Daniel Schmidt

Dr

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https://supervisorconnect.it.monash.edu/

20022021

Research activity per year

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Personal profile

Biography

Daniel Schmidt is a Senior Lecturer in Data Science at the Faculty of Information Technology, Monash University, Melbourne, Australia.

Since completing his PhD in information theoretic inference of linear time series models he has spent 10 years working primarily in the area of Bayesian inference, with specific interest in applications to epidemiological problems, particular the area of statistical genomics.

His specific research interests include:

  • Bayesian inference of high dimensional regression models, particularly linear and generalized linear models. He has, along with Dr. Enes Makalic, written a highly efficient toolbox supporting state-of-the-art Bayesian shrinkage priors for high dimensional regression models (available here);
  • Information theoretic statistics, particularly the application of information theory to statistical inference through the Minimum Message/Description Length principles;
  • Statistical genomics, risk prediction and variant discovery, particularly in the areas of cancer genomics.

He is interested in using mammography and machine learning techniques to improve risk prediction and the stratification of women by their future risk of breast cancer, with the aim of assisting the creation of personalised screening programmes.

 
Recent pre-prints:

  •  Adaptive Bayesian Shrinkage Estimation Using Log-Scale Shrinkage Priors, which describes a new class of shrinkage prior distributions for regression coefficients that can adapt to the sparsity characteristics of the underlying regression coefficients. It also provides simple bounds on behaviour of most existing Bayesian shrinkage priors.
  • A Minimum Message Length Criterion for Robust Linear Regression, which develops a simple, finite sample criterion for model selection in linear models with heavy tailed error distributions using the Minimum Message Length principle. Interestingly, the penalty term can be shown to be related to the signal-to-noise ratio of the fitted model.


External Links:

  • His personal homepage is found at www.dschmidt.org, which contains software and publications/presentations.
  • The BayesReg package for efficient, high dimensional Bayesian penalized regression can be downloaded from here.
  • His google scholar page is here.

 

Monash teaching commitment

Daniel Schmidt has developed, and acted as Chief Examiner and Lecturer, for the following units at the Faculty of Information Technology:

Education/Academic qualification

Computer Science, Doctor of Philosophy, Monash University

Award Date: 16 Oct 2008

Computer Science, Bachelor of Digital Systems (Honours), Monash University

Award Date: 15 Mar 2003

External positions

Senior Research Fellow (Adjunct), University of Melbourne

1 Mar 2018 → …

Research area keywords

  • Bayesian Inference
  • Information Theory
  • Minimum Message Length
  • Minimum Description Length
  • Statistical and Data Analysis
  • Shrinkage Estimation
  • Statistical genomics
  • Cancer genomics
  • Mammography

Network

Recent external collaboration on country level. Dive into details by clicking on the dots or
  • Alcohol consumption is associated with widespread changes in blood DNA methylation: Analysis of cross-sectional and longitudinal data

    Dugué, P-A., Wilson, R., Lehne, B., Jayasekara, H., Wang, X., Jung, C. H., Joo, J. H. E., Makalic, E., Schmidt, D. F., Baglietto, L., Severi, G., Gieger, C., Ladwig, K. H., Peters, A., Kooner, J. S., Southey, M. C., English, D. R., Waldenberger, M., Chambers, J. C., Giles, G. G. & 1 others, Milne, R. L., Jan 2021, In : Addiction Biology. 26, 1, 13 p., e12855.

    Research output: Contribution to journalArticleResearchpeer-review

    3 Citations (Scopus)
  • Bayesian generalized horseshoe estimation of generalized linear models

    Schmidt, D. F. & Makalic, E., 2020, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019 Würzburg, Germany, September 16–20, 2019 Proceedings, Part II. Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M. & Robardet, C. (eds.). Cham Switzerland: Springer, p. 598-613 16 p. (Lecture Notes in Computer Science ; vol. 11907 ).

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

  • Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

    Fachal, L., Aschard, H., Beesley, J., Barnes, D. R., Allen, J., Kar, S., Pooley, K. A., Dennis, J., Michailidou, K., Turman, C., Soucy, P., Lemaçon, A., Lush, M., Tyrer, J. P., Ghoussaini, M., Marjaneh, M. M., Jiang, X., Agata, S., Aittomäki, K., Alonso, M. R. & 234 others, Andrulis, I. L., Anton-Culver, H., Antonenkova, N. N., Arason, A., Arndt, V., Aronson, K. J., Arun, B. K., Auber, B., Auer, P. L., Azzollini, J., Balmaña, J., Barkardottir, R. B., Barrowdale, D., Beeghly-Fadiel, A., Benitez, J., Bermisheva, M., Białkowska, K., Blanco, A. M., Blomqvist, C., Blot, W., Bogdanova, N. V., Bojesen, S. E., Bolla, M. K., Bonanni, B., Borg, A., Bosse, K., Brauch, H., Brenner, H., Briceno, I., Brock, I. W., Brooks-Wilson, A., Brüning, T., Burwinkel, B., Buys, S. S., Cai, Q., Caldés, T., Caligo, M. A., Camp, N. J., Campbell, I., Canzian, F., Carroll, J. S., Carter, B. D., Castelao, J. E., Chiquette, J., Christiansen, H., Chung, W. K., Claes, K. B. M., Clarke, C. L., Mari, V., Berthet, P., Castera, L., Vaur, D., Lallaoui, H., Bignon, Y. J., Uhrhammer, N., Bonadona, V., Lasset, C., Révillion, F., Vennin, P., Muller, D., Gomes, D. M., Ingster, O., Coupier, I., Pujol, P., Collonge-Rame, M. A., Mortemousque, I., Bera, O., Rose, M., Baurand, A., Bertolone, G., Faivre, L., Dreyfus, H., Leroux, D., Venat-Bouvet, L., Bézieau, S., Delnatte, C., Chiesa, J., Gilbert-Dussardier, B., Gesta, P., Prieur, F. P., Bronner, M., Sokolowska, J., Coulet, F., Boutry-Kryza, N., Calender, A., Giraud, S., Leone, M., Fert-Ferrer, S., Stoppa-Lyonnet, D., Jiao, Y., Lesueur, F. L., Mebirouk, N., Barouk-Simonet, E., Bubien, V., Longy, M., Sevenet, N., Gladieff, L., Toulas, C., Reimineras, A., Sobol, H., Paillerets, B. B. D., Cabaret, O., Caron, O., Guillaud-Bataille, M., Rouleau, E., Belotti, M., Buecher, B., Caputo, S., Colas, C., Pauw, A. D., Fourme, E., Gauthier-Villars, M., Golmard, L., Moncoutier, V., Saule, C., Donaldson, A., Murray, A., Brady, A., Brewer, C., Pottinger, C., Miller, C., Gallagher, D., Gregory, H., Cook, J., Eason, J., Adlard, J., Barwell, J., Ong, K. R., Snape, K., Walker, L., Izatt, L., Side, L., Tischkowitz, M., Rogers, M. T., Porteous, M. E., Ahmed, M., Morrison, P. J., Brennan, P., Eeles, R., Davidson, R., Collée, J. M., Cornelissen, S., Couch, F. J., Cox, A., Cross, S. S., Cybulski, C., Czene, K., Daly, M. B., de la Hoya, M., Devilee, P., Diez, O., Ding, Y. C., Dite, G. S., Domchek, S. M., Dörk, T., dos-Santos-Silva, I., Droit, A., Dubois, S., Dumont, M., Duran, M., Durcan, L., Dwek, M., Eccles, D. M., Engel, C., Eriksson, M., Evans, D. G., Fasching, P. A., Fletcher, O., Floris, G., Flyger, H., Foretova, L., Foulkes, W. D., Friedman, E., Fritschi, L., Frost, D., Gabrielson, M., Gago-Dominguez, M., Gambino, G., Ganz, P. A., Gapstur, S. M., Garber, J., García-Sáenz, J. A., Gaudet, M. M., Georgoulias, V., Glendon, G., Godwin, A. K., Goldberg, M. S., Goldgar, D. E., González-Neira, A., Tibiletti, M. G., Greene, M. H., Grip, M., Gronwald, J., Grundy, A., Guénel, P., Hahnen, E., Haiman, C. A., Håkansson, N., Hall, P., Hamann, U., Harrington, P. A., Hartikainen, J. M., Hartman, M., He, W., Healey, C. S., Heemskerk-Gerritsen, B. A. M., Heyworth, J., Hillemanns, P., Hogervorst, F. B. L., Hollestelle, A., Hooning, M. J., Hopper, J. L., Howell, A., Huang, G., Hulick, P. J., Imyanitov, E. N., Sexton, A., Christian, A., Trainer, A., Spigelman, A., Fellows, A., Shelling, A., Fazio, A. D., Blackburn, A., Crook, A., Giles, G., Schmidt, D. F., Southey, M. C., Milne, R. L., ABCTB Investigators, GEMO Study Collaborators, EMBRACE Collaborators, kConFab Investigators & HEBON Investigators, 1 Jan 2020, In : Nature Genetics. 52, 1, p. 56-73 18 p.

    Research output: Contribution to journalArticleResearchpeer-review

    14 Citations (Scopus)
  • InceptionTime: finding AlexNet for time series classification

    Ismail Fawaz, H., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J., Webb, G. I., Idoumghar, L., Muller, P. A. & Petitjean, F., 7 Sep 2020, In : Data Mining and Knowledge Discovery. 34, p. 1936–1962 27 p.

    Research output: Contribution to journalArticleResearchpeer-review

    4 Citations (Scopus)
  • Novel mammogram-based measures improve breast cancer risk prediction beyond an established mammographic density measure

    Nguyen, T. L., Schmidt, D. F., Makalic, E., Maskarinec, G., Li, S., Dite, G. S., Aung, Y. K., Evans, C. F., Trinh, H. N., Baglietto, L., Stone, J., Song, Y. M., Sung, J., MacInnis, R. J., Dugué, P. A., Dowty, J. G., Jenkins, M. A., Milne, R. L., Southey, M. C., Giles, G. G. & 1 others, Hopper, J. L., Dec 2020, (Accepted/In press) In : International Journal of Cancer. 10 p.

    Research output: Contribution to journalArticleResearchpeer-review

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