Academic Activities


Presentations and Invited Talks

Teaching and Mentorship Activities

Journal Articles

  1. Sabelhaus, A. P., Mehta, R. K., Wertz, A. T., & Majidi, C. (2022). In-Situ Sensing and Dynamics Predictions for Electrothermally-Actuated Soft Robot Limbs. ArXiv e-Prints, arXiv–2111.
  2. Zadan, M., Patel, D. K., Sabelhaus, A. P., Liao, J., Wertz, A., Yao, L., & Majidi, C. (2022). Liquid Crystal Elastomer with Integrated Soft Thermoelectrics for Shape Memory Actuation and Energy Harvesting. Advanced Materials, 2200857.
  3. Sabelhaus, A. P., Patterson, Z. J., Wertz, A. T., & Majidi, C. (2022). Safe Supervisory Control of Soft Robot Actuators. ArXiv Preprint ArXiv:2208.01547.
  4. Laird, P., Wertz, A., Welter, G., Maslove, D., Hamilton, A., Yoon, J. H., Lake, D. E., Zimmet, A. E., Bobko, R., Moorman, J. R., & others. (2021). The critical care data exchange format: a proposed flexible data standard for combining clinical and high-frequency physiologic data in critical care. Physiological Measurement.
  5. Pinsky, M. R., Wertz, A., Clermont, G., & Dubrawski, A. (2020). Parsimony of hemodynamic monitoring data sufficient for the detection of hemorrhage. Anesthesia & Analgesia, 130(5), 1176–1187.
  6. Wertz, A., Holder, A. L., Guillame-Bert, M., Clermont, G., Dubrawski, A., & Pinsky, M. R. (2019). Increasing Cardiovascular Data Sampling Frequency and Referencing It to Baseline Improve Hemorrhage Detection. Critical Care Explorations, 1(10).
  7. Hravnak, M., Pellathy, T., Chen, L., Dubrawski, A., Wertz, A., Clermont, G., & Pinsky, M. R. (2018). A call to alarms: Current state and future directions in the battle against alarm fatigue. Journal of Electrocardiology, 51(6), S44–S48.

Conference Articles

  1. Wertz, A., Sabelhaus, A. P., & Majidi, C. (2022). Trajectory Optimization for Thermally-Actuated Soft Planar Robot Limbs. 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft), 439–446.
  2. Potosnak, W., Dufendach, K. A., Wertz, A., Miller, K., Dubrawski, A., & Kilic, A. (2021). Continuous Intraoperative Data Analysis Using Machine Learning Reveals Multiple Parameters to Predict Post-CABG Renal Failure. The Society of Thoracic Surgeons Annual Meeting.
  3. Jeanselme, V., Wertz, A., Clermont, G., Pinsky, M. R., & Dubrawski, A. (2020). Robustness of Machine Learning Models for Hemorrhage Detection. What’s New in Non-Pulmonary Critical Care?, A6320–A6320.
  4. Jeanselme, V., Wertz, A., Clermont, G., Pinsky, M. R., & Dubrawski, A. (2020). Cross-correlation Features of Vital Signs Enable Robust Detection of Hemorrhage.
  5. Wertz, A., Clermont, G., Dubrawski, A., & Pinsky, M. (2019). Hemodynamic monitoring parsimony: minimal information for rapid hemorrhage detection. ESICM LIVES 2019, 7, 91–92.
  6. Chen, L., Dubrawski, A., Clermont, G., Pellathy, T., Wertz, A., Yoon, J. H., Pinsky, M., & Hravnak, M. (2019). Binarized Severity Level Of Future Instability Risk In Continuously Monitored Patients. Critical Care Medicine, 47(1), 605.
  7. Chen, L., Dubrawski, A., Clermont, G., Pellathy, T., Wertz, A., Pinsky, M. R., & Hravnak, M. (2018). Model based estimation of instability severity level in continuously monitored patients. ESICM LIVES 2018, 6, 59–60.
  8. Wertz, A., Hravnak, M., Dubrawski, A., Chen, L., Pellathy, T., Clermont, G., & Pinsky, M. (2018). Sufficient Sampling Frequency For Machine Learning To Separate Monitoring Artifact From Instability. Critical Care Medicine, 46(1), 19.

Genome Announcements

  1. Pope, W. H., Berryman, E. N., Forrest, K. M., McHale, L., Wertz, A. T., Zhuang, Z., Kasturiarachi, N. S., Pressimone, C. A., Schiebel, J. G., Furbee, E. C., Grubb, S. R., Warner, M. H., Montgomery, M. T., Garlena, R. A., Russell, D. A., Jacobs-Sera, D., & Hatfull, G. F. (2016). Genome Sequence of Gordonia Phage BetterKatz. In Microbiology Resource Announcements (Vol. 4, Number 4). American Society for Microbiology Journals.

Other Publications

  1. Gao, C., Falck, F., Goswami, M., Wertz, A., Pinsky, M. R., & Dubrawski, A. (2019). Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning. In NeurIPS.