Hi there!

I am a postdoctoral scholar at the School of Medicine at Stanford University, working with Prof. Zihuai He and Prof. James Zou. We are developing new (explainable) deep learning methods for Alzheimer's disease risk prediction in diverse populations.

I completed my Ph.D. in Computing and Information Sciences from Rochester Institute of Technology under the advisement of Prof. Linwei Wang. In my Ph.D. thesis, I focused on learning from limited labeled data in the biomedical domain. Toward this, I focused on disentanglement and semi-supervised learning. If you’re interested, please take a look at my Ph.D. thesis.

Overall, my research interests are:

  • 1. Machine Learning
  • 2. Generative Models and Disentanglement
  • 3. Learning with limited labelled datasets (e.g, Semi-supervised and self-supervised learning, etc.)
  • 4. Health and AI (Electrocardiograms, ECGI, Medical imaginig and Genomics)

[Scholar Page]

TIMELINE EVENTS

  December 2022 Our paper for monkeypox detection has been accepted by Nature Medicine. Full version will be available soon.
  November 2022 1 paper accepted by Nature Communications and 2 papers by MLCB conference.
  June 2022 1 paper accepted by MICCAI 2022 and 1 paper by ICML-CompBio 2022.
  February 2022 Our paper on single pulse analysis with machine learning is now available at Monthly Notices of the Royal Astronomical Society.
  October 2021 Our paper on medical image latent optimization is accepted by BMVC 2021.
  August 2021 Our study on disentanglement for electrocardiographic signals is accepted by IEEE Transactions of Biomedical Engineering.
  May 2021 Started postdoctoral research at Stanford University.
  March 2021 Our paper "Semi-Supervised Learning for Eye Image Segmentation" is accepted at ETRA 2021.
  Feb 2021 I successfully defended my dissertation.
  Summer 2020 Research Intern at Google.
  May 2020 Two papers accepted in MICCAI 2020.
  Feb 2020 Our work "A Machine Learning Approach for Computer-Guided Localization of the Origin of Ventricular Tachycardia Using 12-Lead Electrocardiograms" is accepted by  Heart Rhythm Society  (HRS 2020) and selected to participate in the special session to highlight the most novel and innovative abstracts of 2020.
  Aug 2019 Our journal paper "Sequential Factorized Autoencoder for Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms" is accepted in IEEE Transactions on Biomedical Engineering.
  Aug 2019 Our paper "Improving Disentangled Representation Learning with the Beta Bernoulli Process" is accepted in ICDM 2019.
  June 2019 Our paper "Semi-Supervised Learning by Disentangling and Self-Ensembling over Stochastic Latent Space" is accepted in MICCAI 2019.
  Summer 2019 Internship at Verisk AI Lab as AI/ML Research Intern.
  Dec 2018 Presented poster at BNP@NeurIPS and SOCML (highly recommended "unconference").
  Nov 2018 Two papers accepted in NeurIPS workshops (ML4H@NeurIPS and BNP@NeurIPS).
  Summer 2018 Mentor for Research Experience for Undergraduates (NSF-REU) program at RIT for the project "Multi-modal sensing and quantification of atypical attention in autism spectrum disorder."
  Nov 2017 Our paper "Learning disentangled representation from 12-lead electrograms: application in localizing the origin of Ventricular Tachycardia" was accepted in the Health Intelligence Workshop, AAAI 2018.
  June 2017 Our paper "Automatic Coordinate Prediction of the Exit of Ventricular Tachycardia from 12-lead Electrocardiogram" was accepted to CinC 2017. The paper was selected as a semi-finalist for Rosanna Degani Young Investigator Award.
  Summer 2017 Mentor for Research Experience for Undergraduates (NSF-REU) program at RIT for the project "Attention and behavior of students in online vs. face-to-face learning contexts."
  May 2017 Our paper "Disentangling Inter-Subject Variations: Automatic Localization of Ventricular Tachycardia Origin from 12-Lead Electrocardiograms" was accepted to ISBI 2017.
  Aug 2016 Joined Rochester Institute of Technology for Ph.D. in Computing and Information Sciences.

RESEARCH WORKS

    2023

    Alexander H Thieme, Yuanning Zheng, Gautam Machiraju, Chris Sadee, Mirja Mittermaier, Maximilian Gertler, Jorge L Salinas, Krithika Srinivasan, Prashnna Gyawali, Francisco Carrillo-Perez, Angelo Capodici, Maximilian Uhlig, Daniel Habenicht, Anastassia Löser, Maja Kohler, Maximilian Schuessler, David Kaul, Johannes Gollrad, Jackie Ma, Christoph Lippert, Kendall Billick, Isaac Bogoch, Tina Hernandez-Boussard, Pascal Geldsetzer, Olivier Gevaert. "A deep-learning algorithm to classify skin lesions from mpox virus infection". Nature Medicine. (2023).

    Zhiyuan Li, Xiajun Jiang, Ryan Missel, Prashnna Kumar Gyawali, Nilesh Kumar, Linwei Wang. "Continual Unsupervised Disentangling of Self-Organizing Representations". International Conference on Learning Representations (ICLR-23) (to appear).

    2022

    Zihuai He, Linxi Liu, Michael E Belloy, Yann Le Guen, Aaron Sossin, Xiaoxia Liu, Xinran Qi, Shiyang Ma, Prashnna K Gyawali, Tony Wyss-Coray, Hua Tang, Chiara Sabatti, Emmanuel Candès, Michael D Greicius, Iuliana Ionita-Laza. "GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies". Nature Communications. (2022).

    Maryam Toloubidokhti, Nilesh Kumar, Zhiyuan Li, Prashnna K Gyawali, Brian Zenger, Wilson W Good, Rob S MacLeod, Linwei Wang. "Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators". International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI-22).

    Prashnna K Gyawali, Xiaoxia Liu, James Zou, Zihuai He. "Ensembling improves stability and power of feature selection for deep learning models". Machine Learning in Computational Biology. (MLCB 2022).

    Carlos O Lousto, Ryan Missel, Harshkumar Prajapati, Valentina Sosa Fiscella, Federico G López Armengol, Prashnna Kumar Gyawali, Linwei Wang, Nathan D Cahill, Luciano Combi, Santiago del Palacio, Jorge A Combi, Guillermo Gancio, Federico García, Eduardo M Gutiérrez and Fernando Hauscarriaga. "Vela pulsar: single pulses analysis with machine learning techniques". Monthly Notices of the Royal Astronomical Society. (2022).

    2021

    Aakash Saboo, Prashnna K Gyawali, Ankit Shukla, Neeraj Jain, Manoj Sharma and Linwei Wang. "Latent-optimization based Disease-aware Image Editing for Medical Image Augmentation". The 32nd British Machine Vision Conference. (BMVC 2021).

    Prashnna K Gyawali, Jaideep Vitthal Murkute, Maryam Toloubidokhti, Xiajun Jiang, B. Milan Horacek, John Sapp and Linwei Wang. "Learning to Disentangle Inter-subject Anatomical Variations in Electrocardiographic Data". IEEE Transacations on Biomedical Engineering. (TBE 2021).  [Dataset]

    Maryam Toloubidokhti, Prashnna K Gyawali, Omar A. Gharbia, Xiajun Jiang, Jaume Coll Font, Jake A. Bergquist, Brain Zenger, Wilson W. Good, Dana H. Brooks, Rob S. MacLeod and Linwei Wang. "Deep Adaptive Electrocardiographic Imaging with Generative Forward Model for Error Reduction". Functional Imaging and Modeling of the Heart (FIMH-2021).

    Aayush K Chaudhary*, Prashnna K Gyawali*, Linwei Wang and Jeff B Pelz. "Semi-Supervised Learning for Eye Image Segmentation". ACM Symposium On Eye Tracking Research & Applications (ETRA-2021). * equal contribution

    2020

    Ryan Missel, Prashnna K Gyawali, Jaideep Vitthal Murkute, Zhiyuan Li, Shijie Zhou, Amir AbdelWahab, Jason Davis, James Warren, John L Sapp and Linwei Wang. "A hybrid machine learning approach to localizing the origin of ventricular tachycardia using 12-lead electrocardiograms". Computers in Biology and Medicine.

    Prashnna K. Gyawali, Sandesh Ghimire and Linwei Wang. "Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization". IEEE International Conference on Data Mining (ICDM-20). [Code]

    Prashnna K. Gyawali, Sandesh Ghimire, Pradeep Bajracharya, Zhiyuan Li and Linwei Wang. "Semi-supervised Medical Image Classification with Global Latent Mixing". International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI-20). (early acceptance). [Code]

    Xiajun Jiang, Sandesh Ghimire, Prashnna K. Gyawali and Linwei Wang. "Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction". International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI-20).

    Zhiyuan Li, Jaideep Vitthal Murkute, Prashnna K. Gyawali and Linwei Wang. "Progressive Learning and Disentanglement of Hierarchical Representations". International Conference on Learning Representations (ICLR-20). (accepted for spotlight).

    2019

    Prashnna K. Gyawali, B. Milan Horacek, John Sapp and Linwei Wang. "Sequential Factorized Autoencoder for Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms". IEEE Transacations on Biomedical Engineering. (TBE 2019).

    Prashnna K. Gyawali, Zhiyuan Li, Cameron Knight, Sandesh Ghimire, B. Milan Horacek, John Sapp and Linwei Wang. "Improving Disentangled Representation Learning with the Beta Bernoulli Process". IEEE International Conference on Data Mining (ICDM-19). [Code]

    Prashnna K. Gyawali*, Zhiyuan Li*, Sandesh Ghimire, and Linwei Wang. "Semi-Supervised Learning by Disentangling and Self-Ensembling over Stochastic Latent Space". International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI-19). * equal contribution [Code]

    Sandesh Ghimire, Prashnna K. Gyawali, Jwala Dhamala, John Sapp, B. Milan Horacek, and Linwei Wang. "Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences". International Conference on Information Processing in Medical Imaging (IPMI-19).

    2018

    Prashnna K. Gyawali, Cameron Knight, Sandehs Ghimire, John Sapp, B. Milan Horacek and Linwei Wang. "Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors". All of Bayesian Nonparametric workshop at NeurIPS 2018 (BNP@NeurIPS 2018).

    Sandesh Ghimire, Jwala Dhamala, Prashnna K. Gyawali, John Sapp, B. Milan Horacek and Linwei Wang. "Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential". International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI-18) (also MLH4@NeurIPS 2018).

    Prashnna K. Gyawali, B. Milan Horacek, John Sapp, and Linwei Wang. "Learning disentangled representation from 12-lead electrocardiograms: application in localizing the origin of Ventricular Tachycardia". Health Intelligence Workshop, 32nd AAAI Conference on Artificial Intelligence (AAAI-18). [Code]

    2017

    Prashnna K. Gyawali, Shuhang Chen, Huafeng Liu, B. Milan Horacek, John Sapp and Wang L. "Automatic Coordinate Prediction of the Exit of Ventricular Tachycardia from 12-lead Electrocardiogram". Computing in Cardiology (CinC-17).

    Erin Coppola, Prashnna K. Gyawali, Nihar Vanjara, Dan Giaime and Linwei Wang. "Atrial Fibrillation Classification from a Short Single Lead ECG Recording Using Hierarchical Classifier". Computing in Cardiology (CinC-17).

    Shuhang Chen, Prashnna K. Gyawali, Huafeng Liu, B. Milan Horacek, John Sapp and Linwei Wang. "Disentangling inter-subject variations: Automatic localization of ventricular tachycardia from 12-lead electrocardiograms". IEEE International Symposium on Biomedical Imaging (ISBI-17).

    2015

    Shailesh Acharya, Ashok K. Pant and Prashnna K. Gyawali. "Deep learning based large scale handwritten Devnagari character recognition". IEEE International Conference on Software, Knowledge, Information Management and Applications (SKIMA-15).   [Dataset]

    Ashok K. Pant, Prashnna K. Gyawali and Shailesh Acharya. "Automatic Nepali Number Plate Recognition with Support Vector Machines". IEEE International Conference on Software, Knowledge, Information Management and Applications (SKIMA).  [Dataset]
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