Publications

Most important publications

  1. Pelofske, E., Hahn, G., and Djidjev, H. (2022). Inferring the Dynamics of the State Evolution During Quantum Annealing. IEEE T Parall Distr, 33(2):310-321.
  2. Hahn, G., Wu, C., Lee, S., Lutz, S., Khurana, S., Baden, L., Haneuse, S., Qiao, D., Hecker, J., DeMeo, D., Tanzi, R., Choudhary, M., Etemad, B., Mohammadi, A., Esmaeilzadeh, E., Cho, M., Li, J., Randolph, A., Laird, N., Weiss, S., Silverman, E., Ribbeck, K., and Lange, C. (2021). Genome-wide association analysis of COVID-19 mortality risk in SARS-CoV-2 genomes identifies mutation in the SARS-CoV-2 spike protein that colocalizes with P.1 of the Brazilian strain. Genet Epidemiol, 45(7):685-693.
  3. Barbosa, A., Pelofske, E., Hahn, G., and Djidjev, H. (2021). Using machine learning for quantum annealing accuracy prediction. Algorithms, 14(6), 187.
  4. Hahn, G., Lutz, S., Laha, N., and Lange, C. (2021). A fast and efficient smoothing approach to LASSO regression and an application in statistical genetics: polygenic risk scores for chronic obstructive pulmonary disease (COPD). Stat Comput, 31(35):1-11.
  5. Hahn, G., Lee, S., Weiss, S., and Lange, C. (2021). Unsupervised cluster analysis of SARS-CoV-2 genomes reflects its geographic progression and identifies distinct genetic subgroups of SARS-CoV-2 virus. Genet Epidemiol, 45(3):316-323.
  6. Hahn, G., Lutz, S., Hecker, J., Prokopenko, D., Cho, M., Silverman, E., Weiss, S., and Lange, C. (2021). locStra: Fast analysis of regional/global stratification in whole genome sequencing (WGS) studies. Genet Epidemiol, 45(1):82-98.
  7. Pelofske, E., Hahn, G., and Djidjev, H. (2021). Decomposition Algorithms for Solving NP-hard Problems on a Quantum Annealer. J Sign Process Syst, 93:405-420.
  8. Hahn, G., Fearnhead, P., and Eckley, I. (2020). BayesProject: Fast computation of a projection direction for multivariate changepoint detection. Stat Comput, 30:1691-1705.
  9. Hahn, G. (2020). On the expected runtime of multiple testing algorithms with bounded error. Stat Probabil Lett, 165:108844.
  10. Gandy, A., Hahn, G., and Ding, D. (2019). Implementing Monte Carlo Tests with P-value Buckets. Scand J Stat, 47(3):950-967.
  11. Ding, D., Gandy, A., and Hahn, G. (2019). A simple method for implementing Monte Carlo tests. Computation Stat, 35:1373-1392.
  12. Hahn, G. (2019). Optimal allocation of Monte Carlo simulations to multiple hypothesis tests. Stat Comput, 30:571-586.
  13. Djidjev, H., Hahn, G., Mniszewski, S., Negre, C., and Niklasson, A. (2019). Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics. Algorithms, 12(9), 187. Invited article for the Special Issue on Graph Partitioning: Theory, Engineering, and Applications.
  14. Chapuis, G., Djidjev, H., Hahn, G., and Rizk G. (2019). Finding Maximum Cliques on the D-Wave Quantum Annealer. J Sign Process Syst, 91(3-4):363-377.
  15. Hahn, G. (2018). Closure properties of classes of multiple testing procedures. AStA Adv Stat Anal, 102(2):167-178.
  16. Ghale, P., Kroonblawd, M., Mniszewski, S., Negre, C., Pavel, R., Pino, S., Sardeshmukh, V., Shi, G., and Hahn, G. (2017). Task-based Parallel Computation of the Density Matrix in Quantum-based Molecular Dynamics using Graph Partitioning. SIAM J Sci Comput, 39(6):C466-C480.
  17. Gandy, A. and Hahn, G. (2017). QuickMMCTest: quick multiple Monte Carlo testing. Stat Comput, 27:823-832.
  18. Gandy, A. and Hahn, G. (2016). A framework for Monte Carlo based multiple testing. Scand J Stat, 43(4):1046-1063.
  19. Gandy, A. and Hahn, G. (2014). MMCTest -- A Safe Algorithm for Implementing multiple Monte Carlo tests. Scand J Stat, 41(4):1083-1101.

Other publications

  1. Hahn, G., Cho, M., Weiss, S., Silverman, E., and Lange, C. (2020). Unsupervised cluster analysis of SARS-CoV-2 genomes indicates that recent (June 2020) cases in Beijing are from a genetic subgroup that consists of mostly European and South(east) Asian samples, of which the latter are the most recent. bioRxiv:2020.06.22.165936.
  2. Gandy, A., Noven, R., and Hahn, G. (2018). Does the success of a grant application depend on gender, nationality, or ethnicity? An observational study. SSRN:3272738.
  3. Hahn, G., Banerjee, M., and Sen, B. (2017). Parameter estimation and inference in a two piece broken hyperplane model. Paper preprint.
  4. Djidjev, H., Chapuis, G., Hahn, G., and Rizk, G. (2016). Efficient Combinatorial Optimization Using Quantum Annealing. Los Alamos National Laboratory Report. arXiv:1801.08653.
  5. Hahn, G. (2015). Statistical Methods for Monte-Carlo based Multiple Hypothesis Testing. Doctoral thesis at Imperial College London.
  6. Hahn, G. (2011). Polynomielle Primzahltests mit elliptischen Kurven. Master thesis at the University of Mainz (translation: "Polynomial primality tests with elliptic curves").
  7. Hahn, G. (2010). Block-Sorting Data Compression. Cambridge Part III Essay.
  8. Hahn, G. (2008). Parallelisierte Faktorisierung mit dem Quadratischen Sieb. Bachelor thesis at the University of Mainz (translation: "Parallelised factorisation using the quadratic sieve").

Conference papers

  1. Pelofske, E., Hahn, G., O'Malley, D., Djidjev, H., and Alexandrov, B. (2021). Boolean Hierarchical Tucker Networks on Quantum Annealers. arXiv:2103.07399. Under review.
  2. Pelofske, E., Hahn, G., and Djidjev, H. (2021). Reducing quantum annealing biases for solving the graph partitioning problem. Computing Frontiers Conference CF'21 and arXiv:2103.04963.
  3. Barbosa, A., Pelofske, E., Hahn, G., and Djidjev, H. (2020). Optimizing embedding-related quantum annealing parameters for reducing hardware bias. PAAP 2020: Parallel Architectures, Algorithms and Programming and arXiv:2011.00719.
  4. Pelofske, E., Hahn, G., and Djidjev, H. (2020). Advanced unembedding techniques for quantum annealers. IEEE Intl Conference on Rebooting Computing 2020 and arXiv:2009.05028.
  5. Pelofske, E., Hahn, G., and Djidjev, H. (2020). Advanced anneal paths for improved quantum annealing. IEEE Quantum Week QCE20 and arXiv:2009.05008.
  6. Pelofske, E., Hahn, G., and Djidjev, H. (2019). Peering into the Anneal Process of a Quantum Annealer. The 20th Intl Conference on Parallel and Distributed Computing, Applications and Technologies PDCAT 2019 and arXiv:1908.02691.
  7. Pelofske, E., Hahn, G., and Djidjev, H. (2019). Optimizing the spin reversal transform on the D-Wave 2000Q. Proceedings of the IEEE Intl Conference on Rebooting Computing 2019 and arXiv:1906.10955.
  8. Pelofske, E., Hahn, G., and Djidjev, H. (2019). Solving large Minimum Vertex Cover problems on a quantum annealer. Proceedings of the Computing Frontiers Conference CF'19 and arXiv:1904.00051.
  9. Pelofske, E., Hahn, G., and Djidjev, H. (2019). Solving large Maximum Clique problems on a quantum annealer. Proceedings of the Intl Workshop on Quantum Technology and Optimization Problems QTOP 2019 and arXiv:1901.07657.
  10. Hahn, G. and Djidjev, H. (2017). Reducing Binary Quadratic Forms for More Scalable Quantum Annealing. IEEE Intl Conference on Rebooting Computing 2017 and arXiv:1801.08652.
  11. Chapuis, G., Djidjev, H., Hahn, G., and Rizk, G. (2017). Finding Maximum Cliques on a Quantum Annealer. Proceedings of the Computing Frontiers Conference CF'17 and arXiv:1801.08649v1.
  12. Pino, S., Kroonblawd, M., Ghale, P., Hahn, G., Sardeshmukh, V., Shi, G., Djidjev, H., Negre, C., Pavel, R., Bergen, B., Mniszewski, S., and Junghans, C. (2015). Task-based parallel computation of the density matrix in quantum-based molecular dynamics using graph partitioning. Supercomputing sc15 and poster pdf.
  13. Djidjev, H., Hahn, G., Mniszewski, S., Negre, C., Niklasson, A., and Sardeshmukh, V. (2015). Graph Partitioning Methods for Fast Parallel Quantum Molecular Dynamics. SIAM Workshop on Combinatorial Scientific Computing (CSC16) and arXiv:1605.01118.

Preprints/ Under review

  1. Hahn, G., Prokopenko, D., Lutz, S., Mullin, K., Tanzi, R., and Lange, C. (2021). A smoothed version of the Lassosum penalty for fitting integrated risk models. bioRxiv:2021.03.09.434653. Under review.
  2. Hahn, G. (2020). Online multivariate changepoint detection with type I error control and constant time/memory updates per series. Under review.
  3. Hahn, G., Lutz, S., Laha, N., and Lange, C. (2020). A framework to efficiently smooth L1 penalties for linear regression. bioRxiv:2020.09.17.301788. Under review.
  4. Hahn, G. (2019). Lossless manipulation of QUBO and Ising connectivity structures. Draft in preparation.
  5. Hahn, G. (2019). Solving NP-complete problems with projections. Draft in preparation.

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