Publications

Most important publications

  1. Lee, S., Hecker, J., Vardarajan, B., Kelly, R., Prince, N., Mullin, K., Lutz, S., Hahn, G., Lasky-Su, J., Mayeux, R., Tanzi, R., Lange, C., Prokopenko D. (2025). Uncovering Ethnicity-Specific Recessive Loci for Alzheimer's Disease in 89 Dominican Families Using Family-Based WGS Analysis. Genet Epidemiol, 49(5):e70014.
  2. Jenei, K., Hahn, G., Kesselheim, A., Tibau, A. (2025). Trends in time to withdrawal and full approval of accelerated approval cancer drug indications (1992–2024). J Cancer Policy, 45:100597.
  3. Henke, K., Pelofske, E., Kenyon, G., Hahn, G. (2025). Comparing quantum annealing and spiking neuromorphic computing for sampling binary sparse coding QUBO problems. Nature npj Unconv Comput, 2:13.
  4. Hurwitz, R., Hahn, G. (2025). Penalized Principal Component Analysis Using Smoothing. Stat Comput, 35(80):1-15.
  5. Wang, S., Egilman, A., Hahn, G., Kesselheim, A. (2025). Therapeutic Benefit of Top-Selling Oncology Drugs in Medicare. JAMA Netw Open, 8(4):e253323.
  6. Hahn, G., Prokopenko, D., Hecker, J., Lutz, S., Mullin, K., Tanzi, R., Lange, C. (2025). Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model. Genet Epidemiol, 49(1):e22581.
  7. Ngai, T., Willett, J., Waqas, M., Fishbein, L., Choi, Y., Hahn, G., Mullin, K., Lange, C., Hecker, J., Tanzi, R., Prokopenko, D. (2024). Assessing polyomic risk to predict Alzheimer's disease using a machine learning model. Alzheimer's & Dementia, 20(12):8700-8714.
  8. Nopsopon, T., Brown, A., Hahn, G., Rank, M., Huybrechts, K., Akenroye, A. (2024). Temporal variation in the effectiveness of biologics in asthma: Effect modification by changing patient characteristics. Respir Med, 234:107802.
  9. Wang, S., Hahn, G., Kesselheim, A. (2024). Impact of Priority Review Voucher Eligibility on Research and Development of Medical Countermeasures. Clin Pharmacol Ther, 116(6):1554-1559.
  10. Hahn, G., Prokopenko, D., Hecker, J., Lutz, S., Mullin, K., Sejour, L., Hide, W., Vlachos, I., DeSantis, S., Tanzi, R., Lange, C. (2024). Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data. Briefings in Bioinformatics, 25(4):bbae267.
  11. Voorhies, K., Young, K., Hsu, F., Palmer, N., McDonald, M., Lee, S., Hahn, G., Hecker, J., Prokopenko, D., Wu, A., Regan, E., DeMeo, D., Kinney, G., Crapo, J., Cho, M., Silverman, E., Lange, C., Budoff, M., Hokanson, J., Lutz, S. (2024). Association of PHACTR1 with Coronary Artery Calcium Differs by Sex and Cigarette Smoking. J Cardiovasc Dev Dis, 11(7), 194.
  12. Voorhies, K., Hecker, J., Lee, S., Hahn, G., Prokopenko, D., McDonald, M., Wu, A., Wu, A., Hokanson, J., Cho, M., Lange, C., Hoth, K., Lutz, S. (2024). Examining the Effect of Genes on Depression as Mediated by Smoking and Modified by Sex. Genes, 15(5):565.
  13. Lee, S., Hecker, J., Hahn, G., Mullin, K., Alzheimer's Disease Neuroimaging Initiative (ADNI), Lutz, S., Tanzi, R., Lange, C., Prokopenko, D. (2024). On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries. Alzheimer's & Dementia, 20(5):3397-3405.
  14. Hahn, G., Lutz, S., Hecker, J., Prokopenko, D., Cho, M., Silverman, E., Weiss, S., Lange, C. (2024). Fast computation of the eigensystem of genomic similarity matrices. BMC Bioinformatics, 25, 43.
  15. Hecker, J., Lee, S., Kachroo, P., Prokopenko, D., Maaser-Hecker, A., Lutz, S., Hahn, G., Irizarry, R., Weiss, S., DeMeo, D., Lange, C. (2023). A consistent pattern of slide effects in Illumina DNA methylation BeadChip array data. Epigenetics, 18(1):2257437.
  16. Hahn, G., Pelofske, E., Djidjev, H. (2023). Posiform planting: generating QUBO instances for benchmarking. Front Comput Sci, 5:1275948.
  17. Pelofske, E., Hahn, G., Djidjev, H. (2023). Initial State Encoding via Reverse Quantum Annealing and H-gain Features. IEEE Transactions on Quantum Engineering, 4(3102221):1-21.
  18. 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., Randolph, A. (2023). Transcriptomic Profiles of Multiple Organ Dysfunction Syndrome Phenotypes in Pediatric Critical Influenza. Front Immunol, 14:1220028.
  19. Hahn, G., Novak, T., Crawford, J., Randolph, A., Lange, C. (2023). Longitudinal Analysis of Contrasts in Gene Expression Data. Genes, 14(6):1134.
  20. Pelofske, E., Hahn, G., Djidjev, H. (2023). Solving larger maximum clique problems using parallel quantum annealing. Quantum Information Processing, 22(219):1-22.
  21. Pelofske, E., Hahn, G., Djidjev, H. (2023). Noise Dynamics of Quantum Annealers: Estimating the Effective Noise Using Idle Qubits. Quantum Science and Technology, 8(3):035005.
  22. 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., 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.
  23. Voorhies, K., Bie, R., Hokanson, J., Weiss, S., Wu, A., Hecker, J., Hahn, G., DeMeo, D., Silverman, E., Cho, M., Lange, C., Lutz, S. (2022). Covariate adjustment of spirometric and smoking phenotypes: The potential of neural network models. PLoS ONE, 17(5):e0266752.
  24. Hahn, G., Lee, S., Prokopenko, D., Abraham, J., Novak, T., Hecker, J., Cho, M., Khurana, S., Baden, L., Randolph, A., Weiss, S., 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.
  25. Pelofske, E., Hahn, G., O'Malley, D., Djidjev, H., Alexandrov, B. (2022). Quantum annealing algorithms for Boolean tensor networks. Sci Rep, 12, 8539.
  26. Pelofske, E., Hahn, G., Djidjev, H. (2022). Parallel quantum annealing. Sci Rep, 12, 4499.
  27. Hahn, G., Prokopenko, D., Lutz, S., Mullin, K., Tanzi, R., Cho, M., Silverman, E., 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.
  28. Pelofske, E., Hahn, G., Djidjev, H. (2022). Inferring the Dynamics of the State Evolution During Quantum Annealing. IEEE T Parall Distr, 33(2):310-321.
  29. Hahn, G. (2022). Online multivariate changepoint detection with type I error control and constant time/memory updates per series. Stat Probabil Lett, 181:109258.
  30. 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., 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.
  31. Barbosa, A., Pelofske, E., Hahn, G., Djidjev, H. (2021). Using machine learning for quantum annealing accuracy prediction. Algorithms, 14(6), 187.
  32. Hahn, G., Lutz, S., Laha, N., Cho, M., Silverman, E., 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.
  33. Hahn, G., Lee, S., Weiss, S., 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.
  34. Hahn, G., Lutz, S., Hecker, J., Prokopenko, D., Cho, M., Silverman, E., Weiss, S., Lange, C. (2021). locStra: Fast analysis of regional/global stratification in whole genome sequencing (WGS) studies. Genet Epidemiol, 45(1):82-98.
  35. Pelofske, E., Hahn, G., Djidjev, H. (2021). Decomposition Algorithms for Solving NP-hard Problems on a Quantum Annealer. J Sign Process Syst, 93:405-420.
  36. Hahn, G., Fearnhead, P., Eckley, I. (2020). BayesProject: Fast computation of a projection direction for multivariate changepoint detection. Stat Comput, 30:1691-1705.
  37. Hahn, G. (2020). On the expected runtime of multiple testing algorithms with bounded error. Stat Probabil Lett, 165:108844.
  38. Gandy, A., Hahn, G., Ding, D. (2019). Implementing Monte Carlo Tests with P-value Buckets. Scand J Stat, 47(3):950-967.
  39. Ding, D., Gandy, A., Hahn, G. (2019). A simple method for implementing Monte Carlo tests. Computation Stat, 35:1373-1392.
  40. Hahn, G. (2019). Optimal allocation of Monte Carlo simulations to multiple hypothesis tests. Stat Comput, 30:571-586.
  41. Djidjev, H., Hahn, G., Mniszewski, S., Negre, C., 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.
  42. Chapuis, G., Djidjev, H., Hahn, G., Rizk, G. (2019). Finding Maximum Cliques on the D-Wave Quantum Annealer. J Sign Process Syst, 91(3-4):363-377.
  43. Hahn, G. (2018). Closure properties of classes of multiple testing procedures. AStA Adv Stat Anal, 102(2):167-178.
  44. Ghale, P., Kroonblawd, M., Mniszewski, S., Negre, C., Pavel, R., Pino, S., Sardeshmukh, V., Shi, G., 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.
  45. Gandy, A., Hahn, G. (2017). QuickMMCTest: quick multiple Monte Carlo testing. Stat Comput, 27:823-832.
  46. Gandy, A., Hahn, G. (2016). A framework for Monte Carlo based multiple testing. Scand J Stat, 43(4):1046-1063.
  47. Gandy, A., 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., Lee, S., Prokopenko, D., Novak, T., Hecker, J., Khurana, S., Baden, L., Randolph, A., Weiss, S., 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.
  2. Hahn, G., Cho, M., Weiss, S., Silverman, E., 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.
  3. Hahn, G., Banerjee, M., Sen, B. (2017). Parameter Estimation and Inference in a Continuous Piecewise Linear Regression Model. arXiv:2503.06303.
  4. Djidjev, H., Chapuis, G., Hahn, G., 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. Henke, K., Pelofske, E., Hahn, G., 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.
  2. Pelofske, E., Hahn, G., O'Malley, D., Djidjev, H., Alexandrov, B. (2021). Boolean Hierarchical Tucker Networks on Quantum Annealers. 13th International Conference on Large-Scale Scientific Computing LSSC 2021 and arXiv:2103.07399.
  3. Pelofske, E., Hahn, G., 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.
  4. Barbosa, A., Pelofske, E., Hahn, G., Djidjev, H. (2020). Optimizing embedding-related quantum annealing parameters for reducing hardware bias. PAAP 2020: Parallel Architectures, Algorithms and Programming and arXiv:2011.00719.
  5. Pelofske, E., Hahn, G., Djidjev, H. (2020). Advanced unembedding techniques for quantum annealers. 2020 International Conference on Rebooting Computing (ICRC), Atlanta, GA, USA and arXiv:2009.05028.
  6. Pelofske, E., Hahn, G., Djidjev, H. (2020). Advanced anneal paths for improved quantum annealing. IEEE Quantum Week QCE20 and arXiv:2009.05008.
  7. Pelofske, E., Hahn, G., 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.
  8. Pelofske, E., Hahn, G., 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.
  9. Pelofske, E., Hahn, G., 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.
  10. Pelofske, E., Hahn, G., 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.
  11. Hahn, G., Djidjev, H. (2017). Reducing Binary Quadratic Forms for More Scalable Quantum Annealing. IEEE Intl Conference on Rebooting Computing 2017 and arXiv:1801.08652.
  12. Chapuis, G., Djidjev, H., Hahn, G., Rizk, G. (2017). Finding Maximum Cliques on a Quantum Annealer. Proceedings of the Computing Frontiers Conference CF'17 and arXiv:1801.08649v1.
  13. Djidjev, H., Hahn, G., Mniszewski, S., Negre, C., Niklasson, A., 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. Dechantsreiter, D., Kelly, R., Lange, C., Lasky-Su, J., Hahn, G. (2025). Quantification of individual dataset contributions to prediction accuracy in cooperative learning. bioRxiv:2025.04.16.649215. Under review.
  2. Hahn, G., Schneeweiss, S., Wang, S. (2025). Adaptive multi-wave sampling for efficient chart validation. arXiv:2503.06308. Under review.
  3. Qian, J., Hahn, G. (2024). Scalable computation of the maximum flow in large brain connectivity networks. arXiv:2412.00106. Under review.
  4. Pelofske, E., Hahn, G., Djidjev, H. (2024). Increasing the Hardness of Posiform Planting Using Random QUBOs for Programmable Quantum Annealer Benchmarking. arXiv:2411.03626. Under review.
  5. Qian, J., Hahn, G. (2024). An efficient heuristic for approximate maximum flow computations. arXiv:2409.08350. Under review.
  6. Hahn, G., Lutz, S., Laha, N., Lange, C. (2022). A framework to efficiently smooth L1 penalties for linear regression. bioRxiv:2020.09.17.301788. Under review.

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