We are delighted to announce the formation of the Machine Learning for Good Laboratory (ML4G Lab) at New York University. Watch this space for lab news, hiring announcements, etc.!
- using health data to predict poor-quality housing
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!
- Congratulations to John!
Congratulations to John Pamplin, who has joined Columbia University’s Mailman School of Public Health as an Assistant Professor of Epidemiology! We are delighted that John will be continuing his association with the ML4G Lab as an affiliated faculty member.
- How can AI combat the opioid crisis?
Daniel presented the invited talk, “Machine Learning for Opioid and Overdose Surveillance”, at the CMU Symposium on AI and Social Good. His talk slides are available here.
- Welcome Ed!
Welcome to Prof. Edward McFowland III, who is joining the ML4G Lab as affiliated faculty. Ed is currently Assistant Professor of Information and Decision Sciences at the University of Minnesota’s Carlson School of Management, and is a co-PI on our new NSF Fairness in AI grant.
- NSF/Amazon Fairness in AI award
Our project on “End-to-End Fairness for Algorithm-in-the-Loop Decision Making in the Public Sector” (NSF IIS-2040898, Neill, PI) was awarded $1M funding from the National Science Foundation Program on Fairness in Artificial Intelligence in Collaboration with Amazon. Our goals are to develop methods and tools that assist public sector organizations with fair and equitable policy interventions in areas including housing, criminal justice, and health. Thanks to Unite.AI for their press coverage, and to NSF and Amazon for their support!
- Why are reports of sexual assault delayed?
Thanks to COSMOS Magazine for their very nice coverage of our recent publication on rape reporting delays, and congratulations Konstantin on the interview!
- Congratulations Dr. Fitzpatrick!
Congratulations to Daniel’s PhD student Dylan Fitzpatrick (at Carnegie Mellon’s Joint PhD Program in Machine Learning and Policy) for the successful defense of his doctoral dissertation, “Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector”. Dylan will be joining the University of Chicago Crime Lab as a Research Director.
- Congratulations Dr. Herlands!
Congratulations to Daniel’s PhD student William Herlands (at Carnegie Mellon’s Joint PhD Program in Machine Learning and Policy) for the successful defense of his doctoral dissertation, “Change Modeling for Understanding our World and the Counterfactual One(s)”.
- New communities, new opportunities…
Daniel is delighted and honored to be part of several new communities, as Affiliated Faculty in the NYU Center for Data Science and as part of the inaugural Editorial Board of the newly formed INFORMS Journal on Data Science.
- Congrats to Konstantin…
… on two new publications!
Luo, M., Du, B., Klemmer, K., Zhu, H., Ferhatosmanoglu, H., & Wen, H. (2020). D3P: Data-driven Demand Prediction for Fast Expanding Electric Vehicle Sharing Systems. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(1), 1-21. (link)
Klemmer, K., Yeboah, G., Porto de Albuquerque, J., Jarvis, S. (2020). Population Mapping in Informal Settlements with High-Resolution Satellite Imagery and Equitable Ground-Truth. ML-IRL Workshop, ICLR 2020, Addis Ababa, Ethiopia. (link)
- Latest papers
Our latest publications: new approaches for finding spatial hotspots of disease infection risk using Twitter data, and performing counterfactual causal inference with change surfaces.
- Congratulations Dr. Nobles!
Congratulations to Daniel’s PhD student Mallory Nobles (at Carnegie Mellon’s Heinz College) for the successful defense of her doctoral dissertation, “Multidimensional Semantic Scan for Pre-Syndromic Surveillance”. This work provides a safety net for public health practitioners, enabling them to detect newly emerging disease threats and other events of interest. Dr. Nobles is now working as a research scientist at MIT Lincoln Labs.
- ACM COMPASS 2020
Daniel is very excited to serve as “AI for Social Impact” area chair for the 3rd ACM Conference on Computing and Sustainable Societies (ACM COMPASS 2020). Please consider submitting your ML/AI for Good work to this great conference!
- ML4G student Katie Rosman awarded NSF Fellowship
Congratulations to Katie on her NSF Graduate Research Fellowship! Here is a great profile by NYU’s Tandon School of Engineering.