The mDOT Center

Transforming health and wellness via temporally-precise mHealth interventions
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Publications

mDOT Center > Resources > Publications
// Our Research

Publications

    1. S.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. M. 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. A. 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. Chatterjee S, Moreno A, Lizotte SL, Akther S, Ertin E, Fagundes CP, Lam C, Rehg JM, Wan N, Wetter DW, Kumar S. SmokingOpp: Detecting the Smoking ‘Opportunity’ Context Using Mobile Sensors. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2020 Mar;4(1):4. doi: 10.1145/3380987. Epub 2020 Mar 18. PMID: 34651096; PMCID: PMC8513752.


    7. Shukla SN, Marlin BM. A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series. 2020. arXiv preprint arXiv:2012.00168.

    8. Meet P. Vadera, Colin Samplawski, and Benjamin M. Marlin. 2023. Uncertainty Quantification Using Query-Based Object Detectors. In Computer Vision – ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VIII. Springer-Verlag, Berlin, Heidelberg, 78–93. DOI: 10.1007/978-3-031-25085-9_5.


    9. Karine K, Klasnja P, Murphy SA, Marlin BM. Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions. Proc Mach Learn Res. 2023 Aug;216:1047-1057. PMID: 37724310; PMCID: PMC10506656.


    10. Chow SM, Nahum-Shani I, Baker JT, Spruijt-Metz D, Allen NB, Auerbach RP, Dunton GF, Friedman NP, Intille SS, Klasnja P, Marlin B, Nock MK, Rauch SL, Pavel M, Vrieze S, Wetter DW, Kleiman EM, Brick TR, Perry H, Wolff-Hughes DL; Intensive Longitudinal Health Behavior Network (ILHBN). The ILHBN: Challenges, Opportunities, and Solutions from Harmonizing Data under Heterogeneous Study Designs, Target Populations, and Measurement Protocols. Transl Behav Med. 2023 Jan 20;13(1):7-16. doi: 10.1093/tbm/ibac069. Erratum in: Transl Behav Med. 2023 Jun 9;13(6):419. PMID: 36416389; PMCID: PMC9853092.


    11. Yang MJ, Sutton SK, Hernandez LM, Jones SR, Wetter DW, Kumar S, Vinci C. A Just-In-Time Adaptive intervention (JITAI) for smoking cessation: Feasibility and acceptability findings. Addict Behav. 2023 Jan;136:107467. doi: 10.1016/j.addbeh.2022.107467. Epub 2022 Aug 23. PMID: 36037610; PMCID: PMC10246550.


    12. Sameer Neupane, Mithun Saha, Nasir Ali, Timothy Hnat, Shahin Alan Samiei, Anandatirtha Nandugudi, David M. Almeida, and Santosh Kumar. 2024. Momentary Stressor Logging and Reflective Visualizations: Implications for Stress Management with Wearables. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 27 pages. DOI: 10.1145/3613904.3642662.

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

    14. 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.

    15. 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.

    16. 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. Psychiatry. 2022 Jul 18;:1-17. doi: 10.1080/00332747.2022.2092828. [Epub ahead of print] PubMed PMID: 35848800; NIHMSID:NIHMS1821198.

    17. 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.

    18. 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.

    19. 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.

    20. Qian T, Klasnja P, Murphy SA. Linear Mixed Models with Endogenous Covariates: Modeling Sequential Treatment Effects with Application to a Mobile Health Study. Statistical science : a review journal of the Institute of Mathematical Statistics. 2020;35(3):375-390. PubMed PMID: 33132496; PubMed Central PMCID: PMC7596885; DOI: 10.1214/19-sts720.

    21. Li S, Psihogios AM, McKelvey ER, Ahmed A, Rabbi M, Murphy S. Microrandomized Trials for Promoting Engagement in Mobile Health Data Collection: Adolescent/Young Adult Oral Chemotherapy Adherence as an Example. Current opinion in systems biology. 2020 June;21:1-8. PubMed PMID: 32832738; PubMed Central PMCID: PMC7437990; DOI: 10.1016/j.coisb.2020.07.002.

    22. Carpenter SM, Menictas M, Nahum-Shani I, Wetter DW, Murphy SA. Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science. Current addiction reports. 2020 September;7(3):280-290. PubMed PMID: 33747711; PubMed Central PMCID: PMC7968352; DOI: 10.1007/s40429-020-00322-y.

    23. Liao P, Klasnja P, Murphy S. Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health. Journal of the American Statistical Association. 2021;116(533):382-391. PubMed PMID: 33814653; PubMed Central PMCID: PMC8014957; DOI: 10.1080/01621459.2020.1807993.

    24. 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. Contemporary clinical trials. 2021 July 24:106513. PubMed PMID: 34314855; DOI: 10.1016/j.cct.2021.106513.

    25. Battalio SL, Conroy DE, Dempsey W, Liao P, Menictas M, Murphy S, 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 Prevention. Contemporary clinical trials. 2021 August 8;109:106534. PubMed PMID: 34375749; DOI: 10.1016/j.cct.2021.106534.

    26. Qian T, Yoo H, Klasnja P, Almirall D, Murphy SA. Estimating Time-Varying Causal Excursion Effect in Mobile Health with Binary Outcomes. Biometrika. 2021 Sep;108(3):507-527. doi: 10.1093/biomet/asaa070. Epub 2020 Sep 4. PMID: 34629476; PMCID: PMC8494142.

    27. Zhang KW, Janson L, Murphy SA. Inference for Batched Bandits. Adv Neural Inf Process Syst. 2020 Dec;33:9818-9829. PMID: 35002190; PMCID: PMC8734616.

    28. 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. PMID: 34735165; PMCID: PMC8738098.

    29. Tomkins S, Liao P, Klasnja P, Murphy S. IntelligentPooling: Practical Thompson Sampling for mHealth. Mach Learn. 2021 Sep;110(9):2685-2727. doi: 10.1007/s10994-021-05995-8. Epub 2021 Jun 21. PMID: 34621105; PMCID: PMC8494236.

    30. Yao J, Brunskill E, Pan W, Murphy S, Doshi-Velez F. Power Constrained Bandits. Proc Mach Learn Res. 2021 Aug;149:209-259. PMID: 34927078; PMCID: PMC8675738.

    31. Saghafian S, Murphy SA. Innovative Health Care Delivery: The Scientific and Regulatory Challenges in Designing mHealth Interventions. NAM perspectives. 2021;2021. PubMed PMID: 34611601; PubMed Central PMCID: PMC8486421; DOI: 10.31478/202108b.

    32. Psihogios AM, Rabbi M, Ahmed A, McKelvey ER, Li Y, Laurenceau JP, Hunger SP, Fleisher L, Pai AL, Schwartz LA, Murphy SA, Barakat LP. Understanding Adolescent and Young Adult 6- Mercaptopurine Adherence and mHealth Engagement During Cancer Treatment: Protocol for Ecological Momentary Assessment. JMIR research protocols. 2021 October 22;10(10):e32789. PubMed PMID: 34677129; PubMed Central PMCID: PMC8571686; DOI: 10.2196/32789.

    33. Trella AL, Zhang KW, Nahum-Shani I, Shetty V, Doshi-Velez F, Murphy SA. Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care. Proc Innov Appl Artif Intell Conf. 2023 Jun 27;37(13):15724-15730. doi: 10.1609/aaai.v37i13.26866. PMID: 37637073; PMCID: PMC10457015.

    34. Zhang, K. W., Janson, L., Murphy, S. A., Statistical Inference After Adaptive Sampling for Longitudinal Data. arXiv preprint arXiv:2202.07098. 2022. DOI: 10.48550/arXiv.2202.07098.

    35. Saengkyongam, S., Pfister, N., Klasnja, P., Murphy, S., and Peters, J., Effect-Invariant Mechanisms for Policy Generalization.  2023. DOI: 10.48550/arXiv.2306.10983.

    36. Guo, Y., & Murphy, S.A. Online Learning in Bandits with Predicted Context. 2023. ArXiv, abs/2307.13916.

    37. Gan, K., Keyvanshokooh, E., Liu, X., & Murphy, S.A. Contextual Bandits with Budgeted Information Reveal. 2023. ArXiv, abs/2305.18511.

    38. Nahum-Shani I, Greer ZM, Trella AL, Zhang KW, Carpenter SM, Ruenger D, Elashoff D, Murphy SA, Shetty V. Optimizing an Aadaptive Digital Oral Health Intervention for Promoting Oral Self-Care Behaviors: Micro-Randomized Trial Protocol. Contemp Clin Trials. 2024 Jan 31:107464. doi: 10.1016/j.cct.2024.107464. Epub ahead of print. PMID: 38307224.

    39. Ghosh, S., Kim, R., Chhabria, P., Dwivedi, R., Klasjna, P., Liao, P., Zhang, K.W., & Murphy, S.A. Did We Personalize? Assessing Personalization by an Online Reinforcement Learning Algorithm Using Resampling. 2023. ArXiv, abs/2304.05365.

    40. Carpenter SM, Greer ZM, Newman R, Murphy SA, Shetty V, Nahum-Shani I. Developing Message Strategies to Engage Racial and Ethnic Minority Groups in Digital Oral Self-Care Interventions: Participatory Co-Design Approach. JMIR Form Res. 2023 Dec 11;7:e49179. doi: 10.2196/49179. PMID: 38079204; PMCID: PMC10750234.

    41. Collins LM, Nahum-Shani I, Guastaferro K, Strayhorn JC, Vanness DJ, Murphy SA. Intervention Optimization: A Paradigm Shift and Its Potential Implications for Clinical Psychology. Annu Rev Clin Psychol. 2024 Feb 5. doi: 10.1146/annurev-clinpsy-080822-051119. Epub ahead of print. PMID: 38316143.

    42. Li, S., Niell, L., Choi, S.W., Nahum-Shani, I., Shani, G., & Murphy, S.A. Dyadic Reinforcement Learning. 2023. ArXiv, abs/2308.07843.

    43. Karine, K., Klasnja, P.V., Murphy, S.A., & Marlin, B.M. Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions. 2023. Proceedings of machine learning research, 216, 1047-1057 .

    44. Nahum-Shani, Inbal. Wetter, David. Murphy, Susan. Adapting Just-in-Time Interventions to Vulnerability and Receptivity: Conceptual and Methodological Considerations. 2023. 10.1016/B978-0-323-90045-4.00012-5.

    45. 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.

    46. 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

    47. 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

    48. 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 Technologies 6, no. 2 (2022): 1-32. NIHMS1839164

    49. 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 Technologies 6, no. 2 (2022): 1-34. NIHMS1839165

    50. 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.

    51. 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.

    52. Bari R, Rahman MM, Saleheen N, Parsons MB, Buder EH, Kumar S. Automated Detection of Stressful Conversations Using Wearable Physiological and Inertial Sensors. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies. 2020 December;4(4). PubMed PMID: 34099995; PubMed Central PMCID: PMC8180313; DOI: 10.1145/3432210.

    53. Akther, S., Saleheen, N., Saha, M., Shetty, V., & Kumar, S. (2021). mTeeth: Identifying Brushing Teeth Surfaces Using Wrist-Worn Inertial Sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5 (2) DOI: 10.1145/3463494.

    54. Wang Z, Wang B, Srivastava M. Poster Abstract: Protecting User Data Privacy with Adversarial Perturbations. IPSN. 2021 May;2021:386-387. doi: 10.1145/3412382.3458776. PMID: 34651144; PMCID: PMC8513393.

    55. Agarwal T,  Ertin E. CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals. 2022. ArXiv, abs/2211.08385.

    56. Wenqiang Chen, Ziqi Wang, Pengrui Quan, Zhencan Peng, Shupei Lin, Mani Srivastava, Wojciech Matusik, and John Stankovic. 2023. Robust Finger Interactions with COTS Smartwatches via Unsupervised Siamese Adaptation. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST ’23). Association for Computing Machinery, New York, NY, USA, Article 25, 1–14. DOI: 10.1145/3586183.3606794.

    57. Y. Chang, N. Sugavanam, E. Ertin. Removing Antenna Effects using an Invertible Neural Network for Improved Estimation of Multilayered Tissue Profiles using UWB Radar. 2023 IEEE USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), Portland, OR, USA, 2023, pp. 53-54, DOI: 10.23919/USNC-URSI54200.2023.10289171.

    58. Kwon S, Wan N, Burns RD, Brusseau TA, Kim Y, Kumar S, Ertin E, Wetter DW, Lam CY, Wen M, Byun W. The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings. Sensors (Basel). 2021 Feb 18;21(4):1411. DOI: 10.3390/s21041411. PMID: 33670507; PMCID: PMC7922785.

    59. Swapnil Sayan Saha, Sandeep Singh Sandha, Mohit Aggarwal, Brian Wang, Liying Han, Julian de Gortari Briseno, and Mani Srivastava. 2023. TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning. ACM Trans. Embed. Comput. Syst. Just Accepted (May 2023). https://doi.org/10.1145/3603171

    60. Ho E, Jeon M, Lee M, Luo J, Pfammatter AF, Shetty V, Spring B. Fostering Interdisciplinary Collaboration: A Longitudinal Social Network Analysis of the NIH mHealth Training Institutes. J Clin Transl Sci. 2021 Sep 20;5(1):e191. doi: 10.1017/cts.2021.859. PMID: 34849265; PMCID: PMC8596066.

    61. Luo J, Jeon M, Lee M, Ho E, Pfammatter AF, Shetty V, et al. (2022) Relationships between Changing Communication Networks and Changing Perceptions of Psychological Safety in a Team Science Setting: Analysis with Actor-Oriented Social Network Models. PLoS ONE 17(8): e0273899.

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