What motivated you two come to the TRANS conference in Taiwan?
I think the biggest reason we came to Taiwan for the conference is one who just you know
the amazing rate of other companies and products that are there and like
the technology research that's being highlighted
You know both in in Asia in general
there's I think this just a lot of work we've done
for preventative health care and even more so than in the United States in many ways
and a lot of the great companies and a lot of the great initiatives when it comes to funding
and when it comes to, you know
new products and technology is happening here
so this is I think a great opportunity for us to come and meet people working on similar ideas
and also share a little bit more about it fellows
Oh yeah, in many ways, yeah
you know I agree with that
I think that the work being done in Asia is in many ways an example to the rest of the world on
how you know, progress and going from, you know
lab work to actual implementation is done a little bit faster here
so I think that's really cool and so it was really cool to see you know
other companies that have done this and also
just got a better understanding of what the healthcare landscape is like in Asia
since it's such a huge huge market
What is the most attractive part of the conference?
I think in my opinion the most attractive part is the other
you know, the attendees, the speakers
There's a chance to meet with them, I
you know, I think that the conversations that we've had
the research we sought at the booths
and then other presentations we got to see
you know, during before and after hours was really compelling and just interesting
you know, like there you go said it highlighted what the landscape looks like
you got an idea of more company like Optum and United Healthcare is on the insurance side
where other point of care diagnostic companies are
what healthcare insurance overall looks like and we're preventative health care
it is heading
and I think it was concentrated in one place
and that's really you don't really find that in many other conferences
Yeah, so you know a lot of this, I think you know
we spend a lot of time to discuss a lot of the same things
so yeah
you know, the people that we met
we're really cool and just also you know it's, it's
It takes a lot of time to go and meet each of these companies or
pharma companies or insurance individually
so to do that all in a in a space where everyone can just do it rapid-fire
it's really efficient for us and really helpful for us to just learn
you know, very quickly in one day
and I thought the booths were also really cool to see what people were working on
and what their people were excited about
I was really cool to see that work
What's the reason that you choose to found Athelas
instead of conducting research?
I also both come from research backgrounds and what we realized is that
while you can do a lot of interesting things in the world like academia in research
in order to really you know
bring it out to fruition and you know
build a product out of it
It takes a little bit more
I guess more of a depth-first approach
and that's what you know fellows or any company or any startup is
it's really it's taking a piece of research and going depth-first and executing it to bring to the masses
and that was I think the most important thing for us
because research and imaging and my research in machine learning and computer vision
in sort of bridging the two of them and
into a singular product that actual people could use
Yeah, I think
I think research is like a really beautiful thing
I think that there's like absolutely a necessity for it
I think that you know in many ways what we're doing
it's just a way to it's the next step so we've done like the problem what our company started
from our research and you know going to the commercialization point was just the next step
you know, now we have a product
now who are we going to sell it to, you know
how can it be, you know
how can it be the most valuable product it can be
so I think that it's just really the next step
So what is the most difficult thing when you make your research into product?
I think the core thing is there's a lot of really cool things and you know
cool things to hack on in a lab
but understanding a core customer need
understanding something that they need, you know
to in a very you know, specific way is a difficult thing to do and
it's a skill set that's a little different from just building things, right
Both of us I think have that transition where we were
why don't we build a hundred different tests and you know do all this stuff and
obviously you know you can do that in a research setting and you have the time that kind of stuff but
when you're working in a startup and on a product
you have to build one product you have to build it really well
and I think I was the hardest transition for awesome and I think the biggest lesson
and it was a very valuable lesson
Yeah, I think that at our core
we're both builders you know engineers
so it's very easy to wanna you know
someone asks for something
it's very easy to say
all right let's, you know
for two days let's go heads down and just build it up
and then you know people keep
people will always ask for you know new features and so
yeah I like to make that
it's an entirely different skill set to really understand the singular focus of
you know what the product is and what people are wanting and
kind of conjoin those two things
What is the potential and application of AI?
Okay I think the you know
one of the most interesting things about AI is it's the way it's going to impact specific verticals
so whether that's you know
it's imaging or whether that's preventive diagnostics or you know predictive diagnostics
these are all things that machine learning models are really well suited to do taking lots of data
and synthesizing some intelligent
you know yes about what's gonna happen next
and I think that really is what a healthcare you know analysis core is
that's what doctors do that's what the medical infrastructure is all about taking the data
and the information we know about a patient and trying to you know
diagnose someone and then after that figure out therapies and treatments that are most likely to work
and this sort of probabilistic niche lends itself really well to machine learning
and I think that's where I will really flourish on healthcare is synthesizing a lot of noisy data
and bringing into one place to extract signal from and then do something very specific to benefit our patient
Yeah, and I think that you know
AI is extremely powerful as a tool and I think that the
one of the really cool things about it I think is that you know
if you can really train a robust system with some of the best doctors or the
best hospitals and get a great probabilistic model on therapies
on different treatments then you can extrapolate that and take it to different parts of the world
that don't have access to these incredible facilities and kind of create you know
a synonymous system in other parts which is really cool I think
can you know
create a really generalizable high-level form of healthcare
And what is the business models of Athelas?
I think our core business model is
is focused on improving outcomes for everyone in the process right
the patients, the oncologists of need and the payers, the insurers
and each one of these people have slightly different incentives but at the end of the day
their core incentive is always how can we make the life better for patient
and for the fellows that's a part of that is selling this as a subscription service to patients
who wanna monitor their own health as well as oncologists and you know treatment facilities that
want to access and extend their sort of reach of care to the patient's home and you know
we're seeing that more and more where the hospital is sort of just the beginning of your care
rather than the end of it and really I think what's
what's compelling for a doctor is oncologist is to extend their practice into the patient's home
and our model is to enable doctors to do that to monitor patients you know
and work with the insurers to build sort of the safety net
where we can identify diseases at a much earlier point treat them for a lower cost
and as a result to improve outcomes and lower costs in healthcare
And our question is we know that you two are co-founders
so did you two encounter any funny things when you work together?
I think that like the the biggest thing is
is you know your friends before and like now a co-founder so
like there's you know
there's a lot of focus that's necessary
so like Deepika said before like naturally
like we're both gonna be glad to build up of each other and be like
hey why don't we try to build this new creative thing or you know do some totally different
the most important thing as co-founders has been for the two of us I think
just keeping each other in check and keeping each other sort of focused on that singular goal
I mean that's that's been the coolest part of that
Yeah, I think that it was really good that you know we were friends because we did science research
we both did research at Stanford and
we both present our research at similar competitions in different places
so we very much came from a background of understanding each other's perspectives
and backgrounds and having respect for that
and so going forward it was good
because we kind of knew you know
where the other person should really be focusing on like Tanay were saying and so, yeah
Okay so could you please say something to encourage healthcare in Asia or students study in this area?
I think healthcare is probably the most important problem that you know
humans face today and like in improving healthcare quality overall
I think is one of the most noble things to work on
and in an area you know like
like Asia was just seen such exponential growth and so many different sectors and technology
and you know
like basically in all different forms of sciences
I think there's a great opportunity to sort of build healthcare the right way from the start
and there's a lot of communities in rural healthcare
and etc in Asia that are just starting to build up their healthcare infrastructures
and I think this is an opportunity to sort of do it right the first time as supposed to you know
in the U.S. healthcare infrastructure if you look at it
while modernized and you know has a lot of great things about
it is also very convoluted and very complex
often in sort of cumbersome ways and
I think if with new people working on new things in new innovative things in Asia
there's a great opportunity to sort of look at this in a very data oriented way and
and build a great healthcare system that rivals the best in the world
Yeah, I think that you know
it's something that healthcare is something that we should have in the smartest minds working on
because I think you know it's you could have the most direct impact to people's lives quite literally
and so I think that you know
it would be one of the problems that
other parts of the world really have is that the healthcare system is very entrenched in that it's very difficult
for change to occur easily in a healthcare system and
in many ways that's a good thing but in many ways that hampers any form of progress that can be made
and so to keep that in mind when developing you know
systems and insurance structures and you know
just the way that doctors interface with patients I think
it's really really important to really understand how to integrate you know
how to consider when new technology comes about or when you the discoveries come about
is there a quick way to integrate that into the infrastructure
so that everyone can benefit from that
so I think you know getting the smartest people working on those problems
it would be huge especially in a place here, like here
The last one is you two have to watch the camera
and read the slogan
the slogan is
TRANS Different Languages Same Purpose
TRANS Different Languages Same Purpose
OK thank you
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