My expertise is the development and application of machine learning techniques to the quantitative analysis of large, high-dimensional data sets. The results of my academic analyses in the fields of genetics and neuroscience have contributed to a better understanding of a wide range of natural phenomena, including multiple sclerosis, cholesterol levels, body shape, nausea, fear, sleep, visual perception, gene expression, and the interaction of genetics with life experience.
My doctoral dissertation, on statistical and machine learning approaches for uncovering the genetic basis of behaviour, is available via the Oxford University Research Archive.
Key Publications
Krohn, Beyleveld, Bassens (2019). Deep Learning Illustrated. New York: Addison-Wesley
Krohn (2017). Deep Learning with TensorFlow. New York: Addison-Wesley
Agakov, McKeigue, Krohn, Storkey (2010). Sparse Instrumental Variables (SPIV) for genome-wide studies. Advances in Neural Information Processing Systems 23 (Edited by: Lafferty et al.)
Additional Publications
Krohn (2018). Deep Reinforcement Learning and Generative Adversarial Networks. New York: Addison-Wesley
Krohn (2017). Deep Learning for Natural Language Processing. New York: Addison-Wesley
Baud, Mulligan, Casale, et al. (2017). Genetic variation in the social environment contributes to health and disease. PLOS Genetics 13: e1006498 [covered by BBC, Sun, Daily Mail, Live Science, Le Temps, and 30 other media outlets, earning a PLOS Genetics Research Prize in 2018]
Robinson, Kleinman, Graff, et al. (2017). Genetic evidence of assortative mating in humans. Nature Human Behaviour 1: 16
Davies, Brown, Cais, et al. (2017). A point mutation in the ion conduction pore of AMPA receptor GRIA3 causes perturbed sleep patterns as well as intellectual disability. Human Molecular Genetics 26: 3869-82
Krohn, Rives-Corbett, Donner (2016). Predicting Job Application Success with Two-Stage, Bayesian Modeling of Features Extracted from Candidate-Role Pairs. In Proceedings of the Joint Statistical Meetings, Section for Statistical Learning and Data Science: 3667-71
Taylor, Martin, Lise, et al. (2015). Factors influencing success of clinical genome sequencing across a broad spectrum of disorders. Nature Genetics 47: 717-26
Krohn, Speed, Palme, et al. (2014). Genetic interactions with sex make a relatively small contribution to the heritability of complex traits in mice. PLOS ONE 9: e96450
Randall, Winkler, Kutalik, et al. (2013). Sex-stratified genome-wide association studies in 270,000 individuals show evidence for sexual dimorphism in genetic loci for anthropometric traits. PLOS Genetics 9: e1003500
Agakov, Krohn, Colombo, McKeigue (2011). Sparse instrumental variables: an integrative approach to biomarker validation. Journal of Epidemiology and Community Health 65: A10
Chen, Krohn, Bhattacharya, Davies (2011). A comparison of exogenous promoter activity at the ROSA26 locus using a PhiC31 integrase mediated cassette exchange approach in mouse ES cells. PLOS ONE 6: e23376
Agakov, McKeigue, Krohn, Flint (2011). Inference of causal relationships between biomarkers and outcomes in high dimensions. Journal of Systemics, Cybernetics and Informatics 9: 1-8
Orton, Wald, Confavreux, et al. (2011). Association of UV radiation with multiple sclerosis prevalence and sex ratio in France. Neurology 76: 425-31
Limebeer, Krohn, Cross-Mellor, et al. (2008). Exposure to a context previously associated with nausea elicits conditioned gaping in rats: A model of anticipatory nausea. Behavioural Brain Research 187: 33-40
