Datacast
Episode 17: Computer Vision Research with Genevieve Patterson
Episode Summary
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 PhD from Brown University in 2016 under the direction of James Hays.
Episode Notes
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.
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