Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., Duke University 2015
• 

  Duke Appointment History

  • Assistant Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2018
• Recognition
• 

  In the News

  • JAN 9, 2018

    Duke Research Blog

    David Carlson: Engineering and Machine Learning for Better Medicine

  • AUG 24, 2017

    Pratt School of Engineering

    David Carlson: Generating Scientific Understanding from Machine Learning
• Research
• 

  Selected Grants

  • QT prolonging medications and sudden cardiac death among individuals on hemodialysis awarded by University of North Carolina - Chapel Hill 2020 - 2025
  • Dissecting and modifying temporal dynamics underlying stress dysfunction awarded by National Institutes of Health 2019 - 2024
  • Duke University Program in Environmental Health awarded by National Institutes of Health 2013 - 2024
  • Predicting Urinary Continence Status with Sacral Neuromodulation and Botulinum Toxin Treatments awarded by National Institutes of Health 2020 - 2023
  • Developing a comprehensive model for peripheral nerve stimulation of gastrointestinal function awarded by National Institutes of Health 2019 - 2023
  • Network Dynamics of Negative and Positive Valence Systems in Decision Making awarded by National Institutes of Health 2019 - 2023
  • Uncovering Population-Level Cellular Relationships to Behavior via Mesoscale Networks awarded by National Institutes of Health 2019 - 2022
  • The neurophysiology of impulsive sensation seeking awarded by University of Pittsburgh 2018 - 2020
  • Enabling Stress Resistance awarded by National Institutes of Health 2012 - 2018
• Publications & Artistic Works
• 

  Selected Publications

  • Academic Articles

    • Zheng, T., M. H. Bergin, S. Hu, J. Miller, and D. E. Carlson. “Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach.” Atmospheric Environment 230 (June 1, 2020). https://doi.org/10.1016/j.atmosenv.2020.117451.
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    • Isaev, Dmitry Yu, Samantha Major, Michael Murias, Kimberly L. H. Carpenter, David Carlson, Guillermo Sapiro, and Geraldine Dawson. “Relative Average Look Duration and its Association with Neurophysiological Activity in Young Children with Autism Spectrum Disorder.” Scientific Reports 10 (2020): 1–11.
    • Lee, JinHyung, Catalin Mitelut, Hooshmand Shokri, Ian Kinsella, Nishchal Dethe, Shenghao Wu, Kevin Li, et al. “YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina.” Biorxiv, 2020.
    • Talbot, Austin, David B. Dunson, Kafui Dzirasa, and David E. Carlson. “Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity.” Corr abs/2004.05209 (2020).
    • Zhou, Tianhui, Yitong Li, Yuan Wu, and David Carlson. “Estimating Uncertainty Intervals from Collaborating Networks.” Arxiv Preprint Arxiv:2002.05212, 2020.
    • Zheng, T., M. H. Bergin, R. Sutaria, S. N. Tripathi, R. Caldow, and D. E. Carlson. “Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi.” Atmospheric Measurement Techniques 12, no. 9 (September 26, 2019): 5161–81. https://doi.org/10.5194/amt-12-5161-2019.
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    • Norris, Christina, Lin Fang, Karoline K. Barkjohn, David Carlson, Yinping Zhang, Jinhan Mo, Zhen Li, et al. “Sources of volatile organic compounds in suburban homes in Shanghai, China, and the impact of air filtration on compound concentrations.” Chemosphere 231 (September 2019): 256–68. https://doi.org/10.1016/j.chemosphere.2019.05.059.
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    • Carlson, David, and Lawrence Carin. “Continuing progress of spike sorting in the era of big data.” Current Opinion in Neurobiology 55 (April 2019): 90–96. https://doi.org/10.1016/j.conb.2019.02.007.
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    • 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.
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    • 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.
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    • 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.
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    • Johnson, K. K., M. H. Bergin, A. G. Russell, and G. S. W. Hagler. “Field test of several low-cost particulate matter sensors in high and low concentration urban environments.” Aerosol and Air Quality Research 18, no. 3 (March 1, 2018): 565–78. https://doi.org/10.4209/aaqr.2017.10.0418.
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    • 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.
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    • 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.
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    • Gallagher, Neil, Kyle R. Ulrich, Austin Talbot, Kafui Dzirasa, Lawrence Carin, and David E. Carlson. “Cross-Spectral Factor Analysis.” Edited by Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett. Nips, 2017, 6842–52.
    • 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.
    • Lee, J. H., D. Carlson, H. Shokri, W. Yao, G. Goetz, E. Hagen, E. Batty, E. J. Chichilnisky, G. Einevoll, and L. Paninski. “YASS: Yet another spike sorter.” Advances in Neural Information Processing Systems 2017-December (January 1, 2017): 4003–13.
    • Li, Yitong, Michael Murias, Samantha Major, Geraldine Dawson, Kafui Dzirasa, Lawrence Carin, and David E. Carlson. “Targeting EEG/LFP Synchrony with Neural Nets.” Edited by Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett. Nips, 2017, 4620–30.
    • 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.
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    • Merel, Josh, David Carlson, Liam Paninski, and John P. Cunningham. “Neuroprosthetic Decoder Training as Imitation Learning.” Plos Computational Biology 12, no. 5 (May 18, 2016): e1004948. https://doi.org/10.1371/journal.pcbi.1004948.
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    • Carlson, D., Y. P. Hsieh, E. Collins, L. Carin, and V. Cevher. “Stochastic Spectral Descent for Discrete Graphical Models.” Ieee Journal on Selected Topics in Signal Processing 10, no. 2 (March 1, 2016): 296–311. https://doi.org/10.1109/JSTSP.2015.2505684.
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    • Gan, Z., C. Li, R. Henao, D. Carlson, and L. Carin. “Deep temporal sigmoid belief networks for sequence modeling.” Advances in Neural Information Processing Systems 2015-January (January 1, 2015): 2467–75.
    • Carlson, David E., Joshua T. Vogelstein, Joshua T. Qisong Wu, Joshua T. Wenzhao Lian, Joshua T. Mingyuan Zhou, Colin R. Stoetzner, Daryl Kipke, Douglas Weber, David B. Dunson, and Lawrence Carin. “Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling.” Ieee Transactions on Bio Medical Engineering 61, no. 1 (January 2014): 41–54. https://doi.org/10.1109/tbme.2013.2275751.
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    • Wang, L., D. E. Carlson, M. R. D. Rodrigues, R. Calderbank, and L. Carin. “A bregman matrix and the gradient of mutual information for vector poisson and gaussian channels.” Ieee Transactions on Information Theory 60, no. 5 (January 1, 2014): 2611–29. https://doi.org/10.1109/TIT.2014.2307068.
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    • Karumbaiah, Lohitash, Tarun Saxena, David Carlson, Ketki Patil, Radhika Patkar, Eric A. Gaupp, Martha Betancur, Garrett B. Stanley, Lawrence Carin, and Ravi V. Bellamkonda. “Relationship between intracortical electrode design and chronic recording function.” Biomaterials 34, no. 33 (November 2013): 8061–74. https://doi.org/10.1016/j.biomaterials.2013.07.016.
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    • Carlson, D., V. Rao, J. Vogelstein, and L. Carin. “Real-time inference for a gamma process model of neural spiking.” Advances in Neural Information Processing Systems, January 1, 2013.
    • Wang, L., D. Carlson, M. D. Rodrigues, D. Wilcox, R. Calderbank, and L. Carin. “Designed measurements for vector count data.” Advances in Neural Information Processing Systems, January 1, 2013.
    • Chen, Minhua, David Carlson, Aimee Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Alfred Hero, Joseph Lucas, and Lawrence Carin. “Detection of viruses via statistical gene expression analysis.” Ieee Trans Biomed Eng 58, no. 3 (March 2011): 468–79. https://doi.org/10.1109/TBME.2010.2059702.
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    • Cheng, Pengyu, Yitong Li, Xinyuan Zhang, Liqun Chen, David Carlson, and Lawrence Carin. “Dynamic Embedding on Textual Networks via a Gaussian Process,” n.d.
    • 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",” n.d. https://doi.org/10.5194/amt-2018-111-supplement.
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    • Zheng, Tongshu, Michael H. Bergin, Ronak Sutaria, Sachchida N. Tripathi, Robert Caldow, and David E. Carlson. “Gaussian Process regression model for dynamically calibrating a wireless low-cost particulate matter sensor network in Delhi,” n.d. https://doi.org/10.5194/amt-2019-55.
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    • Zheng, Tongshu, Michael H. Bergin, Ronak Sutaria, Sachchida N. Tripathi, Robert Caldow, and David E. Carlson. “Supplementary material to "Gaussian Process regression model for dynamically calibrating a wireless low-cost particulate matter sensor network in Delhi",” n.d. https://doi.org/10.5194/amt-2019-55-supplement.
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  • Book Sections

    • Rudin, Cynthia, and David Carlson. “The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to Be More Effective at Data Analysis.” In Operations Research & Management Science in the Age of Analytics, 44–72. INFORMS, 2019.

  • Conference Papers

    • Cheng, Pengyu, Yitong Li, Xinyuan Zhang, Liqun Cheng, David Carlson, and Lawrence Carin. “Gaussian-Process-Based Dynamic Embedding for Textual Networks.” In Aaai Conference on Artificial Intelligence, 2020.
    • Li, Y., M. Murias, S. Major, G. Dawson, and D. E. Carlson. “On target shift in adversarial domain adaptation.” In Aistats 2019  22nd International Conference on Artificial Intelligence and Statistics, 2020.
    • Loring, Zak, Suchit Mehrotra, Jonathan P. Piccini, John Camm, David Carlson, Gregg C. Fonarow, Keith A. Fox, Eric D. Peterson, Karen S. Pieper, and Ajay K. Kakkar. “Machine Learning Does Not Improve Upon Traditional Regression in Predicting Outcomes in Atrial Fibrillation: An Analysis of the ORBIT-AF and GARFIELD-AF Registries.” In Circulation, Vol. 140, 2019.
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    • Li, Y., Z. Gan, Y. Shen, J. Liu, Y. Cheng, Y. Wu, L. Carin, D. Carlson, and J. Gao. “Storygan: A sequential conditional gan for story visualization.” In Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June:6322–31, 2019. https://doi.org/10.1109/CVPR.2019.00649.
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    • Li, Y., M. Murias, S. Major, G. Dawson, and D. E. Carlson. “Extracting relationships by multi-domain matching.” In Advances in Neural Information Processing Systems, 2018-December:6798–6809, 2018.
    • Li, Y., M. R. Min, D. Shen, D. Carlson, and L. Carin. “Video generation from text.” In 32nd Aaai Conference on Artificial Intelligence, Aaai 2018, 7065–72, 2018.
    • Liang, K. J., G. Heilmann, C. Gregory, S. O. Diallo, D. Carlson, G. P. Spell, J. B. Sigman, K. Roe, and L. Carin. “Automatic threat recognition of prohibited items at aviation checkpoint with x-ray imaging: A deep learning approach.” In Proceedings of Spie  the International Society for Optical Engineering, Vol. 10632, 2018. https://doi.org/10.1117/12.2309484.
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    • 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, 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.
    • Pakman, A., D. Gilboa, D. Carlson, and L. Paninski. “Stochastic bouncy particle sampler.” In 34th International Conference on Machine Learning, Icml 2017, 6:4186–4208, 2017.
    • 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.
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    • Chen, C., D. Carlson, Z. Gan, C. Li, and L. Carin. “Bridging the gap between stochastic gradient MCMC and stochastic optimization.” In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Aistats 2016, 1051–60, 2016.
    • Kaganovsky, Y., I. Odinaka, D. Carlson, and L. Carin. “Parallel majorization minimization with dynamically restricted domains for nonconvex optimization.” In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Aistats 2016, 1497–1505, 2016.
    • Li, C., C. Chen, D. Carlson, and L. Carin. “Preconditioned stochastic gradient langevin dynamics for deep neural networks.” In 30th Aaai Conference on Artificial Intelligence, Aaai 2016, 1788–94, 2016.
    • Song, Z., R. Henao, D. Carlson, and L. Carin. “Learning sigmoid belief networks via monte carlo expectation maximization.” In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Aistats 2016, 2016.
    • Carlson, D. E., E. Collins, Y. P. Hsieh, L. Carin, and V. Cevher. “Preconditioned spectral descent for deep learning.” In Advances in Neural Information Processing Systems, 2015-January:2971–79, 2015.
    • Carlson, D., V. Cevher, and L. Carin. “Stochastic spectral descent for restricted boltzmann machines.” In Journal of Machine Learning Research, 38:111–19, 2015.
    • Gan, Z., C. Chen, R. Henao, D. Carlson, and L. Carin. “Scalable deep Poisson factor analysis for topic modeling.” In 32nd International Conference on Machine Learning, Icml 2015, 3:1823–32, 2015.
    • Gan, Z., R. Henao, D. Carlson, and L. Carin. “Learning deep sigmoid belief networks with data augmentation.” In Journal of Machine Learning Research, 38:268–76, 2015.
    • Gan, Zhe, Chunyuan Li, Ricardo Henao, David E. Carlson, and Lawrence Carin. “Deep Temporal Sigmoid Belief Networks for Sequence Modeling.” In Nips, edited by Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett, 2467–75, 2015.
    • Ulrich, K., D. E. Carlson, K. Dzirasa, and L. Carin. “GP kernels for cross-spectrum analysis.” In Advances in Neural Information Processing Systems, 2015-January:1999–2007, 2015.
    • Carlson, D. E., J. S. Borg, K. Dzirasa, and L. Carin. “On the relationship between LFP & spiking data.” In Advances in Neural Information Processing Systems, 3:2060–68, 2014.
    • Carlson, David E., Jana Schaich Borg, Kafui Dzirasa, and Lawrence Carin. “On the relations of LFPs & Neural Spike Trains.” In Nips, edited by Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger, 2060–68, 2014.
    • Hu, C., E. Ryu, D. Carlson, Y. Wang, and L. Carin. “Latent Gaussian models for topic modeling.” In Journal of Machine Learning Research, 33:393–401, 2014.
    • Ulrich, K., D. E. Carlson, W. Lian, J. S. Borg, K. Dzirasa, and L. Carin. “Analysis of brain states from multi-region LFP time-series.” In Advances in Neural Information Processing Systems, 3:2483–91, 2014.
    • Chen, Bo, David E. Carlson, and Lawrence Carin. “On the analysis of multi-channel neural spike data.” In Advances in Neural Information Processing Systems, 936–44, 2011.

  • Theses and Dissertations

    • Carlson, David. “Stochastic Inference and Bayesian Nonparametric Models in Electrophysiological Time Series,” 2015.
• Teaching & Mentoring
• 

  Recent Courses

  • CEE 690: Advanced Topics in Civil and Environmental Engineering 2020
  • CEE 692: Independent Study: Advanced Topics in Civil and Environmental Engineering 2020
  • ECE 494: Projects in Electrical and Computer Engineering 2020
  • EGR 190: Special Topics in Engineering 2020
  • EGR 590: Special Topics in Engineering 2020
  • CEE 780: Internship 2019
  • COMPSCI 393: Research Independent Study 2019
  • ECE 899: Special Readings in Electrical Engineering 2019
  • POE 190: Practice Oriented Education 2019
  • POE 790: Practice Oriented Education 2019
  • ECE 590: Advanced Topics in Electrical and Computer Engineering 2018

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