Chang, Y., Wright, J., & Ertin, E. (2025). Multi-objective deep learning for microwave tomography based lymphedema detection. Proceedings of the IEEE International Radar Conference (RADAR), 1–6. IEEE.
Chang, Y., Zhang, Y., Kiourti, A., & Ertin, E. (2024). MPADA: Open source framework for multimodal time series antenna array measurements. Proceedings of the Antenna Measurement Techniques Association Symposium (AMTA), 1–6. IEEE.
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.
De La Torre, S., El Mistiri, M., Tung, K., Hekler, E., Klasnja, P., Pavel, M., Rivera, D. E., Spruijt-Metz, D., & Marlin, B. M. (2025). A dynamic Bayesian network approach to modeling engagement and walking behavior: Insights from a year-long micro-randomized trial (HeartSteps II). Health Psychology and Behavioral Medicine.
Dong, G., Wu, J., de Gortari Briseno, J., Singh, A. D., Feng, J., Sarker, A., Sehatbakhsh, N., & Srivastava, M. (2024). RefreshChannels: Exploiting dynamic refresh rate switching for mobile device attacks. Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services, 359–371.
Dwivedi, R., Tian, K., Tomkins, S., Klasnja, P., Murphy, S., & Shah, D. (2025). Counterfactual inference in sequential experiments. Annals of Statistics.
El Mistiri, M., De La Torre, S., Marlin, B. M., Pavel, M., Klasnja, P., Spruijt-Metz, D., & Rivera, D. E. (2025). Dynamic modeling and system identification of user engagement in mHealth interventions using a Bayesian approach for missing data imputation. Control Engineering Practice, 164, 106460.
Gao, D., Lai, H., Klasnja, P., & Murphy, S. (2025). Harnessing causality in reinforcement learning with bagged decision times. Proceedings of Machine Learning Research (PMLR), 258, 658–666.
Gazi, A., Gullapalli, B., Gao, D., Marlin, B., Shetty, V., & Murphy, S. A. (2025). SigmaScheduling: Uncertainty-informed scheduling of decision points for intelligent mobile health interventions. IEEE Body Sensor Networks.
Ghosh, S., Hung, P., Bonar, E. E., Coughlin, L. N., Guo, Y., Nahum-Shani, I., Walton, M., Newman, M. W., & Murphy, S. A. (2025). “It felt more real”: Investigating the user experience of the MiWaves personalizing JITAI pilot study. Proceedings of the 19th EAI International Conference on Pervasive Health.
Han, L., Dong, G., Ouyang, X., Kaplan, L., Cerutti, F., & Srivastava, M. (2025). Toward foundation models for online complex event detection in CPS-IoT: A case study. Proceedings of the 2nd International Workshop on Foundation Models for Cyber-Physical Systems & Internet of Things, 1–6.
Ji, S., Zheng, X., Gao, W., & Srivastava, M. (2025). Transforming mental health care with autonomous LLM agents at the edge. Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems, 692–693.
Karine, K., & Marlin, B. (2024). StepCountJITAI: Simulation environment for RL with application to physical activity adaptive intervention. Workshop on Behavioral Machine Learning.
Karine, K., & Marlin, B. M. (2025). Enhancing adaptive behavioral interventions with LLM inference from participant described states. Proceedings of the Machine Learning for Healthcare Conference.
Karine, K., Marlin, B., & Klasnja, P. (2024). BOTS: Batch Bayesian optimization of extended Thompson sampling for severely episode-limited RL settings. NeurIPS Workshop on Bayesian Decision-making and Uncertainty.
LaVine, D., Greer, Z., Kim, J., Kumar, S., Belin, T., & Shetty, V. (2024). A remote oral self-care behaviors assessment system in vulnerable populations: Usability and feasibility study. JMIR Formative Research, 8, e54999.
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.
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.
Moore, I. M., Nofshin, E., Swaroop, S., Murphy, S., Doshi-Velez, F., & Pan, W. (2025). When and why hyperbolic discounting matters for reinforcement learning interventions. Reinforcement Learning Journal.
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.
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.
Nahum-Shani, I., & Murphy, S. (2025). Just-in-time adaptive interventions: Where are we now and what is next? Annual Review of Psychology, 77.
Neupane, S., Dongre, P., Gracanin, D., & Kumar, S. (2025). Wearable meets LLM for stress management: A duoethnographic study integrating wearable-triggered stressors and LLM chatbots for personalized interventions. Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 1–8.
Neupane, S., Saha, M., Almeida, D. M., & Kumar, S. (2025). How many times do people usually experience different kinds of stressors each day? Proceedings of the 10th International Workshop on Mental Health and Well-being at ACM UbiComp.
Nguyen, I., Han, L., Dambly, B., Kazemi, A., Kogan, M., Inman, C., Srivastava, M., & Garcia, L. (2025). Detecting context shifts in the human experience using multimodal foundation models. Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems, 620–621.
Ouyang, X., Wu, J., Kimura, T., Lin, Y., Verma, G., Abdelzaher, T., & Srivastava, M. (2025). Mmbind: Unleashing the potential of distributed and heterogeneous data for multimodal learning in IoT. Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems, 491–503.
Psihogios, A. M., El-Khatib, A., Matos, K., Rabbi, M., Rossoff, J., Dinner, S., Zeng, K., Picos, R., Ahmed, A., Patel, E. M., Fleisher, L., Hunger, S. P., Pai, A., Laurenceau, J. P., Rini, C., Barakat, L. P., Schwartz, L. A., Murphy, S., & Yanez, B. (2025). Human-centered design of a personalized digital health intervention to improve oral chemotherapy adherence in adolescents and young adults with hematologic cancers. Pediatric Blood & Cancer, 72(8), e31756.
Quan, P., Han, L., Hong, D., Berges, M., & Srivastava, M. (2024). Reimagining time series foundation models: Metadata and state-space model perspectives. Proceedings of the NeurIPS Workshop on Time Series in the Age of Large Models.
Quan, P., Ouyang, X., Jeyakumar, J. V., Wang, Z., Xing, Y., & Srivastava, M. (2025). Sensorbench: Benchmarking LLMs in coding-based sensor processing. Proceedings of the 26th International Workshop on Mobile Computing Systems and Applications, 25–30.
Quan, P., Wang, B., Yang, K., Han, L., & Srivastava, M. (2025). Benchmarking spatiotemporal reasoning in LLMs and reasoning models: Capabilities and challenges. arXiv preprint arXiv:2505.11618.
Saha, M., Xu, M. A., Mao, W., Neupane, S., Rehg, J. M., & Kumar, S. (2025). Pulse-PPG: An open-source field-trained PPG foundation model for wearable applications across lab and field settings. Proceedings of the ACM on Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT) & UbiComp 2025.
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.
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.
Steven, A., El Mistiri, M., Hekler, E., Klasnja, P., Marlin, B., Pavel, M., Spruijt-Metz, D., & Rivera, D. E. (2024). Modeling engagement with a digital behavior change intervention (HeartSteps II): An exploratory system identification approach. Journal of Biomedical Informatics, 158, 104721.
Sugavanam, N., & Ertin, E. (2025). Differentiable Gaussian splatting models of limited persistence scattering in SAR backscatter measurements. Proceedings of the IEEE International Radar Conference (RADAR), 1–6.
Sugavanam, N., & Ertin, E. (2025). Joint target recovery and blind calibration of phased-array radar using deep unrolled model. Proceedings of the IEEE International Radar Conference (RADAR), 1–6. IEEE.
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.
Trella, A. L., Dempsey, W., Gazi, A. H., Xu, Z., Doshi-Velez, F., & Murphy, S. A. (2025). Non-stationary latent auto-regressive bandits. Reinforcement Learning Journal.
Trella, A. L., Ghosh, S., Bonar, E., Coughlin, L., Doshi-Velez, F., Guo, Y., Hung, P., Nahum-Shani, I., Shetty, V., Walton, M., Yan, I., Zhang, K. W., & Murphy, S. (2025). Effective monitoring of online decision-making algorithms in digital intervention implementation. Manuscript submitted for publication.
Trella, A. L., Zhang, K. W., Jajal, H., Nahum-Shani, I., Shetty, V., Doshi-Velez, F., & Murphy, S. A. (2025). A deployed online reinforcement learning algorithm in an oral health clinical trial. Proceedings of the AAAI Conference on Artificial Intelligence, 39(28), 28792–28800.
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.
Vinci, C., Sutton, S. K., Yang, M.-J., Jones, S. R., Kumar, S., & Wetter, D. W. (2025). Proximal effects of a just-in-time adaptive intervention for smoking cessation with wearable sensors: Microrandomized trial. JMIR mHealth and uHealth, 13(1), e55379.
Walton, M., Nahum-Shani, I., Campbell, M., Tomlinson, D. C., Florimbio, A. R., Ghosh, S., Guo, Y., Hung, P., Newman, M. W., Lin, J. J., Qian, T., Dziak, J., Pan, H., Zhang, K. W., Zimmerman, L., Bonar, E., Murphy, S., & Coughlin, L. N. (2025). A micro-randomized trial of a mobile intervention for emerging adults with regular cannabis use. Manuscript submitted for publication.
Wang, Z., Hua, D., Jiang, W., Xing, T., Chen, X., & Srivastava, M. (2025). MobiVital: Self-supervised quality estimation for UWB-based contactless respiration monitoring. Proceedings of the 3rd International Workshop on Human-Centered Sensing, Modeling, and Intelligent Systems, 70–75.
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.
Wu, J., Yang, K., Kaplan, L., & Srivastava, M. (2025). ADMN: A layer-wise adaptive multimodal network for dynamic input noise and compute resources. arXiv preprint arXiv:2502.07862.
Xu, M. A., Narain, J., Darnell, G., Hallgrimsson, H., Jeong, H., Forde, D., Fineman, R., Raghuram, K. J., Rehg, J. M., & Ren, S. (2025). RelCon: Relative contrastive learning for a motion foundation model for wearable data. Proceedings of the International Conference on Learning Representations (ICLR), Singapore.
Xu, M. A., Rehg, J. M., Liu, X., & McDuff, D. (2025). LSM-2: Learning from incomplete wearable sensor data. arXiv preprint arXiv:2506.05321.
Xu, Z., Jajal, H., Choi, S. W., Nahum-Shani, I., Shani, G., Psihogios, A. M., Hung, P., & Murphy, S. A. (2025). Reinforcement learning on dyads to enhance medication adherence. Proceedings of the 23rd International Conference on Artificial Intelligence in Medicine (AIME-25).
Xu, Z., Zhang, K., & Murphy, S. (2025). The fallacy of minimizing cumulative regret in the sequential task setting. Manuscript submitted for publication.
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.
Zhang KW, Janson L, Murphy SA. Inference for Batched Bandits. Adv Neural Inf Process Syst. 2020 Dec;33:9818-9829. PMID: 35002190; PMCID: PMC8734616.
Zheng, X., Ji, S., Sun, J., Chen, R., Gao, W., & Srivastava, M. (2025). ProMind-LLM: Proactive mental health care via causal reasoning with sensor data. Findings of the Association for Computational Linguistics: ACL 2025.
