Student Success

Recent Trainee Abstracts/Posters/Presentations: Trainee's underlined (after 2018)


  1. Smruti Deoghare, P. N. T. Nguyen, A. T. Trout, J. R. Dillman, V. B. S. Prasath. Fake it till you make it: Generative AI models for creating realistic artificial pediatric liver ultrasound images. Ultracon, Orlando, FL, USA, March 2023.

  2. J. Dhaliwal, Xiaoxuan Liu, James Reigle, M. Sharma, O. Lopez-Nunez, T. Walters, M. Collins, J. Hyams, I. Siddiqui, L. Denson, A. Jegga, S. Prasath. Development of an optimal machine learning model using treatment naive diagnostic pathology images to predict steroid-free clinical remission at one year in pediatric ulcerative colitis. Crohn's & Colitis Congress, January 2023, Denver, CO, USA.

  3. Xiaoxuan Liu, James Reigle, O. Nunez-Lopez, I. Siddiqui, T. D. Walters, J. S. Hyams, L. A. Denson, S. Prasath, J. Dhaliwal. Machine learning using standard of care pathology images predicts corticosteroid free remission at one year in pediatric ulcerative colitis. NASPGHAN/CPNP/APGNN Annual Meeting, Orlando, FL, USA, October 2022. Poster presentation.

  4. Kang Jin, Daniel Schnell, Guangyuan Li, Nathan Salomonis, V. B. S. Prasath, Rhonda Szczesniak, Bruce Aronow. CellDrift: Inferring perturbation responses in temporally-sampled single cell data. Computational Modeling of Biological Systems (SysMod) Community of Special Interest (COSI), Intelligent Systems for Molecular Biology (ISMB), Madison, WI, USA, July 2022. Poster presentation. (2nd Best Poster Award)

  5. Tareian Cazares, Faiz Rizvi, Balaji Iyer, Xiaoting Chen, Michael Kotliar, Joseph Wayman, Anthony Bejjani, Omer Donmez, Benjamin Wronowski, Sreeja Parameswaran, Leah Kottyan, Artem Barski, Matthew Weirauch, V. B. S. Prasath, Emily Miraldi. maxATAC: Predicting transcription factor binding at disease risk loci from ATAC-seq and DNA sequence with convolutional neural networks. Machine Learning in Computational and Systems Biology (MLCSB) Community of Special Interest (COSI), Intelligent Systems for Molecular Biology (ISMB), Madison, WI, USA, July 2022. Poster presentation.

  6. Xiaoxuan Liu, James Reigle, Erik Drysdale, Oscar Nunez-Lopez, Iram Siddiqui, Thomas D. Walters, Jeffrey S. Hyams, Lee A. Denson, S. Prasath, Jasbir Dhaliwal. One year corticosteroid free remission in pediatric Ulcerative Colitis predicted by machine learning models for histopathological classification. Digestive Disease Week (DDW), San Diego, CA, USA, May 2022. Poster presentation. doi:10.1016/S0016-5085(22)61510-5

  7. Jasbir Dhaliwal, Erik Drysdale, Oscar Nunez-Lopez, Xiaoxuan Liu, James Reigle, Dua Abuquteish, Juan Putra, Jeffrey S. Hyams, S. Prasath, Anna Goldenberg, Thomas D. Walters, Lee A. Denson, Iram Siddiqui. Employing deep learning approaches to automate eosinophilic cell counting in pediatric UC. Digestive Disease Week (DDW), San Diego, CA, USA, May 2022. Poster presentation. doi:10.1016/S0016-5085(22)61512-9

  8. Phuc Ngoc Thien Ngyuen, Smruti Deoghare, Andrew T. Trout, Jonathan R. Dillman, Vasundhara Acharya, V. B. S. Prasath. Fake it till you make it: Synthetic generation of pediatric liver ultrasound images using generative AI models. Undergraduate Research Showcase, University of Cincinnati, April 2022. Poster presentation. Vol. 4 No. 1 (2022): 2022: Undergraduate Scholarly Showcase Proceedings.

  9. Kang Jin, Daniel Schnell, Guangyuan Li, S. Prasath, R. Szczesniak, B. J. Aronow. CellDrift: Identifying cellular and temporal patterns of perturbation responses from single-cell data. Probabilistic Modelling in Genomics (ProbGen), March 2022. Poster presentation.

  10. Xiaoxuan Liu, James Reigle, Erik Drysdale, Oscar Nunez-Lopez, Iram Siddiqui, Thomas Walters, Jeffrey Hyams, Lee Denson, S. Prasath, Jasbir Dhaliwal. Predicting one-year corticosteroid-free remission in pediatric ulcerative colitis with interpretable machine learning. Digestive Health Center (DHC) Annual Scientific Symposium, Cincinnati, February 2022.

  11. Qingqing Wu, Jizhou Zhang, Courtney Johnson, Benjamin Weinhaus, Anastasiya Slaughter, Andre Valladares-Nuez, Andre Sherman, Marie-Dominique Filippi, S. 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

  12. Daiki Katsuma, H. Kawanaka, B. J. Aronow, V. B. S. Prasath. The effects of augmentation using GAN for confocal immunofluorescence image segmentation. 10th International Conference on Informatics, Electronics and Vision (ICIEV), Fukuoka, Japan, August 2021. (Work-in-Progress Best Paper Award)

  13. 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.

  14. Tareian Cazares, Faiz Rizvi, Balaji Iyer, Xiaoting Chen, Michael Kotliar, Leah C. Kottyan, Artem Barski, S. 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. Virtual presentation.

  15. 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.

  16. Smruti Deoghare, Ravi Yadav, Leah A. Gilligan, V. B. S. 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. Virtual presentation.

  17. 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. Virtual presentation.

  18. 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. Virtual Presentation. doi:10.1542/peds.147.3_MeetingAbstract.6-a

  19. Alejandra María 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. Virtual presentation.

  20. Faiz Rizvi, Tareian Cazares, Iyer Balaji, Matthew T. Weirauch, Leah Kottyan, S. 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.

  21. 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.

  22. Samuel W. Hacker, Balaji Iyer, Smruti Deoghare, Vivek J. Khandwala, David Wang, Daniel Woo, Achala S. Vagal, V. B. S.Prasath. Automated ICH outcome prediction from CT scans by ensemble convolutional neural network architecture. Capstone Poster Symposium, University of Cincinnati, Cincinnati, OH, USA, July 2019. (Second Place Award)

  23. Faiz Rizvi, Tareian Cazares, Joseph Wayman, S. Prasath, E. Miraldi. Flexible, scalable methods to infer transcriptional regulatory networks from single-cell genomics data. CCHMC Developmental Biology Retreat, June 2019.

  24. Faiz Rizvi, Tareian Cazares, Balaji Iyer, Matthew T. Weirauch, Leah Kottyan, S. Prasath, E. Miraldi. Using deep learning to predict cell type-specific chromatin accessibility based on genotype alone. CCHMC Immunology Retreat, April 2019.

  25. Harshith Bondada, V. B. S. Prasath. Mosaicking and blending in large-scale neuroimaging for robust dendrite detection. Carnegie Mellon Forum on Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA, 21 September 2018. Poster presentation.

  26. Srinivasa Siddhartha Selagamsetty, V. B. S. Prasath. Human epithelial type-2 cell segmentation with deep convolutional neural networks. Carnegie Mellon Forum on Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA, 21 September 2018. Poster presentation.

  27. 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. Poster presentation. Available at figshare: doi:10.6084/m9.figshare.6394889

Student Theses:


  1. Xiaoxuan Liu. Interpretable Machine Learning for Histopathology Images Classification in Pediatric Ulcerative Colitis Remission Prediction. MS Thesis, Department of Computer Science, University of Cincinnati, USA, 2022.

  2. Srinivasa Siddhartha Selagamsetty. Exploring a Methodology for Segmenting Biomedical Images using Deep Learning. MS Thesis, Electrical Engineering and Computer Science, University of Cincinnati, USA, 2019.

  3. Harshith Bondada. Retinal Vessel Segmentation on Ultra Wide-field Fluorescein Angiography Images. MS Thesis, Electrical Engineering and Computer Science, University of Cincinnati, USA, 2019.

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