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making you lie down and attach a ten different sensors on your body
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you have to be shaved and uh the sensor going to be
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attached and what the sensor going to measure is the um
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electrical activity between each of the to the to censor so you can end
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up with a bunch of one the signals that look like this
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and it takes a very well trained um clinician to look at this and that the mine
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if you have a problem or not okay and is of course has variations in correlations
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we're h. uh you're in the city to your gender so it's a a difficult task
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um another one is a letter unstoppable brown so it's a similar principle
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this is basically trying to measure signals that emitted from your brain
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um it's typically use to diagnose bring conditions but can be used for a number of other things
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we've seen uh the most regions of people uh driving um video games uh using
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e. g. um we have also seen uh people using it for identification
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um it is a useful signal but is also very noisy okay
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it's very cumbersome to put something that when you're had actually expect some if
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um that was the types of bile signals such for example imagery from your
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body could be from one next can but it could also be from
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the phone goes off no i so the back of your mind so this is the typical accent but when you go to the ophthalmologist
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you're basically uh they ask you to put some drops when you're when i would ask you to open
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it and be looking site and they may take a photo and the foot would look like this
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okay um i it's impressive but the little photo can review a lot of different pathologies in the person's
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uh um i okay most um frequent apologies are
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for example by beauties um michael legit degeneration
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and um glaucoma which is a very normal disease the bomb this is
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this is that they can cause blindness so it's a very
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important thing to be able to the pack them as fast as possible and to have a great treatment for those okay
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alright uh last typical example is your face itself it's also set an example about signal
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uh the face sink into its symmetry itself can be used
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to assist on diagnostics of this is in general okay
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and can be also use to match the emotional state of being a
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there was recently a paper published uh about this and that
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so many things i don't know i counted about ten or fifteen different diseases can attack just by looking at somebody's face
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okay so let's look if you use cases okay um the first one is
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of course to have a machine learning and signal processing uh helping
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helps put pictures on the diagnosis uh diseases and pathologies
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so support for diagnostics okay so the idea is that a a particular machine learning
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algorithm would just scan this and have the physician spot the place where
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it should actually look at the more details okay or there's a probability of having a disease because of this particular white
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uh the same here forty i found the same age you can run some segmentation of various
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we can extract the veins you can't figure out that the optical disk is actually here
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and by looking at it if it's damaged or not you can detect if the person for example is subject to ball come or not
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now is it can imagine segmenting something like this is very costly on if you have to do it um
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and we're gonna come back to that later
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um so all the other use cases stella madison so the idea is that uh
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there is um you get involved in the design off cost effective
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devices that can actually be brought home and then a help
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remote clinicians and to actually um on it or your status else okay so you can imagine something
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simple searches loopholes levels but you could imagine something a little bit more sophisticated as well
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any ideas of course that you can decrease was that's the overall cost of insurance
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i'm not sure how many of you were here at the ice yesterday but one
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of the talks was mentioning the we presume price of insurance in general
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so it's an interesting thing to tackle and of course the idea is
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to prevent fit the conditions and to work preventive lean gentle
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um another application is sports medicine so people trying to uh twenty five performance track
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progress portables the been more personal but can also do so professional context
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um and if i look at the challenges at least on the brief amount of time uh
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we started looking to the still doesn't interesting area for the up to spread on um
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i think it would summarise them in this three categories here um the first is how far can we go
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with a a nine to actually help physicians like knowles people or how in the agnostics from biasing
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one gigantic problem is of course overcoming the lack of existing open they can in general
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um so we're truly in the case here that we have a lot of small
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data sets was more data as uh some of my colleagues have already mission
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and the fact as well that's it you know that one of the that's data
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is not as simple as in other areas of actually this medical doctor depend yes eight
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this is a bad a patent for the hard in any c. g. or
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this area here means that the person has that will come okay
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or here is a vein and here's an at okay not my from the same age
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so i can imagine discussed the expense of eternity the the subject is very little data so we have to deal with this
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then there's of course the fact that you have noisy measurements ah high quality cameras
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are available today but of course uh most of the data you have sometimes
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dates from a very long time of also where the the quality was not
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great there's of course a lot of these agreements some doctors would say
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this person is likely to develop a particular disease some others would
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say against okay and of course population biases we see a lot of uh challenges nowadays
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in the area of health going i'm open uh there was one recently me fine
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about the technical palmer from my phone those images which is very interesting but uh sometimes um
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this challenges they don't take in consideration for example your new c. d. or your age
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and the analysis of those as understand now differs depending on this categories before
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well then i would like to also work a bit on improving sensing okay for
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diagnostics we're thinking about where oppose but we also have in house expertise
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emotive spectrum multi device signal position that we can probably leverage as well
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and work on early diagnostics so how can you prevent of really ah
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estimate that somebody's about for example to have a hard stroke or to develop ah
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i'm michael or degeneration for example in the back of the
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okay so
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the question is why tibia up okay so the idea here is that
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we try as much as possible and it's interesting uh platform because
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there's a lot off expertise existing in complimentary area okay
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i've annotated here few but this is by
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no means exclusive as a sign the c. d. and e. g.
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there's a lot of sleep is processing the noisy required ah
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we're going to need of course new a. i. too so do very good in shops segmentation of these
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images measure different parameters but we also gonna be you willing to work with us small data sets
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so this is a quite um difficult problem as already
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um what four by david ah better sensing technologies
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to be able to um actually see where is difficult to see
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we're going to be have to be handling as well uh problems with sort of dayton privacy as
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you know house your personal house is private data about metrics but it's probably even more uh
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sensitive than that because it's talking about your pathologies would be of diseases nobody really wants to get that in your
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connections the prosthetic that work as well um the control of robotics uh
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fact that interact for example with the electron my ram actually control the what the hand i think is also point that
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would uh work altogether and houses of course uh social science
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before there is another set of connections between um
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this study of health in general social computing and even understanding in natural
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language process it as as already mentioned by some of my colleagues
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now it yeah that is a great place to stop is this it has had a policy and pointing is our
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policy of open softer and opened it does so in forcing open science which i think is very good
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and that we see more more but is still lacking uh probably in house
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connections export connections of labs working with sensors without exactly having bill how on how to do the
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analysis of the data so some of it's it's some of the exist are all this year
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in house expertise in complimentary areas as i just mentioned excellent support them system development project
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management infections or you think you have already talked to some of these people
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and uh uh some inspections for
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couple of uh upcoming uh initiatives you
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i see and you know it's okay
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so the ambition is that yep joint of their domain was the work of this group
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vi it's applied the i know how and it becomes instead of artificial
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intelligence for society our division intelligence for how to sew side
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right so and my top here i put here my list of interest to make contact information
00:14:23
if you haven't checked out or posters outside by asking to do so to do in the coffee break
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um there are two that i'm particularly the you're a route off for bob is uh to get four principal research that we
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intend to continue to use in this new group and or be put form which is the but from open signs
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right here yeah um my name is on their ranges thank you for the introduction
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of it so um i i i was just hired a beginning of may
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um so the stock is going to be a little bit different than the other thoughts you have seen
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it's not going to be about is and wants it's going to be about what we can
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be and hopefully it's a future that we're going to be able to do to get
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okay so it's going to be about creating a buyer signal processing reputed yeah
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i'm i'm going to explain a little bit but by the signals are
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in which contacts we would like to uh focus our efforts on to it and
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um briefly discuss the challenges and this would be a quick topic should
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alright so our bodies are constantly in meeting information
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about our health right you can be uh
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just by looking at some by me you know that it's been a state of pain
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or to a certain but carries a certain apologise certain genetic apology
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or ah can also be done through uh the means of sensors right
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so here's a few example of sensors that are commonly found in
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the medical the main uh like trance several grams um
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uh elect from your fee who brought a volume polls as a button was
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using for example to measure a ground truth for these are people you work
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a temperature heart rate ah electrical diagrams in general respiration is one so
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okay
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now it so happens that physicians and health care protection as in general
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we use this values are the values measured from the systems to make treatment decisions okay
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so it again a matter this we're gonna check with you and then they will why treatment
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now biomedical signal or in short while signal
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processing is the analysis of such signals to provide
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how to how skip addictions to do that in a nice way okay
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so basically very full the
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if i follow the flow of dayton so the patient rate signals that are some processing and some decision
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that can be eyes uh made by the house get production or or by the system okay
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and we interest of course in the whole set of things
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in this uh data flow but i'm more particularly
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um the three less tap so the generation of the single itself the processing of the signal the refinement
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the segmentation the understanding of that signal and the support the final decision uh there's going to help
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the protection of to actually take the final rule we'll find decision to um up by the tree
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so let me go fulfil examples in the area soul probably the
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best known type of bile signals are electric audio grounds
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everybody has already seen or been in contact with uh this kind of uh exam
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um it's frequently used to to in house uh a house rate that
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applications for detecting heart rate pathologies a hot related pathologies okay
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it's until to day the most reliable way to measure your heart rate variability were holes
00:03:31
and this might be very a great it's also very cumbersome the
00:03:35
current technique today tomb uh doing c. g. is by

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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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