About me

I am a Ph.D. candiate in the B. Thomas College of Computing and Information Sciences at the Rochester Institute of Technology (RIT). My advisor is Dr. Linwei Wang. My research focuses on deep learning, machine learning, and optimization under uncertainty applied to to model personalization and inverse problems.

My husband, Kushal Kafle, is also a Ph.D. candidate at RIT and works on awesome research in artificial intelligence for viso-lisgusitic reasoning.

Curriculum vitae

News

[Oct, 2018] Internship work (in collaboration with MIT CSAIL) accepted at IEEE Sensors Letters and NIPS ML4H.
[Oct, 2018] Abstract accepted in WiML Workshop 2018 and received WiML 2018 travel grant.
[Sep, 2018] My MICCAI 2018 paper is nominated for young scientist award.
[Sep, 2018] I received MICCAI 2018 travel grant.
[May, 2018] My paper is accepted for publication in Medical Image Analysis (MedIA).
[Mar, 2018] I passed Ph.D. proposal defense.
[Mar, 2018] My paper is accepted in MICCAI 2018 (oral presentation, 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] My paper is accepted for publication in IEEE Transactions on Medical Imaging (IEEE TMI).
[Mar, 2017] I received IPMI Scholarship and GCCIS Student Travel Fund.
[Feb, 2017] My paper is accepted in the prestigious IPMI 2017 (acceptance rate ~30%).
[Jun, 2016] I received MICCAI 2016 Student Travel Award.
[Apr, 2016] My paper is accepted in MICCAI 2016 (early-accept, acceptance rate~25%).
[May, 2015] I passed the Ph.D. research potential assesment.

Major projects

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, NIPS ML4H 2018
Paper

In this work, we propose to eliminate the high cost of engineering hand-crafted features from multivariate time-series of physiologic signals by learning its representation with a sequence-to-sequence auto-encoder. We then propose to hash the learned multivariate time-series representations of labeled dataset to enable signal similarity assessment for the prediction of critical events. We evaluate this methodological framework to predict Acute Hypotensive Episodes (AHE) on a large and diverse dataset of 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, WiML 2018
Nominated for young scientist award | Oral presentaiton | Early accept | Acceptance rate~4%
Paper

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, Medical Image Analysis (MedIA)
Acceptance rate~30%
Paper

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 learn 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, IEEE TMI
Early accept | Acceptance rate~25%
Paper

We present a novel framework that, going beyond a uniform low-resolution approach, is able toobtain 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 andallows higher resolution in heterogeneous tissue regions. Thepresented 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]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



Student co-organizer

Women in Computing, Pre-orientation program

Rochester Institute of Technology

Selected Publications

IEEE Sensors Letters,
NIPS ML4H

[1] Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili, Jonathan Rubin, and Una-May O'Reilly. “Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection”. In: IEEE Sensors Letters, (2018)

MICCAI,
NIPS ML4H

[2] Sandesh Ghimire, Jwala Dhamala, John L. Sapp, B. Milan Horacek, and Linwei Wang. “Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential”. In: Proc. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, (2018)

MICCAI,
WiML 2018

[3] Jwala Dhamala, Sandesh Ghimire, John L. Sapp, B. Milan Horacek, and Linwei Wang. “ High-dimensional Bayesian Optimization of Personalized Cardiac Model Parameters via an Embedded Generative Model”. In: Proc. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, ORAL presentation, (2018)

MedIA

[4] Jwala Dhamala, Hermenegild J. Arevalo, John L. Sapp, Milan Horacek, Katherine C. Wu, Natalia A. Trayanova, and Linwei Wang. “Quantifying the Uncertainty in Model Parameters using Gaussian Process-Based Markov Chain Monte Carlo in Cardiac Electrophysiology”. In: Medical Image Analysis (2018) [PDF].

IEEE TMI

[5] Jwala Dhamala, Hermenegild J. Arevalo, John L. Sapp, Milan Horacek, Katherine C. Wu, Natalia A. Trayanova, and Linwei Wang. “Spatially-Adaptive Multi-Scale Optimization for Local Parameter Estimation in Cardiac Electrophysiology”. In: IEEE Transactions on Medical Imaging 36 (2017), pp. 1966-1978.[PDF]

IPMI

[6] Jwala Dhamala, John L. Sapp, B. Milan Horacek, and Linwei Wang. “Quantifying the Uncertainty in Model Parameters using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models”. In: Proc. IPMI: International Conference on Information Processing in Medical Imaging, LNCS 10265 (2017), pp. 223-235.[PDF]

MICCAI

[7] Jwala Dhamala, John L. Sapp, B. Milan Horacek, and Linwei Wang. “Spatially-Adaptive Multi-scale Optimization for Local Parameter Estimation: Application in Cardiac Electrophysiological Models”. In: Proc. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, LNCS 9902 (2016), pp. 282-290.[PDF]