[CMU AI Seminar] Feb 22 at 12pm (Zoom) -- Gaurav Manek (CMU) -- Hough and Cover: 2D Bin Packing with Classic Computer Vision Techniques -- AI Seminar sponsored by Morgan Stanley

[Asher Trockman]

Dear all,

We look forward to seeing you *tomorrow,* *this Tuesday (2/22)* from
*1**2:00-1:00
PM (U.S. Eastern time)* for the next talk of our *CMU AI seminar*,
sponsored by Morgan Stanley
<https://www.morganstanley.com/about-us/technology/>.

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

Tomorrow (2/22), *Gaurav Manek *(CMU) will be giving a talk titled *"**Hough
and Cover: 2D Bin Packing with Classic Computer Vision Techniques**".*

*Title*: Hough and Cover: 2D Bin Packing with Classic Computer Vision
Techniques

*Talk Abstract*: Classic computer vision has, unfortunately, fallen by the
wayside in favor of neural networks. In this talk I introduce a novel
use-case for some such techniques, applying them to 2D bin-packing. The 2D
bin packing problem is a classic NP-hard optimization problem in which a
number of rectangles need to be packed into a minimal number of larger
rectangular bins. I present Hough and Cover, a technique that leverages
transformations found in classic computer vision to greatly reduce the
number of constraints of a novel integer linear programming (ILP)
formulation. This approach is evaluated on classic datasets against a
state-of-the-art ILP approach, where it achieves a speed-up of 5x in many
cases. This problem has many industrial applications such as wood or glass
industries, sheet metal fabrication, and even in scheduling heterogeneous
resources. Solving this optimization problem optimally is important since
it can save money, human resources, and time.
*Presented in Partial Fulfillment of the CSD Speaking Skills Requirement.*

*Speaker Bio*: Gaurav Manek is a PhD student at Carnegie Mellon University,
advised by Zico Kolter. Gaurav's research centers around novel ways to
learn dynamics models and value functions for reinforcement learning and
similar applications, and to understand the subtle mechanisms at work when
these models fail. His interests stretch from theoretical contributions to
industry-facing applications. His personal website is at
https://www.gauravmanek.com/.

*Zoom Link*:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher Trockman
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