In this Machine Learning virtual conference session, Sanjoy Dasgupta gives an introduction to machine learning. He goes through a detailed prediction problem and surveys the landscape of the field.

Speaker

Sanjoy Dasgupta, PhD

Sanjoy Dasgupta is a professor of computer science and engineering at the University of California, San Diego, where he has been since 2002. Previously, he worked in the machine learning group at AT&T Labs Research. He obtained a BA from Harvard University in 1993 and a PhD from the University of California, Berkeley. Dasgupta works on algorithmic statistics, with a particular focus on unsupervised and minimally supervised learning. He is author (with Christos Papadimitriou and Umesh Vazirani) of a textbook, Algorithms. He was program co-chair of the Conference on Learning Theory in 2009 and of the International Conference on Machine Learning in 2013.

In this video, Talmo Pereira describes the use of pose estimation instead of classical tracking to quantify animal behavior from videos.

Note: this video has no audio.

Speaker

Talmo Pereira

Talmo Pereira is a PhD candidate in neuroscience at Princeton University. Advised by Mala Murthy and Joshua Shaevitz, Pereira has developed quantitative methods for studying animal behavior by employing a combination of computer vision and deep learning.

In this video, Eyrún Eyjólfsdóttir describes a method for predicting the behavior of fruit flies.

Speaker

Eyrún Eyjólfsdóttir, PhD

Eyrún Eyjólfsdóttir is a research scientist at Vicarious, working on a computational vision system for robots. She completed her PhD at the California Institute of Technology, where she worked with Pietro Perona on automated analysis of behavior, particularly of fruit flies, in a collaboration with the David Anderson Lab. Prior to working at Caltech, she received her MS in computer science from the University of California, Santa Barbara, and her BS in mathematics from the University of Iceland.

In this video, Larissa Heinrich explains her work in connectomics, reconstructing synapses from large electron microscopy volumes (in this case, the entire drosophila brain) using convolutional neural networks.

Speaker

Larissa Heinrich

Larissa Heinrich is a PhD student in the Saalfeld Lab at Janelia Research Campus, where she works on machine learning approaches for automated analysis of large electron microscopy datasets of neural tissue. She earned her BS and MS in physics at the University of Heidelberg, completing her thesis under Winfried Denk at the Max Planck Institute for Medical Research.

In this video, Beth Cimini talks about the use of machine learning for morphological profiling, extracting many features of the cells to build up its “fingerprint.”

Speaker

Beth Cimini, PhD

Beth Cimini is the lead image assay developer in the Carpenter Lab at the Broad Institute, collaborating with researchers around the world to build custom open-source image analysis workflows. She is also the co-maintainer of the lab's CellProfiler software. Always passionate about microscopy, she received her BA from Boston University while studying cholinergic signaling in the salamander retina, and her PhD in biochemistry and molecular biology from the University of California, San Francisco, studying telomere biology in cancer cells.