The mDOT Center

Transforming health and wellness via temporally-precise mHealth interventions
mDOT@MD2K.org
901.678.1526
 

Publications

mDOT Center > Resources > Publications
  1. N. Shukla, B.M. Marlin. Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series. In Proceedings of the International Conference on Learning Representations. 2022.

  2. Tung, K., Torre, S.D., Mistiri, M.E., Braganca, R.B., Hekler, E.B., Pavel, M., Rivera, D.E., Klasnja, P., Spruijt-Metz, D., & Marlin, B.M. (2022). BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data. Accepted at IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) 2022.  ArXiv, abs/2209.05581.

  3. A. Xu, A. Moreno, S. Nagesh, V. B. Aydemir, D. W. Wetter, S. Kumar, and J. M. Rehg. PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation. Proceedings 36th Conference on Neural Information Processing Systems (NeurIPS), Track on Datasets and Benchmarks, 2022. Accepted for publication.  NIHMS1839168.

  4. Md Azim Ullah, Soujanya Chatterjee, Christopher P. Fagundes, Cho Lam, Inbal Nahum-Shani, James M. Rehg, David W. Wetter, and Santosh Kumar. 2022. mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 3, Article 143 (September 2022), 29 pages.

  5. Moreno, Z. Wu, S. Nagesh, W. Dempsey, and J. M. Rehg. Kernel Multimodal Continuous Attention. Proceedings 36th Conference on Neural Information Processing Systems (NeurIPS), 2022. Accepted for publication.

  6. Liao, Z. Qi, R. Wan, P. Klasnja, S. Murphy Batch Policy Learning in Average Reward Markov Decision Processes. Annals of Statistics. 2022 Sept 17.

  7. Trella AL, Zhang KW, Nahum-Shani I, Shetty V, Doshi-Velez F, Murphy SA. Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines. Algorithms. 2022; 15(8). NIHMSID: NIHMS1825651.

  8. Bertsimas D., Klasnja, P., Murphy, S., & L. Na (2022) Data-driven Interpretable Policy Construction for Personalized Mobile Health. 2022 IEEE International Conference on Digital Health. 2022, pp. 13-22.

  9. Coppersmith DDL, Dempsey W, Kleiman EM, Bentley KH, Murphy SA, Nock MK. Just-in-Time Adaptive Interventions for Suicide Prevention: Promise, Challenges, and Future Directions.  2022 Jul 18;:1-17. doi: 10.1080/00332747.2022.2092828. [Epub ahead of print] PubMed PMID: 35848800; NIHMSID:NIHMS1821198.

  10. Nahum-Shani I, Shaw SD, Carpenter SM, Murphy SA, Yoon C.Engagement in digital interventions. Am Psychol. 2022 Mar 17;. doi: 10.1037/amp0000983. [Epub ahead of print] PubMed PMID: 35298199; NIHMSID:NIHMS1800077.

  11. Qian T, Walton AE, Collins LM, Klasnja P, Lanza ST, Nahum-Shani I, Rabbi M, Russell MA, Walton MA, Yoo H, Murphy SA.The microrandomized trial for developing digital interventions: Experimental design and data analysis considerations. Psychol Methods. 2022 Jan 13;. doi: 10.1037/met0000283. [Epub ahead of print] PubMed PMID: 35025583; PubMed Central PMCID: PMC9276848.

  12. Nahum-Shani I, Rabbi M, Yap J, Philyaw-Kotov ML, Klasnja P, Bonar EE, Cunningham RM, Murphy SA, Walton MA.Translating strategies for promoting engagement in mobile health: A proof-of-concept microrandomized trial. Health Psychol. 2021 Dec;40(12):974-987. doi: 10.1037/hea0001101. Epub 2021 Nov 4. PubMed PMID: 34735165; PubMed Central PMCID: PMC8738098.

  13. Zhang KW, Janson L, Murphy SA.Statistical Inference with M-Estimators on Adaptively Collected Data. Adv Neural Inf Process Syst. 2021 Dec;34:7460-7471. PubMed PMID: 35757490; PubMed Central PMCID: PMC9232184.

  14. Nahum-Shani I, Potter LN, Lam CY, Yap J, Moreno A, Stoffel R, Wu Z, Wan N, Dempsey W, Kumar S, Ertin E, Murphy SA, Rehg JM, Wetter DW.The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocol. Contemp Clin Trials. 2021 Nov;110:106513. doi: 10.1016/j.cct.2021.106513. Epub 2021 Jul 24. PubMed PMID: 34314855; PubMed Central PMCID: PMC8824313 .

  15. Civek, Burak C., and Emre Ertin. “Bayesian Sparse Blind Deconvolution Using MCMC Methods Based on Normal-Inverse-Gamma Prior.” IEEE Transactions on Signal Processing 70 (2022): 1256-1269. NIHMS1839071.

  16. Civek, Burak C., and Emre Ertin.”MCMC Methods for Estimation of Thoracic Fluid Levels using UWB Radar,.” Poster at IEEE BHI-BSN 2022.

  17. Saleheen, Nazir, Md Azim Ullah, Supriyo Chakraborty, Deniz S. Ones, Mani Srivastava, and Santosh Kumar. “WristPrint: Characterizing User Re-identification Risks from Wrist-worn Accelerometry Data.” In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, pp. 2807-2823. 2021. NIHMS1839082

  18. Liu, Renju, Luis Garcia, and Mani Srivastava. “Aerogel: Lightweight Access Control Framework for WebAssembly-Based Bare-Metal IoT Devices.” In 2021 IEEE/ACM Symposium on Edge Computing (SEC), pp. 94-105. IEEE, 2021. NIHMS1839084

  19. Saha, Swapnil Sayan, Sandeep Singh Sandha, Luis Antonio Garcia, and Mani Srivastava. “Tinyodom: Hardware-aware efficient neural inertial navigation.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies6, no. 2 (2022): 1-32. NIHMS1839164

  20. Saha, Swapnil Sayan, Sandeep Singh Sandha, Siyou Pei, Vivek Jain, Ziqi Wang, Yuchen Li, Ankur Sarker, and Mani Srivastava. “Auritus: An Open-Source Optimization Toolkit for Training and Development of Human Movement Models and Filters Using Earables.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies6, no. 2 (2022): 1-34. NIHMS1839165

  21. Saha, Swapnil Sayan, Sandeep Singh Sandha, and Mani Srivastava. “Machine Learning for Microcontroller-Class Hardware–A Review.” arXiv preprint arXiv:2205.14550(2022). Accepted for IEEE J. Sensors ’22.

  22. Saha, Swapnil Sayan, Sandeep Singh Sandha, Mohit Aggarwal, and Mani Srivastava. “THIN-Bayes: Platform-Aware Machine Learning for Low-End IoT Devices.” Poster at the tinyML Summit 2022.

  23. Ho E, Jeon MJ, Lee MH, Lou JW, Pfammatter A, Shetty V, Spring. Fostering Interdisciplinary Collaboration: A Longitudinal Social Network Analysis of the mHealth Training Institutes. Journal of Clinical and Translational Science 5:e191, 1-12

  24. Lee MH, Ho E, Lou JW, Pfammatter A, Shetty V, Spring, Jeon MJ. Dynamics between mHealth scholars’ communication networks and team psychological safety: Analysis with actor-oriented social network models.PLOS One (in press).

  25. Ho E, Jeon MJ, Lee MH, Lou JW, Pfammatter A, Shetty V, Spring. Fostering Interdisciplinary Collaboration: A Longitudinal Social Network Analysis of the mHealth Training Institutes. Journal of Clinical and Translational Science 5:e191, 1-12

  26. Lee MH, Ho E, Lou JW, Pfammatter A, Shetty V, Spring, Jeon MJ. Dynamics between mHealth scholars’ communication networks and team psychological safety: Analysis with actor-oriented social network models.PLOS One (in press).

  27. Akther, S., Saleheen, N., Saha, M., Shetty, V., Kumar, S. mTeeth: Identifying Brushing Teeth Surfaces Using Wrist-Worn Inertial SensorsIn the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2021; 5(2):1-25. PUBMEDPUBMED CENTRALDOIPDF

  28. Zhang, K.W., Janson, L., Murphy S.A. Inference for Batched BanditsAdvances in Neural Information Processing Systems. 2020, December;33:9818-9829. PUBMEDPUBMED CENTRALDOIPDF

  29. Nahum-Shani, I., Rabbi, M., Yap, J., Philyaw-Kotov, M.L., Klasnja, P., Bonar, E.E., Cunningham, R.M., Murphy, S.A., Walton, M.A. Translating strategies for promoting engagement in mobile health: A proof-of-concept microrandomized trialHealth psychology : official journal of the Division of Health Psychology, American Psychological Association. 2020;40(12):974-987. PUBMEDPUBMED CENTRALDOIPDF

  30. Tomkins, S., Liao, P., Klasnja, P., Murphy S.A. IntelligentPooling: Practical Thompson Sampling for mHealthMachine learning. 2021; 110(9):2685-2727. PUBMEDPUBMED CENTRALDOIPDF

  31. Yao, J., Brunskill, E., Pan, W., Murphy, S.A., Doshi-Velez, F. Power Constrained BanditsIn The Proceedings of Machine Learning Research. 2021, August; 149:209-259. PUBMEDPUBMED CENTRALDOIPDF

  32. Chatterjee, S., Moreno, A., Lizotte, S.L., Akther, S., Ertin, E., Fagundes, C.P., Lam, C., Rehg, J. M., Wan, N., Wetter, D.W., & Kumar, S. SmokingOpp: Detecting the Smoking ‘Opportunity’ Context Using Mobile SensorsIn The Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies. 2021, October; 4(1):4. PUBMEDPUBMED CENTRALDOIPDF

  33. Wang, Z., Wang, B., Srivastava, M. Protecting User Data Privacy with Adversarial Perturbations (Poster Abstract)IPSN (Conference). 2021, May; 386-387. PUBMEDPUBMED CENTRALDOIPDF

  34. Qian T, Klasnja P, Murphy S.A. Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health studyStatistical science : a review journal of the Institute of Mathematical Statistics. 2020;35(3):375-390. PUBMEDPUBMED CENTRALDOIPDF

  35. Carpenter, S.M., Menictas, M., Nahum-Shani, I., Wetter, D.W., Murphy, S.A. Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction ScienceProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies. 2020, December;4(4):280-290. PUBMEDPUBMED CENTRALDOIPDF

  36. Bari R, Rahman MM, Saleheen N, Parsons MB, Buder EH, Kumar S. Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial SensorsCurrent addiction reports. 2020, September;7(3):280-290. PUBMEDPUBMED CENTRALDOIPDF

  37. Liao, P., Klasnja, P., Murphy, S.A. Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile HealthJournal of the American Statistical Association. 2021;116(533):382-391. PUBMEDPUBMED CENTRALDOIPDF

  38. Nahum-Shani, I., Potter, L.N., Lam, C.Y., Yap, J., Moreno, A., Stoffel, R., Wu, Z., Wan, N., Dempsey, W., Kumar, S., Ertin, E., Murphy, S.A,, Rehg, J.M., Wetter, D.W. The mobile assistance for regulating smoking (MARS) micro-randomized trial design protocolContemporary clinical trials. 2021, July 24. PUBMEDPUBMED CENTRALDOIPDF

  39. Saleheen, N., Ullah, M.A., N., Chakraborty, S., Ones, D.S., Srivastava, M., Kumar, S. WristPrint: Characterizing User Re-identification Risks from Wrist-worn Accelerometry DataIn the Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. 2021:2807-2823. PUBMEDPUBMED CENTRALDOIPDF

  40. Battalio, S.L., Conroy, D.E., Dempsey, W., Liao, P., Menictas, M., Murphy, S.A., Nahum-Shani, I., Qian, T., Kumar, S., Spring, B. Sense2Stop: A micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse preventionContemporary clinical trials. 2021, August 8;109(106534). PUBMEDPUBMED CENTRALDOIPDF

  41. Li, S., Psihogios, A.M., McKelvey, E.R., Ahmed, A., Rabbi, M., Murphy, S.A. Microrandomized trials for promoting engagement in mobile health data collection: Adolescent/young adult oral chemotherapy adherence as an exampleCurrent opinion in systems biology. 2020 June;21:1-8. PUBMEDPUBMED CENTRALDOIPDF