Eric Schulz

Hello World.

I am a Cognitive Scientist and Data Science Postdoctoral Fellow in Sam Gershman's Computational Cognitive Neuroscience Lab and the Harvard Data Science Initiative. Here is my CV.

I am interested in learning and decision making from a computational and cognitive perspective. To this end, I use a combination of behavioral experiments and computational modeling to probe the fundamental basis of cognition.

A particular focus for me is the role of generalization in cognition. How do people know what to think and do in novel situations? And how does generalization support inference, problem solving, and the search for rewards?

I model human generalization as a form of function approximation, whereby a function can be any mapping between inputs and outputs, from simple regression over compositional structure search all the way to induced programs. As our brain is still the best known ``prediction machine'' out there, algorithms that can capture human generalization promise to be beneficial for theories of biological and artificial intelligence alike. My aim is to further unravel these algorithms.

Publications.

Google scholar

Working papers:

Wu, C.M., Schulz, E., Speekenbrink, M., Nelson, J.D., & Meder, B. (submitted). Exploration and generalization in vast spaces. [PDF]

Schulz, E., Speekenbrink, M., & Krause, A. (submitted). A tutorial on Gaussian process regression with a focus on exploration-exploitation scenarios. [PDF]

Schulz, E., Tenenbaum, J.B., Duvenaud, D., Speekenbrink, M., & Gershman, S.J. (submitted). Compositional Inductive Biases in Function Learning. [PDF]

2017:

Schulz, E., Konstantinidis, E., & Speekenbrink, M. (2017). Putting bandits into context: How function learning supports decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition. [PDF]

Dasgupta, I., Schulz, E., & Gershman, S.J. (2017). Where do hypotheses come from? Cognitive Psychology. Cognitive Psychology, 96, 1-25. [PDF]

Schulz, E., Klenske, E.D., Bramley, N.R., & Speekenbrink, M. (2017). Strategic exploration in human adaptive control. Proceedings of the Thirty-Ninth Annual Conference of the Cognitive Science Society. [PDF]

Wu, C.M., Schulz, E., Speekenbrink, M., Nelson, J.D., & Meder, B. (2017). Mapping the unknown: The spatially correlated multi-armed bandit. Proceedings of the Thirty-Ninth Annual Conference of the Cognitive Science Society. [PDF]

Dasgupta, I., Schulz, E., Goodman, N.D., & Gershman, S.J. (2017). Amortized Hypothesis Generation. Proceedings of the Thirty-Ninth Annual Conference of the Cognitive Science Society. [PDF]

2016:

Schulz, E., Speekenbrink, M., Hernández Lobato J. M., Ghahramani, Z., & Gershman, S.J. (2016). Quantifying mismatch in Bayesian optimization. In NIPS Workshop on Bayesian Optimization: Black-box Optimization and beyond, Barcelona, Spain, 2016. [PDF]

Schulz, E., Tenenbaum, J.B., Duvenaud, D., Speekenbrink, M., & Gershman, S.J. (2016). Probing the Compositionality of Intuitive Functions. In Advances in Neural Information Processing Systems. [PDF]

Schulz, E., Huys, Q. J. M., Bach, D.R., Speekenbrink, M., & Krause, A. (2016). Better safe than sorry: Risky function exploitation through safe optimization. Proceedings of the Thirty-Eighth Annual Conference of the Cognitive Science Society. [PDF]

Schulz, E., Speekenbrink, M., & Meder, B. (2016). Simple trees in complex forests: Growing Take The Best by Approximate Bayesian Computation. Proceedings of the Thirty-Eighth Annual Conference of the Cognitive Science Society. [PDF]

2015:

Schulz, E., Konstantinidis, E., & Speekenbrink, M. (2015). Learning and decisions in contextual multi-armed bandit tasks. Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science Society. [PDF]

Schulz, E., Tenenbaum, J.B., Reshef, D.N., Speekenbrink, M., & Gershman, S.J. (2015). Assessing the perceived predictability of functions. Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science Society. [PDF]

Parpart, P., Schulz, E., Speekenbrink, M., & Love, B.C. (2015). Active learning as a means to distinguish among prominent decision strategies. Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science Society. [PDF]

Schulz, E., Konstantinidis, E., & Speekenbrink, M. (2015). Exploration-Exploitation in a Contextual Multi-Armed Bandit Task. Proceedings of the International Conference on Cognitive Modeling. [PDF]

2014:

Schulz, E., Speekenbrink, M., & Shanks, D.R. (2014). Predict choice – a comparison of 21 mathematical models. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. [PDF]

Before 2014:

Cokely, E.T., Ghazal, S., Galesic, M., Garcia-Retamero, R., & Schulz, E.(2013). How to measure risk comprehension in educated samples. Transparent Communication of Health Risks, 29-52. [PDF]

Cokely E.T., Galesic, M., Schulz, E., Ghazal, S., & Garcia-Retamero, R. (2012).Measuring risk literacy: The Berlin numeracy test. Judgment and Decision Making 7 (1), 25-47. [PDF]

Schulz,E., Cokely, E.T., & Feltz, A. (2011). Persistent bias in expert judgments about free will and moral responsibility: A test of the expertise defense. Consciousness and cognition 20 (4), 1722-1731. [PDF]

Theses:

Schulz, E. (2017). Towards a unifying model of generalization. PhD-thesis. [PDF]

Schulz, E. (2014). Function learning as Gaussian Process optimization. Unpublished MRes-thesis supervised by Maarten Speekenbrink.

Schulz, E. (2012). Measuring the behaviour of Monte Carlo Markov chain sampling schemes. Unpublished MSc-thesis supervised by Brian Ripley.

Schulz, E. (2011). Choosing how to predict choices. Unpublished MSc-thesis supervised by Maarten Speekenbrink & David Shanks.

About me.

I grew up in Colditz (Saxony, Germany) and did my undergrad in Psychology at Humboldt University in Berlin.

Afterwards, I did a MSc in Cognitive and Decision Sciences at University College London, a MSc in Applied Statistics at the University of Oxford, worked as a Machine Learning Analyst in Berlin and as a volunteer in Uganda’s Virus Research Institute, and then went on to do a MRes in Computer Science again at UCL.

I received my PhD in Experimental Psychology from UCL in 2017, where I worked with Maarten Speekenbrink.

Currently, I am a Data Science Postdoctoral Fellow at Harvard University.

Contact.

Questions, comments, and ideas? Please get in contact.