Yogatheesan Varatharajah

Yogatheesan Varatharajah
Yogatheesan Varatharajah
Research Assistant Professor, Bioengineering
3146E Everitt Laboratory

For More Information


  • M.S., Electrical and Computer Engineering, the University of Illinois at Urbana Champaign, 2015
  • Ph.D., Electrical and Computer Engineering, the University of Illinois at Urbana Champaign, 2020


I am currently a Research Assistant Professor in the Department of Bioengineering at the University of Illinois at Urbana-Champaign. I obtained my Ph.D. and M.S. degrees from the Department of Electrical and Computer Engineering at the same university under the supervision of Prof. Ravi Iyer. During my graduate program, I was fortunate to be mentored by Dr. Gregory Worrell at the Mayo Clinic through the Mayo-Clinic-Illinois Partnership. Prior to that, I obtained my bachelor's degree in Electronic and Telecommunication Engineering at the University of Moratuwa in Sri Lanka. I have also spent a summer at Google and collaborated with Google Accelerated Science and Medical Brain teams.

Academic Positions

  • Visiting Scientist, Mayo Clinic, Rochester, 2020 - Present
  • Faculty Affiliate, Center for AI Innovations, NCSA, 2021 - Present
  • Research Assistant Professor, Department of Electrical and Computer Engineering (by courtesy), the University of Illinois at Urbana-Champaign, 2020-present
  • Research Assistant Professor, Department of Bioengineering, the University of Illinois at Urbana-Champaign, 2020-present

Professional Highlights

  • Served on an NSF Review Panel
  • Served as an area chair in the Machine Learning for Health (ML4H) conference

Teaching Statement

My primary goals in teaching health data science are to lay out a holistic understanding of data science and provide hands-on experience in designing end-to-end data science pipelines. While the underlying signal processing and statistical methods are very important, I believe that a dedication to understanding the data and the domain, the ability to identify important scientific problems, the ability to formulate analytical approaches to solve such problems, and being versatile with computing platforms and programming languages are equally important in creating a successful data scientist.

Resident Instruction

  • BIOE 488, Applied High-performance Computing for Biomedical Imaging, Fall 2022 - Present
  • BIOE 485, Computation Math, Fall 2021 - Present

Research Statement

I am broadly interested in health data analytics, machine learning, and computational neuroscience. Specifically, I am interested in developing domain-guided machine learning models for health data analytics, with specific applications in neurological diseases. My research is experimental in nature, with firm foundations in signal processing, machine learning, probabilistic graphical models, statistics, algorithms, and fundamental neuroscience.

Undergraduate Research Opportunities

I am available to mentor motivated undergraduates interested in machine learning and healthcare.

Research Interests

  • Neurological Diseases
  • Personalized Medicine
  • Deep Learning
  • Graphical Models
  • Neural Engineering
  • Machine Learning for Healthcare

Research Areas

  • Embedded, real-time, and hybrid systems
  • Fault tolerance and reliability
  • Machine learning
  • Machine learning and pattern recognition

Research Topics

Selected Articles in Journals

Articles in Conference Proceedings

  • Gupta, T., Wagh, N., Rawal, S., Berry, B., Worrell, G. and Varatharajah, Y., 2022. Tensor Decomposition of Large-scale Clinical EEGs Reveals Interpretable Patterns of Brain Physiology. In 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE, 2023.
  • Wagh, N., Wei, J., Rawal, S., Berry, B.M. and Varatharajah, Y., 2022. Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts. Advances in Neural Information Processing Systems, 35, pp.21142-21156.
  • Wagh, N., Wei, J., Rawal, S., Berry, B., Barnard, L., Brinkmann, B., Worrell, G., Jones, D. and Varatharajah, Y., 2021, November. Domain-guided self-supervision of eeg data improves downstream classification performance and generalizability. In Machine Learning for Health (pp. 130-142). PMLR.
  • Rawal, S. and Varatharajah, Y., 2021, December. Score-it: A machine learning framework for automatic standardization of eeg reports. In 2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) (pp. 1-4). IEEE.
  • N. Wagh, Y. Varatharajah, EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network, Accepted for publication, ML4H Workshop, Thirty-fourth Conference on Neural Information Processing Systems 2020.
  • Varatharajah, Yogatheesan, Haotian Chen, Andrew Trotter, and Ravishankar Iyer. "A Dynamic Human-in-the-loop Recommender System for Evidence-based Clinical Staging of COVID-19.", ACM RecSys Workshop on Health Recommender Systems, 2020.
  • Varatharajah, Yogatheesan, Brent Berry, Boney Joseph, Irena Balzekas, Vaclav Kremen, Benjamin Brinkmann, Gregory Worrell, and Ravishankar Iyer. "Electrophysiological Correlates of Brain Health Help Diagnose Epilepsy and Lateralize Seizure Focus." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 3460-3464. IEEE, 2020.
  • Saboo, Krishnakant, Chang Hu, Yogatheesan Varatharajah, Prashanthi Vemuri, and Ravishankar Iyer. "Predicting Longitudinal Cognitive Scores Using Baseline Imaging and Clinical Variables." In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1326-1330. IEEE, 2020.
  • Saboo, Krishnakant V., Yogatheesan Varatharajah, Brent M. Berry, Michael R. Sperling, Richard Gorniak, Kathryn A. Davis, Barbara C. Jobst et al. "A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection." In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 323-327. IEEE, 2019.
  • Varatharajah, Yogatheesan, Krishnakant Saboo, Ravishankar Iyer, Scott Przybelski, Christopher Schwarz, Ronald Petersen, Clifford Jack, and Prashanthi Vemuri. "A joint model for predicting structural and functional brain health in elderly individuals." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1657-1664. IEEE, 2019.
  • Varatharajah, Yogatheesan, Sujeeth Baradwaj, Atilla Kiraly, Diego Ardila, Ravishankar Iyer, Shravya Shetty, and Kai Kohlhoff. "Predicting brain age using structural neuroimaging and deep learning." bioRxiv (2018): 497925.
  • Varatharajah, Yogatheesan, Brent Berry, Sanmi Koyejo, and Ravishankar Iyer. "A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials." arXiv preprint arXiv:1809.00258 (2018).
  • Varatharajah, Yogatheesan, Brent M. Berry, Zbigniew T. Kalbarczyk, Benjamin H. Brinkmann, Gregory A. Worrell, and Ravishankar K. Iyer. "Inter-ictal seizure onset zone localization using unsupervised clustering and bayesian filtering." In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 533-539. IEEE, 2017.
  • Varatharajah, Yogatheesan, Min Jin Chong, Krishnakant Saboo, Brent Berry, Benjamin Brinkmann, Gregory Worrell, and Ravishankar Iyer. "EEG-GRAPH: A factor-graph-based model for capturing spatial, temporal, and observational relationships in electroencephalograms." In Advances in Neural Information Processing Systems, pp. 5371-5380. 2017.


Other Scholarly Activities

  • Member of the Machine Learning for Healthcare (ML4H) program committee (2020).
  • Peer reviewer for Neurips, AISTATS, MICCAI, and ICML (2017 - 2020).
  • Peer reviewer for Nature Scientific Reports and Neurobiology of Aging (2018 - 2020).

Professional Societies

  • Member, American Epilepsy Society
  • Member, IEEE Engineering in Medicine and Biology Society


  • NSF CISE Research Initiation Award (CRII)
  • Coordinated Science Laboratory Ph.D. Thesis Award (2021)
  • Young Investigator Award, American Epilepsy Society (2020)
  • Rambus Computer Engineering Fellowship (2018)
  • Fellowship of the Mayo Clinic - Illinois Alliance for Technology-based Healthcare Research (2016)

Research Honors

  • Best Poster Award, Machine Learning for Health Symposium (2021)
  • Best Paper Award, IEEE Signal Processing in Medicine and Biology Conference (2021)

Recent Courses Taught

  • BIOE 485 - Computational Math
  • BIOE 488 - Applied Hi-Per Comp Image Sci