| Opportunities for doctoral students to work with Glenn Shafer
I enthusiastically welcome doctoral students to participate in my
research. I work with doctoral
students in two institutions: (1) the
In the Rutgers Business School, I work with full-time doctoral students in several different majors, including accounting, accounting information systems, finance, information technology, and management science. I also serve as faculty director of the school’s doctoral program, and in this role I would like to encourage potential applicants to consult the program’s extensive website, including the frequently asked questions and the online application.
In the Department of Computer Science at Royal Holloway, I am affiliated with the Computer Learning Research Centre, where doctoral students work on the theory of machine learning and its applications to a wide variety of fields. Potential students may apply online for full-time or part-time study.
Some topics for research with doctoral students
For more than thirty-five years, I have worked on the foundations of probability, statistics, artificial intelligence, machine learning, accounting, and finance. I continue to be fascinated by new methods for prediction and the weighing of evidence, their connections with older ideas about probability, and their implication for the philosophy of probability. I am particularly interested in applications of game-theoretic probability, defensive forecasting, conformal prediction, and the Dempster-Shafer theory of evidence.
I enjoy working with students in accounting, finance, and marketing who can formulate their own substantive questions, to which machine-learning methods can be applied. I am also interested in working with students who want to explore the implications of the game-theoretic formulation of the efficient market hypothesis.
I would also like to work with students with strong enough computational or mathematical skills that they can help
· extend existing systems for defensive forecasting, conformal prediction, and Venn prediction,
· develop the predictive method of causal analysis to compete with the more established counter-factual method, or
· develop systems that guide Dempster-Shafer analyses of evidence using Cournotian judgements.
These topics would be appropriate for students in information technology or management science at Rutgers or in machine learning at Royal Holloway.
Recent and current work with doctoral students
Tanya Levin completed a doctoral dissertation in information technology at the
Elaine Henry completed
a doctoral dissertation in accounting at the
In 2004, as a doctoral student in finance at the
Wei Wu, a
doctoral student in finance at the
another doctoral student in finance at the
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This page last revised December 2, 2007
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