Publications

2019

Carlson, David, and Lawrence Carin. “Continuing progress of spike sorting in the era of big data.” Current Opinion in Neurobiology 55 (2019): 90–96.

2018

Zheng, T., M. H. Bergin, K. K. Johnson, S. N. Tripathi, S. Shirodkar, M. S. Landis, R. Sutaria, and D. E. Carlson. “Field evaluation of low-cost particulate matter sensors in high-and low-concentration environments.” Atmospheric Measurement Techniques 11, no. 8 (August 22, 2018): 4823–46. https://doi.org/10.5194/amt-11-4823-2018.

Goldstein, Benjamin A., David Carlson, and Nrupen A. Bhavsar. “Subject Matter Knowledge in the Age of Big Data and Machine Learning.” JAMA Netw Open 1, no. 4 (August 3, 2018): e181568. https://doi.org/10.1001/jamanetworkopen.2018.1568.

Zheng, Tongshu, Michael H. Bergin, Karoline K. Johnson, Sachchida N. Tripathi, Shilpa Shirodkar, Matthew S. Landis, Ronak Sutaria, and David E. Carlson. “Supplementary material to "Field evaluation of low-cost particulate matter sensors in high and low concentration environments",” April 23, 2018. https://doi.org/10.5194/amt-2018-111-supplement.

Hultman, Rainbo, Kyle Ulrich, Benjamin D. Sachs, Cameron Blount, David E. Carlson, Nkemdilim Ndubuizu, Rosemary C. Bagot, et al. “Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability.” Cell 173, no. 1 (March 22, 2018): 166-180.e14. https://doi.org/10.1016/j.cell.2018.02.012.

Vu, Mai-Anh T., Tülay Adalı, Demba Ba, György Buzsáki, David Carlson, Katherine Heller, Conor Liston, et al. “A Shared Vision for Machine Learning in Neuroscience.” J Neurosci 38, no. 7 (February 14, 2018): 1601–7. https://doi.org/10.1523/JNEUROSCI.0508-17.2018.

Li, Yitong, Martin Renqiang Min, Dinghan Shen, David Carlson, and Lawrence Carin. “Video Generation From Text.” In AAAI Conference on Artificial Intelligence, 2018.

Liang, Kevin J., Geert Heilmann, Christopher Gregory, Souleymane O. Diallo, David Carlson, Gregory P. Spell, John B. Sigman, Kris Roe, and Lawrence Carin. “Automatic threat recognition of prohibited items at aviation checkpoint with x-ray imaging: a deep learning approach.” In Anomaly Detection and Imaging with X-Rays (ADIX) III, 10632:1063203–1063203, 2018.

Li, Yitong, David E. Carlson, and David E. others. “Extracting relationships by multi-domain matching.” In Advances in Neural Information Processing Systems, 6798–6809, 2018.

Zheng, Tongshu, Michael H. Bergin, Karoline K. Johnson, Sachchida N. Tripathi, Shilpa Shirodkar, Matthew S. Landis, Ronak Sutaria, and David E. Carlson. “Field evaluation of low-cost particulate matter sensors in high and low concentration environments.” Atmospheric Measurement Techniques, 2018.

2017

Carlson, David, Lisa K. David, Neil M. Gallagher, Mai-Anh T. Vu, Matthew Shirley, Rainbo Hultman, Joyce Wang, et al. “Dynamically Timed Stimulation of Corticolimbic Circuitry Activates a Stress-Compensatory Pathway.” Biol Psychiatry 82, no. 12 (December 15, 2017): 904–13. https://doi.org/10.1016/j.biopsych.2017.06.008.

Hultman, Rainbo, Kyle Ulrich, Benjamin Sachs, Cameron Blount, David Carlson, Nkemdilim Ndubuizu, Rosemary Bagot, et al. “A convergent depression vulnerability pathway encoded by emergent spatiotemporal dynamics.” BioRxiv, 2017, 154708–154708.

Pakman, Ari, Dar Gilboa, David Carlson, and Liam Paninski. “Stochastic Bouncy Particle Sampler.” In International Conference on Machine Learning, 2017.

Lee, JinHyung, David Carlson, Hooshmand Shokri, Weichi Yao, Georges Goetz, Espen Hagen, Eleanor Batty, E. J. Chichilnisky, Gaute Einevoll, and Liam Paninski. “YASS: Yet Another Spike Sorter.” Advances in Neural Information Processing Systems, 2017.

Li, Y., M. Murias, S. Major, G. Dawson, K. Dzirasa, L. Carin, and D. E. Carlson. “Targeting EEG/LFP synchrony with neural nets.” In Advances in Neural Information Processing Systems, 2017-December:4621–31, 2017.

Gallagher, N. M., K. Ulrich, A. Talbot, K. Dzirasa, L. Carin, and D. E. Carlson. “Cross-spectral factor analysis.” In Advances in Neural Information Processing Systems, 2017-December:6843–53, 2017.

Li, Yitong, Michael Murias, Samantha Major, Geraldine Dawson, Kafui Dzirasa, Lawrence Carin, and David E. Carlson. “Targeting EEG/LFP Synchrony with Neural Nets.” Advances in Neural Information Processing Systems, 2017.

Gallagher, Neil M., Kyle Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, and David E. Carlson. “Cross-Spectral Factor Analysis.” Advances in Neural Information Processing Systems, 2017.

2016

Hultman, Rainbo, Stephen D. Mague, Qiang Li, Brittany M. Katz, Nadine Michel, Lizhen Lin, Joyce Wang, et al. “Dysregulation of Prefrontal Cortex-Mediated Slow-Evolving Limbic Dynamics Drives Stress-Induced Emotional Pathology.” Neuron 91, no. 2 (July 20, 2016): 439–52. https://doi.org/10.1016/j.neuron.2016.05.038.

Carlson, D. E., P. Stinson, A. Pakman, and L. Paninski. “Partition functions from rao-blackwellized tempered sampling.” In 33rd International Conference on Machine Learning, ICML 2016, 6:4248–62, 2016.

Kaganovsky, Yan, Ikenna Odinaka, David Carlson, and Lawrence Carin. “Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization.” In Artificial Intelligence and Statistics, 1497–1505, 2016.

Song, Zhao, Ricardo Henao, David Carlson, and Lawrence Carin. “Learning sigmoid belief networks via Monte Carlo expectation maximization.” In Artificial Intelligence and Statistics, 1347–55, 2016.

Chen, Changyou, David Carlson, Zhe Gan, Chunyuan Li, and Lawrence Carin. “Bridging the gap between stochastic gradient MCMC and stochastic optimization.” In Artificial Intelligence and Statistics, 1051–60, 2016.

Li, Chunyuan, Changyou Chen, David Carlson, and Lawrence Carin. “Preconditioned stochastic gradient Langevin dynamics for deep neural networks.” In AAAI Conference on Artificial Intelligence, 2016.

Carlson, David, Ya-Ping Hsieh, Edo Collins, Lawrence Carin, and Volkan Cevher. “Stochastic Spectral Descent for Discrete Graphical Models,” 2016.