LinkedInGoogle Scholar

Hello! I'm a machine learning researcher in NYC currently working as a software engineer at Google AI. Before graduating in 2018, I was a Ph.D. student at the Cornell Tech SE(3) Vision Group under Dr. Serge Belongie. I previously worked as a research assistant in the Vision and Security Technology lab and as a software engineer at Securics, Inc., both under Terrance E. Boult.

My hobbies include peer counseling, bouldering (still a beginner, VB-V1), lifting (beginner), hiking, and finding delicious quiet places in the city to drink tea and read books.

https://s3-us-west-2.amazonaws.com/secure.notion-static.com/e28994c3-d5db-4346-9622-b28809ed483e/Untitled.png

All Publications

My full publication list is available on my CV or my **Google Scholar Profile Page.**

Download CV

CV kwilber Fall 2019.pdf

Selected Publications and Projects

<aside> ✏️ Note that some work before 2018 is listed under a previous name.

</aside>

Online Trust

We created a system that can understand the aesthetic quality of user-created photos on online marketplaces like Letgo or eBay. This was a collaboration between Cornell Tech, eBay, and the Oath connected experiences lab.

We created a system that can understand the aesthetic quality of user-created photos on online marketplaces like Letgo or eBay. This was a collaboration between Cornell Tech, eBay, and the Oath connected experiences lab.

Artistic Aesthetics

We taught a computer about artwork! Our efforts led to the creation of "BAM," currently the largest semisupervised dataset of digital artwork on the Internet freely available for researchers.

We taught a computer about artwork! Our efforts led to the creation of "BAM," currently the largest semisupervised dataset of digital artwork on the Internet freely available for researchers.

Deep learning theory

What happens when you perform brain surgery on a ResNet? Surprisingly, performance still stays the same when deleting several layers, even without fine-tuning. We investigate why in this paper.

What happens when you perform brain surgery on a ResNet? Surprisingly, performance still stays the same when deleting several layers, even without fine-tuning. We investigate why in this paper.

On flagship datasets like LFW and Caltech-256, the current top performing algorithms at time of writing do not satisfy the triangle inequality, symettricity, or even identity. Why are top-performing algorithms non-metric?

On flagship datasets like LFW and Caltech-256, the current top performing algorithms at time of writing do not satisfy the triangle inequality, symettricity, or even identity. Why are top-performing algorithms non-metric?