Our paper, Housing-Sensitive Health Conditions Can Predict Poor-Quality Housing, is now out (open access) in the February 2024 issue of Health Affairs. Thanks to Health Affairs for highlighting our work in their Feb. 6 special issue briefing and Feb. 26 Health Affairs Insider Journal Club. Thanks also to David Brand (Gothamist), Steve Scott (WCBS 880 Newsradio), and Robert Polner (NYU) for their wonderful press coverage!
2 AAAI paper accepts!
The ML4G Lab had two papers accepted to AAAI 2023:
Katie Rosman and Daniel B. Neill. Detecting anomalous networks of opioid prescribers and dispensers in prescription drug data. Proc. 37th AAAI Conf. on Artificial Intelligence, 2023, in press.
Pavan Ravishankar, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. Provable detection of propagating sampling bias in prediction models. Proc. 37th AAAI Conf. on Artificial Intelligence, 2023, in press.
Congratulations all!
pre-syndromic surveillance paper
Our paper on pre-syndromic disease surveillance is now out (open-access) in Science Advances! Thanks to Kimberly Adams (Marketplace Tech), Ruth Reader (Politico), Shania Kennedy (HealthITAnalytics), and Robert Polner (NYU) for their wonderful press coverage. For more details, please see our pre-syndromic surveillance project page here.
ethical and equitable opioid responses
Congratulations to Bennett on his recent opinion piece, “Public health and police: Building ethical and equitable opioid responses,” published in the Proceedings of the National Academy of Sciences.
3 AAAI paper accepts!
The ML4G Lab had three papers accepted to AAAI 2022:
Konstantin Klemmer, Tianlin Xu, Beatrice Acciaio, and Daniel B. Neill. SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss. Proc. 36th AAAI Conf. on Artificial Intelligence, 2022.
Chunpai Wang, Daniel B. Neill, and Feng Chen. Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs. Proc. 36th AAAI Conf. on Artificial Intelligence, 2022.
G. Reiersen, D. Dao, B. Lütjens, K. Klemmer, K. Amara, A. Steinegger, C. Zhang, and X.X. Zhu. ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery. Proc. 36th AAAI Conf. on Artificial Intelligence, 2022.
Congratulations Konstantin and Daniel!