Student Success

Undergraduate Research in Biomedical Informatics

State-of-the-Art Methods

Discover and explore Biomedical Informatics. Apply new methods to interesting open problems in the Pediatrics domain.

Advanced Training

Work in collaborative teams to develop new algorithms, hone soft skills, and enhance your scientific background.

Hands-on Experience

Get hands-on experience in scientific research and explore career opportunities in science, technology, engineering and mathematics (STEM) - in an interdisciplinary and applied approach.

Have NO-FEAR (New Opportunities - For Engineering and Advanced undergraduates in Research) to do ARISE (AI Research In Science and Engineering)! So Apply Now to join us!

Check our Research and Projects!

Recent Trainee Abstracts/Posters/Presentations:

  1. Qingqing Wu, Jizhou Zhang, Courtney Johnson, Benjamin Weinhaus, Anastasiya Slaughter, Andre Valladares-Nuez, Andre Sherman, Marie-Dominique Filippi, Surya Prasath, Sing Sing Way, J Mathew Koffron, Daniel Lucas-Alcaraz. A durable anatomy with local plasticity enables normal and stress hematopoiesis. 63rd Annual American Society of Hematology (ASH) Meeting. Blood, vol 138 (Supplement 1): 297, November 2021. doi:10.1182/blood-2021-153083

  2. Guangyuan Li, Song Baobao, H.L. Grimes, V. B. S. Prasath, Nathan Salomonis. scTriangulate - Decision-level integration of multimodal single-cell data. Single Cell Analyses, CSHL, 10 - 12 November 2021. Poster Presentation.

  3. Tareian Cazares, Faiz Rizvi, Balaji Iyer, Xiaoting Chen, Michael Kotliar, Leah C. Kottyan, Artem Barski, Surya Prasath, Matthew T. Weirauch and Emily R. Miraldi. maxATAC: a suite of user-friendly, deep neural network models for transcription factor binding prediction from ATAC-seq. Great Lakes Bioinformatics Conference (GLBIO), May 2021.

  4. Manan Shah, Derek Shu, Yizhao Ni, S. Prasath, Andrew Schapiro, Kevin Dufendach. Feasibility of machine learning to automatically extract PICC tip locations from unstructured radiology reports. Pediatric Academic Societies (PAS) Virtual Meeting, May 2021.

  5. Smruti Deoghare, Ravi Yadav, Leah A. Gilligan, V. B. Surya Prasath, Andrew T. Trout, Jonathan R. Dillman. Deep learning predicts ultrasound SWE liver stiffness in children. 106th RSNA Annual Meeting, 29 November - 5 December 2020.

  6. Manan Shah, Yizhao Ni, S. Prasath, Andrew Schapiro, Kevin Dufendach. Machine learning to identify peripherally inserted central catheter (PICC) tip position from radiology reports. American Medical Informatics Association (AMIA) Annual Symposium, Chicago, USA, November 2020.

  7. Manan Shah, Kevin Dufendach, Andrew Schapiro, Yizhao Ni, S. Prasath. Comparison of various machine learning models to identify peripherally inserted central catheter (PICC) tip position from radiology reports. American Academy of Pediatrics (AAP) Virtual National Conference and Exhibition, San Diego, USA, October 2020. doi:10.1542/peds.147.3_MeetingAbstract.6-a

  8. Alejandra MarĂ­a Casar Berazaluce, Ravi Yadav, Smruti Deoghare, Alexander Gibbons, V. B. S. Prasath, Todd A. Ponsky, B. A. Rymeski. Artificial intelligence driven automated detection of pyloric stenosis in ultrasound imaging. International Pediatric Endosurgery Group (IPEG), Vienna, Austria, June 2020.

  9. Faiz Rizvi, Tariean Cazares, Iyer Balaji, Matt Weirauch, Leah Kottyan, Surya Prasath, Emily R. Miraldi. Using deep learning to predict cell type-specific chromatin accessibility based on genotype alone. 12th annual RECOMB/ISCB Conference on Regulatory and Systems Genomics, New York, USA, November 2019. Poster presentation.

  10. Balaji Iyer, Smruti Deoghare, Samuel Hacker, Vivek Khandwala, David Wang, Daniel Woo, Achala S. Vagal, V. B. S. Prasath. Predicting ICH patient outcome from brain CT scans using an ensemble deep learning framework. Advanced Computational Neuroscience Network (ACNN), University of Michigan, Ann Arbor, MI, USA, 19 - 20 September, 2019.

  11. Asami Yonekura, H. Kawanaka, V. B. S. Prasath, B. J. Aronow, H. Takase. Automatic disease stage classification of brain Glioblastoma Multiforme histopathological images using deep convolutional neural networks. Machine Learning in Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, USA, 6 - 8 June, 2018. Available at figshare: doi:10.6084/m9.figshare.6394889