Connectionists: Postdoc Position in Expanding Machine Learning beyond Social Prediction to Explanation, Intervention

[Candice Lewis]

Title: Postdoctoral Position in Expanding Machine Learning Beyond Social
Prediction to Explanation & Intervention @ the Knowledge Lab
<https://www.knowledgelab.org/>, UChicago <https://www.uchicago.edu/>

The Knowledge Lab at the University of Chicago seeks to hire outstanding
candidates for a postdoctoral research project with support from DARPA to
extend the limits of machine learning from the prediction of social systems
to explaining those systems and intervening in them. This is in association
with the “Ground Truth” program at DARPA. Other teams will generate
reasonable agent-based models of diverse social systems, and our task is to
build automated, analytical techniques that induce the "ground truth" or
structure of the model and program used to generate them. We will also
predict future instances of these social systems, and propose desirable and
pragmatic interventions in them. Our team, the "Social MIND (Machine
Inference for Novel Discovery)”, is exploring approaches that use
large-scale Bayesian inference, probabilistic programming, deep learning
neural networks, and approaches that link them together. We are recruiting
for 1-2 postdoc positions at the intersection of data science, machine
learning, automated scientific discovery, and social science.

Postdoctoral candidates will design and conduct independent research, in
close collaboration with UChicago professor, Santa Fe Institute external
faculty member, and Director of Knowledge Lab James Evans, along with
Joshua Tenenbaum, computational cognitive scientist from MIT, and Michael
Franklin, a computer scientist and leader in systems design at the
University of Chicago. Candidates must hold a PhD in Computer Science, or
have substantial computational and data science background and a Ph.D. in
Statistics, Applied Math, Physics, Sociology or another Social or
Behavioral Science, or a related field. Candidates should have a strong
publishing record. Experience in a social science discipline a strong plus.
Comfort working collaboratively with a team is essential.

Specifically, successful candidates will be responsible for generating and
automatically decoding agent-models, and applying these techniques to real
social systems. Experience with some of the following will be helpful: deep
neural networks, Bayesian inference, probabilistic programming, machine
learning and machine understanding. Candidates will be involved in both
innovating new methods for specific inference tasks, and assembling
approaches into automated data analytic pipelines. The broader project will
also involve crowdsourcing alternative approaches, so experience with
crowdsourcing and intelligent model combination also a plus. Because we
will be requesting social data from the agent-modeling teams, understanding
social science data gathering methods and familiarity with game theoretic
and agent-based models will be very helpful.

To apply, please send CV, cover letter and names for letters from at least
two references to Candice Lewis, cllewis at uchicago.edu.

Candice Lewis, Ph.D.
Assistant Director
The Knowledge Lab <https://www.knowledgelab.org/>
University of Chicago
5735 S Ellis Ave| Room 221
Chicago, IL 60637
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