Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (www.epistasis.org)

Friday, May 26, 2017

The NIH rule of 21

I have worked my ass off my entire career publishing nearly 500 scientific papers (H-index = 73), training more than 50 undergraduate researchers, graduating 20 PhD students, training numerous postdocs, employing dozens of technical staff, mentoring dozens of faculty, and providing extensive service to the NIH as a good citizen. Despite all of this, Francis Collins and the NIH want to take grants away from me because I have not been productive enough. By all accounts this seems very likely. As you can tell I am quite steamed about this. I am not sure I will ever be able to forgive them should this come to pass [UPDATE: this policy was abandoned - see link below]. I have helped my institution respond to this. Not sure it will make a difference.

Here is the announcement from the NIH.

Here is a report on changes in response to the concerns of the research community.

Here is the announcement from the NIH about their plans to back away from this policy.

Here is information about the NIH Next Generation Researcher Initiative that seems like a much more sensible solution to the problem.

Tuesday, May 16, 2017

Detecting Statistical Interactions from Neural Network Weights

A new preprint on detecting interactions from deep learning models:

Detecting Statistical Interactions from Neural Network Weights 

Michael Tsang Dehua Cheng Yan Liu 
University of Southern California 
May 16, 2017 


Interpreting deep neural networks can enable new applications for predictive modeling where both accuracy and interpretability are required. In this paper, we examine the underlying structure of a deep neural network to interpret the statistical interactions it captures. Our key observation is that any input features that interact with each other must follow strongly weighted connections to a common hidden unit before the final output. We propose a novel framework for detecting feature interactions of arbitrary order by interpreting neural network weights. Our framework, which we call Neural Interaction Detector (NID), accurately identifies meaningful interactions without an exhaustive search on an exponential solution space of interaction candidates. Empirical evaluation on both synthetic and real-world data showed the effectiveness of NID, which can uncover interactions omitted by other methods in orders of magnitude less time.

Saturday, May 06, 2017

Accessible Artificial Intelligence

We are developing an open-source and user-friendly AI system for machine learning analysis of data. We call this PennAI. We have posted a preprint on arXiv. This paper will be presented and published as part of the 2017 Genetic Theory and Practice Workshop (GPTP) that will take place later this month. Our project was written up in a nice piece published by Motherboard.

Monday, May 01, 2017

Elected Fellow of the American Statistical Association

It is a great honor to be elected as a Fellow of the American Statistical Association. I have a Masters degree in statistics and have worked my entire career at the interface between stats and computer science. It means a lot to be recognized by my stats peers. Thanks!

Here is a list of all 62 newly elected fellow for 2017