Episode 51: Research and Tooling for Computer Vision Systems with Jason Corso

Show Notes(2:13) Jason went over his experience studying Computer Science at Loyola College in Baltimore for undergraduate, where he got an early exposure to academic research in image registration.(4:31) Jason described his graduate school experience at John Hopkins University, where he completed his Ph.D. on “Techniques for Vision-Based Human-Computer Interaction” that proposed the Visual Interaction Cues paradigm.(9:31) During his time as a Post-Doc Fellow at UCLA, Jason helped develop automatic segmentation and recognition techniques for brain tumors to improve the accuracy of diagnosis and treatment accuracy(14:27) From 2007 to 2014, Jason was a professor in the Computer Science and Engineering department at SUNY-Buffalo. He covered the content of two graduate-level courses on Bayesian Vision and Intro to Pattern Recognition that he taught.(18:20) On the topic of metric learning, Jason proposed an approach to data analysis and modeling for computer vision called "Active Clustering."(21:35) On the topic of image understanding, Jason created Generalized Image Understanding - a project that examined a unified methodology that integrates low-, mid-, and high-level elements for visual inference (equivalent to image captioning today).(24:51) On the topic of video understanding, Jason worked on ISTARE: Intelligent Spatio-Temporal Activity Reasoning Engine, whose objective is to represent, learn, recognize, and reason over activities in persistent surveillance videos.(27:46) Jason dissected Action Bank - a high-level representation of activity in video, which comprises of many individual action detectors sampled broadly in semantic space and viewpoint space.(35:30) Jason unpacked LIBSVX - a library of super voxel and video segmentation methods coupled with a principled evaluation benchmark based on quantitative 3D criteria for good super voxels.(40:06) Jason gave an overview of AI research activities at the University of Michigan, where he was a professor of Electrical Engineering and Computer Science from 2014 to 2020.(41:09) Jason covered the problems and projects in his graduate-level courses on Foundations of Computer Vision and Advanced Topics in Computer Vision at Michigan.(44:56) Jason went over his recent research on video captioning and video description.(47:03) Jason described his exciting software called BubbleNets, which chooses the best video frame for a human to annotate.(51:44) Jason shared anecdotes of Voxel51's inception and key takeaways that he has learned.(01:05:25) Jason talked about Voxel51's Physical Distancing Index that tracks the coronavirus global pandemic's impact on social behavior.(01:07:47) Jason discussed his exciting new chapter as the new director of the Stevens Institute for Artificial Intelligence.(01:11:28) Jason identified the differences and similarities between being a professor and being a founder.(01:14:55) Jason gave his advice to individuals who want to make a dent in AI research.(01:16:14) Jason mentioned the trends in computer vision research that he is most excited about at the moment.(01:17:23) Closing segment.His Contact InfoWikipediaGoogle ScholarWebsiteTwitterLinkedInHis Recommended ResourcesBubblenets: Video Object Segmentation for Computer VisionVoxel51's FiftyOne Open-Sourced LibraryJeff Siskind (Professor at Purdue University)CJ Taylor (Professor at the University of Pennsylvania)Kristen Grauman (Professor at the University of Austin)"An Introduction to Mathematical Statistics"

Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.

Datacast is produced and edited by James Le. For inquiries about sponsoring the podcast, email [email protected].

Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:

If you’re new, see the podcast homepage for the most recent episodes to listen to, or browse the full guest list.