Warped Autoregressive Hidden Markov Models (BioRxiv Paper, COSYNE Poster)
In this work, we take the autoregressive hidden Markov model employed in MoSeq and add an additional latent “warping” variable, which modulates discrete syllables via a time-warping (T-WARHMM) or Gaussian process (GP-WARHMM) format. While we believe this model class can be generally useful, in our work it is applied to unsupervised behavioral segmentation of mouse movement. Graduate research with Scott Linderman, Lea Duncker, and Alex Williams.
bioRxiv paper: J.C. Costacurta, L. Duncker, B. Sheffer, C. Weinreb, W. Gillis, J. Markowitz, S.R. Datta, A.H. Williams, and S.W. Linderman (2022). Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs. bioRxiv 2022.06.10.495690; doi: https://doi.org/10.1101/2022.06.10.495690
COSYNE abstract/poster: J.C. Costacurta, A.H. Williams, B. Sheffer, C. Weinreb, W. Gillis, J. Markowitz, S.R. Datta, and S.W. Linderman (2022). Time-warped state space models for distinguishing movement type and vigor. COSYNE Abstracts 2022.
Designing Feedback Controllers for Human-Prosthetic Systems Using H-Infinity Model Matching (EMBC Paper, Slides)
Brain-controlled (myoelectric) upper-limb prosthetic devices are advancing at a fast pace, but current methods of delivering sensory feedback to users are lagging. In particular, some external forms of feedback (force, sound, etc) have been shown to be more distracting than helpful to users. In this work, we attempt to use feedback control theory to investigate how we can improve sensory feedback experiences for amputees. To do so, we build a multi-controller closed-loop system involving the user and prosthesis. Slides are from 2019 JHU Computational Medicine Night talk. Undergraduate research with Sridevi Sarma and Luke Osborn.
Conference paper: J. Costacurta, L. Osborn, N. Thakor, and S. Sarma, “Designing feedback controllers for human-prosthetic systems using h-infinity model matching,” in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, pp. 2316 – 2319.
Traditional numerical methods for solving PDEs, such as finite difference or finite element method, are of limited use in problems with complex domains. We attempted to use theory from stochastic calculus to code an alternative solution method in Python. We used methods from machine learning, such as temporal difference learning and PyTorch, to enhance our method. Research conducted as part of 2019 Fields Undergraduate Summer Research Program at Fields Institute/University of Toronto. Presented slides at end-of-summer symposium. Summer undergraduate research with Adam Stinchcombe and Mihai Nica.
Journal paper: Martin, C., Zhang, H., Costacurta, J. et al. Solving Elliptic Equations with Brownian Motion: Bias Reduction and Temporal Difference Learning. Methodol Comput Appl Probab 24, 1603–1626 (2022). https://doi.org/10.1007/s11009-021-09871-9
JHU Directed Reading Program (Slides, 2018)
Independent study in mathematics department in Spring 2018 and Spring 2020. Studied mathematical foundations of control theory (2018) and elementary applied topology (2020).
An interactive module for teaching biomedical applications of feedback control theory (Link, Poster)
Systems and Controls is a required introductory control course for biomedical engineers at JHU. The limited timescale of this course requires focus on theory rather than application, and students expressed a desire to learn exactly how control theory can be relevant in medicine. This applet was developed to expose students to a previously investigated biomedical control problem. Received funding from JHU Center for Educational Resources (CER) Tech Fellowship. Presented poster at 2019 Biomedical Engineering Society Conference.
Effect of Ankle-Foot Orthosis Stiffness on Muscle Coordination During Transient Walking in Healthy Adults (Poster)
Ankle-Foot Orthoses are devices prescribed to children with cerebral palsy in order to enhance their mobility. To inform how they are tuned and prescribed, we collected data and analyzed the effects of these devices on the first six steps of motion, called gait initiation or transient walking. Research conducted as part of 2018 Center for Neurotechnology REU at University of Washington. Presented poster at end-of-summer symposium. Summer undergraduate research with Kat Steele and Michael Rosenberg.