[CMU AI Seminar] Mar 9 (Zoom) -- Kayvon Fatahalian (Stanford) -- Keeping the Domain Expert in the Loop: Ideas to Models in Hours, Not Weeks -- AI Seminar sponsored by Fortive

[Shaojie Bai]

Dear all,

We look forward to seeing you *next Tuesday (3/9)* from 12:00-1:00 PM (U.S.
Eastern time) for the next talk of our *CMU AI seminar*, sponsored by
Fortive <https://careers.fortive.com/>.

To learn more about the seminar series or see the future schedule, please
visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
<http://www.cs.cmu.edu/~aiseminar/>

On 3/9, *Kayvon Fatahalian <http://graphics.stanford.edu/~kayvonf/>* (Stanford
University) will be giving a talk on "*Keeping the Domain Expert in the
Loop: Ideas to Models in Hours, Not Weeks*."

*Title*: Keeping the Domain Expert in the Loop: Ideas to Models in Hours,
Not Weeks

*Talk Abstract*: My students and I often find ourselves as "subject matter
experts" needing to create models for use in big data computer graphics and
video analysis applications. Yet it is frustrating that a capable grad
student, armed with a large unlabeled image/video collection, a palette of
modern pre-trained models, and an idea of what novel object or event they
want to detect, still requires days-to-weeks to create good models for
their task. In this talk, I will discuss challenges we've faced carrying
out the iterative process of data curation, model training, and model
validation for the specific case of rare events and categories in image and
video collections (such as professional broadcast sports and cable TV). Our
ultimate goal (not yet achieved) is to create training techniques and data
selection interfaces that enable interactive, grad-student-in-the-loop
workflows where the expert human is working concurrently with massive
amounts of parallel processing to interactively and continuously perform
cycles of data acquisition, training, and validation.

*Speaker Bio*: Kayvon Fatahalian is an Assistant Professor in the Computer
Science Department at Stanford University. His lab works on visual
computing systems projects, including high-performance rendering for RL,
large-scale video analytics, programming systems for video data mining, and
compilation techniques for optimizing image processing pipelines. In all
these efforts, the goal is to enable rapid development of applications that
involve image and video processing at scale.

*Zoom Link*:
https://cmu.zoom.us/j/92196990617?pwd=Zk1vZkhzbTkwTE1nNzcyMm5JTFRpUT09


Thanks,
Shaojie Bai (MLD)
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