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.
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.
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.
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.
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.
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.
 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
 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