Juampablo Heras Rivera

Program Year:

University:

University of Washington

Field of Study:

Mechanical Engineering

Advisor:

Mehmet Kurt

Degree(s):

M.S. Mechanical Engineering, University of New Mexico, 2023; B.S. Mechanical Engineering, and B.S. Mathematics, University of New Mexico, 2022

Practicum Experience(s)

Lawrence Berkeley National Laboratory (2024)

Practicum Supervisor(s):

Daniela

Ushizima

Practicum Title:

Developing and evaluating deep learning segmentation models in the biological and medical domain.

Annual Program Review Abstracts

Fellow Poster

: Improving Glioma Segmentation in Low-Resolution Domains With Transfer Learning (

2024

)

Publications

Journal Papers:

B. Lindquist, R. Jadrich, J. E. Heras Rivera, L. Rondini; Uncertainty quantification for high explosive reactant and product equations of state. J. Appl. Phys. 21 August 2023; 134 (7): 075102. https://doi.org/10.1063/5.0157842

J. E. Heras Rivera, S. V. Poroseva; DNN power grid classifier as a surrogate for graph-search algorithms for the survivability analysis. International Journal of Energy for a Clean Environment 2024 (under review).

J. E. Heras Rivera, C. M. Neher, M. Kurt; Real-time nonlinear inversion of magnetic resonance elastography with operator learning. Radiology: Artificial Intelligence 2025 (under preparation).

Conference Papers:

J. E. Heras Rivera, D. T. Chen, T. Ren, D. K. Low, A. B. Abacha, A. Santamaria-Pang, et al.; BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features. Proc. Medical Imaging with Deep Learning, MIDL 2026.

T. Ren, D. Low, R. Xiang, P. Jaengprajak, J. E. Heras Rivera, R. Olson, J. Ruzevick, et al.; Can You Trust Your Model? Constructing Uncertainty Approximations Guaranteeing Validity of Glioma Segmentation Explanations. Proc. Medical Imaging with Deep Learning, MIDL 2026.

J. E. Heras Rivera, D. K. Low, X. Xiong, J. J. Ruzevick, D. D. Child, W. Yim, M. Kurt, et al.; CoRe-BT: A Multimodal Radiology-Pathology-Text Benchmark for Robust Brain Tumor Typing. Proc. Medical Image Computing and Computer Assisted Intervention, MICCAI 2026 (under review).

J. E. Heras Rivera, H. Oswal, T. Ren, Y. Pan, W. Henry, C. M. Neher, M. Kurt; Clinically-Informed Preprocessing Improves Stroke Segmentation in Low-Resource Settings. Proc. Medical Image Computing and Computer Assisted Intervention, MICCAI 2025.

E. de la Rosa, R. Su, M. Reyes, R. Wiest, E. O. Riedel, F. Kofler, K. Yang, J. E. Heras Rivera, et al.; Isles' 24: Improving final infarct prediction in ischemic stroke using multimodal imaging and clinical data. ISLES'24 Challenge 2024.

T. Ren, A. Sharma, J. E. Heras Rivera, H. Rebala, E. Honey, A. Chopra, J. Ruzevick, et al.; Re-DiffiNet: modeling discrepancies in tumor segmentation using diffusion models. Proc. Medical Imaging with Deep Learning, MIDL 2024.

J. E. Heras Rivera, H. Rebala, T. Ren, A. Sharma, M. Kurt; Improving glioma segmentation in low-resolution domains with transfer learning. Proc. Medical Imaging with Deep Learning, MIDL 2024.

S. V. Poroseva, J. E. Heras Rivera; Benefits and challenges of data-driven models for velocity/pressure-gradient correlations in turbulent flows. Proc. the 10th International Symposium on Turbulence, Heat and Mass Transfer, THMT-23, Rome, Italy, 11-15 September 2023.

S. V. Poroseva, J. E. Heras Rivera; On the Contribution of Data Errors in DNS Data-Driven VPG Correlation Models. Proc. the AIAA Aviation Forum, Chicago, IL, June 27 - July 1, 2022.

J. E. Heras Rivera, S. V. Poroseva; Application of Multiple Linear Regression to Deriving Data-Driven Models for Velocity/Pressure-Gradient Correlations in Turbulent Flows. Proc. the AIAA Region IV Student Conference, San Antonio, TX, April 1- April 2, 2022.

Awards

2023-2024 University of Washington College of Engineering Deans Fellowship

2020-2021 Outstanding Senior of the Year award for the Mechanical Engineering Department at the University of New Mexico (UNM).

2019-2022 Presidential Scholarship at UNM - covered my undergraduate education.