I work primarily in Minimum Message Length (MML) - a unifying tool in machine learning (computer science, artificial intelligence), statistics, econometrics, inductive inference (philosophy of science) and ``data mining''. MML unifies these areas by combining Bayesianism, (algorithmic) information theory and Kolmogorov complexity. MML was first published in Wallace and Boulton (Computer J, 1968) and is relevant whenever data needs to be analysed.
Some of the many areas in which I have applied MML include statistical inference (and model selection and point estimation), prediction, machine learning, econometrics (including time series and panel data), proofs of financial market inefficiency, knowledge discovery, ``data mining'', theories of (quantifying) intelligence and new forms of (universal) intelligence test (for biological and non-biological agents), philosophy of science, the problem of induction, bioinformatics, linguistics (evolutionary [tree] models), image analysis, etc.
My Comley and Dowe (2003, 2005) are the first two papers on MML Bayesian nets which can deal with both discrete (multi-valued) and continuous-valued variables.
Wallace and Dowe (1999a) is the most cited Wallace paper with a co-author still actively working on MML.
In Chris Wallace (1933-2004)'s posthumous ``Statistical and Inductive Inference by Minimum Message Length'' (2005),
(a) I am given special mention in the preface on page vi,
(b) I am the outright most mentioned living person in the table of contents, where my name appears twice,
(c) I am the living person whose name and work are most mentioned in the index,
(d) I am the outright most cited living author.
I am a native English speaker, at least partly competent in both French and (Castillian) Spanish.
My paid research invitations include at least twice as an invited conference speaker in the northern hemisphere.
My Wallace & Dowe (1999a) and my Hernandez-Orallo & Dowe (2010) have both been the most downloaded articles in their respective journals.