Jwala Dhamala

Ph.D. Student

I am a Ph.D. candiate in the College of Computing and Information Sciences at the Rochester Institute of Technology (RIT). I work with Dr. Linwei Wang in the Computational Biomedicine Lab.

The main focus of my research is machine/deep learning approaches to integrate patient's measurements with physics-based simulations for probabilistic personalization of the simulation models. To this end, I have experience working with machine learning methods like Gaussian processes, Bayesian optimization and MCMC; and deep learning methods like variational auto-encoders (VAE), DNN, CNN and geometric deep learning. Last summer, I was an intern at Philips Research where I worked on unsupervised representation learning of multivariate time-series physiological signals with sequence-to-sequence auto-encoders.


News

  • [Jun, 2019] NEW! Paper early accepted and finalist for Young Scientist Award at MICCAI 2019

  • [Oct, 2018] Paper (collaboration of Philips Research and MIT CSAIL) accepted at IEEE Sensors Letters and NeurIPS ML4H; received NeurIPS ML4H workshop travel grant

  • [Oct, 2018] Abstract accepted in WiML Workshop 2018 and received WiML 2018 travel grant

  • [Sep, 2018] MICCAI 2018 paper is finalist for young scientist award; received MICCAI 2018 travel grant

  • [May, 2018]Paper accepted for publication in Medical Image Analysis (MedIA)

  • [Mar, 2018] Passed Ph.D. proposal defense

  • [Mar, 2018] Paper accepted for oral presentation in MICCAI 2018 (acceptance rate ~4%)

  • [Jan, 2018] I will be joining Philips Research, Cambridge, MA for summer internship

  • [Apr, 2017] Our paper is accepted for publication in CinC 2017

  • [Apr, 2017] Paper accepted for publication in IEEE Transactions on Medical Imaging (IEEE TMI)

  • [Feb, 2017] Paper accepted in the prestigious IPMI 2017 (acceptance rate ~30%); received IPMI Scholarship and GCCIS Student Travel Fund

  • [Apr, 2016] Paper accepted in MICCAI 2016 (early-accept, acceptance rate~25%); received MICCAI 2016 travel grant

  • [May, 2015] Passed the Ph.D. research potential assesment


Projects

Graph Convolutional Generative Model for Bayesian Optimization on Large Graphs
Jwala Dhamala, Sandesh Ghimire, John L. Sapp, Milan Horacek, Linwei Wang
MICCAI 2019 (early accept, finalist Young Scientist Award)

We present a novel graph convolutional VAE to allow generative modeling of non-Euclidean data, and utilize it to embed Bayesian optimization of large graphs into a small latent space. This approach bridges the gap of previous works by introducing an expressive generative model that is able to incorporate the knowledge of spatial proximity and hierarchical compositionality of the underlying geometry. It further allows transferring of the learned features across different geometries.

Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing
Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili, Jonathan Rubin, Una-May O'Reilly
IEEE Sensors Letters, NeurIPS ML4H 2018

We learn the representations of multivariate time-series of physiologic signals with a sequence-to-sequence auto-encoder. We then hash the learned multivariate time-series representations of labeled dataset to enable signal similarity assessment. This methodological framework is evaluated to predict Acute Hypotensive Episodes (AHE) on vital signal recordings extracted from eICU Collaborative Research Database.

High-dimensional Bayesian Optimization via an Embedded Generative Model
Jwala Dhamala, Sandesh, Ghimire, John L. Sapp, B. Milan Horacek, Linwei Wang
MICCAI 2018 (oral presentation, finalist young scientist award), WiML 2018

We devise a novel concept that embeds a generative variational auto-encoder (VAE) into the objective function of Bayesian optimization, providing an implicit low-dimensional (LD) search space that represents the generative code of the HD spatially-varying tissue properties. In addition, the VAE-encoded knowledge about the generative code is used to guide the exploration of the search space. It is applied to estimating high-dimensional tissue excitability in a cardiac electrophysiological model.

Quantifying the Uncertainty in Model Parameters using Gaussian Process-Based Markov Chain Monte Carlo
Jwala Dhamala, Hermenegild J. Arevalo, John L. Sapp, Milan Horacek, Katherine C. Wu, Natalia A. Trayanova, Linwei Wang
IPMI 2017 (acceptance rate~30%), Medical Image Analysis

The quantification of uncertainty in model parameters is challenging because the posterior distribution of the parameters given the measurement data is non-Gaussian and the evaluation of the model is computationally expensive. In this project, we l earn a surrogate of this complicated and computationally expensive posterior distribution and utilize it to obtain a MCMC sampling with higher acceptance rate. The surrogate posterior pdf is used to accelerate the sampling of the true posterior pdf and not as a replacement.

Spatially-Adaptive Multi-scale Optimization for Local Parameter Estimation
Jwala Dhamala, Hermenegild J. Arevalo, John L. Sapp, Milan Horacek, Katherine C. Wu, Natalia A. Trayanova, Linwei Wang
MICCAI 2016 (early accept, acceptance rate~25%), IEEE TMI

We present a novel framework that, going beyond a uniform low-resolution approach, is able to obtain a higher resolution estimation of tissue properties represented by spatially non-uniform resolution. This is achieved by two central elements: 1) a multi-scale coarse-to-fine optimization that facilitates higher resolution optimization using the lower resolution solution, and 2) a spatially adaptive decision criterion that retains lower resolution in homogeneous tissue regions and allows higher resolution in heterogeneous tissue regions. The presented framework is evaluated in estimating the local tissue excitability properties of a cardiac EP model.


Activities

Student co-organizer
Hackathon on Premature Ventricular Contraction
Consortium of ECG Imaging

Relevant Publications:
[1] Sandesh, Ghimire, Jwala Dhamala, Jaume Coll-Font, Jess D Tate, Maria S Guillem, Dana H Brooks, Rob S MacLeod, and Linwei Wang. “Overcoming Barriers to Quantification and Comparison of Electrocardiographic Imaging Methods: A Community-Based Approach”. In: Proc. CinC: Computing in Cardiology, IEEE (2017) To Appear
[2] Jaume Coll-Font*, Dana H Brooks, Peter M van Dam, Jwala Dhamala, Olaf Dössel, Maria de la Salud Guillem Sánchez, Rob MacLeod, Danila Potyagaylo, Walther Schulze, Jess D Tate and Linwei Wang, “The Consortium on Electrocardiographic Imaging”, Computing in Cardiology (CINC), Vancouver, Canada, September 2016

2015 - 2017
Student co-organizer
Pre-orientation Program, Women in Computing
Rochester Institute of Technology
2018

Publications

Journal Publications

  • Embedding High-dimensional Bayesian Optimization via Generative Modeling: Parameter Personalization of Cardiac Electrophysiological Models
    Jwala Dhamala, Hermenegild J. Arevalo, John L. Sapp, Milan Horacek, Katherine C. Wu, Natalia A. Trayanova, and Linwei Wang
    Medical Image Analysis (MedIA), in submission
  • Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection
    Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili, Jonathan Rubin, and Una-May O'Reilly.
    IEEE Sensors Letters, 2018; NeurIPS Machine Learning for Health Workshop, 2018
  • Quantifying the Uncertainty in Model Parameters using Gaussian Process-Based Markov Chain Monte Carlo in Cardiac Electrophysiology
    Jwala Dhamala, Hermenegild J. Arevalo, John L. Sapp, Milan Horacek, Katherine C. Wu, Natalia A. Trayanova, and Linwei Wang
    Medical Image Analysis (MedIA), 2018
  • Spatially-Adaptive Multi-Scale Optimization for Local Parameter Estimation in Cardiac Electrophysiology
    Jwala Dhamala, Hermenegild J. Arevalo, John L. Sapp, Milan Horacek, Katherine C. Wu, Natalia A. Trayanova, and Linwei Wang
    IEEE Transactions on Medical Imaging (IEEE TMI), 2017

Conference Publications

  • Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization
    Jwala Dhamala, John L. Sapp, Milan Horacek, and Linwei Wang
    Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019
  • High-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model
    Jwala Dhamala, John L. Sapp, Milan Horacek, and Linwei Wang
    Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018
  • Quantifying the Uncertainty in Model Parameters using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models
    Jwala Dhamala, John L. Sapp, Milan Horacek, and Linwei Wang
    Information Processing in Medical Imaging (IPMI), 2017
  • Spatially-Adaptive Multi-scale Optimization for Local Parameter Estimation: Application in Cardiac Electrophysiological Models
    Jwala Dhamala, John L. Sapp, Milan Horacek, and Linwei Wang
    Medical Image Computing and Computer Assisted Intervention (MICCAI), 2016