Author Archives: Joris Mooij


About Joris Mooij

Associate Professor

Thijs van Ommen joins AMLab

Thijs van Ommen joined AMLab as a postdoc. Thijs studied mathematics and
computer science in Leiden and did his PhD on model selection and prediction
at the CWI. After that, he was lecturer for a Machine Learning course in
Utrecht, and will now work on causal inference in the CAFES project.

Video Club: Sackler Colloquium “Drawing Causal Inference from Big Data”

On Wednesdays 12:30-13:30 we will jointly watch some of the video recordings of presentations given at the recent Sackler Colloquium Drawing Causal Inference from Big Data of the National Academy of Sciences. (Lunch is not included – bring it yourself!)

Schedule: at the bottom of the Meetings page

Abstract: This colloquium was motivated by the exponentially growing amount of information collected about complex systems, colloquially referred to as “Big Data”. It was aimed at methods to draw causal inference from these large data sets, most of which are not derived from carefully controlled experiments. Although correlations among observations are vast in number and often easy to obtain, causality is much harder to assess and establish, partly because causality is a vague and poorly specified construct for complex systems. Speakers discussed both the conceptual framework required to establish causal inference and designs and computational methods that can allow causality to be inferred. The program illustrates state-of-the-art methods with approaches derived from such fields as statistics, graph theory, machine learning, philosophy, and computer science, and the talks will cover such domains as social networks, medicine, health, economics, business, internet data and usage, search engines, and genetics. The presentations also addressed the possibility of testing causality in large data settings, and will raise certain basic questions: Will access to massive data be a key to understanding the fundamental questions of basic and applied science? Or does the vast increase in data confound analysis, produce computational bottlenecks, and decrease the ability to draw valid causal inferences?

Talk by Karen Sachs (Stanford University)

You are cordially invited for the following talk:

Date: Monday August 31, 2015
Time: 11:00-12:00
Location: SP C3.163

Speaker: Karen Sachs, Stanford University School of Medicine

Title: Use of single cell proteomics measurements for elucidation of biomolecular regulatory relationships

Abstract: We have previously introduced the application of probabilistic models for elucidation of statistical relationships from single cell proteomic data (Sachs et al, Science 2005), an approach enabled by the key insight that each cell may be considered an observation of the underlying biological system. The application of this approach has been partially limited by the low dimensionality of the data modality (flow cytometry), which enabled measurements of only up to ~10 proteins of interest per cell. In 2011, we introduced a next generation single cell proteomic technology, mass cytometry or CyTOF (cytometry time of flight, Bendall et all, Science 2011), which enables quantification of 30-40 parameters per cell. In this talk, I will describe the CyTOF technology and illuminate its advantages and potential follies. I will then discuss my perspective on causal modeling of single cell data and what hurdles remain in this application.