Publications

Jain, V., A. Mukherjee, S. Banerjee, S. Madhwal, M. H. Bergin, P. Bhave, D. Carlson, et al. “A hybrid approach for integrating micro-satellite images and sensors network-based ground measurements using deep learning for high-resolution prediction of fine particulate matter (PM2.5) over an indian city, lucknow.” Atmospheric Environment 338 (December 1, 2024). https://doi.org/10.1016/j.atmosenv.2024.120798.

Carson, William E., Samantha Major, Harshitha Akkineni, Hannah Fung, Elias Peters, Kimberly L. H. Carpenter, Geraldine Dawson, and David E. Carlson. “Model selection to achieve reproducible associations between resting state EEG features and autism.” Sci Rep 14, no. 1 (October 25, 2024): 25301. https://doi.org/10.1038/s41598-024-76659-5.

Walder-Christensen, Kathryn K., Jack Goffinet, Alexandra L. Bey, Reah Syed, Jacob Benton, Stephen D. Mague, Elise Adamson, et al. “Sleep-wake states are encoded across emotion-regulation regions of the mouse brain.” Cold Spring Harbor Laboratory, September 15, 2024. https://doi.org/10.1101/2024.09.15.613104.

Calhoun, Z. D., M. S. Black, M. Bergin, and D. Carlson. “Refining Citizen Climate Science: Addressing Preferential Sampling for Improved Estimates of Urban Heat.” Environmental Science and Technology Letters 11, no. 8 (August 13, 2024): 845–50. https://doi.org/10.1021/acs.estlett.4c00296.

Isaev, Dmitry, Samantha Major, Kimberly L. H. Carpenter, Jordan Grapel, Zhuoqing Chang, Matias Di Martino, David Carlson, Geraldine Dawson, and Guillermo Sapiro. “Use of Computer Vision Analysis for Labeling Inattention Periods in Eeg Recordings With Visual Stimuli.” Springer Science and Business Media LLC, August 10, 2024. https://doi.org/10.21203/rs.3.rs-4637470/v1.

Hughes, Dalton N., Michael Hunter Klein, Kathryn Katsue Walder-Christensen, Gwenaëlle E. Thomas, Yael Grossman, Diana Waters, Anna E. Matthews, et al. “A widespread electrical brain network encodes anxiety in health and depressive states.” BioRxiv, June 30, 2024. https://doi.org/10.1101/2024.06.26.600900.

Sui, C., Z. Jiang, G. Higueros, D. Carlson, and P. C. Hsu. “Designing electrodes and electrolytes for batteries by leveraging deep learning.” Nano Research Energy 3, no. 2 (June 1, 2024). https://doi.org/10.26599/NRE.2023.9120102.

Brandsen, Sam, Tara Chandrasekhar, Lauren Franz, Jordan Grapel, Geraldine Dawson, and David Carlson. “Prevalence of bias against neurodivergence-related terms in artificial intelligence language models.” Autism Res 17, no. 2 (February 2024): 234–48. https://doi.org/10.1002/aur.3094.

Walder-Christensen, Kathryn, Karim Abdelaal, Hunter Klein, Gwenaëlle E. Thomas, Neil M. Gallagher, Austin Talbot, Elise Adamson, et al. “Electome network factors: Capturing emotional brain networks related to health and disease.” Cell Rep Methods 4, no. 1 (January 22, 2024): 100691. https://doi.org/10.1016/j.crmeth.2023.100691.

Calhoun, Zachary D., Frank Willard, Chenhao Ge, Claudia Rodriguez, Mike Bergin, and David Carlson. “Estimating the effects of vegetation and increased albedo on the urban heat island effect with spatial causal inference.” Scientific Reports 14, no. 1 (January 2024): 540. https://doi.org/10.1038/s41598-023-50981-w.

Scott, S. R., P. E. Hailemariam, P. V. Bhave, M. H. Bergin, and D. E. Carlson. “Identifying Waste Burning Plumes Using High-Resolution Satellite Imagery and Machine Learning: A Case Study in the Maldives.” Environmental Science and Technology Letters 10, no. 8 (August 8, 2023): 642–48. https://doi.org/10.1021/acs.estlett.3c00225.

Talbot, Austin, David Dunson, Kafui Dzirasa, and David Carlson. “Estimating a brain network predictive of stress and genotype with supervised autoencoders.” J R Stat Soc Ser C Appl Stat 72, no. 4 (August 2023): 912–36. https://doi.org/10.1093/jrsssc/qlad035.

Sui, Chenxi, Ziyang Jiang, Genesis Higueros, David Carlson, and Po-Chun Hsu. “Designing electrodes and electrolytes for batteries by leveraging deep learning.” American Chemical Society (ACS), July 7, 2023. https://doi.org/10.26434/chemrxiv-2023-jvfqq.

Jiang, Ziyang, Zhuoran Hou, Yiling Liu, Yiman Ren, Keyu Li, and David Carlson. “Estimating Causal Effects using a Multi-task Deep Ensemble.” In Proceedings of Machine Learning Research, 202:15023–40, 2023.

Grossman, Yael S., Austin Talbot, Neil M. Gallagher, Gwenaëlle E. Thomas, Alexandra J. Fink, Kathryn K. Walder-Christensen, Scott J. Russo, David E. Carlson, and Kafui Dzirasa. “A widespread oscillatory network encodes an aggressive internal state.” Cold Spring Harbor Laboratory, December 7, 2022. https://doi.org/10.1101/2022.12.07.519272.

Block, Carina L., Oznur Eroglu, Stephen D. Mague, Caroline J. Smith, Alexis M. Ceasrine, Chaichontat Sriworarat, Cameron Blount, et al. “Prenatal environmental stressors impair postnatal microglia function and adult behavior in males.” Cell Rep 40, no. 5 (August 2, 2022): 111161. https://doi.org/10.1016/j.celrep.2022.111161.

Thornton, Luka Lila, David E. Carlson, and Mark R. Wiesner. “Predicting emerging chemical content in consumer products using machine learning.” The Science of the Total Environment 834 (August 2022): 154849. https://doi.org/10.1016/j.scitotenv.2022.154849.

Jiang, Z., T. Zheng, M. Bergin, and D. Carlson. “Improving spatial variation of ground-level PM2.5 prediction with contrastive learning from satellite imagery.” Science of Remote Sensing 5 (June 1, 2022). https://doi.org/10.1016/j.srs.2022.100052.

Mague, Stephen D., Austin Talbot, Cameron Blount, Kathryn K. Walder-Christensen, Lara J. Duffney, Elise Adamson, Alexandra L. Bey, et al. “Brain-wide electrical dynamics encode individual appetitive social behavior.” Neuron 110, no. 10 (May 18, 2022): 1728-1741.e7. https://doi.org/10.1016/j.neuron.2022.02.016.

Yuan, S., Y. Li, D. Wang, K. Bai, L. Carin, and D. Carlson. “Learning to Weight Filter Groups for Robust Classification.” In Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, 3321–30, 2022. https://doi.org/10.1109/WACV51458.2022.00338.

Tu, Liyun, Austin Talbot, Neil M. Gallagher, and David E. Carlson. “Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility.” IEEE Transactions on Signal Processing : A Publication of the IEEE Signal Processing Society 70 (January 2022): 5954–66. https://doi.org/10.1109/tsp.2022.3230329.

Thornton, Luka Lila, David Carlson, and Mark Wiesner. “Predicting emerging chemical content in consumer products using machine learning.” American Chemical Society (ACS), December 21, 2021. https://doi.org/10.26434/chemrxiv-2021-96wvd.

Gallagher, Neil M., Kafui Dzirasa, and David Carlson. “Directed Spectral Measures Improve Latent Network Models Of Neural Populations.” Adv Neural Inf Process Syst 34 (December 2021): 7421–35.

Bey, Alexandra L., Kathryn K. Walder-Christensen, Jack Goffinet, Elise Adamson, Noah Lanier, Stephen D. Mague, David Carlson, and Kafui Dzirasa. “6.28 Identifying Networks Underlying Sleep Disruption in Autism Spectrum Disorder Mouse Models.” In Journal of the American Academy of Child & Adolescent Psychiatry, 60:S167–S167. Elsevier BV, 2021. https://doi.org/10.1016/j.jaac.2021.09.101.

Dunn, Timothy W., Jesse D. Marshall, Kyle S. Severson, Diego E. Aldarondo, David G. C. Hildebrand, Selmaan N. Chettih, William L. Wang, et al. “Geometric deep learning enables 3D kinematic profiling across species and environments.” Nat Methods 18, no. 5 (May 2021): 564–73. https://doi.org/10.1038/s41592-021-01106-6.