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ah hello everyone
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i'd like to present my group of other computational by you in e. g.
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so here's what i'm gonna talk about or did you were you were you what
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were doing uh i'll be talking first about what actually computationally meeting is
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you know i'll talk a bit about how we do my cross you the
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ball developing hearts and on it should use e. g. apps imaging platform
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and then i'll give you an idea about how we can get sharper images uh with the use of a artificial intelligence
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here's my group ah actually there's a two members wrap posters outside so encourage you to over
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lunch break to go in and see them olivia and uh if you and not
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h. zero posters in uh in the second so what is computational imaging our
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goal is actually to make images based on data that we collect
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and so the definition here that's used by recently watched journal
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is uh that it uses innovative algorithms computation sensor design
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to dress image formation problems so it's very close to
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image processing and actually to compared to uh the
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inputs in the outputs might similar uh image processing typically you take an image given other image out
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in our case we have also uh collect data or it could be images
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and also get an image out we try and also incorporate some knowledge about how
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this image has been collected in order to get the better image yeah
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concrete example image processing you can take a bad image as an input and get a much better image
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as an output by using your photoshop insert grammar or whatever your favourite image processing to liz
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ah in our case uh we're we're looking into trying to figure
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out things in seeing things that wouldn't be directly accessible
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with your typical imaging device so in our case that could
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be tomorrow if he were you start out with projections
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so you look sense you could be similar as with x. rays you could look
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through a a body but you cannot actually slides however you want to
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do is you can only do after you've actually done some computations
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uh inverted uh the the image formation process in once you've done
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so you can essentially look at your data for in
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you can look at your data in all directions uh you can move it around and slice it however you want
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so let me let me give you a one no uh one example of a project that that uh
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we're working on and that's a collaboration that we have with the uh three groups were in france
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use but to have a good critic need who's uh who's developing optical microscopes uh you
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don't go more who's in a stress ball was the developmental biologist studying hard development
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and so is a person an agenda that there was a she's in uh which isn't universe you burn
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and so the goal there is to try and figure out how
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the heart develops and uh why sometimes the development goes along
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and one of the things that i'd like to be able to do is to actually be able to see
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what happens in particular particular see uh what happens when things go wrong
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what special with the heart it's a little bit different from other organs inner body is that
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the hearts actually already functional which means that the heart is beating
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long before it's fully formed and so it starts out
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in the human looks a bit like it to be first and that tubes already being it's already pumping but
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and this is typically when you go from here to here this is where a lot
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of the effects might actually happen and it's very hard to image something that's
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small and be that uh uh moving or beating very
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quickly uh several uh heart beats per sec
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so can we image so when you put and and in this case we're we're
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looking at the uh an animal mall that's a zebra fish which is great
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transparent and you can see all the organs without
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doing anything special using any uh special
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uh_huh nation just plain like
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and when you look at a time lapse we can essentially folder whole development from the signals from a single cell all the way to
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when the organ start forming here on top you can actually see by uh that's right here this
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is the brain uh here you have the heart that's already bleeding and you can see that
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can barely see although you can see the other organs forming in the
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heart starts beating it's way too fast captured by a simple timeout
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so that problem has pretty much everything that somebody who does
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image processing or computational imaging uh likes to tap
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we're looking at things that are very small uh some aspects are
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slow development some aspects are fast the heart that's beating
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original looking at objects like to get differentials shape you have things that you'd like to imagine multiple
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channels the signal is very weak in noisy and the sample itself is very bad child
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and of course if you want to do any valid here is this sixteen people to do experiments multiple time
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this is pretty much the world that we're living in and we're trying to find the solutions solutions are trying to find
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have to essentially a depending on on the day and the problem have to uh have to comply with this constraint
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so let me that show you one of the problems that the actually has been working on a
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uh recently and that's uh the idea of trying to make a small microscope that has some
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very nice features namely is mike of microscopes out there that allow you to actually image
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in three dimensions so get data from one points inside uh the inside
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a a three dimensional object at a time that second focal microscope
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something has been out there for a very long time which is great but
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it has the major drawback that it's very slow because we get
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one point of time so we have two centuries stand uh through your object in order to form an entire image
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so now imagine if you're a teacher imaging the heart this is what actually
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happens you have the heart here on the left that's leading and what
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you're doing you're standing well you don't really get something that looks like a
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heart get more something that um looks well it looks like this
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so now the question was can we take those images that are horribly a matter rated
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and try and reconstruct something that looks more like a true shape of the heart so
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this is what uh the kind of images that only yes starts with it
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and you can see that the heart is is uh where there's something happening those
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images but uh it doesn't look anything like a part two should look like
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and then finally uh when you when we do the reconstruction uh were able to uh
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which is able to actually put the images back into the right word or
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and to onto this that's standing artifacts to actually get the heart of beat as it should be beating
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another problem that we're facing a so i'm i mention all the challenges that we have
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one thing that's expected from uh the imaging community is that uh we
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other people are able to actually produce the results with the the idea is that we haven't publish
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and uh there's some alarming don't use that and many of those
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results a general cannot be reproduced at least in the
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biomedical science i think that in majoring in computer science uh this is something that's very easy
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to do because we you know we are browns and the about is what turns out
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it's actually not always all that easy especially because uh
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often very often the data that you collect
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uh has multiple purposes a very often uh your your uh
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you know it's very specific they that that you get from a collaborator who's working biology
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who has a different agenda about when and where you'd like to should like to to publish that data
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and so uh the other thing is that if you actually want to
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distribute these large data set it actually uh requires lots of
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resources because you don't just need to do it for six months but you have to do it on the on the long
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so we've uh uh we we've got two colleagues at any gaps so it's it's much general
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that's the best enough that we decide to uh put together a platform and that platforms in imaging platform
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that that looks like this that's down here in the basement
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and that allows us to acquire dynamic that imaging data
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with a robotic arms there is a more drives microscope that's it down here to
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the microscope that's has been partly built here in valet with the help of
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the mechanical uh apprentices and install and that allows us to uh collect data in a
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very controlled manner and uh in the repeatable manner so that we can uh
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for instance in this case we're we're looking at an algorithm for super resolution can image essentially the same
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motion multiple times with different kinds of cameras different
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kinds of ruminations strategies and should we compare
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a a tree compare the and two and a quality of our
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algorithms uh to see how we're uh how we're too
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and so uh uh show you the two arms that are there that that the reviews for that experiments
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the one of the nice things that you country constrain them in in ways that the motion will um
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will fall essentially either something that has been preprogrammed were that will explore certain space
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within a certain number of of uh of uh of constraints that you set for
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finally uh i'd like to talk about the problem that uh adrian has has been working on
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and that's the idea uh another classical problem in computational imaging
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is that of trying to people are blurry images
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and uh when i said for what we do is collect images that
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look like this one here on the right which is completely blurred
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and if you're lucky enough to know what kind of microscope you've been
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using and what's the four characteristic of that microscopes or microscope or
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and that's typically uh inside this magical function that's yours called the point spread function
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which pretty much cracked rises your entire optical system then there is hope that you might
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eventually be able to pull back from this average image back to know original
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so still that even if you have that functions a very difficult problem and now if you don't have that much but
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function or if there's not one but many depending on where
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you are in your image becomes an even harder pop
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so uh what we in uh ask their cells is whether we could essentially
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instead of having to meet your happens but function which is something that's very painful to do whether
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we could have a program actually recognise when you give it an image recognise what kind of
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a degradation was the source what what was the what was that points but
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so here is how we tackle that problem we've taken a very big library of good images
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and then uh we've parameter tries the point spread functions with decorations
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and we've generated a a hole to have a lot of as possible decorations
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and we've taken all these images and we've degraded them with different kinds of of course but
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then we had this big library and uh we trained uh uh
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i would betray a convolutional neural net work to actually be able when
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you get it in in each without telling it's the parameters
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that it will tell you what kind of parameters were actually uh what kind of parameters was a great were the parameters of the post
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what you've trained that the nice thing is that you know can now take and on known image
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that can have that in uh that was degraded in different places with
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different ways but functions this case it's something that's flat that's inclined
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respect to the microscope so some regions are in focus some other regions are not in focus
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and you can send small patches into that uh into that and then that will don't
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tell you oh in this region image has been degree of this kind of stuff
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and then uh you can run a space apparently convolution can get an
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image that now is word focus in all regions of your age
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when you want a little bit more details about how are we doing this and how it performs 'cause visit uh engines post
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just outside
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so in summary ah computational imaging there's many applications
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ranging now from entertainment to science were all have them in our cell phones a
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group mostly focusing on applications uh in in the area of of science
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for austin sounds to which usually that we're able to reproduce
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things and crack tries things a very carefully otherwise
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uh maybe we uh have an issue with our partners will will show us
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people just like to see pretty images but the actual wanna know exactly whether
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they also are meaningful
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ah specifically in the area by you imaging uh been able to make slow
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microscopes faster or teach uh did you computer to recogniser cracked arise
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uh optical system which is something that's really painful if use you know lab never major points but function it's not
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something that's actually on a to do and having a computer do that for you is a is a big
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our imaging platforms actually uh it's open uh and uh no
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welcomes actually poppers if you're interested in using that well
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so would i like to acknowledge the you know the people actually found our our research the national science
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foundation and also uh the value value some still initiative which has been a a launched by page
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up a few years ago and it allowed us to allow me to recruit you know excellent students
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who had been a train their their their elementary school in a in a in that it
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and uh
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bring them back to uh nothing and find the
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any questions on show will help us build the uh

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Conference program

Introduction by Hervé Bourlard
BOURLARD, Hervé, Idiap Director, EPFL Full Professor
29 Aug. 2018 · 9:03 a.m.
Presentation of the «Speech & Audio Processing» research group
MAGIMAI DOSS, Mathew, Idiap Senior Researcher
29 Aug. 2018 · 9:22 a.m.
Presentation of the «Robot Learning & Interaction» research group
CALINON, Sylvain, Idiap Senior Researcher
29 Aug. 2018 · 9:43 a.m.
Presentation of the «Machine Learning» research group
FLEURET, François, Idiap Senior Researcher, EPFL Maître d'enseignement et de recherche
29 Aug. 2018 · 10:04 a.m.
Presentation of the «Uncertainty Quantification and Optimal Design» research group
GINSBOURGER, David, Idiap Senior Researcher, Bern Titular Professor
29 Aug. 2018 · 11:05 a.m.
Presentation of the «Perception and Activity Understanding» research group
ODOBEZ, Jean-Marc, Idiap Senior Researcher, EPFL Maître d'enseignement et de recherche
29 Aug. 2018 · 11:24 a.m.
Presentation of the «Computational Bioimaging» research group
LIEBLING, Michael, Idiap Senior Researcher, UC Santa Barbara Adjunct Professor
29 Aug. 2018 · 11:45 a.m.
Presentation of the «Natural Language Understanding» research group
HENDERSON, James, Idiap Senior Researcher
29 Aug. 2018 · 2:03 p.m.
Presentation of the «Biometrics Security and Privacy» research group
MARCEL, Sébastien, Idiap Senior Researcher
29 Aug. 2018 · 2:19 p.m.
Presentation of the «Biosignal Processing» research group
RABELLO DOS ANJOS, André, Idiap Researcher
29 Aug. 2018 · 2:43 p.m.
Presentation of the «Social Computing» research group
GATICA-PEREZ, Daniel, Idiap Senior Researcher, EPFL Adjunct Professor
29 Aug. 2018 · 2:59 p.m.

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