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Department of Statistical Science
Stochastic Modelling and Time Series
General Theory and Methodology
|Curriculum Vitae (2016/09)|
|Franz Kiraly @|
|Google Scholar||arXiv||The Alan Turing Institute & Data Study Groups||IRIS|
|Core interests||Recent projects||PhD applications||Upcoming events|
|Short CV||Publications||Slides and videos|
As a practical statistician and machine learner, I am interested in creating a data analytics workflow which is empirically solid, quantitative, and useful in the real world, with a focus on predictive modelling.
I am working on what I consider to be two of the most pressing challenges in a practical and data-centric context: namely, how to deal with structured data, such as learning with data samples of series, sequences, matrices, or graphs; and how to quantitatively assess and compare methods against each other, for example whether complicated algorithm X is really better than a random guess.
These are especially relevant in applications where usually the data and the associated scientific questions, and not a single method class is in the focus of interest; current project and collaboration domains include the medical sciences, sports and prevention, geoscience, physics and finance.
Recently, I have been doing research on these applications:
Prediction and Prevention of Falls in a Neurological In-Patient Population. Falling, and associated injuries such as hip fracture, are a major strain on health and health resources, especially in the elderly or hospitalized. We are able to predict, with high accuracy in a neurological population, whether a patient is likely to fall during their stay, using only a number connecting test (the Trail making test).
Quantification and Prediction in Running Sports. Characterizing the training state of running athletes, and making predictions for race planning and training. We can predict Marathon times with an error in the order of a few minutes, and we are able to accurately summarize an athlete by three characteristic numbers.
My current work on data analysis methodology includes:
Non-linear prediction and dimension reduction with series-valued samples. We propose a new learning framework for the situation where the data samples are time series or otherwise sequentially ordered, based on kernels whose features are ordered variants of sample moments.
Single-Entry Matrix Completion and Local Matrix Completion. Our new methods can (i) reliably impute or predict single missing entries in a numerical data table, with error bars, and (ii) do so without necessarily reading in all entries in a big data table. They are the first of their kind under the common low-rank assumption.
Kernel Learning with Invariances. Encoding known invariances of the data, say sign/mirror symmetry, scaling or phase invariance, efficiently with a kernel (work in progress).
I am currently accepting applications for PhD supervision and PhD funding. If you would like to do your PhD with me as your advisor, please contact me initially, preferably by email, so we can discuss the feasibility of working together. Please include a CV, a short description of your research interests and why you would like to pursue your PhD with me. You are also advised to consult the UCL guidelines on formal requirements for obtaining a graduate research degree. Possible (not mutually exclusive) topic areas are:
Statistics and Machine Learning. This is were my core research interests lie. Please have a look at my recent work in methodology and domain applications. The general themes of most interest to me are model selection, model validation and learning tasks in a structured data context.
Engineering and Health Applications at the Alan Turing Institute. I am a faculty fellow at the Alan Turing Institute whose mission is to conduct cutting-edge data science research, and a closely involved with its translational efforts in engineering and health applications.
Other Interdisciplinary Applications. There are a variety of other data-driven applications I am currently working on or I am potentially interested in, and I am generally interested in applied data science meta-methodology.
Additionally, if you have your own innovative idea you would like to do research on (assuming it makes sense and our interests match), I would be happy to help you to pursue that goal even if it is not specifically something I am currently working on.
(times are local time and in 24h format)
Upcoming events will be announced here.
At the University of Ulm, I have obtained my Diplomae (equivalent to MSc or MD, as regards content) in Computer Science, Mathematics, Medicine and Physics in the years 2003, 2005, 2006 and 2011; in 2008, I recieved my PhD in Medicine.
From 2007 to 2010, I have completed my PhD thesis in Mathematics on the topic of Arithmetic Geometry, under supervision of and in cooperation with Prof. Werner Lütkebohmert in Ulm.
From 2010 to 2013, I have worked as a postdoctoral researcher in Prof. Klaus-Robert Müller's Machine Learning Group, at the Technische Universität Berlin, and I have been an associate member of Prof. Günter Ziegler's Discrete Geometry Group, at the Freie Universität Berlin.
Since 2013, I am working as a lecturer (comparable to a tenured assistant professor) at University College London.
In 2015, I have been visiting the Aalto Science Institute as an AScI Visiting Fellow.
Since 2016, I am also a faculty fellow at the newly founded Alan Turing Institute whose vision is to bundle and catalyze the UK's efforts in modern data science, and have been recently co-organizing its Data Study Groups as a member of the DSG coordination team.
[Download Curriculum Vitae] (2016/09)
(the arXiv versions are usually the most up-to-date)
Király FJ, Qian. Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes. Preprint, 53 pages, arXiv 1701.08055. 2017.
Mateen BA, Bussas M, Doogan C, Waller D, Saverino A, Király FJ, Playford ED. Machine Learning in Falls Prediction; A cognition-based predictor of falls for the acute neurological in-patient population. Preprint, 37 pages, arXiv 1607.07751. 2016.
Király FJ, Oberhauser H. Kernels for sequentially ordered data. Preprint, 48 pages, arXiv 1601.08169. 2016.
Király FJ, Ziehe A, Müller K-R. Learning with algebraic invariances, and the invariant kernel trick. Preprint, 17 pages, arXiv 1411.7817. 2014.
Blythe DAJ, Király FJ, Theran L. Algebraic combinatorial methods for low-rank matrix completion with application to athletic performance prediction. Preprint, 13 pages, arXiv 1406.2864. 2014.
Király FJ, Kreuzer M, Theran L. Learning with cross-kernels and Ideal PCA. Preprint, 14 pages, arXiv 1406.2646. 2014.
Király FJ, Theran L. Matroid Regression. Preprint, 16 pages, arXiv 1403.0873. 2014.
Király FJ, Ehler M. The algebraic approach to phase retrieval and explicit inversion at the identifiability threshold. Preprint, 26 pages, arXiv 1402.4053. 2014.
Király FJ, Kreuzer M, Theran L. Dual-to-kernel learning with ideals. Preprint, 15 pages, arXiv 1402.0099. 2014.
Király FJ, Rosen Z, Theran L. Algebraic matroids with graph symmetry. Preprint, 70 pages, arXiv 1312.3777. 2013.
Király FJ. Efficient orthogonal tensor decomposition, with an application to latent variable model learning. Preprint, 14 pages, arXiv 1309.3233. 2013.
Király FJ, Theran L. Coherence and sufficient sampling densities for reconstruction in compressed sensing. Preprint, 18 pages, arXiv 1302.2767. 2013.
Refereed conference publications
Király FJ, Ehler M. Algebraic reconstruction bounds and explicit inversion for phase retrieval at the identifiability threshold. Journal of Machine Learning Research Workshop & Conference Proceedings Vol.24 – Proceedings on the Seventeenth International Conference on Artificial Intelligence and Statistics. 9 pages. 2014.
Király FJ, Theran L. Obtaining error-minimizing estimates and universal entry-wise error bounds for low-rank matrix completion. Neural Information Processing Systems 2013, to appear in Proceedings. Preprint version available as arXiv 1302.5337, 14 pages. 2013.
[arXiv 1302.5337] [code, mloss]
Király FJ, Ziehe A. Approximate rank-detecting factorization of low-rank tensors. IEEE Internatioal Conference of Acoustics, Speech, and Signal Processing 2013, to appear in Proceedings. Preprint version available as arXiv 1211.7369, 5 pages. 2013.
[arXiv 1211.7369] [code, mloss]
Király FJ, Tomioka R. A combinatorial algebraic approach for the identifiability of low-rank matrix completion. International Conference on Machine Learning 2012. Published in ICML Proceedings, made available by ICML as arXiv 1206.4670, 8 pages. 2012.
Király FJ, Von Buenau P, Müller JS, Blythe DAJ, Meinecke FC, Müller K-R. Regression for sets of polynomial equations. Journal of Machine Learning Research Workshop & Conference Proceedings Vol.22 – Proceedings on the Fifteenth International Conference on Artificial Intelligence and Statistics, 22:628-637. 2012.
[arXiv 1110.4531] [code] (ZIP, 17,4 KB)
[JMLR W&CP 2012-22]
Király FJ, Ziehe A, Müller K-R. An algebraic method for approximate rank one factorization of rank deficient matrices. Latent Variable Analysis and Signal Separation 2012 Conference Proceedings, 272-279. 2012.
Refereed journal publications
Ioannidis K, Chamberlain SR, Treder M, Király FJ, Leppink EW, Redden SA, Stein DJ, Lochner C, Grant JE. Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. Accepted in Journal of Psychiatric Research. 2016.
Ehler M, Graef M, Király FJ. Phase retrieval using random cubatures and fusion frames of positive semidefinite matrices. Waves, Wavelets and Fractals – Advanced Analysis. Dec 2015.
Király FJ, Von Buenau P, Blythe DAJ, Meinecke FC, Müller K-R. Algebraic geometric comparison of probability distributions. Journal of Machine Learning Research 13(Mar):855-903. 2012.
[JMLR 2012-13] [code] (ZIP, 3,8 KB)
Preprint published in the Oberwolfach Preprint Series as
Müller JS, von Bünau P, Meinecke FC, Király FJ, Müller K-R. The Stationary Subspace Analysis Toolbox. Journal of Machine Learning Research 12(Oct):3065−3069. 2011.
Kilian H-G, Kazda M, Király FJ, Kaufmann D, Kemkemer R, Bartkowiak D. On the structure-bounded growth processes in plant population. Cell Biochemistry and Biophysics 57:87-100. 2010.
Schlenk RF, Döhner K, Mack S, Stoppel M, Király F, Götze K, Hartmann F, Horst HA, Koller E, Petzer A, Grimminger W, Kobbe G, Glasmacher A, Salwender H, Kirchen H, Haase D, Kremers S, Matzdorff A, Benner A, Döhner H. Prospective evaluation of allogeneic hematopoietic stem-cell transplantation from matched related and matched unrelated donors in younger adults with high-risk Acute Myeloid Leukemia: German-Austrian trial AMLHD98A. Journal of Clinical Oncology 20;28(30):4642-4648. 2010.
Von Bünau P, Meinecke FC, Király FJ, Müller K-R. Finding stationary subspaces in multivariate time series. Physics Review Letters. 103, 214101. 2009.
Király FJ, Kletting P, Reske SN, Glatting G. Modelling radioimmunotherapy (RIT) with anti-CD45 antibody to obtain a more favourable biodistribution. Nuklearmedizin 48:113-119. 2009.
Király FJ. Wild quotient singularities of surfaces and their regular models. Doctoral dissertation, Ulm. 2010.
[e-print VTS Univ. Ulm]
Király FJ. Vergleich verschiedener Postremissionsstrategien bei der akuten myeloischen Leukämie mit normalem Karyotyp. Doctoral dissertation, Ulm. 2008.
[e-print VTS Univ. Ulm]
2012, June 29, 14:00-14:20, ICML 2012
University of Edinburgh, Appleton Tower, Room AT LT 2
A Combinatorial Algebraic Approach for the Identifiability of Matrix Completion
2012, April 23, 19:35-20:00, AISTATS 2012
La Palma, Los Cancajos, H10 Taburiente Playa, Las Nieves/Tenguía room
Regression for sets of polynomial equations
[Video, unfortunately incomplete]