.

How do you find combining those two, and plus, as

well, I know you're a father, a husband. How do you

coming all those things? When do you find the time?

Raul Popa: I don't know. I don't have a prepared answer for that. I

think that the key is that I really like the data science

part. It's like a hobby for me. The CEO part or the

founder part makes sense because I really think typing

biometrics can make a difference. I think in the future

people will type more than ever. Today, we're

communicating through voice or typing. I think if you

look at young people, 11 years daughter that you

mentioned, every time I talk to her, even if we're in the

same room, she would WhatsApp me, right?

Kirill Eremenko: Yeah. In the same room, she would WhatsApp you.

Raul Popa: Yeah, so it's like asynchronous type of communication

in which you type whenever you want. The other

person replies whenever they want. I think this is the

future of communication. If we look at young people,

we know that. It's no-brainer. I had to do this because

I realized that we will type more than we used to, and

with devices and with other people. I think having a

layer of security based on typing is really a key to a

better world in a way.

Raul Popa: I have sort of motivation to be a CEO and founder, and

I have love for data science, so it's like a hobby or

something that I really like to do. I have to find time

for everything, of course, family as well. For sure, of

course, you can work full days on whatever you need

to, but eventually, you have to find time for family and

friends. I think that's really important.

Kirill Eremenko: Okay. Got you. Just to clarify because I only realized

this now when you're talking about WhatsApp and

typing on your phone, so TypingDNA and, in general,

typing biometrics is not only designed for keyboards

and computers. It can also work on your phone. Am I

getting that right?

Raul Popa: Definitely.

Kirill Eremenko: Wow.

Raul Popa: Yeah, it's a different thing. It's a different thing. It's not

the same thing, but we're not focused only desktops.

We also have algorithms for mobile phones. On mobile

phones, we think we're even better at some points.

Mobile phones, you move them a lot. You have

pressure and you have a lot of things, at which you

can do when people type. Yeah, we're quite good in

mobile phones, and we have a few really important

projects on that.

Kirill Eremenko: You can also measure not just the typing speed and

how, in some phones, how much force people applied

to press the buttons, but also how the people are

holding the phone, orientation in space, as you said,

how they're moving their phone as they're typing,

those types of things.

Raul Popa: Yeah. Yeah. Yeah. Yeah, so we can do those as well. As

I said, people are using, whether it's mobile phones or

desktops, it defers a lot for that use case. I think in

enterprise if you look at what people used to perform

their task is probably always going to be their

computer. You'll rarely see people using mobile phones

or tablets in their office for office business. It's like for

personal rather than business. On the other side, you

have personal communication and entertainment that,

typically, you use mobile phones for that. We have to

understand these both.

Raul Popa: When we're talking about banks or financial apps,

people are using both mobile and desktops. A lot of

focus on mobile later, and so people are checking their

bank accounts over mobile phones. They're typing in

some pins or some other sensitive information that we

can look at how they type this and use that for

authentication and flagging suspicious users and so

forth.

Kirill Eremenko: Got you. One of the fears that entrepreneurs or

entrepreneurs-to-be have, so data scientists that may

have even come up with an idea, really, genius idea

that can revolutionize the whole industry, one of the

fears that stops them from starting a business is the

fear that as soon as it starts, and if it proving to be

working, a large company can come in with lots of

R&D, lots of funds, budget, presence, huge user base.

Kirill Eremenko: We're talking likes of Google, Facebook, LinkedIn,

Microsoft can come in and just copy the idea, not

necessarily like create a solution for the same problem.

You've identified the problem, but somebody else can

come in and do the same thing. How did you feel about

that when starting your business? Did that hold you

back or not at all? How has that played out? Do you

have any major competitors at the moment?

Raul Popa: What you're saying is a real thing. It happens every

time, right? Think about this, this is not a bad

problem to have. It's actually a very good problem to

have. Google coming in the game, just saying, just

dropping a name, right, means validation for our

technology, means that this is mainstream now,

means that anyone who started working on the

technology like three or four or five years ago has a leg

up, right? It means that other competitors of Google

will want to buy a company, or investors will say, "Hey,

we want to fund this company because this thing is

mainstream, and they have a leg."

Raul Popa: I think, definitely, a good problem to have. Clients will

want to use the technology that we're building rather

than other alternatives that are not typing biometrics.

Of course, some of that competition will go to Google in

that case, probably most of it, but when a big player

like that comes into a new space, I think they create a

large ocean that didn't exist before that you can

benefit as well and everyone in the space will benefit at

some point [crosstalk 00:47:18].

Kirill Eremenko: Got you. Got you. Basically, you're saying it's better to

have, I don't know, 1% of a huge pie than 100% of a

tiny pie.

Raul Popa: Yeah, I know it sounds less. 1% always sounds less

than 100%. I think what you said is like is true.

Kirill Eremenko: Yeah. You imagine if you have 100,000 users and you

have 100%, or you 1% of 7 billion users, better, 7

billion.

Raul Popa: Yeah. Yeah.

Kirill Eremenko: Got you. Got you. Okay, very, very cool. How is this

industry right now? We didn't talk about this. How

long have you been working on TypingDNA and has

this industry or this technology matured from your

perspective? What's the competitive space right now?

Raul Popa: Yeah, I'm working from 2014 on this technology as a

side project. Actually, I researched for two years. I

actually started working in 2016, but I already

connected most of the dots, and I knew that I can do

it, sort of. I had the conviction that I can pull this off.

Regarding the competition space, a lot of people tried

to create algorithms, recording typing patterns. There

are patterns that are almost 30 years old in the space.

I mean, now, there are public space, right?

Kirill Eremenko: Yeah.

Raul Popa: People try all sorts of things, but without machine

learning, statistical models are not really accurate.

You can use them, but before I started, the most

valuable technologies that were built around this thing

needed you to type for a day or two for the technology

to be able to recognize you with a 70-something

percent, 80% accuracy. We can do zero false positives

with three samples of you typing in your email and

password, for example, I think, for your credit card.

That's like rare, right? We don't want to replace

passwords. We just want to have a second layer of

security that can go with anything that you type.

Raul Popa: Also, we have an algorithm that works when somebody

types anything. You can type in the chat window and

just chat with somebody else, and we'll look at what

you're typing statistically and we can say ... I mean, we

create a statistical profile of your typing. We don't

record exactly what you're typing, only how you type

letter A, letter B, stuff like that, right, the averages,

standard deviations, that kind of thing, and we build a

profile, and whenever you're typing again, we can do

matching and that's the machine learning part.

Raul Popa: We can do matching and we can say whether it is the

same person or not on any text when you type

completely different things. The downside of that is

that you have to type about a tweet-length but imagine

you got to a country where you don't live. It's the first

time you get to Vietnam and somebody steals your

computer, your mobile phone, everything. You try to

get online.

Kirill Eremenko: As it happens, yeah.

Raul Popa: Yeah, and you try to get online to connect to your

people, to your friends, ask for some money to be able

to go back to your home, whatever hotel, wherever you

are, you will need a new computer or you will try to log

in to Gmail, let's say, from a new computer, a new

location without your phone, without your UBQ,

whatever you have. Your password, you don't

remember it because it was in your OnePass,

whatever, LastPass, whatever you're using for

password managing.

Raul Popa: All of a sudden, you're in front of a computer unable to

log into your account or to talk with your people, your

friends and ask them for help. That's, really, a

situation when you are into that, you will just have no

escape. One thing that Google can do, for example, or

anyone, any big application like that, Facebook,

anyone like that, they could ask you to type a sample,

a sentence, whatever.

Raul Popa: They can use that along with, let's say, a question that

they can ask, stuff like, "When did you log in last

time? From where?" They can combine pseudo-

information with typing biometrics and other things

like that and be able to reset your account or enter

your account like that. That's really-

Kirill Eremenko: Yeah. Yeah. Yeah. Yeah, fantastic, definitely a great ...

It's actually bringing a lot of convenience, a lot of, even

in this case, certainty to the world that if you go

traveling and you lose all your things, you can still get

in touch and log into your accounts because I think

that's, for me anyway, that's always a concern, when

I'm in third-world countries and so, exactly what do

you do in that situation?

Kirill Eremenko: Moreover, as we discussed before, typing biometrics

brings a whole new user experience where instead of

waiting for that SMS or doing those CAPTCHA, when

they're showing you images of a bus and I find those

so time-consuming when you have a grid of nine

images or an image broken down into nine parts, and

you have to point out where there's a bus, or where

there's a car to authenticate yourself or to show that

you're not a robot. I think that could be also removed

with typing biometrics, and definitely a whole new user

experience that can be introduced. That's a very cool

example.

Kirill Eremenko: I wanted to ask you another question. What is the

future, what future do you see for TypingDNA? I think

it's a very cool thing to think about and always wonder

about. Do you see an IPO some time in the future? I

know it's probably very early stages right now. Do you

see the company growing and, I don't know, building

teams around the world, getting clients in lots of

different countries? What is your vision for your

business right now?

Raul Popa: Well, I'm not thinking of the IPOS at this point. We're

really small. We're a startup. We raised that seed

round two months ago. I believe that the tech can

become mainstream and can save a lot of situations,

can be used to protect all sorts of accounts and

businesses and money and assets. We have a lot of

crypto exchanges, crypto wallets, crypto projects that

want to use our technology for making people safer

when they do transactions like that.

Raul Popa: We are happy that we can play a role in this entire

scheme, and we can make authentication and

communication easier, so you don't have to go to

friction in order to authenticate or to send some

important messages or to deal with private data. On

the how big the company can get, I think it really

depends on whether the technology gets adopted or

not on the wider scale. We believe there are clear use

cases on which the company can grow to multi-billion-

dollar size, but it all depends on how the audience and

how the market will receive that. We do have good

science though.

Kirill Eremenko: Got you. Got you. Also, well, hopefully, it all goes really

well, and this will be the first interview that you did

just before your company becoming a billion-dollar

company. Before we finish up, I wanted to ask you,

from everything that you've gone through because I

think your journey is very exciting from data scientist

to research to founder and CEO to growing the

business and disrupting a whole new space.

Kirill Eremenko: What would you say has been your one biggest

learning, something that you can share with our

listeners out there who are data scientists who might

be considering starting a business, who might be

considering staying in their current positions or

progressing with their careers in data science?

Nonetheless, what is one learning, something that

you've found very useful for yourself in your life that

you can share with them?

Raul Popa: I think data scientists are typically very intelligent

people, and so I think typical intelligent people have

this problem of overthinking. By far, this is like the

biggest problem that, collectively, data scientists have.

All that, I know. Also, on the other side, entrepreneurs

are rarely overthinkers. Usually, they are more

opportunistic, kind of optimists, that think that things

will somehow solve by themselves in the future.

Raul Popa: This is why they start companies, they do a lot of

things like that, so making the switch between those

two is really, really hard. The key is to, first, you have

to be less anxious, to overthink less. I think, to do

that, you have to be more relaxed. I have a mentor,

Kevin O'Brien from GreatHorn. Actually, this is his

advice. Whenever you're entering the ring to box with

an opponent, you can't be that stiff, kind of rigid

opponent that you would fear at first, right, that you

see that there's a lot of fear on his face as well, or you

can be that relaxed guy who just enters the ring by

knowing things will work out.

Raul Popa: By being laid back, you will be able to spot the other

person mistakes because people make mistakes al the

time. If you relax, you can do that. You can use those

mistakes to get an advantage. I think that's really

important. Things that you can learn when you want

to turn into an entrepreneur from a data scientist,

relax. That's really important. Become curious about

things that are not really as important and you think

they should do more things for fun, get out of your

comfort zone, stuff like that. I think those are really

important things.

Kirill Eremenko: How do you relax?

Raul Popa: Yeah, as I told you, I am a curious person, so I like to

learn things about all sorts of things. Sometimes,

when I'm really stressed about something, I find a

topic on which I want to learn more. I always want to

learn more about something. Trying to do that, you

will focus on something new. Entering that learning

mode, really, really valuable. Two, three, four hours,

learning something about something else like your

normal life, you will just find yourself in a comfortable

position where you don't see the risk anymore, you

don't see the pressure anymore, and you can think

clearly, really important.

Kirill Eremenko: Okay. Got you. Got you. Raul, that's fantastic advice.

I'm going to take on board as well. I sometimes

overthink a lot of things as well. Yeah, good, good

advice. On that note, we've slowly approached the end

of this amazing podcast. Raul, thank you so much.

Before I let you go, can you tell us a bit about where

can our listeners find you, connect with you, maybe

for a career, maybe some business owners then get in

touch about trying out TypingDNA, or maybe if some

data scientists that are looking for jobs that might be

interested in this space and looking for job

opportunities that you might have. What are some of

the best places to find you?

Raul Popa: Typingdna.com, we have a lot of demos there, a lot of

information. Our recorders for typing patterns are

actually open sourced, so if you have another project

in typing biometrics, you want to use these for, I don't

know, detecting how people type, you know, use that,

you can contact me on raul@typingdna.com or R-A-U-

L@typingdna.com. That's pretty much it.

Kirill Eremenko: Awesome. Is LinkedIn okay as well?

Raul Popa: Yeah, LinkedIn is fine, Raul Popa at LinkedIn, sure.

Kirill Eremenko: Got you. Okay. Awesome, and guys, make sure to get

in touch. One final question before we finish up today.

What's a book that you can recommend to our

listeners to help them in their careers and life

journeys?

Raul Popa: I don't recommend data science books, typically. I

recommend online courses, but one book I recommend

for everyone doing data science is Black Swan from

Nassim Taleb, also the last books from this author like

Antifragile and Skin in the Game touch the essence of

the rare asymmetries that we find in the real world

and how these may lead to positive outcome. I think

these are extremely satisfying books for me as a data

scientist.

Kirill Eremenko: Got you. Awesome. That's Black Swan, Antifragile,

Skin in the Game.

Raul Popa: Yeah.

Kirill Eremenko: Great, great advice, and on that none, thanks so

much, Raul, for coming on the show, very, very

exciting podcast. I'm sure our listeners enjoyed it as

well, and best of luck with TypingDNA. I hope you

change the world.

Raul Popa: Thank you very much. Thank you for having me.

Kirill Eremenko: There you have it. That was Raul Popa, CEO and Data

Scientist at TypingDNA, what a discussion. I hope you

enjoyed this conversation as much as I did. We went

into, first of all, the world of typing biometrics. How

crazy is that? It blows my mind. Just by typing in my

logging and password a couple of times, I can then be

identified in the future as myself, and that is crazy, as

you can imagine that, or from what we discussed in

the podcast, there are plenty of applications that can

make our lives easier and safer in many, many ways.

Kirill Eremenko: Also, it's very cool to learn both the data science

aspect of things and the different approaches,

techniques, algorithms that are used in the space as

well as setting up a startup around a data science

idea. If you have a data science idea, maybe now, you

have some better ideas of what is coming up for you,

what to expect. My personal favorite part of this

podcast, something that stood out to me the most was

the creative ways of collecting data, as Raul

mentioned, on when they needed those patterns, how

they created that fun tool for people to use to learn a

bit about themselves, their personality type, and

things like that based on their typing pattern. That

allowed them to collect data. I think that's a very out-

of-the-box type of thinking, type of idea.

Kirill Eremenko: On that note, make sure to connect with Raul and you

can find his URL to his LinkedIn as well as all other

materials that we mentioned on this episode at

www.superdatascience.com/251. That is

superdatascience.com/251. Make sure to hit up Raul

on LinkedIn. If you know anybody who is looking to

create a startup in data science, who has a really cool

idea that can be empowered with machine learning or

data science, or any other data-driven technologies,

then make sure to send them this episode, and maybe

they can cut through their learning curve and get

some really cool ideas from here. On that note, thanks

so much for being here today. I look forward to seeing

you back here next time. Until then, happy analyzing.