Dam, R., Mponzi, W., Msaky, D., Mwandyala, T., Kaindoa, E.W., Sinka, M.E., Kiskin, I., Herreros-Moya, E., Messina, J., Shah, S.G.S., Roberts, S., Willis, K.J., 2023: What incentives encourage local communities to collect and upload mosquito sound data by using smartphones? A mixed methods study in Tanzania. Global Health Research and Policy 8, 18 (2023).

Kiskin I., Sinka M., Cobb A.D., Rafique W., Wang L., Zilli D., Gutteridge B., Dam R., Marinos T., Li Y., Msaky D., Kaindoa E., Killeen G., Herreros-Moya E., Willis K.J., Roberts S.J., 2021: HumBugDB: A Large-scale Acoustic Mosquito Dataset. Preprint available on

Kiskin I., Cobb A.D., Wang L., Sinka M., Willis K., Roberts S, 2021: Automatic Acoustic Mosquito Tagging with Bayesian Neural Networks. In ECML PKDD 2021: Machine Learning and Knowledge Discovery in Databases.

Sinka M.E., Zilli D., Li Y., Kiskin I., Kirkham D., Rafique W., Wang L., Chan H., Gutteridge B., Herreros-Moya E., Portwood H., Roberts S., Willis K.J., 2021: HumBug – An Acoustic Mosquito Monitoring Tool for use on budget smartphones. In Methods in Ecology and Evolution June 2021. 

Sinka M.E., Pironon S., Massey N.C., Longbottom J., Hemingway J., Moyes C. L., Willis K.J., 2020: A new malaria vector in Africa: Predicting the expansion range of Anopheles stephensi and identifying the urban populations at risk. In PNAS Sep 2020, 202003976; DOI: 10.1073/pnas.2003976117

Kiskin I., Cobb A.D., Wang L., Li Y., Zilli D., Sinka M., Willis K., Roberts S., 2020: HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset. Best paper award at NeurIPS 2019 Workshop: Machine Learning For the Developing WorldarXiv:2001.04733

Kiskin I., Zilli D., Li Y., Sinka M., Willis K., Roberts S., 2020: Bioacoustic detection with wavelet-conditioned convolutional neural networks. In Neural Computing and Applications 32 (4), 915-927.

Li Y., Kiskin I., Sinka M. , Zilli D, Chan H., Herreros-Moya E., Chareonviriyaphap T., Tisgratog R., Willis K., Roberts S., 2018: Fast mosquito acoustic detection with field cup recordings: an initial investigation. In Detection and Classification of Acoustic Scenes and Events; Tampere University of Technology 2018: 153-157.

Li Y., Zilli D., Chan H., Kiskin I., Sinka M., Roberts S., Willis K., 2017: Mosquito detection with low-cost smartphones: data acquisition for malaria research. In NIPS 2017 Workshop on Machine Learning for the Developing World 2017. arXiv:1711.06346

Li Y., Kiskin I., Zilli D., Sinka M., Chan H., Willis K., Roberts S, 2017.: Cost-sensitive detection with variational autoencoders for environmental acoustic sensing. Presented at the NIPS 2017 Workshop on Machine Learning for Audio Signal ProcessingarXiv:1712.02488

Kiskin I., Orozco B.P., Windebank T., Zilli D., Sinka M., Willis K, Roberts S., 2017: Mosquito detection with neural networks: the buzz of deep learning. In ArXiv 2017. arXiv:1705.05180

Kiskin I.,  Zilli D., Roberts S.: Autonomous Machines and Intelligent Systems (AIMS) Centre for Doctoral Training project. 



"New Asian mosquito could bring malaria to African cities, warn scientists" The Guardian (14/09/2020)

"How an invasive mosquito could bring malaria to Africa’s cities" BBC News World Service (09/10/2020) - Interview to Dr Marianne Sinka