Datacast Episode 17: Computer Vision Research with Genevieve Patterson

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.

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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