Show Notes:

• (2:09) Genevieve discussed her undergraduate experience studying Electrical Engineering and Mathematics at the University of Arizona.


• (3:18) Genevieve talked about her Master’s work in Electrical Machines from the University of Tokyo.


• (6:59) Genevieve went in-depth about her research work on transverse-flux motor design during her Master’s, in which she won the Outstanding Paper award at ICEMS 2009.


• (11:39) Genevieve talked about her motivation to pursue a Ph.D. degree in Computer Science at Brown University after coming back from Japan.


• (14:17) Genevieve shared her story of finding her research advisor (Dr. James Hays) as a graduate student.


• (18:44) Genevieve discussed her work building and maintaining the SUN Attributes dataset, a widely used resource for scene understanding, during her first year of her Ph.D. degree.


• (21:52) Genevieve talked about the paper Basic Level Scene Understanding (2013), her collaboration with researchers from MIT, Princeton, and University of Washington to build a system that can automatically understand 3D scenes from a single image.


• (24:32) Genevieve talked about the paper Bootstrapping Fine-grained Classifiers: Active Learning with a Crowd in the Loop presented at the NIPS conference in 2013, her collaboration with researchers from UCSD and Cal-Tech to propose an iterative crowd-enabled active learning algorithm for building high-precision visual classifiers from unlabeled images.


• (28:25) Genevieve discussed her Ph.D. thesis titled “Collective Insight: Crowd-Driven Image Understanding.”


• (34:02) Genevieve mentioned her next career move - becoming a Postdoctoral Researcher at Microsoft Research New England.


• (36:40) Genevieve talked about her teaching experience for 2 graduate-level courses: Data-Driven Computer Vision at Brown University in Spring 2016 and Deep Learning For Computer Vision at Tufts University in Spring 2017.


• (38:04) Genevieve shared her 2 advice for graduate students who want to make a dent in the AI/Machine Learning research community.


• (41:45) Genevieve went over her startup TRASH, which develops computational filmmaking tools for mobile iphono-graphers.


• (43:45) Genevieve mentioned the benefit of having TRASH as part of the NYU Tandon Future Labs, which is a network of business incubator and accelerators that support early stage ventures in NYC.


• (45:00) Genevieve talked about the research trends in computer vision, augmented reality, and scene understanding that she’s most interested in at the moment.


• (45:59) Closing segment.

Her Contact Info:

• Website


• GitHub


• LinkedIn


• Twitter


• CV

Her Recommended Resources:

• The Trouble with Trusting AI to Interpret Police Body-Cam Video


• Microsoft Research Podcast


• Stanford’s CS231n: Convolutional Neural Networks for Visual Recognition


• Yann LeCun's letter to CVPR chair after bad reviews on a Vision System that "learnt" features & reviews


• Paperspace


• CVPR 2019


• Michael Black’s Perceiving Systems Lab at the Max Planck Institute for Intelligent Systems


• Nassim Taleb’s “The Black Swan”

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