Publications

Most important publications

  1. Hahn, G., Lutz, S., Laha, N., Hecker, J., Prokopenko, D., Lee, S., Lee, W., Cho, M., Silverman, E., Lange, C. (2026). A framework to efficiently smooth L1 penalties for linear regression. Comput Stat, 41(48).
  2. Jo, S., Ye, S., Hahn, G., Lee, W. (2026). Accelerated Bayesian Kernel Machine Regression: A Gaussian Variational Approximation with the Horseshoe Prior. Stat Comput, 36(79).
  3. Ray, A., Sreedhara, S., Paik, J., Cromer, S., Bykov, K., Hahn, G., Glynn, R., Wexler, D., Patorno, E. (2026). Comparative Effectiveness of Current Glucose-lowering Medications in Type 2 Diabetes Mellitus: Emulation of a Modified GRADE Trial. J Gen Intern Med.
  4. Wang, S., Hahn, G., Sreedhara, S., Mufaddal, M., Pillai, H., Aldis, R., Lii, J., Dutcher, S., Eniafe, R., Jones, J., Kim, K., He, J., Lee, H., Toh, S., Desai, R., Yang, J. (2026). An expedited chart review process for large database studies using natural language processing and multi-wave adaptive sampling. Epidemiology, 37(4), 504-514.
  5. Friedrich, S., Huybrechts, K., Straub, L., Hernandez-Diaz, S., Zhu, Y., Hahn, G., Mogun, H., Jones, H., Connery, H., Davis, J., Gray, K., Lester, B., Terplan, M., Bateman, B. (2026). Prenatal exposure to buprenorphine or methadone and adverse neurodevelopmental outcomes: population based cohort study. BMJ, 393(e087321).
  6. Hahn, G., Schneeweiss, S., Wang, S. (2026). Adaptive Multi-Wave Sampling for Efficient Chart Validation. Clin Epidemiol, 18(573511).
  7. Wyss, R., Hansen, B., Hahn, G., van der Laan, L., Lin, K. (2026). Tuning LASSO Models for Propensity Score Weighting and Using Synthetic Negative Control Exposures for Residual Bias Detection. Stat Med, 45(8-9), e70503.
  8. Weberpals, J., Schneeweiss, S., Kehl, K., Rivera, D., Mishra-Kalyani, P., Lerro, C., Larkins, E., Narayan, P., Curley, R., Hahn, G., Anand, P., Natanzon, Y., Belli, A., Wang, C., Collins, J., Kish, J., Espirito, J., Robert, N., Glynn, R., Wang, S. (2026). Emulating Comparative Oncology Trials With Real-World Evidence Studies (ENCORE): Process Development and Methodological Considerations for Oncology Real-World Data. Clin Pharmacol Ther, 119(4), 881-890.
  9. Bea, S., Patorno, E., Hahn, G., Paik, J., Wexler, D., Bykov, K. (2026). Medications associated with increased risk of hypoglycemia in older adults on sulfonylureas: a high-throughput case-crossover-based screening study. Drug Saf, 49, 471-480.
  10. Krüger, N., Schneeweiss, S., Desai, R., Sreedhara, S., Kehoe, A., Fuse, K., Hahn, G., Schunkert, H., Wang, S. (2026). Cardiovascular outcomes of semaglutide and tirzepatide for patients with type 2 diabetes in clinical practice. Nat Med, 32, 342-352.
  11. Htoo, P., Paik, J., Everett, B., Glynn, R., Bykov, K., Hahn, G., Liu, J., Wexler, D., Patorno, E. (2025). Machine learning for predicting cardiovascular events in older adults with type 2 diabetes using Medicare claims and electronic health records. J Clin Epidemiol, 188(112001).
  12. Nopsopon, T., Cabrera-Perez, J., Lee, P., Brodeur, K., Lugogo, N., Hsu, E., LeSon, C., Hahn, G., Carr, S., Weiss, S., Akenroye, A. (2025). Impact of clinical factors and season on inflammatory cytokines in biologic-treated and untreated asthma. Respir Res, 26(1), 291.
  13. 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).
  14. Krüger, N., Schneeweiss, S., Fuse, K., Matseyko, S., Sreedhara, S., Hahn, G., Schunkert, H., Wang, S. (2025). Semaglutide and Tirzepatide in Patients With Heart Failure With Preserved Ejection Fraction. JAMA, 334(14), 1255-1266.
  15. Pelofske, E., Hahn, G., Djidjev, H. (2025). Increasing the hardness of posiform planting using random QUBOs for programmable quantum annealer benchmarking. npj Unconv Comput, 2(17).
  16. Gauron, M., Prokopenko, D., Lee, S., Wolfe, S., Hecker, J., Willett, J., Waqas, M., Lordén, G., Yang, Y., Mayfield, J., Castanho, I., Mullin, K., Morgan, S., Hahn, G., DeMeo, D., Hide, W., Bertram, L., Lange, C., Newton, A., Tanzi, R. (2025). A PKCη missense mutation enhances Golgi-localized signaling and is associated with recessively inherited familial Alzheimer's disease. Science Signaling, 18(893), eadv0970.
  17. 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.
  18. 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).
  19. Lee, S., Kelly, R., Chen, Y., Waqas, M., Mendez, K., Hecker, J., Hahn, G., Lutz, S., Celedón, J., Clish, C., Litonjua, A., Chen, Q., McGeachie, M., Choi, Y., Weiss, S., Tanzi, R., Lange, C., Prokopenko, D., Lasky-Su, J., NHLBI Trans-Omics for Precision Medicine (TOPMed) (2025). Associations of APOE variants with sphingomyelin and cholesterol metabolites across the life-course in diverse populations. Metabolomics, 21(64).
  20. Hurwitz, R., Hahn, G. (2025). Penalized Principal Component Analysis Using Smoothing. Stat Comput, 35(80).
  21. Wang, S., Egilman, A., Hahn, G., Kesselheim, A. (2025). Therapeutic Benefit of Top-Selling Oncology Drugs in Medicare. JAMA Netw Open, 8(4), e253323.
  22. 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.
  23. 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 Dement, 20(12), 8700-8714.
  24. 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.
  25. 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).
  26. 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.
  27. 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.
  28. 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 Dement, 20(5), 3397-3405.
  29. 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.
  30. 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).
  31. Hahn, G., Pelofske, E., Djidjev, H. (2023). Posiform planting: generating QUBO instances for benchmarking. Front Comput Sci, 5(1275948).
  32. Pelofske, E., Hahn, G., Djidjev, H. (2023). Initial State Encoding via Reverse Quantum Annealing and H-gain Features. IEEE Trans Quantum Eng, 4(3102221), 1-21.
  33. 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.
  34. 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.
  35. Hahn, G., Novak, T., Crawford, J., Randolph, A., Lange, C. (2023). Longitudinal Analysis of Contrasts in Gene Expression Data. Genes, 14(6), 1134.
  36. Pelofske, E., Hahn, G., Djidjev, H. (2023). Solving larger maximum clique problems using parallel quantum annealing. Quantum Inf Process, 22(219).
  37. Pelofske, E., Hahn, G., Djidjev, H. (2023). Noise dynamics of quantum annealers: estimating the effective noise using idle qubits. Quantum Sci Technol, 8(3), 035005.
  38. 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.
  39. 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).
  40. 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.
  41. Pelofske, E., Hahn, G., O'Malley, D., Djidjev, H., Alexandrov, B. (2022). Quantum annealing algorithms for Boolean tensor networks. Sci Rep, 12(8539).
  42. Pelofske, E., Hahn, G., Djidjev, H. (2022). Parallel quantum annealing. Sci Rep, 12(4499).
  43. Pelofske, E., Hahn, G., Djidjev, H. (2022). Inferring the Dynamics of the State Evolution During Quantum Annealing. IEEE Trans Parallel Distrib Syst, 33(2), 310-321.
  44. Hahn, G. (2022). Online multivariate changepoint detection with type I error control and constant time/memory updates per series. Stat Probabil Lett, 181(109258).
  45. 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.
  46. 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.
  47. Barbosa, A., Pelofske, E., Hahn, G., Djidjev, H. (2021). Using machine learning for quantum annealing accuracy prediction. Algorithms, 14(6), 187.
  48. 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).
  49. 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.
  50. 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.
  51. 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.
  52. Hahn, G., Fearnhead, P., Eckley, I. (2020). BayesProject: Fast computation of a projection direction for multivariate changepoint detection. Stat Comput, 30, 1691-1705.
  53. Ding, D., Gandy, A., Hahn, G. (2020). A simple method for implementing Monte Carlo tests. Computation Stat, 35, 1373-1392.
  54. Gandy, A., Hahn, G., Ding, D. (2020). Implementing Monte Carlo Tests with P-value Buckets. Scand J Stat, 47(3), 950-967.
  55. Hahn, G. (2020). On the expected runtime of multiple testing algorithms with bounded error. Stat Probabil Lett, 165(108844).
  56. Hahn, G. (2020). Optimal allocation of Monte Carlo simulations to multiple hypothesis tests. Stat Comput, 30, 571-586.
  57. Djidjev, H., Hahn, G., Mniszewski, S., Negre, C., Niklasson, A. (2019). Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics (Invited article for the Special Issue on Graph Partitioning: Theory, Engineering, and Applications). Algorithms, 12(9), 187.
  58. Chapuis, G., Djidjev, H., Hahn, G., Rizk, G. (2019). Finding Maximum Cliques on the D-Wave Quantum Annealer. J Sign Process Syst, 91, 363-377.
  59. Hahn, G. (2018). Closure properties of classes of multiple testing procedures. AStA Adv Stat Anal, 102, 167-178.
  60. Gandy, A., Hahn, G. (2017). QuickMMCTest: quick multiple Monte Carlo testing. Stat Comput, 27, 823-832.
  61. 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.
  62. Gandy, A., Hahn, G. (2016). A framework for Monte Carlo based multiple testing. Scand J Stat, 43(4), 1046-1063.
  63. 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., Banerjee, M., Sen, B. (2025). Parameter Estimation and Inference in a Continuous Piecewise Linear Regression Model. arXiv:2503.06303.
  2. 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.
  3. 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.
  4. Djidjev, H., Chapuis, G., Hahn, G., Rizk, G. (2018). Efficient Combinatorial Optimization Using Quantum Annealing. 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 (Polynomial primality tests with elliptic curves). Master thesis at the University of Mainz.
  7. Hahn, G. (2010). Block-Sorting Data Compression. Cambridge Part III Essay.
  8. Hahn, G. (2008). Parallelisierte Faktorisierung mit dem Quadratischen Sieb (Parallelised factorisation using the quadratic sieve). Bachelor thesis at the University of Mainz.

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, arXiv:2306.01940, 38, 1-5.
  2. Pelofske, E., Hahn, G., O'Malley, D., Djidjev, H., Alexandrov, B. (2022). Boolean Hierarchical Tucker Networks on Quantum Annealers. Large-Scale Scientific Computing. LSSC 2021. Lecture Notes in Computer Science, arXiv:2103.07399, 13127, 351-358.
  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, arXiv:2103.04963, 133-139.
  4. Barbosa, A., Pelofske, E., Hahn, G., Djidjev, H. (2021). Optimizing Embedding-Related Quantum Annealing Parameters for Reducing Hardware Bias. Parallel Architectures, Algorithms and Programming. PAAP 2020. Communications in Computer and Information Science, arXiv:2011.00719, 1362, 162-173.
  5. Pelofske, E., Hahn, G., Djidjev, H. (2020). Advanced unembedding techniques for quantum annealers. 2020 International Conference on Rebooting Computing (ICRC), Atlanta, GA, USA, arXiv:2009.05028, 34-41.
  6. Pelofske, E., Hahn, G., Djidjev, H. (2020). Advanced anneal paths for improved quantum annealing. 2020 IEEE International Conference on Quantum Computing and Engineering (QCE), arXiv:2009.05008, 256-266.
  7. Pelofske, E., Hahn, G., Djidjev, H. (2019). Peering into the Anneal Process of a Quantum Annealer. 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Gold Coast, QLD, Australia, arXiv:1908.02691, 184-189.
  8. Pelofske, E., Hahn, G., Djidjev, H. (2019). Optimizing the spin reversal transform on the D-Wave 2000Q. 2019 IEEE International Conference on Rebooting Computing (ICRC), San Mateo, CA, USA, arXiv:1906.10955, 1-8.
  9. Pelofske, E., Hahn, G., Djidjev, H. (2019). Solving large Minimum Vertex Cover problems on a quantum annealer. Proceedings of the 16th ACM International Conference on Computing Frontiers, arXiv:1904.00051, 76-84.
  10. Pelofske, E., Hahn, G., Djidjev, H. (2019). Solving Large Maximum Clique Problems on a Quantum Annealer. Quantum Technology and Optimization Problems. QTOP 2019. Lecture Notes in Computer Science, arXiv:1901.07657, 11413, 123–135.
  11. Hahn, G., Djidjev, H. (2017). Reducing Binary Quadratic Forms for More Scalable Quantum Annealing. 2017 IEEE International Conference on Rebooting Computing (ICRC), Washington, DC, USA, arXiv:1801.08652, 1-8.
  12. Chapuis, G., Djidjev, H., Hahn, G., Rizk, G. (2017). Finding Maximum Cliques on a Quantum Annealer. Proceedings of the Computing Frontiers Conference, arXiv:1801.08649, 63-70.
  13. Djidjev, H., Hahn, G., Mniszewski, S., Negre, C., Niklasson, A., Sardeshmukh, V. (2016). Graph Partitioning Methods for Fast Parallel Quantum Molecular Dynamics. 2016 Proceedings of the SIAM Workshop on Combinatorial Scientific Computing (CSC), arXiv:1605.01118, 42-51.

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.
  2. Qian, J., Hahn, G. (2024). Scalable computation of the maximum flow in large brain connectivity networks. arXiv:2412.00106.
  3. Qian, J., Hahn, G. (2024). An efficient heuristic for approximate maximum flow computations. arXiv:2409.08350.

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