Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., Duke University 2015
• 

  Previous Appointments & Affiliations

  • Assistant Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2018 - 2021
  • Assistant Professor in Biostatistics and Bioinformatics, Biostatistics & Bioinformatics, Basic Science Departments 2017 - 2020
• Recognition
• 

  In the News

  • JAN 21, 2021

    Students Find Interdisciplinary Exploration and Connection in Winter Breakaway Courses

  • 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

  • NeuroSimNIBS: Integrated electric field and neuronal response modeling for transcranial electric and magnetic stimulation awarded by National Institutes of Health 2022 - 2027
  • Title: NRT-FW-HTF: NSF Traineeship in the Advancement of Surgical Technologies awarded by  National Science Foundation 2021 - 2026
  • MULTIREGIONAL ELECTRICAL ENCODING OF SOCIAL AGGRESSION awarded by National Institutes of Health 2021 - 2026
  • From Deep Neural Networks to Kernel Machines: Advanced Data Science for Verification and Forensics awarded by Georgia Institute of Technology 2019 - 2024
  • 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
• 

  External Relationships

  • Jannsen Pharmaceuticals
• Publications & Artistic Works
• 

  Selected Publications

  • Academic Articles

    • Jiang, Ziyang, Tongshu Zheng, Mike Bergin, and David Carlson. “Improving spatial variation of ground-level PM2.5 prediction with contrastive learning from satellite imagery.” Science of Remote Sensing 5 (June 2022): 100052–100052. https://doi.org/10.1016/j.srs.2022.100052.
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    • 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.
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    • Zheng, T., M. Bergin, G. Wang, and D. Carlson. “Local PM2.5 hotspot detector at 300 m resolution: A random forest-convolutional neural network joint model jointly trained on satellite images and meteorology.” Remote Sensing 13, no. 7 (April 1, 2021). https://doi.org/10.3390/rs13071356.
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    • Carson, William, Austin Talbot, and David Carlson. “AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models.” Neurips Workshop on Learning Meaningful Representations of Life, 2021.
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    • Dunn, Timothy, 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.” Nature Methods (In press) (2021).
    • Zhou, Tianhui, Yitong Li, Yuan Wu, and David Carlson. “Estimating Uncertainty Intervals from Collaborating Networks.” Journal of Machine Learning Research, 2021.
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    • Isaev, Dmitry Yu, Dmitry Tchapyjnikov, C Michael Cotten, David Tanaka, Natalia Martinez, Martin Bertran, Guillermo Sapiro, and David Carlson. “Attention-Based Network for Weak Labels in Neonatal Seizure Detection.” Proc Mach Learn Res 126 (August 2020): 479–507.
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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 Dunson, Kafui Dzirasa, and David Carlson. “Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity.” Arxiv Preprint Arxiv:2004.05209, 2020.
       Open Access Copy
    • Zheng, Tongshu, Michael H. Bergin, Shijia Hu, Joshua Miller, and David E. Carlson. “Estimating ground-level PM2. 5 using micro-satellite images by a convolutional neural network and random forest approach.” Atmospheric Environment, 2020, 117451–117451.
    • 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 (2019): 256–68.
    • Carlson, David, and Lawrence Carin. “Continuing progress of spike sorting in the era of big data.” Current Opinion in Neurobiology 55 (2019): 90–96.
    • 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.” Atmos. Meas. Tech. Discuss., 2019, 1–28.
    • 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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    • 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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    • 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.
    • 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.
    • 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.
    • 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.” Biological Psychiatry 82 (2017): 904–13.
    • 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.
    • 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.
    • 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 (2016).
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    • Carlson, David, Ya-Ping Hsieh, Edo Collins, Lawrence Carin, and Volkan Cevher. “Stochastic Spectral Descent for Discrete Graphical Models,” 2016.
    • Gan, Zhe, Chunyuan Li, Ricardo Henao, David E. Carlson, and Lawrence Carin. “Deep temporal sigmoid belief networks for sequence modeling.” Advances in Neural Information Processing Systems, 2015, 2467–75.
    • Wang, Liming, David Edwin Carlson, Miguel R. D. Rodrigues, Robert Calderbank, and Lawrence Carin. “A Bregman matrix and the gradient of mutual information for vector Poisson and Gaussian channels.” Ieee Transactions on Information Theory 60 (2014): 2611–29.
    • Carlson, David E., Joshua T. Vogelstein, Qisong Wu, Wenzhao Lian, 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 Biomedical Engineering 61 (2013): 41–54.
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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 (2013): 8061–74.
    • Carlson, David E., Vinayak Rao, Joshua T. Vogelstein, and Lawrence Carin. “Real-time inference for a gamma process model of neural spiking.” Advances in Neural Information Processing Systems, 2013, 2805–13.
    • Wang, Liming, David E. Carlson, Miguel Rodrigues, David Wilcox, Robert Calderbank, and Lawrence Carin. “Designed measurements for vector count data.” Advances in Neural Information Processing Systems, 2013, 1142–50.
    • 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. “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

    • 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 &Amp; Adolescent Psychiatry, 60:S167–S167. Elsevier BV, 2021. https://doi.org/10.1016/j.jaac.2021.09.101.
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    • Loring, Zak, Suchit Mehrotra, Jonathan P. Piccini, John Camm, David Carlson, Gregg C. Fonarow, Keith A. A. Fox, Eric D. Peterson, Karen 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 Europace, 22:1635–44, 2020. https://doi.org/10.1093/europace/euaa172.
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    • 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., 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, Yitong, Michael Murias, Samantha Major, Geraldine Dawson, and David E. Carlson. “On Target Shift in Adversarial Domain Adaptation.” In International Conference on Artificial Intelligence and Statistics, 2019.
    • 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.
    • 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.
    • 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, Ari, Dar Gilboa, David Carlson, and Liam Paninski. “Stochastic Bouncy Particle Sampler.” In International Conference on Machine Learning, 2017.
    • 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.
    • 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.
    • 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.
    • 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.
    • Gan, Zhe, Ricardo Henao, David Carlson, and Lawrence Carin. “Learning deep sigmoid belief networks with data augmentation.” In Artificial Intelligence and Statistics, 268–76, 2015.
    • Carlson, David E., Edo Collins, Ya-Ping Hsieh, Lawrence Carin, and Volkan Cevher. “Preconditioned spectral descent for deep learning.” In Advances in Neural Information Processing Systems, 2971–79, 2015.
    • Carlson, David, Volkan Cevher, and Lawrence Carin. “Stochastic spectral descent for restricted Boltzmann machines.” In Artificial Intelligence and Statistics, 111–19, 2015.
    • Gan, Zhe, Changyou Chen, Ricardo Henao, David Carlson, and Lawrence Carin. “Scalable Deep Poisson Factor Analysis for Topic Modeling.” In Icml, 2015.
    • Ulrich, Kyle R., David E. Carlson, Kafui Dzirasa, and Lawrence Carin. “GP kernels for cross-spectrum analysis.” In Advances in Neural Information Processing Systems, 1999–2007, 2015.
    • 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, Changwei, Eunsu Ryu, David Carlson, Yingjian Wang, and Lawrence Carin. “Latent Gaussian models for topic modeling.” In Artificial Intelligence and Statistics, 393–401, 2014.
    • Carlson, David E., Jana Schaich Borg, Kafui Dzirasa, and Lawrence Carin. “On the Relationship Between LFP & Spiking Data.” In Advances in Neural Information Processing Systems, 2014.
    • Ulrich, Kyle, David E. Carlson, Wenzhao Lian, Jana Schaich Borg, Kafui Dzirasa, and Lawrence Carin. “Analysis of Brain States from Multi-Region LFP Time-Series.” In Advances in Neural Information Processing Systems, 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 2022
  • CEE 780: Internship 2022
  • COMPSCI 394: Research Independent Study 2022
  • CEE 690: Advanced Topics in Civil and Environmental Engineering 2021
  • CEE 780: Internship 2021
  • 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

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