Years at WID2012 - present
- Ph.D., Computer Science, University of Illinois at Urbana-Champaign.
Thesis: Anti-Unification in Constraint Logics: Foundations and Applications to Learnability in First-Order Logic, to Speed-Up Learning, and to Deduction.
- Postdoctoral Research Officer, Oxford University Computing Laboratory, member of Mathematics Faculty, Sub-Faculty on Computation (4 years).
Machine learning and data mining, especially techniques such as inductive logic programming (ILP) that can utilize background knowledge and return human-comprehensible results. Applications to bioinformatics, chemoinformatics, and health sciences are of particular interest. For more information on David Page’s research interests, see his Computer Science Web Page.
University of Wisconsin–Madison, Department of Computer Sciences
Vilas Distinguished Achievement Professor
- M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page and T. Ristenpart. Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. 23rd USENIX Security Symposium (Best Paper Award), 2014.
- Jie Liu, David Page, Peggy Peissig, Catherine McCarty, Adedayo A. Onitilo, Amy Trentham-Dietz and Elizabeth Burnside. New Genetic Variants Improve Personalized Breast Cancer Diagnosis. AMIA Summit on Translational Bioinformatics (AMIA-TBI) (Marco Ramoni Distinguished Paper Award), 2014.
- J. Liu, C. Zhang, E. Burnside and D. Page. Multiple Testing under Dependence via Semiparametric Graphical Models. The 31st International Conference on Machine Learning (ICML), 2014.
- F. Kuusisto, V. Santos Costa, H. Nassif, E.S. Burnside, D. Page, J.W. Shavlik. Support Vector Machines for Differential Prediction. To appear in Proceedings of the European Conference on Machine Learning, ECML-PKDD, 2014.
- P.L. Peissig, V. Santos Costa, M.D. Caldwell, C. Rottscheit, R.L. Berg, E.A. Mendonca, and D. Page. Relational Machine Learning for Electronic Health Record-Driven Phenotyping. Journal of Biomedical Informatics, 2014.
- Jie Liu, Chunming Zhang, Elizabeth Burnside and David Page. Learning Heterogeneous Hidden Markov Random Fields. The 17th International Conference on Artificial Intelligence and Statistics (AISTATS), 2014.
- Chang TS, Lemanske RF, Mauger D, Fitzpatrick A, Sorkness CA, Szefler SJ, Gangnon RE, Page CD, Jackson DJ. Childhood Asthma Clusters and Response to Therapy in Clinical Trials. Journal of Allergy and Clinical Immunology, 133(2), pp. 363-369, 2014.
- Z. Ye, J. Mayer, L. Ivacic, Z. Zhou, M. He, S.J. Schrodi, D. Page, M.H. Brilliant, and S.J. Hebbring. Phenome-wide association studies (PheWASs) for functional variants. Eur J Hum Genet., 2014.