Sensitivity Analysis of Social Network Data and Methods
45:13
Sensitivity Analysis of Social Network Data and Methods
Diversity and Inequality in Social Networks
54:13
Diversity and Inequality in Social Networks
Histogram Pooling and Symbolic Regression
53:27
Histogram Pooling and Symbolic Regression
Multiagent reasoning for social impact: Results from deployments for public health and conservation
57:13
Multiagent reasoning for social impact: Results from deployments for public health and conservation
The Energetic Basis of Psychobiology and Health
1:04:50
The Energetic Basis of Psychobiology and Health
Mechanism Design for Social Good: Maximising the utility of a limited number of COVID tests
1:08:55
Mechanism Design for Social Good: Maximising the utility of a limited number of COVID tests
ArrayFire Mission and Thoughts on a Future Shaped by Machine Learning and Internet Decentralization
1:17:34
ArrayFire Mission and Thoughts on a Future Shaped by Machine Learning and Internet Decentralization
Accelerating Empirical Dynamic Modeling using High Performance Computing
1:16:59
Accelerating Empirical Dynamic Modeling using High Performance Computing
How to use Causality without Correlation to Download Brains into Computers
1:06:49
How to use Causality without Correlation to Download Brains into Computers
A Software Engineer's Toolkit for Quantitative Research
54:00
A Software Engineer's Toolkit for Quantitative Research
Interpretable and Generalizable Machine Learning for Fluid Dynamics
1:03:55
Interpretable and Generalizable Machine Learning for Fluid Dynamics
(Mis)(Dis)information flow in online social networks
58:45
(Mis)(Dis)information flow in online social networks
Data Science: Meta-Expertise or Interstitial Craft?
1:00:13
Data Science: Meta-Expertise or Interstitial Craft?
A university-wide approach to integrative data science education and career paths
1:12:53
A university-wide approach to integrative data science education and career paths
Predicting what will happen when you act: A casual-process-tracing theory of change
1:10:21
Predicting what will happen when you act: A casual-process-tracing theory of change
Some Applications of Latent Variable Modeling Using Mplus (Mplus series part 2)
1:27:25
Some Applications of Latent Variable Modeling Using Mplus (Mplus series part 2)
Advances in Latent Variable Modeling with Bayesian Estimation (Mplus series part 1)
1:36:53
Advances in Latent Variable Modeling with Bayesian Estimation (Mplus series part 1)
Stability and Complexity: New insights from Random Matrix Theory
1:07:31
Stability and Complexity: New insights from Random Matrix Theory
Bridging Causality and Machine Learning: How Do They Benefit from Each Other?
1:03:32
Bridging Causality and Machine Learning: How Do They Benefit from Each Other?
A tractable latent variable model for nonlinear dimensionality reduction
1:19:27
A tractable latent variable model for nonlinear dimensionality reduction
Rastermap: An embedding algorithm for interpreting high-dimensional neural activity
59:19
Rastermap: An embedding algorithm for interpreting high-dimensional neural activity
Attraction and Repulsion for Visualizing High-Dimensional Data (Manifold Learning)
1:10:31
Attraction and Repulsion for Visualizing High-Dimensional Data (Manifold Learning)
Funny Numbers: Toward a Political Economy of Data and Information
54:03
Funny Numbers: Toward a Political Economy of Data and Information
Empirical Dynamic Modeling: The New edm Package for Stata
1:07:15
Empirical Dynamic Modeling: The New edm Package for Stata
Missing Data Imputation with Low Rank Models
1:16:36
Missing Data Imputation with Low Rank Models
Using real-world data to evaluate and optimize personalized oncology
56:02
Using real-world data to evaluate and optimize personalized oncology
Interdisciplinary Comparison When Analyzing Time Series
1:01:44
Interdisciplinary Comparison When Analyzing Time Series
Privacy-Sensitive Data Analysis: Addressing Failures of Existing Statistical Programming Languages
54:06
Privacy-Sensitive Data Analysis: Addressing Failures of Existing Statistical Programming Languages
Manifold Learning for Biomedical, Health, & Social Sciences, A/Prof. Smita Krishnaswamy
1:10:42
Manifold Learning for Biomedical, Health, & Social Sciences, A/Prof. Smita Krishnaswamy
Manifold Learning for The Scientist, Prof. Marina Meila
1:14:04
Manifold Learning for The Scientist, Prof. Marina Meila