About me

Yogatheesan Varatharajah,

245, Coordinated Science Laboratory,

Urbana, IL, USA.

Email: varatha[number2][at]illinois[dot]edu

I am Yogatheesan (Yoga) Varatharajah, a PhD candidate in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. I have previously obtained my master's degree from the same university and bachelor's degree in Electronic and Telecommunication Engineering at the University of Moratuwa in Sri Lanka. I am currently in my 4th year of the PhD program and I am advised by Prof. Ravishankar Iyer. I spend most of my summers at the Mayo Clinic, working with Dr. Gregory Worrell and others on big data problems in healthcare. I have also spent a summer at Google and collaborated with Google Accelerated Science and Medical Brain teams.

My interests are in developing domain-guided models for analyzing healthcare datasets. In my view, the gap between off-the-shelf machine learning methods and those that are clinically applicable is the incorporation of domain knowledge. My PhD research focuses on bridging this gap by developing domain-guided models, mainly for neurological applications working in collaboration with clinical experts at the Mayo Clinic and ML experts at UIUC and Google. I have developed a unique expertise to augment probabilistic graphical models to encode clinical domain knowledge and successfully use them in clinical applications. My PhD research is being generously supported by the Mayo Clinic-Illinois Alliance Fellowship for Technology based Healthcare Research and Rambus Computer Engineering Fellowship.

You can find my latest cv here. And a list of my publications is here.

Receiving best student paper finalist award from Dr. Nigel Lovell of the IEEE.

Received the Rambus computer engineering fellowship certificate from dept. head Prof. William Sanders at the annual ECE award ceremony.

Recent news

  • Dec 2019 - Talk at the Clinical Data Animation Center (CDAC) at the Massachusetts General Hospital and Harvard University.
  • Nov 2019 - Invited talk at the Swartz Center for Computational Neuroscience at the University of California San Diego.
  • Nov 2019 - Our work received funding from the NSF center for computational biotechnology and genomic medicine for further investigation.
  • Nov 2019 - Invited talk at the University of Chicago.
  • Nov 2019 - An article on my work related to Alzheimer's disease was featured by CSL.
  • Oct 2019 - Our work on brain health modeling using neuroimaging was accepted for publication at the IEEE BIBM conference.
  • Aug 2019 - Invited talk at JP Morgan Chase AI research team.
  • Mar 2019 - Talk at Mayo Clinic department of Radiology.
  • Jan 2019 - Our work on early prediction of Alzheimer's disease was accepted for publication at Nature scientific reports.
  • Nov 2018 - Successfully completed my PhD preliminary examination.
  • Aug 2018 - My Mayo Fellowship was extended for another year (2018/2019)
  • May 2018 - Started my summer internship at Google Research
  • Apr 2018 - My work appeared in CSL spring 2018 newsletter
  • Mar 2018 - Received the RAMBUS COMPUTER ENGINEERING FELLOWSHIP for 2018-2019!
  • Jan-Mar 2018 - My work was featured in UIUC and CSL news, Daily Illini, Post Bulletin.
  • Dec 2017 - Presented my work at the 31st annual NIPS conference in Long Beach, LA.
  • Nov 2017 - Presented my poster at the American Epilepsy Society annual meeting in Washington DC.
  • Oct 2017 - Gave a talk at the first Feedback Friday meeting of Fall 2017, CSL, UIUC.
  • Sep 2017 - Spotlight talk at the ACNN Bigdata Neuroscience Workshop, Indiana University.
  • Sep 2017 - My paper titled 'EEG-GRAPH: A Factor Graph Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms' gets accepted at 31st Annual NIPS conference.
  • Jul 2017, My poster titled 'Unsupervised Analysis of Transcriptomic Data for Demystifying the Cause of Alzheimer's Disease' was featured at the Alzheimer's Association International Conference in London, UK.
  • May 2017 - My paper titled 'Inter-ictal Seizure Onset Zone localization using unsupervised clustering and Bayesian Filtering. ' was selected as one of the best six student authored papers at the 8th International IEEE/EMBS Conference on Neural Engineering. (link)
  • Feb 2017 - I was selected as an excellent graduate teaching assistant for ECE 313 (link)
  • Aug 2016 - Received the inaugural Mayo Clinic-Illinois Alliance Fellowship for Technology based Healthcare Research.
  • May 2016 - Passed my PhD qualifying examination!

Videos on my research