Jessica B. Hamrick · Curriculum VitæWebsite: http://www.jesshamrick.com/
Email: jessica.b.hamrick@gmail.com(Last updated: June 8, 2019)

Education
University of California, Berkeley
Ph.D. in Psychology
Thesis: Metareasoning and Mental Simulation
Advised by: Thomas L. Griffiths

August 2012 – October 2017

Massachusetts Institute of Technology
September 2011 – June 2012
M.Eng. in Electrical Engineering and Computer Science
Thesis: Physical Reasoning in Complex Scenes is Sensitive to Mass
Advised by: Joshua B. Tenenbaum
Massachusetts Institute of Technology
B.S. in Computer Science and Engineering
Advised by: Gerald Jay Sussman

September 2007 – June 2012

Research Experience
Senior Research Scientist
Research Scientist
DeepMind

May 2019 – present
November 2017 – April 2019

Graduate Student Researcher
Computational Cognitive Science Lab, UC Berkeley

August 2012 – October 2017

InternDeepMind

May 2016 – August 2016

Graduate Research Assistant
Undergraduate Research AssistantComputational Cognitive Science Lab, MIT

June 2011 – August 2012
January 2010 – June 2011

Undergraduate Research Assistant
Personal Robots Group, MIT Media Lab

June 2008 – December 2009

Fellowships, Grants, and Awards• 2017 ACM Software System Award for Project Jupyter. Awarded May 2018.
• 2015-2016 Outstanding Graduate Student Instructor Award. Awarded March 2016.
• Cognitive Science Society Student Travel Grant. Awarded July 2015. Based on merit.
• National Science Foundation Graduate Fellowship (three years, 2014–2017), tuition and stipend.
Awarded April 2013.
• Berkeley Fellowship, University of California Berkeley (two years, 2012–2014), tuition andstipend. Awarded March 2012. Berkeley’s most prestigious fellowship awarded to incoming
graduate students, based on merit.

Journal Articles• Hamrick, J. B. (2019). Analogues of mental simulation and imagination in deep learning.
Current Opinion in Behavioral Sciences, 29, 8–16.
• Project Jupyter, Blank, B., Bourgin, D., Brown, A., Bussonnier, M., Frederic, J., Granger, B.,
Griffiths, T. L., Hamrick, J. B., Kelley, K., Pacer, M, Page, L., Pérez, F., Ragan-Kelley, B.,
Suchow, J. W., & Willing, C. (2019). nbgrader: A Tool for Creating and Grading Assignmentsin the Jupyter Notebook. Journal of Open Source Education, 2(11), 32.
• Hamrick, J. B., Battaglia, P. W., Griffiths, T. L., & Tenenbaum, J. B. (2016). Inferring massin complex scenes by mental simulation. Cognition, 157, 61–76.
• Gureckis, T. M., Martin, J., McDonnell, J., Rich, A. S., Markant, D. B., Coenen, A., Halpern,
D., Hamrick, J. B., & Chan, P. (2016). psiTurk: An open-source framework for conducting
replicable behavioral experiments online. Behavioral Research Methods, 48(3), 829–842.
• Goodman, N. D., Frank, M. C., Griffiths, T. L., Tenenbaum, J. B., Battaglia, P. W., & Hamrick, J. B. (2015). Relevant and robust: A response to Marcus & Davis (2013). Psychological
Science, 26(4), 539–-541.
• Battaglia P. W., Hamrick J. B., & Tenenbaum J. B. (2013). Simulation as an engine of physicalscene understanding. Proceedings of the National Academy of Sciences, 110(45), 18327–18332.

Refereed Conference Proceedings• Bapst∗ , V., Sanchez-Gonzalez∗ , A., Doersch, C., Stachenfeld, K., Kohli, P., Battaglia, P. W.,& Hamrick, J. B. (2019). Structured agents for physical construction. In Proceedings of theInternational Conference of Machine Learning (ICML 2019).
• Hamrick∗ , J. B., Allen∗ , K. R., Bapst, V., Zhu, T., McKee, K. R., Tenenbaum, J. B., &Battaglia, P. W. (2018). Relational inductive bias for physical construction in humans andmachines. In Proceedings of the 40th Annual Conference of the Cognitive Science Society.
Austin, TX: Cognitive Science Society.
• Fisac, J. F., Gates, M. A., Hamrick, J. B., Liu, C., Hadfield-Menell, D., Palaniappan, M.,
Malik D., Sastry, S., Griffiths, T. L., & Dragan, A. D. (2017). Pragmatic-Pedagogic Value
Alignment. In Proceedings of the International Symposium on Robotics Research (ISRR 2017).
Winner of the Computing Community Consortium Blue Sky Award.
• Callaway, F., Hamrick, J. B., & Griffiths, T. L. (2017). Discovering simple heuristics frommental simulation. In Proceedings of the 39th Annual Conference of the Cognitive Science
Society. Austin, TX: Cognitive Science Society.
• Hamrick, J. B., Ballard, A. J., Pascanu, R., Vinyals, O., Heess, N., & Battaglia, P. W.
(2017). Metacontrol for Adaptive Imagination-Based Optimization. In Proceedings of the 5thInternational Conference on Learning Representations (ICLR 2017).
• Hamrick, J. B., Pascanu, R., Vinyals, O., Ballard, A. J., Heess, N., & Battaglia, P. W.
(2016). Imagination-Based Decision Making with Physical Models in Deep Neural Networks. In
Proceedings of the NeurIPS 2016 Workshop on Intuitive Physics.
• Fisac∗ , J. F., Liu∗ , C., Hamrick∗ , J. B., Sastry, S., Hedrick, J. K., Griffiths, T. L., & Dragan,
A. D. (2016). Generating plans that predict themselves. In Proceedings of the 12th International
Workshop on the Algorithmic Foundations of Robotics (WAFR 2016).
∗

Contributed equally.

• Liu∗ , C., Hamrick∗ , J. B., Fisac∗ , J. F., Dragan, A. D., Hedrick, J. K., Sastry, S. S., & Griffiths,
T. L. (2016). Goal Inference Improves Objective and Perceived Performance in Human-Robot
Collaboration. In J. Thangarajah, K. Tuyls, C. Jonker, & S. Marsella (Eds.), Proceedings of the
15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016).
• Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley,K., Hamrick, J. B., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., & Willing, C.
(2016). Jupyter Notebooks—a publishing format for reproducible computational workflows. In
Proceedings of the 20th International Conference on Electronic Publishing, 87–90.
• Hamrick, J. B., Smith, K. A., Griffiths, T. L., & Vul, E. (2015). Think again? The amountof mental simulation tracks uncertainty in the outcome. In Proceedings of the 37th Annual
Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
• Lieder, F., Plunkett, D., Hamrick, J. B., Russell, S. J., Hay, N. J., & Griffiths, T. L. (2014).
Algorithm selection by rational metareasoning as a model of human strategy selection. Advancesin Neural Information Processing Systems, 27.
• Hamrick J. B. & Griffiths T. L. (2014). What to simulate? Inferring the right direction for
mental rotation. In P. Bello, M. Guarini, M. McShane & B. Scassellati (Eds.), Proceedings of the36th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
• Hamrick J. B. & Griffiths T. L. (2013). Mental Rotation as Bayesian Quadrature. In NeurIPS
Bayesian Optimization Workshop.
• Abbott J. T., Hamrick J. B., & Griffiths T. L. (2013). Approximating Bayesian inferencewith a sparse distributed memory system. In M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth(Eds.), Proceedings of the 35th Annual Conference of the Cognitive Science Society. Austin, TX:
Cognitive Science Society.
• Hamrick J. B., Battaglia P. W., & Tenenbaum J. B. (2011). Internal physics models guideprobabilistic judgments about object dynamics. In L. Carlson, C. Hölscher, & T. Shipley (Eds.),
Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.

Non-Refereed Publications• Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski,
M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., Gulcehre, C., Song, F., Ballard, A.,
Gilmer, J., Dahl, G., Vaswani, A., Allen, K., Nash, C., Langston, V., Dyer, C., Heess, N.,
Wierstra, D., Kohli, P., Botvinick, M., Vinyals, O., Li, Y., & Pascanu, R. (2018). Relational
inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261.
• Hamrick, J. B. (2017). Metareasoning and Mental Simulation. Ph.D. thesis.
• Hamrick, J. B. (2016). A Rejection Sampler. In M. DiBernardo & A. Brown (Eds.), The
Architecture of Open Source Applications, Volume 4: 500 Lines or Less.
• Hamrick, J. B. (2012). Physical Reasoning in Complex Scenes is Sensitive to Mass. Master’s
thesis.

Invited Presentations• Hamrick, J. B. (2019, July). Talk to be presented at the Workshop on Heuristics, Hacks, andHabits, CogSci 2019. Montreal, Canada.
• Hamrick, J. B. (2019, June). Talk to be presented at the Workshop on Generative Modelingand Model-Based Reasoning for Robotics and AI, ICML 2019. Long Beach, CA, USA.

• Hamrick, J. B. (2019, May). nbgrader: A Tool for Creating and Grading Assignments in the
Jupyter Notebook. Talk presented at the nbgrader Jupyter Community Workshop. Edinburgh,UK.
• Hamrick, J. B. (2019, May). Construction as the manipulation of structure. Presented at theWorkshop on Representation Learning for Graphs and Manifolds, ICLR 2019. New Orleans,
LA, USA.
• Hamrick, J. B. (2019, May). Structured Computation and Representation in Deep Reinforcement Learning. Presented at the Workshop on Structured Prediction meets Deep Reinforcement
Learning, ICLR 2019. New Orleans, LA, USA.
• Hamrick, J. B. (2019, April). Relational inductive bias, deep learning, and graph networks.
Linear Accelerator Laboratory (LAL) Seminar Series. Paris, France.
• Hamrick, J. B. (2019, April). Mental simulation, imagination, and model-based deep RL.
Lecture for Stanford CS379. Palo Alto, CA.
• Hamrick, J. B. (2018, October). nbgrader: A Tool for Creating and Grading Assignments in
the Jupyter Notebook. Talk presented at PyData Paris. Paris, France.
• Hamrick, J. B. (2018, July). Meta-reasoning for adaptive physical strategy selection andcontrol. Presented at the Symposium for Strategies and Representations in Physical Inference,
CogSci 2018. Madison, WI, USA.
• Hamrick, J. B. (2018, July). Structured Computation and Representation in Deep Reinforcement Learning. Presented at the Workshop on Prediction and Generative Modeling in
Reinforcement Learning, ICML 2018. Stockholm, Sweden.
• Hamrick, J. B. (2018, March). Metareasoning and mental simulation in humans and artificial
agents. Presented at the Workshop on Model-Based Cognition: Hierarchical Reasoning andSequential Planning, COSYNE 2018. Breckenridge, CO, USA.
• Hamrick, J. B. (2017, July). Metacontrol for Adaptive Imagination-Based Optimization.
Presented at the Workshop on Deep Learning in Computational Cognitive Science, CogSci 2017.
London, UK.
• Hamrick, J. B. (2017, July). Think again? Adaptive allocation of resources for mental simulation. Presented at the Symposium on Bridging Levels of Analysis with Rational Process Models,
MathPsych 2017. Coventry, UK.
• Hamrick, J. B. (2016, October). Metareasoning and mental simulation. Stanford University.
Palo Alto, CA, USA.
• Hamrick, J. B. (2015, December). Think again? Bounded optimality in decisions from internally generated evidence. Presented at the Bounded Optimality and Rational Metareasoning
Workshop, NeurIPS 2015. Montreal, Canada.
• Hamrick, J. B. (2015, November). Teaching with Jupyter Notebooks. Presented at CodeNeuro2015. San Francisco, CA, USA.
• Hamrick, J. B. (2015, November). Mental Simulation in Humans and Robots. Presented at
the Algorithms for Human-Robot Interaction Workshop. Berkeley, CA, USA.

Conference Presentations• Bapst∗ , V., Sanchez-Gonzalez∗ , A., Doersch, C., Stachenfeld, K., Kohli, P., Battaglia, P. W., &Hamrick, J. B. (2019). Structured agents for physical construction. Talk and poster presented
at ICML 2019. Long Beach, CA, USA.

• Hamrick∗ , J. B., Allen∗ , K. R., Bapst, V., Zhu, T., McKee, K. R., Tenenbaum, J. B., &Battaglia, P. W. (2018, July). Relational inductive bias for physical construction in humans
and machines. Poster presented at the 40th Annual Conference of the Cognitive Science Society.
Madison, WI, USA.
• Pacer, M, Avila, D., & Hamrick, J. B. (2017, August). The Jupyter notebook as document:from structure to application. Talk presented at JupyterCon 2017. New York, NY, USA.
• Hamrick, J. B., Bourgin, D., Langlois, T., & Griffiths, T. L. (2017, July). Exploring inductive
bias of visual scenes. Poster presented at the 39th Annual Conference of the Cognitive Science
Society. London, UK.
• Hamrick, J. B., Bussonnier, M., Frederic, J., Granger, B., Page, L., & Willing., C. (2017,
July). nbgrader: A Tool for Creating and Grading Assignments in the Jupyter Notebook. Talk
presented at SciPy 2017. Austin, TX, USA.
• Hamrick, J. B., Ballard, A. J., Pascanu, R., Vinyals, O., Heess, N., & Battaglia, P. W. (2017,
April). Metacontrol for Adaptive Imagination-Based Optimization. Poster presented at the 5thInternational Conference on Learning Representations (ICLR 2017). Toulon, France.
• Hamrick, J. B., Pascanu, R., Vinyals, O., Ballard, A. J., Heess, N., & Battaglia, P. W.
(2016, December). Imagination-Based Decision Making with Physical Models in Deep Neural
Networks. Talk and poster presented at the NeurIPS 2016 Workshop on Intuitive Physics.
Barcelona, Spain.
• Hamrick, J. B. and Griffiths, T. L. (2016, December). Metareasoning and mental simulation.
Poster presented at the 11th Women in Machine Learning Workshop (WiML 2016). Barcelona,
Spain.
• Suchow, J. W., Morgan, T. J. H., Hamrick, J. B., Pacer, M. D., Meylan, S. C., & Griffiths, T. L.
(2016, August). Wallace: Automating Cultural Evolution Experiments Through Crowdsourcing.
Tutorial presented at the 38th Annual Conference of the Cognitive Science Society. Philadelphia,
PA, USA.
• Hamrick, J. B. (2016, July). Reproducible, One-Button Workflows with the Jupyter Notebook
and SCons. Talk presented at SciPy 2016. Austin, TX, USA.
• Liu, C., Hamrick, J. B., Fisac, J. F., Dragan, A. D., Hedrick, J. K., Sastry, S. S., & Griffiths, T.
L. (2016, May). Goal Inference Improves Objective and Perceived Performance in Human-Robot
Collaboration. Poster presented at AAMAS 2016. Singapore.
• Hamrick, J. B. (2016, March). Creating and Grading Assignments in the IPython/Jupyter
Notebook with nbgrader. Demo presented at SIGCSE 2016. Memphis, TN, USA.
• Hamrick, J. B., Smith, K. A., Griffiths, T. L., & Vul, E. (2015, July). Think again? The
amount of mental simulation tracks uncertainty in the outcome. Talk presented at the 37th
Annual Conference of the Cognitive Science Society. Pasadena, CA, USA.
• Hamrick, J. B., Ragan-Kelley, M., & Kelley, K. (2015, July). Teaching with IPython/
Jupyter Notebooks and JupyterHub. Talk presented at SciPy 2015. Austin, TX, USA.
• Hamrick J. B. & Griffiths, T. L. (2014, August). What to simulate? Inferring the right
direction for mental rotation. Talk presented at the 36th Annual Conference of the Cognitive
Science Society. Quebec City, Canada.
• Hamrick J. B. & Battaglia, P. W. (2014, April). Games for Science: Creating interactive
psychology experiments in Python with Panda3D. Talk presented at PyCon 2014. Montreal,
Canada.
• Hamrick J. B. & Griffiths, T. L. (2013, December). Mental Rotation as Bayesian Quadrature.
Poster presented at the NeurIPS Bayesian Optimization Workshop.

• Hamrick J. B. (2013, November). Rewriting Python Docstrings with a Metaclass. Talk
presented at the San Francisco Python Meetup.
• Hamrick J. B., Battaglia P. W., Griffiths T. L, & Tenenbaum J. B. (2013, August). Inferring
mass in complex physical scenes via probabilistic simulation. Talk presented at the 46th Annual
Meeting of the Society of Mathematical Psychology. Potsdam, Germany.
• Hamrick J. B., Battaglia P. W., Griffiths T. L, & Tenenbaum J. B. (2013, August). Inferring
mass in complex physical scenes via probabilistic simulation. Poster presented at the 35th
Annual Conference of the Cognitive Science Society. Berlin, Germany.
• Battaglia P. W., Hamrick J. B., & Tenenbaum J. B. (2012, May). Intuitive mechanics in visualreasoning about complex scenes with unknown forces. Poster presented by Peter Battaglia at
Annual Meeting of the Vision Sciences Society. Naples, FL, USA.
• Hamrick J. B., Battaglia P. W., & Tenenbaum J. B. (2012, May). Physics knowledge aids
object perception in dynamic scenes. Poster presented at the Annual Meeting of the Vision
Sciences Society. Naples, FL, USA.
• Hamrick J. B., Battaglia P. W., & Tenenbaum J. B. (2011, July). Internal physics models
guide probabilistic judgments about object dynamics. Paper presented at the 33rd Annual
Conference of the Cognitive Science Society. Boston, MA, USA.
• Battaglia P. W., Hamrick J. B., & Tenenbaum J. B. (2011, May). Intuitive physics judgments
guided by probabilistic dynamics model. Poster presented by Peter Battaglia at the Annual
Meeting of the Vision Sciences Society. Naples, FL, USA.

Reviewing• Annual reviewer for CogSci, 2014 – present• Annual reviewer for NeurIPS, 2017 – present. Awarded the Best Reviewer Award in 2017and 2018.
• Annual reviewer for ICLR, 2018 – present• Annual reviewer for ICML, 2017-2018• Occasional reviewer for Cognitive Science, 2017 – present

Mentoring• Elena Ouyang (undergraduate research assistant), September 2014 – May 2016
• Crystal Chen (undergraduate research assistant), February 2013 – May 2014

Teaching• Berkeley Review of CogSci Articles (September 14, 2016, UC Berkeley)
http://www.decal.org/courses/4179
Guest lecturer.
• COGSCI 88: Data Science and the Mind (November 24, 2015, UC Berkeley)
http://data8.org/connector/mind/
Guest lecturer.
• Software Carpentry (July 6-7, 2015, Austin, TX)
http://software-carpentry.org/
Instructor for a boot camp teaching computer skills to scientists at the SciPy 2015 conference,including Python programming, Git version control, the command line, and various scientific
Python libraries.
• COGSCI 131: Computational Models of Cognition (Spring 2015, UC Berkeley)
http://www.jesshamrick.com/2014/03/24/deploying-jupyterhub-for-education
Graduate student instructor. Spearheaded an effort to convert assignments from MATLAB to
IPython notebooks and maintained a deployment of JupyterHub for the 220 students to workon their IPython notebook assignments.
• Berkeley Review of CogSci Articles (October 15, 2014, UC Berkeley)
http://www.decal.org/courses/3192
Guest lecturer.
• Software Carpentry (September 22-23, 2014, Berkeley, CA)
http://software-carpentry.org/
Instructor for a boot camp teaching computer skills to scientists at the Lawrence Berkeley
National Laboratory, including Python programming, Git version control, the command line,
and data processing.
• Software Carpentry (April 14-15, 2014, Montreal, Canada)
http://software-carpentry.org/
Instructor for a boot camp teaching computer skills to librarians, including Python programming,
Git version control, the command line, and data processing.
• COGSCI 131: Computational Models of Cognition (Spring 2014, UC Berkeley)
Graduate student instructor.
• Berkeley Review of CogSci Articles (October 16, 2013, UC Berkeley)
http://www.decal.org/courses/2827
Guest lecturer.
• Python FUNdamentals (August 19-22, 2013, UC Berkeley)https://github.com/dlab-berkeley/python-fundamentals
Helper for a crash course teaching introductory Python to graduate students.
• Software Carpentry (April 13-14, 2013, UC Berkeley)
http://software-carpentry.org/
Helper for a boot camp teaching scientists computer skills in Python programming, Git version
control, testing, the command line, and data management.
• Python for Computational Cognitive Science (Summer 2012, MIT).
https://github.com/jhamrick/python-course
Co-instructor for a crash course in scientific Python for computational cognitive science.
• Boston Python Workshop (May, July, and December 2011, Boston, MA)
http://openhatch.org/wiki/Boston_Python_Workshop
Helper for workshops teaching Python to women and their friends.

Service and Societies• Jupyter Steering Council (March 2015 – Present), Member
https://github.com/jupyter/governance• Cognitive Science Society (July 2011 – Present), Member
http://www.cognitivesciencesociety.org/• MIT Student Information Processing Board (September 2008 – June 2012)
Volunteer student computing group, http://sipb.mit.edu/

– Associate Member (June 2012 – present)– Full Member (February 2009 – June 2012)– Executive Committee (February 2009 – February 2012)– Chair (February 2010 – February 2011). While I was Chair, SIPB received the Karl Taylor
Compton prize, MIT’s most prestigious award given to students or student groups.

SoftwareNote: for a full list of projects I maintain or have contributed to, please see
https://github.com/jhamrick?tab=repositories.
• nbgrader (primary maintainer)
https://github.com/jupyter/nbgrader
A tool to help instructors create, distribute, collect, and grade assignments in the Jupyter/
IPython notebook.
• Jupyter/IPython (maintainer)
http://jupyter.org/
An architecture for interactive computing.
• nbflow (primary maintainer)
https://github.com/jhamrick/nbflow
A tool that supports one-button reproducible workflows with the Jupyter/IPython Notebook
and Scons.
• Wallace (contributor)
https://github.com/berkeley-cocosci/Wallace
A platform for running iterated learning experiments on Amazon’s Mechanical Turk.
• psiTurk (contributor)
https://github.com/NYUCCL/psiTurk
A platform for running psychological experiments on Amazon’s Mechanical Turk.
• Gaussian Process and Bayesian Quadrature libraries for Python (primary maintainer)
https://github.com/jhamrick/bayesian-quadrature
http://github.com/jhamrick/gaussian_processes
While other Python implementations of Gaussian processes exist, this library is specificallydesigned to expose the underlying math (e.g., log-likelihoods, derivatives of the log-likelihood,etc.).