Dr. Kamel Lahouel is interested in formulating and analyzing mathematical/statistical models with applications to biology. These applications mainly focus on machine learning tools using signal originating from cell free DNA, such as fragmentation patterns or mutation frequencies, for cancer early detection and minimal residual disease testing (MRD). In his research, he is also developing mathematical models of evolutionary dynamics of cancer.
On the methodological side, he focuses on two areas of mathematics. The first is stochastic processes where his current work uses branching processes and Markov chains techniques. The second is statistics, including pattern recognition. More precisely, he uses non-parametric statistics for tasks such as stochastic optimization or multiple hypothesis testing. In addition, Dr. Lahouel works on pattern recognition problems involving latent dynamical systems.
DR. Lahouel joined TGen in February 2022. He completed his Ph.D. in Applied Mathematics and Statistics at Johns Hopkins University in May 2018, where he worked on multiple comparisons strategies. He then joined the division of Oncology Biostatistics /Bioinformatics as a research postdoctoral fellow in the Johns Hopkins
University School of Medicine, where he started focusing on cancer early detection and monitoring.
Revisiting the tumorigenesis timeline with a data-driven generative model
Kamel Lahouel, Laurent Younes, Ludmila Danilova, Francis M. Giardiello, Ralph H. Hruban, John Groopman, Kenneth W. Kinzler, Bert Vogelstein, Donald Geman, and Cristian Tomasetti. Proceedings of the National Academy of Sciences 117, no. 2 (2020): 857-864.
Coarse-to-fine multiple testing strategies
Kamel Lahouel, Donald Geman, and Laurent Younes. Electronic Journal of Statistics 13, no. 1 (2019): 1292-1328.
Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention
Lennon, Anne Marie, Adam H. Buchanan, Isaac Kinde, Andrew Warren, Ashley Honushefsky, Ariella T. Cohain, David H. Ledbetter, Kamel Lahouel et al. Science 369, no. 6499 (2020): eabb9601.
Supervised mutational signatures for obesity and other tissue-specific etiological factors in cancerAfsari, Bahman, Albert Kuo, YiFan Zhang, Lu Li, Kamel Lahouel, Ludmila Danilova, Alexander Favorov et al. Elife 10 (2021): e61082.
Non-adaptive policies for 20 questions target localization
Variani, Ehsan, Kamel Lahouel, Avner Bar-Hen, and Bruno Jedynak. In 2015 IEEE International Symposium on Information Theory (ISIT), pp. 775-778. IEEE, 2015.
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