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Paul Rauwolf
E-mail: [email protected]
Summary:
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Graduated first in class with an MSc in Artificial Intelligence, focusing on nontask specific learning within the field of machine learning.
Pursuing doctorate under the David Parkin scholarship (three years, full tuition)
3 years experience as a project lead for software development.
10 years experience as a java developer (Sun certified java programmer).
Member of the American branch of Mensa.
Technical Skills:
Machine Learning
Techniques
Probability Models:
Languages (years)
Web Languages
Databases
Version Control
Backpropagated Neural Networks, K-means clustering, PCA,
Genetic Algorithms, Simulated Annealing, Reinforcement
Learning
Bayesian Networks, Markov Models
Java(10 years), Matlab(2), Octave(1), VB.net(4), C#(1), VB 6.0,
C++, VBA, SAS, ML, Prolog
J2EE, PHP, Javascript, CSS, HTML
Mysql, MS Access
Perforce
Education:
2012-2015: PhD., Computer Science, University of Bath, In Progress
 Research Area: The evolution of Self-Deception.
MSc., Informatics, University of Sussex, 2010.
 Major: Artificial Intelligence focusing on Evolutionary and Adaptive Systems.
 Graduated first in class with distinction.
 Concentrations:
o Machine learning – Neural Networks, Reinforcement Learning, K-Means,
PCA, Simulated Annealing, Genetic Algorithms, Evolutionary
Algorithms.
o Published a paper implementing a neural network to disprove a claim in a
publication within developmental psychology.
o For my dissertation I employed simulated annealing and neural networks
to compare non-task specific learning mechanisms (Abstract below).
o Utilized genetic algorithms to falsify claims by Robert Axelrod, regarding
the evolutionary formation of social norms.
o Studied reinforcement learning for a final project for an advanced
mathematics class.
B.S., Major: Computer Science, Minor: Philosophy, University of Iowa, 2002.
Additional studies:
 Online University of Stanford classes:
o Machine learning (Linear Regression, Backpropagated Neural Networks,
K-Means Clustering, PCA, Support Vector Machines,
Unsupervised Recommender machines)
o Introduction to Artificial Intelligence
o Game Theory
Publications:
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Rauwolf, Paul and Berthouze, Luc (2010). “Transfer or no Transfer: The Key
Role of Learning Specificity” In Proceedings of the 10th International Conference
on Epigenetic Robotics, pp. 97-104.
Ghostwriter/editor in: Witchel, H. (2010). You Are What You Hear : How Music
and Territory Make Us Who We Are. New York: Algora.
Work Experience:
NCS Pearson, Iowa City, IA (02/15/00 – 09/20/09; 11/20/10 - Present)
 Automation Lead: Software Developer (11/20/10 – Present)
o Lead designer and developer for creating applications to augment a onehundred person testing team.
o Required to solve complex testing needs generically, despite a lack of
standardization between the hundreds of projects in need of aid.
 Team Lead: Software Developer (06/01/07 – 09/20/09)
o Managed a group of seven developers in designing and programming a
$250,000 application.
o Collaborated with customers and management as the point of contact for
all deliverables. Presented proposals for applications, including
timeframes, budgets, and workflows.
 Software Developer I-III (02/15/00 – 06/01/07)
o Designed and developed an application which saved the company 20,000
man-hours every year.
o Developed a code-base for semi-automated test case generation.
Independent Researcher and Writer (11/01/09 – 05/20/10)
 Dr. Harry Witchel (see: Publications)
o Wrote rough drafts of chapters.
o Researched.
o Debated logical flow and inclusion of topics.
Awards and Honors:
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David Parkin Scholarship, 2012 (full tuition for 3 years to pursue PhD)
Chancellor’s International Scholarship, 2009
Sun Certified Java Programmer, 2008
Qualified Member of the American Branch of Mensa (http://www.us.mensa.org)
Volunteer Experience:
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Developed the University of Iowa Chess Club website, 1999
Taught elementary school chess, 2008
MSc Dissertation Abstract:
Within the field of developmental robotics, recent advances in the study of intrinsic
motivation algorithms have lent credence to the potential of non-task specific learning
(Oudeyer, Kaplan, & Hafner, 2007) (Singh, Barto, & Chentanez, 2005) (Schmidhuber,
2002). In a relatively brief period, the literature has explored several unique intrinsic
motivation mechanisms (Oudeyer & Kaplan, 2007). However, to the author’s knowledge,
no work has been conducted which systematically compares such algorithms via an indepth study. This work initiated such research by contrasting the advantages and
disadvantages of two unique intrinsically motivated heuristics: (1) which sought novel
experiences and (2) which attempted to accurately predict the consequences of
experiences. However, it was hypothesized that the benefits of intrinsic motivation
algorithms could not be analyzed in isolation. A case was set forward to validate the
postulation that the values of intrinsic motivation heuristics are contingent on at least
three factors: (1) the environment / the agent’s sensory coupling to the environment, (2)
the agent’s learning mechanism, and (3) an experimenter’s criteria for success. It was
shown that altering any these contingencies could affect the comparative advantages of
employing either intrinsic motivation heuristic. Hence, this work advocates that future
advances in intrinsic motivation would be aided by a thorough examination into the
factors on which intrinsically motivated agents depend. This would permit experimenters
to adopt an intrinsic motivation mechanism with the knowledge of its dependencies,
strengths, and weaknesses, thus encouraging more focused research into the potential of
intrinsic motivation heuristics.
Bibliography
Oudeyer, P.-Y., & Kaplan, F. (2007). What is Intrinsic Motivation? A Typology of
Computational Approaches. Front. Neurorobot , 1-14.
Oudeyer, P.-Y., Kaplan, F., & Hafner, V. (2007). Intrinsic Motivation Systems for
Autonomous Mental Development. IEEE Transactions on Evolutionary Computation , 122.
Schmidhuber, J. (2002). Exploring the Predictable. In S. Ghosh, & S. Tsutsui, Advances
in Evolutionary Computing (pp. 579-612).
Singh, S., Barto, A. G., & Chentanez, N. (2005). Intrinsically Motivated Reinforcement
Learning. Advances in Neural Information Processing 18., (pp. 1-8).