James Le

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Datacast Episode 17: Computer Vision Research with Genevieve Patterson

James Le

Datacast’s 17th episode is my conversation with Genevieve Patterson, the chief scientist at TRASH — a startup that is developing computational filmmaking tools for mobile iphono-graphers. Give it a listen to learn about her academic background studying Electrical Machines in Japan, her Ph.D. research in Computer Vision at Brown, her transition to building her own startup, and much more.

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Genevieve Patterson is the chief scientist at TRASH, a startup that is developing computational filmmaking tools for mobile iphono-graphers. Before that, she was a Postdoctoral Researcher at Microsoft Research New England. Her work is about creating dialog between AI and people. Her interests include video understanding, visual attribute discovery, human-in-the-loop systems, fine-grained object recognition, medical image understanding, and active learning. Genevieve received her Ph.D. from Brown University in 2016 under the direction of James Hays.

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 incubators 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.

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