David Carlson

Civil and Environmental Engineering

Associate Professor of Civil and Environmental Engineering

David Carlson Profile Photo

Research Interests

Machine learning, predictive modeling, health data science, statistical neuroscience

Bio

My general research focus is on developing novel machine learning and artificial intelligence techniques that can be used to accelerate scientific discovery. I work extensively both on the fundamental theory and algorithms as well as translating them into scientific applications. I have extensive partnerships deploying machine learning techniques in environmental health, mental health, and neuroscience.

Education

Ph.D. Duke University, 2015

Positions

Associate Professor of Civil and Environmental Engineering
Assistant Professor in Biostatistics & Bioinformatics
Assistant Professor in the Department of Electrical and Computer Engineering
Assistant Professor of Computer Science
Faculty Network Member of the Duke Institute for Brain Sciences

Courses Taught

ME 555: Advanced Topics in Mechanical Engineering
EGR 393: Research Projects in Engineering
ECE 899: Special Readings in Electrical Engineering
ECE 494: Projects in Electrical and Computer Engineering
ECE 493: Projects in Electrical and Computer Engineering
ECE 392: Projects in Electrical and Computer Engineering
COMPSCI 394: Research Independent Study
COMPSCI 393: Research Independent Study
CEE 780: Internship
CEE 702: Graduate Colloquium
CEE 690: Advanced Topics in Civil and Environmental Engineering

Publications

Jain V, Mukherjee A, Banerjee S, Madhwal S, Bergin MH, Bhave P, 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 (Accepted). Atmospheric Environment. 2024 Dec 1;338.
Calhoun ZD, Black MS, Bergin M, Carlson D. Refining Citizen Climate Science: Addressing Preferential Sampling for Improved Estimates of Urban Heat. Environmental Science and Technology Letters. 2024 Aug 13;11(8):845–50.
Isaev D, Major S, Carpenter KLH, Grapel J, Chang Z, Martino MD, et al. Use of Computer Vision Analysis for Labeling Inattention Periods in Eeg Recordings With Visual Stimuli. Springer Science and Business Media LLC. 2024.
Hughes DN, Klein MH, Walder-Christensen KK, Thomas GE, Grossman Y, Waters D, et al. A widespread electrical brain network encodes anxiety in health and depressive states. bioRxiv. 2024 Jun 30;
Sui C, Jiang Z, Higueros G, Carlson D, Hsu PC. Designing electrodes and electrolytes for batteries by leveraging deep learning. Nano Research Energy. 2024 Jun 1;3(2).
Brandsen S, Chandrasekhar T, Franz L, Grapel J, Dawson G, Carlson D. Prevalence of bias against neurodivergence-related terms in artificial intelligence language models. Autism Res. 2024 Feb;17(2):234–48.
Walder-Christensen K, Abdelaal K, Klein H, Thomas GE, Gallagher NM, Talbot A, et al. Electome network factors: Capturing emotional brain networks related to health and disease. Cell Rep Methods. 2024 Jan 22;4(1):100691.
Calhoun ZD, Willard F, Ge C, Rodriguez C, Bergin M, Carlson D. Estimating the effects of vegetation and increased albedo on the urban heat island effect with spatial causal inference. Scientific reports. 2024 Jan;14(1):540.
Scott SR, Hailemariam PE, Bhave PV, Bergin MH, Carlson DE. Identifying Waste Burning Plumes Using High-Resolution Satellite Imagery and Machine Learning: A Case Study in the Maldives. Environmental Science and Technology Letters. 2023 Aug 8;10(8):642–8.
Talbot A, Dunson D, Dzirasa K, Carlson D. Estimating a brain network predictive of stress and genotype with supervised autoencoders. J R Stat Soc Ser C Appl Stat. 2023 Aug;72(4):912–36.
Sui C, Jiang Z, Higueros G, Carlson D, Hsu P-C. Designing electrodes and electrolytes for batteries by leveraging deep learning. American Chemical Society (ACS). 2023.
Jiang Z, Hou Z, Liu Y, Ren Y, Li K, Carlson D. Estimating Causal Effects using a Multi-task Deep Ensemble. In: Proceedings of machine learning research. 2023. p. 15023–40.
Grossman YS, Talbot A, Gallagher NM, Thomas GE, Fink AJ, Walder-Christensen KK, et al. A widespread oscillatory network encodes an aggressive internal state. Cold Spring Harbor Laboratory. 2022.
Block CL, Eroglu O, Mague SD, Smith CJ, Ceasrine AM, Sriworarat C, et al. Prenatal environmental stressors impair postnatal microglia function and adult behavior in males. Cell Rep. 2022 Aug 2;40(5):111161.
Thornton LL, Carlson DE, Wiesner MR. Predicting emerging chemical content in consumer products using machine learning. The Science of the total environment. 2022 Aug;834:154849.
Jiang Z, Zheng T, Bergin M, Carlson D. Improving spatial variation of ground-level PM2.5 prediction with contrastive learning from satellite imagery. Science of Remote Sensing. 2022 Jun 1;5.
Mague SD, Talbot A, Blount C, Walder-Christensen KK, Duffney LJ, Adamson E, et al. Brain-wide electrical dynamics encode individual appetitive social behavior. Neuron. 2022 May 18;110(10):1728-1741.e7.
Tu L, Talbot A, Gallagher NM, Carlson DE. Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility. IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society. 2022 Jan;70:5954–66.
Yuan S, Li Y, Wang D, Bai K, Carin L, Carlson D. Learning to Weight Filter Groups for Robust Classification. In: Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022. 2022. p. 3321–30.
Thornton LL, Carlson D, Wiesner M. Predicting emerging chemical content in consumer products using machine learning. American Chemical Society (ACS). 2021.
Gallagher NM, Dzirasa K, Carlson D. Directed Spectral Measures Improve Latent Network Models Of Neural Populations. Adv Neural Inf Process Syst. 2021 Dec;34:7421–35.
Bey AL, Walder-Christensen KK, Goffinet J, Adamson E, Lanier N, Mague SD, et al. 6.28 Identifying Networks Underlying Sleep Disruption in Autism Spectrum Disorder Mouse Models. In: Journal of the American Academy of Child & Adolescent Psychiatry. Elsevier BV; 2021. p. S167–S167.
Dunn TW, Marshall JD, Severson KS, Aldarondo DE, Hildebrand DGC, Chettih SN, et al. Geometric deep learning enables 3D kinematic profiling across species and environments. Nat Methods. 2021 May;18(5):564–73.
Zheng T, Bergin M, Wang G, Carlson D. 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. 2021 Apr 1;13(7).
Carson W, Talbot A, Carlson D. AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models. NeurIPS Workshop on Learning Meaningful Representations of Life. 2021;
Zhou T, Li Y, Wu Y, Carlson D. Estimating Uncertainty Intervals from Collaborating Networks. Journal of Machine Learning Research. 2021;
Loring Z, Mehrotra S, Piccini JP, Camm J, Carlson D, Fonarow GC, et al. 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. 2020. p. 1635–44.
Block CL, Eroglu O, Mague SD, Sriworarat C, Blount C, Malacon KE, et al. Prenatal Environmental Stressors Impair Postnatal Microglia Function and Adult Behavior in Males. Cold Spring Harbor Laboratory. 2020.
Isaev DY, Tchapyjnikov D, Cotten CM, Tanaka D, Martinez N, Bertran M, et al. Attention-Based Network for Weak Labels in Neonatal Seizure Detection. Proc Mach Learn Res. 2020 Aug;126:479–507.
Mague SD, Talbot A, Blount C, Duffney LJ, Walder-Christensen KK, Adamson E, et al. Brain-wide electrical dynamics encode an appetitive socioemotional state. Cold Spring Harbor Laboratory. 2020.
Lee J, Mitelut C, Shokri H, Kinsella I, Dethe N, Wu S, et al. YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina. Cold Spring Harbor Laboratory. 2020.
Isaev DY, Major S, Murias M, Carpenter KLH, Carlson D, Sapiro G, et al. Relative Average Look Duration and its Association with Neurophysiological Activity in Young Children with Autism Spectrum Disorder. Sci Rep. 2020 Feb 5;10(1):1912.
Cheng P, Li Y, Zhang X, Cheng L, Carlson D, Carin L. Gaussian-Process-Based Dynamic Embedding for Textual Networks. In: AAAI Conference on Artificial Intelligence. 2020.
Isaev DY, Major S, Murias M, Carpenter KLH, Carlson D, Sapiro G, et al. Relative Average Look Duration and its Association with Neurophysiological Activity in Young Children with Autism Spectrum Disorder. Scientific Reports. 2020;10:1–11.
Lee J, Mitelut C, Shokri H, Kinsella I, Dethe N, Wu S, et al. YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina. bioRxiv. 2020;
Zheng T, Bergin MH, Hu S, Miller J, Carlson DE. Estimating ground-level PM2. 5 using micro-satellite images by a convolutional neural network and random forest approach. Atmospheric Environment. 2020;117451–117451.
Talbot A, Dunson D, Dzirasa K, Carlson D. Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity. arXiv preprint arXiv:200405209. 2020;
Zheng T, Bergin MH, Sutaria R, Tripathi SN, Caldow R, Carlson DE. Gaussian process regression model for dynamically calibrating and surveilling a wireless low-cost particulate matter sensor network in Delhi. Atmospheric Measurement Techniques. 2019 Sep 26;12(9):5161–81.
Li Y, Gan Z, Shen Y, Liu J, Cheng Y, Wu Y, et al. Storygan: A sequential conditional gan for story visualization. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2019. p. 6322–31.
Zheng T, Bergin MH, Sutaria R, Tripathi SN, Caldow R, Carlson DE. Supplementary material to "Gaussian Process regression model for dynamically calibrating a wireless low-cost particulate matter sensor network in Delhi". 2019 Mar 1;
Rudin C, Carlson D. 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. INFORMS; 2019. p. 44–72.
Zheng T, Bergin MH, Sutaria R, Tripathi SN, Caldow R, Carlson DE. Gaussian Process regression model for dynamically calibrating a wireless low-cost particulate matter sensor network in Delhi. Atmos Meas Tech Discuss. 2019;1–28.
Norris C, Fang L, Barkjohn KK, Carlson D, Zhang Y, Mo J, et al. Sources of volatile organic compounds in suburban homes in Shanghai, China, and the impact of air filtration on compound concentrations. Chemosphere. 2019;231:256–68.
Li Y, Murias M, Major S, Dawson G, Carlson DE. On Target Shift in Adversarial Domain Adaptation. In: International Conference on Artificial Intelligence and Statistics. 2019.
Carlson D, Carin L. Continuing progress of spike sorting in the era of big data. Current opinion in neurobiology. 2019;55:90–6.
Zheng T, Bergin MH, Johnson KK, Tripathi SN, Shirodkar S, Landis MS, et al. Field evaluation of low-cost particulate matter sensors in high-and low-concentration environments. Atmospheric Measurement Techniques. 2018 Aug 22;11(8):4823–46.
Goldstein BA, Carlson D, Bhavsar NA. Subject Matter Knowledge in the Age of Big Data and Machine Learning. JAMA Netw Open. 2018 Aug 3;1(4):e181568.
Zheng T, Bergin MH, Johnson KK, Tripathi SN, Shirodkar S, Landis MS, et al. Supplementary material to "Field evaluation of low-cost particulate matter sensors in high and low concentration environments". 2018 Apr 23;
Hultman R, Ulrich K, Sachs BD, Blount C, Carlson DE, Ndubuizu N, et al. Brain-wide Electrical Spatiotemporal Dynamics Encode Depression Vulnerability. Cell. 2018 Mar 22;173(1):166-180.e14.
Vu M-AT, Adalı T, Ba D, Buzsáki G, Carlson D, Heller K, et al. A Shared Vision for Machine Learning in Neuroscience. J Neurosci. 2018 Feb 14;38(7):1601–7.
Zheng T, Bergin MH, Johnson KK, Tripathi SN, Shirodkar S, Landis MS, et al. Field evaluation of low-cost particulate matter sensors in high and low concentration environments. Atmospheric Measurement Techniques. 2018;
Li Y, Min MR, Shen D, Carlson D, Carin L. Video Generation From Text. In: AAAI Conference on Artificial Intelligence. 2018.
Li Y, Carlson DE, others. Extracting relationships by multi-domain matching. In: Advances in Neural Information Processing Systems. 2018. p. 6798–809.
Liang KJ, Heilmann G, Gregory C, Diallo SO, Carlson D, Spell GP, et al. 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. 2018. p. 1063203–1063203.
Carlson D, David LK, Gallagher NM, Vu M-AT, Shirley M, Hultman R, et al. Dynamically Timed Stimulation of Corticolimbic Circuitry Activates a Stress-Compensatory Pathway. Biol Psychiatry. 2017 Dec 15;82(12):904–13.
Hultman R, Ulrich K, Sachs BD, Blount C, Carlson DE, Ndubuizu N, et al. A convergent depression vulnerability pathway encoded by emergent spatiotemporal dynamics. Cold Spring Harbor Laboratory. 2017.
Lee J, Carlson D, Shokri H, Yao W, Goetz G, Hagen E, et al. YASS: Yet Another Spike Sorter. Cold Spring Harbor Laboratory. 2017.
Hultman R, Ulrich K, Sachs B, Blount C, Carlson D, Ndubuizu N, et al. A convergent depression vulnerability pathway encoded by emergent spatiotemporal dynamics. bioRxiv. 2017;154708–154708.
Pakman A, Gilboa D, Carlson D, Paninski L. Stochastic Bouncy Particle Sampler. In: International Conference on Machine Learning. 2017.
Gallagher NM, Ulrich K, Talbot A, Dzirasa K, Carin L, Carlson DE. Cross-Spectral Factor Analysis. Advances in Neural Information Processing Systems. 2017;
Li Y, Murias M, Major S, Dawson G, Dzirasa K, Carin L, et al. Targeting EEG/LFP synchrony with neural nets. In: Advances in Neural Information Processing Systems. 2017. p. 4621–31.
Lee J, Carlson D, Shokri H, Yao W, Goetz G, Hagen E, et al. YASS: Yet Another Spike Sorter. Advances in Neural Information Processing Systems. 2017;
Gallagher NM, Ulrich K, Talbot A, Dzirasa K, Carin L, Carlson DE. Cross-spectral factor analysis. In: Advances in Neural Information Processing Systems. 2017. p. 6843–53.
Li Y, Murias M, Major S, Dawson G, Dzirasa K, Carin L, et al. Targeting EEG/LFP Synchrony with Neural Nets. Advances in Neural Information Processing Systems. 2017;
Hultman R, Mague SD, Li Q, Katz BM, Michel N, Lin L, et al. Dysregulation of Prefrontal Cortex-Mediated Slow-Evolving Limbic Dynamics Drives Stress-Induced Emotional Pathology. Neuron. 2016 Jul 20;91(2):439–52.
Chen C, Carlson D, Gan Z, Li C, Carin L. Bridging the gap between stochastic gradient MCMC and stochastic optimization. In: Artificial Intelligence and Statistics. 2016. p. 1051–60.
Li C, Chen C, Carlson D, Carin L. Preconditioned stochastic gradient Langevin dynamics for deep neural networks. In: AAAI Conference on Artificial Intelligence. 2016.
Carlson DE, Stinson P, Pakman A, Paninski L. Partition functions from rao-blackwellized tempered sampling. In: 33rd International Conference on Machine Learning, ICML 2016. 2016. p. 4248–62.
Kaganovsky Y, Odinaka I, Carlson D, Carin L. Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization. In: Artificial Intelligence and Statistics. 2016. p. 1497–505.
Song Z, Henao R, Carlson D, Carin L. Learning sigmoid belief networks via Monte Carlo expectation maximization. In: Artificial Intelligence and Statistics. 2016. p. 1347–55.
Merel J, Carlson D, Paninski L, Cunningham JP. Neuroprosthetic decoder training as imitation learning. PLoS computational biology. 2016;12.
Carlson D, Hsieh Y-P, Collins E, Carin L, Cevher V. Stochastic Spectral Descent for Discrete Graphical Models. 2016;
Gan Z, Li C, Henao R, Carlson DE, Carin L. Deep temporal sigmoid belief networks for sequence modeling. Advances in Neural Information Processing Systems. 2015;2467–75.
Carlson D. Stochastic Inference and Bayesian Nonparametric Models in Electrophysiological Time Series. 2015.
Carlson D, Cevher V, Carin L. Stochastic spectral descent for restricted Boltzmann machines. In: Artificial Intelligence and Statistics. 2015. p. 111–9.
Carlson DE, Collins E, Hsieh Y-P, Carin L, Cevher V. Preconditioned spectral descent for deep learning. In: Advances in Neural Information Processing Systems. 2015. p. 2971–9.
Gan Z, Henao R, Carlson D, Carin L. Learning deep sigmoid belief networks with data augmentation. In: Artificial Intelligence and Statistics. 2015. p. 268–76.
Ulrich KR, Carlson DE, Dzirasa K, Carin L. GP kernels for cross-spectrum analysis. In: Advances in neural information processing systems. 2015. p. 1999–2007.
Gan Z, Chen C, Henao R, Carlson D, Carin L. Scalable Deep Poisson Factor Analysis for Topic Modeling. In: ICML. 2015.
Gan Z, Li C, Henao R, Carlson DE, Carin L. Deep Temporal Sigmoid Belief Networks for Sequence Modeling. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R, editors. NIPS. 2015. p. 2467–75.
Carlson DE, Borg JS, Dzirasa K, Carin L. On the Relationship Between LFP & Spiking Data. In: Advances in Neural Information Processing Systems. 2014.
Hu C, Ryu E, Carlson D, Wang Y, Carin L. Latent Gaussian models for topic modeling. In: Artificial Intelligence and Statistics. 2014. p. 393–401.
Wang L, Carlson DE, Rodrigues MRD, Calderbank R, Carin L. A Bregman matrix and the gradient of mutual information for vector Poisson and Gaussian channels. IEEE Transactions on Information Theory. 2014;60:2611–29.
Ulrich K, Carlson DE, Lian W, Borg JS, Dzirasa K, Carin L. Analysis of Brain States from Multi-Region LFP Time-Series. In: Advances in Neural Information Processing Systems. 2014.
Carlson DE, Borg JS, Dzirasa K, Carin L. On the relations of LFPs & Neural Spike Trains. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, editors. NIPS. 2014. p. 2060–8.
Carlson DE, Rao V, Vogelstein JT, Carin L. Real-time inference for a gamma process model of neural spiking. Advances in Neural Information Processing Systems. 2013;2805–13.
Wang L, Carlson DE, Rodrigues M, Wilcox D, Calderbank R, Carin L. Designed measurements for vector count data. Advances in neural information processing systems. 2013;1142–50.
Carlson DE, Vogelstein JT, Wu Q, Lian W, Zhou M, Stoetzner CR, et al. Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling. IEEE Transactions on Biomedical Engineering. 2013;61:41–54.
Karumbaiah L, Saxena T, Carlson D, Patil K, Patkar R, Gaupp EA, et al. Relationship between intracortical electrode design and chronic recording function. Biomaterials. 2013;34:8061–74.
Chen M, Carlson D, Zaas A, Woods CW, Ginsburg GS, Hero A, et al. Detection of viruses via statistical gene expression analysis. IEEE Trans Biomed Eng. 2011 Mar;58(3):468–79.
Chen B, Carlson DE, Carin L. On the analysis of multi-channel neural spike data. In: Advances in Neural Information Processing Systems. 2011. p. 936–44.
Cheng P, Li Y, Zhang X, Chen L, Carlson D, Carin L. Dynamic Embedding on Textual Networks via a Gaussian Process.

In The News

For Many Urban Residents, It’s Hotter Than Their Weather App Says (Jun 27, 2024 | Pratt School of Engineering)
Eyes in the Sky Bring Good News on Trash Burning in the Maldives (Jul 14, 2023 | Pratt School of Engineering)
Students Find Interdisciplinary Exploration and Connection in Winter Breakaway Courses (Jan 21, 2021 | )
David Carlson: Engineering and Machine Learning for Better Medicine (Jan 9, 2018 | Duke Research Blog)
David Carlson: Generating Scientific Understanding from Machine Learning (Aug 24, 2017 | Pratt School of Engineering)