00:00 - Introduction 02:28 - Career path and how ML came into play 04:20 - Opportunities in Materials and ML 16:05 - Instrumentation, Cloud Computing and User Facilities 19:10 - Workflow design and implementation 29:33 - Machine Learning for physical sciences 35:24 - Machine Learning in industry 39:49 - Data analysis using Machine Learning (in microscopy) 47:20 - Automated experiments for aspects that we know in advance 55:49 - Automated experiments via Bayesian Optimization 1:07:31 - Hypothesis Active Learning 1:14:23 - Concluding remarks and the future of the field Q&A: 1:20:33 - Q1: Advice for newcomers 1:28:40 - Q2: Commercialization of AI-controlled instruments 1:30:18 - Q3: Machine learning for exploring complex phenomena 1:33:40 - Q4: Accidental discoveries 1:39:03 - Q5: Machine learning with small datasets (e.g. at synchrotrons) 1:44:58 - Q6: AI-assisted discovery at a scale: synthesis, characterization, etc. 1:49:55 - Q7: Discoveries by learning on large-scale datasets 1:14:49 - Q8: Is there a need for Department of Machine Learning and AI?
![Wolfram Physics Project: Working Session Wednesday, Apr. 29, 2020 [Finding Black Hole Structures]](http://i.ytimg.com/vi/SbDzqlOLIZA/mqdefault.jpg)
Wonderful talk! Can’t appreciate enough how Prof. Kalinin explains complex concepts in a very simple and understandable manner.
Thank you for watching, Elizaveta!
Fantastic lecture. Even more than the lecture I enjoyed the question answer part. Thanks for sharing!
Thanks for watching!
00:00 - Introduction
02:28 - Career path and how ML came into play
04:20 - Opportunities in Materials and ML
16:05 - Instrumentation, Cloud Computing and User Facilities
19:10 - Workflow design and implementation
29:33 - Machine Learning for physical sciences
35:24 - Machine Learning in industry
39:49 - Data analysis using Machine Learning (in microscopy)
47:20 - Automated experiments for aspects that we know in advance
55:49 - Automated experiments via Bayesian Optimization
1:07:31 - Hypothesis Active Learning
1:14:23 - Concluding remarks and the future of the field
Q&A:
1:20:33 - Q1: Advice for newcomers
1:28:40 - Q2: Commercialization of AI-controlled instruments
1:30:18 - Q3: Machine learning for exploring complex phenomena
1:33:40 - Q4: Accidental discoveries
1:39:03 - Q5: Machine learning with small datasets (e.g. at synchrotrons)
1:44:58 - Q6: AI-assisted discovery at a scale: synthesis, characterization, etc.
1:49:55 - Q7: Discoveries by learning on large-scale datasets
1:14:49 - Q8: Is there a need for Department of Machine Learning and AI?