Most important publications
- Hahn, G., Lutz, S., Hecker, J., Prokopenko, D., Cho, M., Silverman, E., Weiss, S., and Lange, C. (2024).
Fast computation of the eigensystem of genomic similarity matrices.
BMC Bioinformatics, 25, 43.
- Hecker, J., Lee, S., Kachroo, P., Prokopenko, D., Maaser-Hecker, A., Lutz, S., Hahn, G., Irizarry, R., Weiss, S., DeMeo, D., and Lange, C. (2023).
A consistent pattern of slide effects in Illumina DNA methylation BeadChip array data.
Epigenetics, 18(1):2257437.
- Hahn, G., Pelofske, E., and Djidjev, H. (2023).
Posiform planting: generating QUBO instances for benchmarking.
Front Comput Sci, 5:1275948.
- Pelofske, E., Hahn, G., and Djidjev, H. (2023).
Initial State Encoding via Reverse Quantum Annealing and H-gain Features.
IEEE Transactions on Quantum Engineering, 4(3102221):1-21.
- Novak, T., Crawford, J., Hahn, G., Hall, M., Thair, S., Newhams, M., Chou, J., Mourani, P., Tarquinio, K., Markovitz, B., Loftis, L., Weiss, S., Higgerson, R., Schwarz, A., Pinto, N., Thomas, N., Gedeit, R., Sanders, R., Mahapatra, S., Coates, B., Cvijanovich, N., Ackerman, K., Tellez, D., McQuillen, P., Kurachek, S., Shein, S., Lange, C., Thomas, P., and Randolph, A.~(2023).
Transcriptomic Profiles of Multiple Organ Dysfunction Syndrome Phenotypes in Pediatric Critical Influenza.
Front Immunol, 14:1220028.
- Hahn, G., Novak, T., Crawford, J., Randolph, A., and Lange, C. (2023).
Longitudinal Analysis of Contrasts in Gene Expression Data.
Genes, 14(6):1134.
- Pelofske, E., Hahn, G., and Djidjev, H. (2023).
Solving larger maximum clique problems using parallel quantum annealing.
Quantum Information Processing, 22(219):1-22.
- Pelofske, E., Hahn, G., and Djidjev, H. (2023).
Noise Dynamics of Quantum Annealers: Estimating the Effective Noise Using Idle Qubits.
Quantum Science and Technology, 8(3):035005.
- Lee, S., Hahn, G., Hecker, J., Lutz, S., Mullin, K., Alzheimer's Disease Neuroimaging Initiative (ADNI), Hide, W., Bertram, L., DeMeo, D., Tanzi, R., Lange, C., and Prokopenko, D. (2023).
A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets.
Briefings in Bioinformatics, 24(1):bbac611.
- Voorhies, K., Bie, R., Hokanson, J., Weiss, S., Wu, A., Hecker, J., Hahn, G., DeMeo, D., Silverman, E., Cho, M., Lange, C., and Lutz, S. (2022).
Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models.
PLoS ONE, 17(5):e0266752.
- Hahn, G., Lee, S., Prokopenko, D., Abraham, J., Novak, T., Hecker, J., Cho, M., Khurana, S., Baden, L., Randolph, A., Weiss, S., and Lange, C. (2022).
Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest.
BMC Bioinformatics, 23, 547.
- Pelofske, E., Hahn, G., O'Malley, D., Djidjev, H., and Alexandrov, B. (2022).
Quantum annealing algorithms for Boolean tensor networks.
Sci Rep, 12, 8539.
- Pelofske, E., Hahn, G., and Djidjev, H. (2022).
Parallel quantum annealing.
Sci Rep, 12, 4499.
- Hahn, G., Prokopenko, D., Lutz, S., Mullin, K., Tanzi, R., Cho, M., Silverman, E., and Lange, C. (2022).
A Smoothed Version of the Lassosum Penalty for Fitting Integrated Risk Models Using Summary Statistics or Individual-Level Data.
Genes, 13(1):112.
- 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.
- Hahn, G. (2022).
Online multivariate changepoint detection with type I error control and constant time/memory updates per series.
Stat Probabil Lett, 181:109258.
- 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.
- Barbosa, A., Pelofske, E., Hahn, G., and Djidjev, H. (2021).
Using machine learning for quantum annealing accuracy prediction.
Algorithms, 14(6), 187.
- Hahn, G., Lutz, S., Laha, N., Cho, M., Silverman, E., 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.
- 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.
- 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.
- 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.
- Hahn, G., Fearnhead, P., and Eckley, I. (2020).
BayesProject: Fast computation of a projection direction for multivariate changepoint detection.
Stat Comput, 30:1691-1705.
- Hahn, G. (2020).
On the expected runtime of multiple testing algorithms with bounded error.
Stat Probabil Lett, 165:108844.
- Gandy, A., Hahn, G., and Ding, D. (2019).
Implementing Monte Carlo Tests with P-value Buckets.
Scand J Stat, 47(3):950-967.
- Ding, D., Gandy, A., and Hahn, G. (2019).
A simple method for implementing Monte Carlo tests.
Computation Stat, 35:1373-1392.
- Hahn, G. (2019).
Optimal allocation of Monte Carlo simulations to multiple hypothesis tests.
Stat Comput, 30:571-586.
- 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.
- 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.
- Hahn, G. (2018).
Closure properties of classes of multiple testing procedures.
AStA Adv Stat Anal, 102(2):167-178.
- 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.
- Gandy, A. and Hahn, G. (2017).
QuickMMCTest: quick multiple Monte Carlo testing.
Stat Comput, 27:823-832.
- Gandy, A. and Hahn, G. (2016).
A framework for Monte Carlo based multiple testing.
Scand J Stat, 43(4):1046-1063.
- Gandy, A. and Hahn, G. (2014).
MMCTest -- A Safe Algorithm for Implementing multiple Monte Carlo tests.
Scand J Stat, 41(4):1083-1101.
- Hahn, G., Lee, S., Prokopenko, D., Novak, T., Hecker, J., Khurana, S., Baden, L., Randolph, A., Weiss, S., and Lange, C. (2022).
Unsupervised genome-wide cluster analysis: nucleotide sequences of the omicron variant of SARS-CoV-2 are similar to sequences from early 2020.
bioRxiv:2021.12.29.474469.
- 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.
- 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.
- Hahn, G., Banerjee, M., and Sen, B. (2017).
Parameter estimation and inference in a two piece broken hyperplane model.
Paper preprint.
- Djidjev, H., Chapuis, G., Hahn, G., and Rizk, G. (2016).
Efficient Combinatorial Optimization Using Quantum Annealing.
Los Alamos National Laboratory Report. arXiv:1801.08653.
- Hahn, G. (2015).
Statistical Methods for Monte-Carlo based Multiple Hypothesis Testing.
Doctoral thesis at Imperial College London.
- Hahn, G. (2011).
Polynomielle Primzahltests mit elliptischen Kurven.
Master thesis at the University of Mainz
(translation: "Polynomial primality tests with elliptic curves").
- Hahn, G. (2010).
Block-Sorting Data Compression.
Cambridge Part III Essay.
- Hahn, G. (2008).
Parallelisierte Faktorisierung mit dem Quadratischen Sieb.
Bachelor thesis at the University of Mainz
(translation: "Parallelised factorisation using the quadratic sieve").
- Henke, K., Pelofske, E., Hahn, G., and Kenyon, G. (2023).
Sampling binary sparse coding QUBO models using a spiking neuromorphic processor.
Proceedings of the 2023 International Conference on Neuromorphic Systems (ICONS'23), 38:1–5.
- Pelofske, E., Hahn, G., O'Malley, D., Djidjev, H., and Alexandrov, B. (2021).
Boolean Hierarchical Tucker Networks on Quantum Annealers.
13th International Conference on Large-Scale Scientific Computing LSSC 2021 and
arXiv:2103.07399.
- Pelofske, E., Hahn, G., and Djidjev, H. (2021).
Reducing quantum annealing biases for solving the graph partitioning problem.
Proceedings of the 18th ACM International Conference on Computing Frontiers CF'21 and
arXiv:2103.04963.
- 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.
- Pelofske, E., Hahn, G., and Djidjev, H. (2020).
Advanced unembedding techniques for quantum annealers.
2020 International Conference on Rebooting Computing (ICRC), Atlanta, GA, USA and
arXiv:2009.05028.
- Pelofske, E., Hahn, G., and Djidjev, H. (2020).
Advanced anneal paths for improved quantum annealing.
IEEE Quantum Week QCE20 and
arXiv:2009.05008.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Hurwitz, R., and Hahn, G. (2023).
Penalized Principal Component Analysis using Nesterov Smoothing.
arXiv:2309.13838. Under review.
- Henke, K., Pelofske, E., Hahn, G., and Kenyon, G. (2023).
Sampling binary sparse coding QUBO models using a spiking neuromorphic processor.
arXiv:2306.01940. Under review.
- Hahn, G., Prokopenko, D., Hecker, J., Lutz, S., Mullin, K., Tanzi, R., and Lange, C. (2023).
Prediction of Alzheimer's free survival using cooperative learning. Under review.
- Hahn, G., Prokopenko, D., Hecker, J., Lutz, S., Mullin, K., Tanzi, R., and Lange, C. (2023).
Polygenic Hazard Score Models for the Prediction of Alzheimer's free survival using the Lasso for Cox's proportional hazards model.
Under review.
- Hahn, G., Lutz, S., Hecker, J., Prokopenko, D., Cho, M., Silverman, E., Weiss, S., and Lange, C. (2022).
Fast computation of principal components of genomic similarity matrices.
bioRxiv:2022.10.06.511168. Under review.
- Hahn, G., Lutz, S., Laha, N., and Lange, C. (2022).
A framework to efficiently smooth L1 penalties for linear regression.
bioRxiv:2020.09.17.301788. Under review.