Jeffrey F. Collamore

Primary fields of research

Probability theory:  large deviations, Harris recurrent Markov chains, iterated random systems and multidimensional recursions.  Applications to insurance, financial risk management, Monte Carlo simulation.

Please see http://www.math.ku.dk/~collamore/ for detailed information.

CV

Professional Biography:

Associate Professor, Department of Mathematical Sciences, University of Copenhagen, 2002-present. 

Post-doctoral research fellow, Department of Mathematics (RiskLab), ETH Zürich, Switzerland, 2000-2002.

Post-doctoral research fellow, Financial Mathematics, EURANDOM, The Netherlands, 1999-2000.

Post-doctoral research fellow, Mathematical Statistics, Lund University, Sweden, 1998-1999.

Visiting Assistant Professor, Department of Statistics, University of Illinois, Urbana-Champaign, 1996-1998.

Long-term (one-month) visiting appointments at the following universities:  University of Wisconsin, Madison (1998); ETH Zürich (2000); University of Helsinki (2005); University of Wroclaw (2012).

Education:

M.A., Ph.D., Mathematics; University of Wisconsin, Madison.

B.S., Physics, Mathematics; University of California, San Diego.

Ph.D. dissertation: Large Deviation Techniques for the Study of the Hitting Probabilities of Rare Sets, 1996.  (Advisor:  Peter Ney.)

Societies:

Insititute of Mathematics Statistics; Danish Actuarial Society.

Precise research interests:

Pure and applied probability.  Large deviations.  Stochastic fixed point equations and iterated random systems.  Multidimensional recursions and random matrices.  Markov chain theory for Harris recurrent chains.  Stochastic simulation techniques for rare event estimation.  Related aspects of convexity theory; networks; branching random walk and branching Markov chains; large deviation methods in statistics and information theory.

Insurance and finance. Risk theory; particularly, multidimensional ruin problems and stochastic economic processes arising in risk theory.  Insurance-based methods for quantitative risk management.  Mathematical aspects of financial time series modeling.  Large deviations methods in finance.