Terenin, A., Burt, D. R., Artemev, A., Flaxman, S., van der Wilk, M., Rasmussen, C. E., & Ge, H. (2024). Numerically stable sparse gaussian processes via minimum separation using cover trees. Journal of Machine Learning Research, 25, 1-36.

Pesudovs, Konrad, et al. “Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020.” Eye (2024): 1-15.

Cluver, Lucie, Jeffrey W. Imai-Eaton, Lorraine Sherr, Mary Mahy, and Seth Flaxman. “Reauthorise PEPFAR to prevent death, orphanhood, and suffering for millions of children.” The Lancet 402, no. 10404 (2023): 769-770.

Flaxman, Seth, Lackson Kasonka, Lucie Cluver, Andrea Santos Souza, Charles A. Nelson III, Alexandra Blenkinsop, H. Juliette T. Unwin, and Susan Hillis. “List child dependents on death certificates.” Science 380, no. 6644 (2023): 467-467.

Hawryluk, Iwona, Swapnil Mishra, Seth Flaxman, Samir Bhatt, and Thomas A. Mellan. “Application of referenced thermodynamic integration to Bayesian model selection.” PLoS One 18, no. 8 (2023): e0289889.

Howes A, Risher KA, Nguyen VK, Stevens O, Jia KM, Wolock TM, Esra RT, Zembe L, Wanyeki I, Mahy M, Benedikt C, Flaxman SR, Eaton JW, “Spatio-temporal estimates of HIV risk group proportions for adolescent girls and young women across 13 priority countries in sub-Saharan Africa.” PLOS Global Public Health 3(4): e0001731 (2023).

Monod, M., Blenkinsop, A., Brizzi, A., Chen, Y., Perello, C.C.C., Jogarah, V., Wang, Y., Flaxman, S., Bhatt, S. and Ratmann, O. “Regularised B-splines Projected Gaussian Process Priors to Estimate Time-trends in Age-specific COVID-19 Deaths,” Bayesian Analysis (2023).

Bennett, J. E., Rashid, T., Zolfaghari, A., Doyle, Y., Suel, E., Pearson-Stuttard, J., … & Ezzati, M. “Changes in life expectancy and house prices in London from 2002 to 2019: hyper-resolution spatiotemporal analysis of death registration and real estate data.” The Lancet Regional Health-Europe (2023).

Flaxman, Whittaker, Semenova, Rashid, Parks, Blenkinsop, Unwin, Mishra, Bhatt, Gurdasani, and Ratmann, “Assessment of COVID-19 as the underlying cause of death among children and young people aged 0 to 19 years in the US,” JAMA Network Open (2023).

S Lamprinakou, M Barahona, S Flaxman, S Filippi, A Gandy, EJ McCoy, “BART-based inference for Poisson processes,” Computational Statistics & Data Analysis (2023).

Charles, G., Wolock, T. M., Winskill, P., Ghani, A., Bhatt, S., & Flaxman, S. “Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference,” AAAI (2023).


Hillis, S., N’konzi, J. P. N., Msemburi, W., Cluver, L., Villaveces, A., Flaxman, S., & Unwin, H. J. T. “Orphanhood and caregiver loss among children based on new global excess COVID-19 death estimates,” JAMA Pediatrics (2022).

Brizzi, Andrea, et al. “Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals.” Nature Medicine (2022).

Bourne, […], Flaxman, Keel, Resnikoff, “Effective refractive error coverage in adults aged 50 years and older: estimates from population-based surveys in 61 countries,” Lancet Global Health (2022).

Brito, Semenova, et al, “Global disparities in SARS-CoV-2 genomic surveillance,” Nature Communications (2022).

Ball, Petrova, Coomes, and Flaxman, “Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation,” Methods in Ecology and Evolution (2022).

Mishra, Flaxman, Berah, Zhu, Pakkanen, and Bhatt, “πVAE: a stochastic process prior for Bayesian deep learning with MCMC,” Statistics & Computing (2022).

Semenova, Xu, Howes, Rashid, Bhatt, Mishra, and Flaxman, “PriorVAE: Encoding spatial priors with VAEs for small-area estimation,” Royal Society Interface (2022).

Bhatt, Ferguson, Flaxman, Gandy, Mishra, and Scott, “Semi-Mechanistic Bayesian modeling of COVID-19 with Renewal Processes,” Forthcoming in JRSS A (2022). Read paper at the Royal Statistical Society (16 June 2022).

Stevens, G.A., Paciorek, C.J., Flores-Urrutia, M.C., Borghi, E., Namaste, S., Wirth, J.P., Suchdev, P.S., Ezzati, M., Rohner, F., Flaxman, S.R. and Rogers, L.M., 2022. “National, regional, and global estimates of anaemia by severity in women and children for 2000–19: a pooled analysis of population-representative data.” The Lancet Global Health (2022).

Monod, M., Blenkinsop, A., Brizzi, A., Chen, Y., Perello, C.C.C., Jogarah, V., Wang, Y., Flaxman, S., Bhatt, S. and Ratmann, O. “Regularised B-splines Projected Gaussian Process Priors to Estimate Time-trends in Age-specific COVID-19 Deaths,” Bayesian Analysis (2022).

Zhang, Q., Wild, V., Filippi, S., Flaxman, S., & Sejdinovic, D. (2022). “Bayesian kernel two-sample testing,” Journal of Computational and Graphical Statistics, (2022).

Nyberg, Ferguson, et al, “Comparative Analysis of the Risks of Hospitalisation and Death Associated with SARS-CoV-2 Omicron (B.1.1.529) and Delta (B.1.617.2) Variants in England,” the Lancet (2022).

Unwin et al, “Global, regional, and national minimum estimates of children affected by COVID-19-associated orphanhood and caregiver death, by age and family circumstance up to Oct 31, 2021,” Lancet Child & Adolescent Health (2022).


Scott et al, “Track Omicron’s spread with molecular data,” Science (2021).

Bradley, Kuriwai, Isakov, Sejdinovic, Meng, Flaxman, “Unrepresentative big surveys significantly overestimated US vaccine uptake,” Nature (2021).

Hillis et al, “COVID-19–associated orphanhood and caregiver death in the United States,” Pediatrics (2021).

Hawryluk et al, “Gaussian process nowcasting: Application to COVID-19 mortality reporting,” UAI (2021).

Wilde et al, “The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study,” BMC Medicine (2021).

Dhar et al, “Genomic characterization and epidemiology of an emerging SARS-CoV-2 variant in Delhi, India,” Science (2021).

Mlcochova et al, “SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion,” Nature (2021).

Sharma et al, “Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe,” Nature Communications (2021).

Gurdasani et al, “Vaccinating adolescents against SARS-CoV-2 in England: a risk–benefit analysis,” Journal of the Royal Society of Medicine (2021).

Rashid et al, “Life expectancy and risk of death in 6791 communities in England from 2002 to 2019: high-resolution spatiotemporal analysis of civil registration data,” Lancet Public Health (2021).

Hillis, Unwin, Chen, Cluver, Sherr, Goldman, Ratmann, Donnelly, Bhatt, Villaveces, Butchart, Bachman, Rawlings, Green, Nelson, Flaxman, “Global Minimum Estimates for COVID-19-associated Orphanhood and Deaths among Caregivers during 2020,” the Lancet (2021).

Smith, Flaxman, et al, “Environment influences SARS-CoV-2 transmission in the absence of non-pharmaceutical interventions,” PNAS (2021).

Lightley, Gorlitz, Kumar, Kalita, Kolbeinnson, Garcia, Alexandrov, Bousgouni, Wysoczanski, Barnes, Donnelly, Bakal, Dunsby, Neil, Flaxman, French, “Robust optical autofocus system utilizing neural networks trained for extended range and time-course applied to automated multiwell plate single molecule localization microscopy,” Journal of Microscopy (2021).

Wolock, Flaxman, Risher, Dadirai, Gregson, Eaton, “Evaluating distributional regression strategies for modelling self-reported sexual age-mixing,” eLife (2021).

Faria et al, “Genomics and epidemiology of a novel SARS-CoV-2 lineage in Manaus, Brazil,”  Science (2021).

Volz et al, “Transmission of SARS-CoV-2 Lineage B.1.1.7 in England: Insights from linking epidemiological and genetic data,” Nature (2021).

Ratmann, Bhatt, Flaxman, “Implications of a highly transmissible variant of SARS-CoV-2 for children,” Archives of Disease in Childhood (2021).

Laydon, Mishra, Hinsley, Samartsidis, Flaxman, Gandy, Ferguson, Bhatt, “Impact of the Tier system on SARS-CoV-2 transmission in the UK between the first and second national lockdowns,” BMJ Open (2021).

Vollmer et al, “The impact of the COVID-19 pandemic on patterns of attendance at emergency departments in two large London hospitals: an observational study,” BMC Health Services Research (2021).

Vollmer, Glampson, Mellan, Mishra, Mercuri, Costello, Klaber, Cooke, Flaxman, Bhatt, “A unified machine learning approach to time series forecasting applied to demand at emergency departments”, BMC Emergency Medicine (2021).

Bourne, Steinmetz, Flaxman, et al, “Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the Global Burden of Disease Study,” Lancet Global Health (2021).

Monod et al, “Age groups that sustain resurging COVID-19 epidemics in the United States” Science 2021.

AJ Holbrook, CE Loeffler, SR Flaxman, MA Suchard, “Scalable Bayesian inference for self-excitatory stochastic processes applied to big American gunfire data,” Statistics and Computing 31 (1), 1-15.

E Suel, S Bhatt, M Brauer, S Flaxman, M Ezzati, “Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas,” Remote Sensing of Environment 257, 112339.

Hawryluk et al, “Inference of COVID-19 epidemiological distributions from Brazilian hospital data,” Journal of the Royal Society Interface 17 (172), 20200596.

Unwin, [et al…], Flaxman, “State-level tracking of COVID-19 in the United States” Nature Communications, 2020.

Flaxman, S., Mishra, S., Gandy, A. et al. “Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe.” Nature (2020).

Okell, L., Verity, R., Watson, OJ, et al. Have deaths from COVID-19 in Europe plateaued due to herd immunity? Lancet (2020). [supplementary figures.]

Lisi, Malekzadeh, Haddadi, Lau, Flaxman. “Modelling and forecasting art movements with CGANs.” Royal Society Open Science. 7: 191569, 2020.


S. Flaxman, M. Chirico, P. Pereira, C. Loeffler. ‘Scalable high-resolution forecasting of sparse spatiotemporal events with kernel methods: a winning solution to the NIJ “Real-Time Crime Forecasting Challenge,”‘ Annals of Applied Statistics, 2019.

L. Tusting, D. Bisanzio, G. Alabaster, E. Cameron, R. Cibulskis, M. Davies, S. Flaxman, H. Gibson, S. Hay, J. Knudsen, C. Mbogo, F. Okumu, K. Rust, L. von Seidlein, D. Weiss, S. Lindsay, P. Gething, and S. Bhatt, “Mapping changes in housing in Africa, 2000 to 2015,” Nature, 2019.

L. Crawford, S. Flaxman, D. Runcie, M. West, ‘Predictor Variable Prioritization in Nonlinear Models: A Genetic Association Case Study’,  Annals of Applied Statistics 2019.

Davis, Kumar, Alexandrov, Bhargava, Sahai, Flaxman, French, McGinty, “Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish,” Journal of Biophotonics, 2019.

Jakob Runge, Peer Nowack, Marlene Kretschmer, Seth Flaxman, and Dino Sejdinovic, “Detecting causal associations in large nonlinear time series datasets”, forthcoming at Science Advances, https://arxiv.org/abs/1702.07007


Vision Loss Expert Group series of publications in the British Journal of Ophthalmology: Keeffe et al [2018], Kahloun et al [2018], Leasher et al [2018], Nangia et al [2018], Bourne et al [2018], with more forthcoming.

Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim CD Lucas, Seth Flaxman, Katherine Battle, Kenji Fukumizu, “Variational Learning on Aggregate Outputs with Gaussian Processes”, NeurIPS 2018.

A. Hu and S. Flaxman, “Multimodal Sentiment Analysis To Explore the Structure of Emotions”, Applied Data Science track, KDD 2018.

Law, Sutherland, Sejdinovic, Flaxman, Bayesian Approaches to Distribution Regression, AISTATS 2018.

Abbati, Tosi, Osborne, Flaxman, “AdaGeo: Adaptive Geometric Learning for Optimization and Sampling”, AISTATS 2018.

J-F Ton, S. Flaxman, D. Sejdinovic, S. Bhatt, “Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features”, Spatial Statistics 2018.

S. Flaxman, R. Bourne, S. Resnikoff, P. Ackland, et al. “Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis.” Lancet Global Health 6, no. 12 (2017).

R. Bourne, S. Flaxman, T. Braithwaite, M.V. Cicinelli, A. Das, et al. “Global Prevalence of Blindness and Distance and Near Vision Impairment: Magnitude, Temporal Trends, and Projections.” Lancet Global Health.

Charles Loeffler and Seth Flaxman, “Is Gun Violence Contagious?” Journal of Quantitative Criminology [code].

Bryce Goodman and Seth Flaxman, ‘European Union regulations on algorithmic decision-making and a “right to explanation” ‘ AI Magazine, 38, no. 3 (2017): 50-57.

Seth Flaxman, Yee Whye Teh, and Dino Sedjdinovic. “Poisson intensity estimation with reproducing kernels”, AISTATS 2017, selected for oral presentation [code]. Journal version: Electronic Journal of Statistics, Vol. 11 (2017) 5081-5104.

S Bhatt, E Cameron, SR Flaxman, DJ Weiss, DL Smith, PW Gething, “Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation”, Journal of the Royal Society Interface.

Qinyi Zhang, Sarah Filippi, Seth Flaxman, Dino Sejdinovic, “Feature-to-Feature Regression for a Two-Step Conditional Independence Test,” UAI 2017 [code]

S. Flaxman, D. Sejdinovic, J. P. Cunningham, and S. Filippi, “Bayesian Learning of Kernel Embeddings”, Uncertainty in Artificial Intelligence (UAI), 2016. [supplementary]

W. Herlands, A. Wilson, H. Nickisch, S. Flaxman, D. Neill, W. van Panhuis, E. Xing, Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces, AISTATS 2016.

S. Flaxman, S. Goel, J. Rao, Ideological Segregation and the Effects of Social Media on News Consumption, Public Opinion Quarterly.

Seth Flaxman, Daniel Neill, Alex Smola. “Gaussian Processes for Independence Tests with non-iid Data in Causal Inference.” ACM Transactions on Intelligent Systems and Technology. [code]

Unpublished manuscripts
Hyunjik Kim, Xiaoyu Lu, Seth Flaxman, and Yee Whye Teh. “Tucker Gaussian Process for Regression and Collaborative Filtering.”

Seth Flaxman, Danica J. Sutherland, Yu-Xiang Wang, and Yee Whye Teh, “Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata”.

Seth Flaxman and Karim Kassam, “On #agony and #ecstasy: Potential and pitfalls of linguistic sentiment analysis.”

S. Flaxman, A. Gelman, D. Neill, A. Smola, A. Vehtari, and A. G. Wilson, “Fast hierarchical Gaussian processes.”

S. Flaxman, D. Neill, A. Smola, New Space/Time Interaction Tests for Spatiotemporal Point Processes, Heinz College Working Paper series. (This was my Heinz first paper and it won the Suresh Konda award.)

Seth Flaxman, Andrew Wilson, Daniel Neill, Hannes Nickisch, and Alex Smola. “Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods,” International Conference on Machine Learning 2015, Lille. [code]

Seth Flaxman, Yu-Xiang Wang, and Alex Smola. “Who supported Obama in 2012? Ecological inference through distribution regression,” KDD 2015, Best Student Paper Award. [third party code / replication]

Stevens, Bennett, Hennocq, Lu, De-Regil, Rogers, Danaei, Li, White, Flaxman, Oehrle, Finucane, Guerrero, Bhutta, Then-Paulino, Fawzi, Black, Ezzati, “Trends and mortality effects of vitamin A deficiency in children in 138 low- and middle-income countries: pooled analysis of population-based surveys,” accepted for publication in The Lancet Global Health.

J. B. Jonas, R.A. Bourne, R. A. White, S. R. Flaxman, J. Keeffe, J. Leasher, K. Naidoo, K. Pesudovs, H. Price, T. Y. Wong, S. Resnikoff, and H. R. Taylor. Visual Impairment and Blindness Due to Macular Diseases Globally: A Systematic Review and Meta-Analysis. American Journal of Ophthalmology Volume 158, Issue 4, Pages 808–815, October 2014.

May 2014, 98(5), issue of the British Journal of Ophthalmology has a series of articles by the Vision Loss Expert Group of the Global Burden of Disease Study.

N. J. Kassebaum, R. Jasrasaria, M. Naghavi, S. K. Wulf, N. Johns, R. Lozano, M. Regan, D. Weatherall, D. P. Chou, T. P. Eisele, S. R. Flaxman, R. L. Pullan, S. J. Brooker, and C. J. L. Murray. A systematic analysis of global anemia burden from 1990 to 2010. Blood. January 2014, 123 (5) 615-624.

R. Bourne, G. Stevens, R. A. White, J. Smith, S. Flaxman, H. Price, J. B Jonas, J. Keeffe, J. Leasher, K. Naidoo, K. Pesudovs, S. Resnikoff, H. R. Taylor. Causes of vision loss worldwide, 1990—2010: a systematic analysis. The Lancet Global Health, 11 November 2013.

G. Stevens, R.A. White, S. Flaxman, H. Price, J.B. Jonas, J. Keeffe, J. Leasher, K. Naidoo, K. Pesudovs, S. Resnikoff, H. Taylor, R. Bourne. Global Prevalence of Vision Impairment and Blindness: Magnitude and Temporal Trends, 1990-2010. Ophthalmology (2013).

Stevens GA, Finucane MM, De-Regil LM, Paciorek CJ, Flaxman SR, Branca F, Peña-Rosas JP, Bhutta ZA, Ezzati M, Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: a systematic analysis of population-representative data, The Lancet Global Health (2013), 1(1): 16-25

Olofin I, McDonald CM, Ezzati M, Flaxman S, Black RE, et al. Associations of Suboptimal Growth with All-Cause and Cause-Specific Mortality in Children under Five Years: A Pooled Analysis of Ten Prospective Studies. PLoS ONE 8(5): e64636. 2013.

US Burden of Disease Collaborators, The State of US Health, 1990-2010: Burden of Diseases, Injuries, and Risk Factors, JAMA. (2013) 310(6):591-608.

Global Burden of Disease papers in the Lancet, 2013: 400+ authors; I’m an author on the years lived with disability (YLDs), disability-adjusted life years (DALYs), and risk factors papers.

C. McDonald, I. Olofin, S. Flaxman, W. Fawzi, D. Spiegelman, L. Caulfield, R. Black, M. Ezzati, G. Danaei, The effect of multiple anthropometric deficits on child mortality: meta-analysis of individual data in ten prospective studies from developing countries, American Journal of Clinical Nutrition, 2013.

M. Mascarenhas, S. Flaxman, T. Boerma, S. Vanderpoel, G. Stevens. National, regional and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys,” PLoS Medicine, 9(12), 2012. [I’m a co-first author.]

Gretchen A Stevens, Mariel M Finucane, Christopher J Paciorek, Seth R Flaxman, Richard A White, Abigail J Donner, Majid Ezzati, on behalf of Nutrition Impact Model Study Group (Child Growth). “Trends in mild, moderate, and severe stunting and underweight, and progress towards MDG 1 in 141 developing countries: a systematic analysis of population representative data.” The Lancet, 5 July 2012. Find out more and view interactive visualizations.

Gretchen Stevens, Seth Flaxman, Emma Brunskill, Maya Mascarenhas, Colin D. Mathers, Mariel Finucane, on behalf of the Global Burden of Disease Hearing Loss Expert Group. (2011). “Global and regional hearing impairment prevalence: an analysis of 42 studies in 29 countries,” The European Journal of Public Health 2011. [PDF]

K. Corcoran, S. Flaxman, M. Neyer, P. Scherpelz, C. Weidert, and R. Libeskind-Hadas. (2009). “Approximation Algorithms for Traffic Grooming in WDM Rings,” IEEE International Conference on Communications 2009. [PDF]

S. Flaxman, J. Huang, J. Stephenson, and X. Comtesse. (2009). “CityRank: a dynamic tool for exploring and generating new indices of cities.” Information Design Journal. 17:3, January 2010. [pdf]

S. Flaxman. (2008). “On the Optimality of Some Semide finite Programming-Based Approximation Algorithms under the Unique Games Conjecture.” Senior honors thesis in computer science. Unpublished manuscript available in Harvard University Archives. [PDF]