Working at IPAM at UCLA. Video editor and producer with news, corporate, government, and PBS background.
Culver City, CA
Joined October 2007
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Time for a break. I only use this service to index things that I do at work and it doesn't feel like a good match right now.
Today @ipam_ucla : Jamie Haddock of Harvey Mudd College - Hierarchical and neural nonnegative tensor factorizations. Watch on YouTube-> youtu.be/0-kwIBAQ1hg
Today @ipam_ucla : Kevin Kelly of Rice University Electrical and Computer Engineering - Machine Learning Enhanced Compressive Hyperspectral Imaging. Watch on YouTube-> youtu.be/7gsVPBLC08c
Today @ipam_ucla : Palina Salanevich of Utrecht University - STFT Phase retrieval: robustness and generative priors. Watch on YouTube-> youtu.be/SUEfH0OUScY
Today @ipam_ucla : Reinhard Heckel of the Technical University of Munich - The role of data and models for deep-learning based image reconstruction. Watch on YouTube-> youtu.be/chpBnMAKhHs
Today @ipam_ucla : Mark Iwen of Michigan State University - Provably Accurate Recovery of Compactly Supported Smooth Functions from Spectrogram Measurements. Watch on YouTube-> youtu.be/OpnJf1BwpnA
Today @ipam_ucla : Elizaveta Rebrova of Princeton University - Low-rank tensor recovery from memory-efficient measurements. Watch on YouTube-> youtu.be/x6MyawY5zdE
Today @ipam_ucla : Mary Scott of the University of California, Berkeley - Supervised and Unsupervised approaches for Electron Microscopy Data Analysis. Watch on YouTube-> youtu.be/Y4jVMQE5l-E
Today @ipam_ucla : Mahdi Soltanolkotabi of the University of Southern California - Medical image reconstruction via deep learning: new architectures, data reduction and robustness. Watch on YouTube-> youtu.be/85IKRhmk3lI
Today @ipam_ucla : Ben Recht of the University of California, Berkeley - Splitting the difference between deep and shallow solutions of inverse problems. Watch on YouTube-> youtu.be/Vp0BEhtnT6o
New from @ipam_ucla : Juan-Carlos Idrobo of the University of Washington - Into the Realms of Quantum Phenomena with Electron Microscopy: Exploring New Possibilities & Limitations. Watch on YouTube-> youtu.be/SnW1X_OE8FU
Today @ipam_ucla : Sarah Haigh of the University of Manchester - Probing atomic behaviour in liquids with STEM: opportunities for machine learning. Watch on YouTube-> youtu.be/YUcCcPkzRV4
New from @ipam_ucla : Paul Hand of Northeastern University - Signal Recovery with Generative Priors. Watch on YouTube-> youtu.be/obt1yRRt3qM
Today @ipam_ucla : Robert Wolkow of the University of Alberta - Atom-Defined Devices, Ultra-Fast, Ultra-Low Power Classical Devices and Diverse Quantum Devices. Watch on YouTube-> youtu.be/Yf8gcBGyxHA
Today @ipam_ucla : Piotr Indyk of the Massachusetts Institute of Technology - Learning-Based Low-Rank Approximations. Watch on YouTube-> youtu.be/gtsFOkIMjZc
Today @ipam_ucla : Rama Vasudevan of Oak Ridge National Laboratory - Advancing Microscopy with Machine Learning: Lessons and Insights from Scanning Probe Microscopy. Watch on YouTube-> youtu.be/xhHLYlrz5A0
Today @ipam_ucla : Daniel Cremers of Technische Universtitat München - Deep Learning: Challenges and Perspectives. Watch on YouTube-> youtu.be/24lBl7KciPg
Rich Bartlebaugh retweeted
I had the immense pleasure of working with the amazing team at @Slate, including @susanematthews and @DerekLJohn on the special episodes for this feature, including the episode on how they made this show. What an incredible dive into the origins of this situation in America.🔥
🎉 Congrats to our Show of the Year: @Slate's Slow Burn: Roe v. Wade.
When the decision dominated headlines this year, the podcast was perfectly timed to give listeners an in-depth perspective on the 1973 ruling and provide context for the current moment. apple.co/ShowOfTheYear
Today @ipam_ucla : Michael Lustig of the University of California, Berkeley - Multi-modal Motion Imaging using Microwave tones in an MRI scanner. Watch on YouTube-> youtu.be/bx66hVdqlg0
Today @ipam_ucla : Hanbaek Lyu of the University of Wisconsin-Madison - Mesoscale reconstruction of images and networks using tensor decomposition. Watch on YouTube-> youtu.be/HCy04Gu9-vc