Prashnna K Gyawali



Contact

prashnna.gyawali@mail.wvu.edu
AERB (Room: 260)
           


Hi there!

I am an Assistant Professor at West Virginia University in the Lane Department of Computer Science and Electrical Engineering. Before my stint at WVU, I did my postdoctoral training at Stanford University (under the mentorship of Prof. Zihuai He and Prof. James Zou) and completed my PhD in Computer Science from RIT (advisor Prof. Linwei Wang).

My research interests lie within the broad area of machine learning for healthcare. More specifically, my research spans unbiased, fair, and explainable ML. I am also interested in generative AI and multimodal AI and their implications in various domains, including health and medicine.

NOTE: I am looking for motivated graduate students broadly interested in robust and explainable machine learning and its applications to health care. If you are excited about this line of research and would like to work with me, please read this before contacting me.

Research

See Google Scholar Page for latest preprints. * below indicates equal contribution

  • A deep-learning algorithm to classify skin lesions from mpox virus infection.
    Alexander H Thieme, Yuanning Zheng, Gautam Machiraju, Chris Sadee, Mirja Mittermaier, Maximilian Gertler, Jorge L Salinas, Krithika Srinivasan, Prashnna K 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.
    Nature Medicine, 2023.

  • Continual Unsupervised Disentangling of Self-Organizing Representations.
    Zhiyuan Li, Xiajun Jiang, Ryan Missel, Prashnna K Gyawali, Nilesh Kumar, Linwei Wang.
    International Conference on Learning Representations (ICLR), 2023.

  • GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.
    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.
    Nature Communications, 2022.

  • Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators.
    Maryam Toloubidokhti, Nilesh Kumar, Zhiyuan Li, Prashnna K Gyawali, Brian Zenger, Wilson W Good, Rob S MacLeod, Linwei Wang.
    International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), 2022.

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

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

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

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

  • Deep Adaptive Electrocardiographic Imaging with Generative Forward Model for Error Reduction.
    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.
    Functional Imaging and Modeling of the Heart (FIMH), 2021.

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

  • A hybrid machine learning approach to localizing the origin of ventricular tachycardia using 12-lead electrocardiograms.
    Ryan Missel, Prashnna K Gyawali, Jaideep Vitthal Murkute, Zhiyuan Li, Shijie Zhou, Amir AbdelWahab, Jason Davis, James Warren, John L Sapp and Linwei Wang.
    Computers in Biology and Medicine, 2020.

  • Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization.
    Prashnna K Gyawali, Sandesh Ghimire, Linwei Wang.
    IEEE International Conference on Data Mining (ICDM), 2020.
    Code

  • Semi-supervised Medical Image Classification with Global Latent Mixing.
    Prashnna K Gyawali, Sandesh Ghimire, Pradeep Bajracharya, Zhiyuan Li, Linwei Wang.
    International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), 2020.
    Code

  • Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction.
    Xiajun Jiang, Sandesh Ghimire, Prashnna K Gyawali, Linwei Wang.
    International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), 2020.

  • Progressive Learning and Disentanglement of Hierarchical Representations.
    Zhiyuan Li, Jaideep Vitthal Murkute, Prashnna K Gyawali, Linwei Wang.
    International Conference on Learning Representations (ICLR), 2020.

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

  • Improving Disentangled Representation Learning with the Beta Bernoulli Process.
    Prashnna K Gyawali, Zhiyuan Li, Cameron Knight, Sandesh Ghimire, B. Milan Horacek, John Sapp, Linwei Wang.
    IEEE International Conference on Data Mining (ICDM), 2019.
    Code

  • Semi-Supervised Learning by Disentangling and Self-Ensembling over Stochastic Latent Space.
    Prashnna K Gyawali*, Zhiyuan Li*, Sandesh Ghimire, Linwei Wang.
    International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), 2019.
    Code

  • Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences.
    Sandesh Ghimire, Prashnna K Gyawali, Jwala Dhamala, John Sapp, B. Milan Horacek, Linwei Wang.
    International Conference on Information Processing in Medical Imaging (IPMI), 2019.

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

  • Automatic Coordinate Prediction of the Exit of Ventricular Tachycardia from 12-lead Electrocardiogram.
    Prashnna K Gyawali, Shuhang Chen, Huafeng Liu, B. Milan Horacek, John Sapp, Wang L.
    Computing in Cardiology (CinC), 2017. (Semifinalist for Young Investigator Award)

  • Atrial Fibrillation Classification from a Short Single Lead ECG Recording Using Hierarchical Classifier.
    Erin Coppola, Prashnna K Gyawali, Nihar Vanjara, Dan Giaime, Linwei Wang.
    Computing in Cardiology (CinC), 2017.

  • Disentangling inter-subject variations: Automatic localization of ventricular tachycardia from 12-lead electrocardiograms.
    Shuhang Chen, Prashnna K Gyawali, Huafeng Liu, B. Milan Horacek, John Sapp, Linwei Wang.
    IEEE International Symposium on Biomedical Imaging (ISBI), 2017.

  • Deep learning based large scale handwritten Devnagari character recognition.
    Shailesh Acharya, Ashok K Pant, Prashnna K Gyawali..
    IEEE International Conference on Software, Knowledge, Information Management and Applications (SKIMA), 2015.
    Dataset

  • Automatic Nepali Number Plate Recognition with Support Vector Machines.
    Ashok K. Pant, Prashnna K Gyawali, Shailesh Acharya.
    IEEE International Conference on Software, Knowledge, Information Management and Applications (SKIMA), 2015.
    Dataset

Advising

I am grateful to be collaborating with the following students:

  • Chowdhury Mohammad Abid Rahman (PhD Student, WVU CS)
  • Jacob Thrasher (PhD Student, WVU CS)
  • Shivam (PhD student, WVU CS, co-advising with Prof. Yemunala Reddy)

Previous mentee:

  • Aaksh Saboo (served as undergraduate mentor, now PhD student at King's College London)
  • Prasiddha Siwakoti (served as graduate mentor)
  • Sudarshan Regmi (served as undergraduate mentor)

Teaching

  • Advanced Image Processing
    Instructor
    West Virginia University, 2023.

  • Generative Modeling and Variational Autoencoder
    Invited Lecturer
    Nepal AI School, 2021.

  • Image Processing and Pattern Recognition
    Instructor
    Institute of Engineering, Tribhuwan University, 2015.