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Hi, I’m Cristian Moran, a Texas A&M University–San Antonio student and researcher focused on turning data and AI into practical, impact-driven solutions. My work spans machine learning, graph neural networks, GIS, and interactive visualization to support resilience, accessibility, and informed decision-making. I’ve contributed to NSF and SROP programs, built surrogate models to accelerate simulations, and co-authored research on trustworthy medical imaging, while mentoring peers and engaging with community partners. I’m passionate about AI-powered decision support, hazard modeling, and human-centered interfaces. I enjoy translating complex technical results into accessible demos—like immersive mixed-reality hazard simulations and Gradio apps—that nontechnical stakeholders can explore. My goal is to apply computation and data science to real-world challenges in climate, transportation, and public service, while growing as a developer and researcher.…Hi, I’m Cristian Moran, a Texas A&M University–San Antonio student and researcher focused on turning data and AI into practical, impact-driven solutions. My work spans machine learning, graph neural networks, GIS, and interactive visualization to support resilience, accessibility, and informed decision-making. I’ve contributed to NSF and SROP programs, built surrogate models to accelerate simulations, and co-authored research on trustworthy medical imaging, while mentoring peers and engaging with community partners. I’m passionate about AI-powered decision support, hazard modeling, and human-centered interfaces. I enjoy translating complex technical results into accessible demos—like immersive mixed-reality hazard simulations and Gradio apps—that nontechnical stakeholders can explore. My goal is to apply computation and data science to real-world challenges in climate, transportation, and public service, while growing as a developer and researcher.

Cristian Moran

Data Scientist, AI Engineer, Developer, +4

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Hi, I’m Cristian Moran, a Texas A&M University–San Antonio student and researcher focused on turning data and AI into practical, impact-driven solutions. My work spans machine learning, graph neural networks, GIS, and interactive visualization to support resilience, accessibility, and informed decision-making. I’ve contributed to NSF and SROP programs, built surrogate models to accelerate simulations, and co-authored research on trustworthy medical imaging, while mentoring peers and engaging with community partners. I’m passionate about AI-powered decision support, hazard modeling, and human-centered interfaces. I enjoy translating complex technical results into accessible demos—like immersive mixed-reality hazard simulations and Gradio apps—that nontechnical stakeholders can explore. My goal is to apply computation and data science to real-world challenges in climate, transportation, and public service, while growing as a developer and researcher.…Hi, I’m Cristian Moran, a Texas A&M University–San Antonio student and researcher focused on turning data and AI into practical, impact-driven solutions. My work spans machine learning, graph neural networks, GIS, and interactive visualization to support resilience, accessibility, and informed decision-making. I’ve contributed to NSF and SROP programs, built surrogate models to accelerate simulations, and co-authored research on trustworthy medical imaging, while mentoring peers and engaging with community partners. I’m passionate about AI-powered decision support, hazard modeling, and human-centered interfaces. I enjoy translating complex technical results into accessible demos—like immersive mixed-reality hazard simulations and Gradio apps—that nontechnical stakeholders can explore. My goal is to apply computation and data science to real-world challenges in climate, transportation, and public service, while growing as a developer and researcher.

Available to hire

Hi, I’m Cristian Moran, a Texas A&M University–San Antonio student and researcher focused on turning data and AI into practical, impact-driven solutions. My work spans machine learning, graph neural networks, GIS, and interactive visualization to support resilience, accessibility, and informed decision-making. I’ve contributed to NSF and SROP programs, built surrogate models to accelerate simulations, and co-authored research on trustworthy medical imaging, while mentoring peers and engaging with community partners.

I’m passionate about AI-powered decision support, hazard modeling, and human-centered interfaces. I enjoy translating complex technical results into accessible demos—like immersive mixed-reality hazard simulations and Gradio apps—that nontechnical stakeholders can explore. My goal is to apply computation and data science to real-world challenges in climate, transportation, and public service, while growing as a developer and researcher.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

Student at CodeTheDream

May 1, 2023 - October 1, 2023

240 hours of HTML/CSS/JavaScript learning; built a personal website; created a solid HTML boilerplate; used objects, arrays, AJAX, JSON, and the fetch API; integrated Font Awesome and Google Fonts to improve visuals and usability.

Business Development Intern at Snapbrilla

September 1, 2022 - May 1, 2023

Supported business development by researching partnerships, market trends, and lead generation; crafted outreach materials; contributed to early-stage marketing to grow brand visibility in the blockchain space.

Marketing Intern at Trending Socials

April 1, 2021 - October 1, 2021

Assisted with social media strategy and content creation; collaborated with Mission Lotería on cultural initiatives; supported branding and campaign performance tracking across platforms to improve reach.

Research Mentor at NSF LCCF ACSC Program

November 1, 2025 - December 1, 2025

Returned to guide student teams on data-driven social impact projects using leadership-class computing resources; led hands-on labs in Python, Linux, SLURM, and data visualization; coached teams on scoping research questions and presenting posters.

Research Participant at NSF LCCF ACSC Program

November 1, 2024 - December 1, 2024

Led a project on neighborhood-level poverty in San Antonio; coded Python and GIS workflows to analyze Census, infrastructure, and service location data; produced neighborhood indicators of poverty, access, and public investment; summarized results for community partners.

Research Assistant at University of Michigan Garcia Lab (SROP)

May 1, 2025 - August 1, 2025

Created improved models to inform housing infrastructure decisions; built a two-level surrogate for granular DEM (GNN + 1D CNN); reduced runtime from hours to seconds with ~0.4 mm error; designed a Python pipeline handling ~3.6 TB into ~120 GB per-step GPU-ready files; documented data provenance and uncertainty.

Research Fellow at CRSA at TACC, UT Austin

June 1, 2024 - August 1, 2024

Conducted GNN-based prediction of granular flow dynamics; generated 3D reconstructions via COLMAP; built mixed reality demo with human-in-the-loop controls; ran experiments on Lonestar6 and linked outputs to hazard simulations for preparedness and recovery planning.

Research Assistant at Medical Imaging Research, San Antonio

February 1, 2024 - June 1, 2024

Developed a calibration framework for deep learning models in medical imaging; improved calibration metrics by 35.83%; integrated evaluation into deployment pipeline; created visuals for clinical use; co-authored EMBC 2024 paper.

Project Lead at Toxicity Comment Detector

January 1, 2024 - May 1, 2024

Led a TensorFlow/Keras-based project to identify toxicity in comments; built text preprocessing and vectorization; evaluated with standard metrics; developed a Gradio interface to enable hands-on testing and study of online harm.

Research Assistant at Data Analyzation & Visualization Research, San Antonio

November 1, 2023 - February 1, 2024

Analyzed 40+ years of Bexar County crash records; built Python and GIS pipeline to clean data, join Census and roadway layers; produced interactive maps and identified hotspots; presented findings to the university symposium and the San Antonio Public Works Department.

Research Mentor at NSF LCCF ACSC Program

November 1, 2025 - December 1, 2025

Returned as research mentor, guiding student teams on data-driven social impact projects; led labs in Python, Linux, SLURM, and data visualization; coached students on scoping questions and posters.

Research Assistant at Garcia Lab, University of Michigan

May 1, 2025 - August 1, 2025

Created improved models to inform housing infrastructure decisions regarding exposure to different fault and material settings; built a two-level surrogate for granular DEM using a Graph Neural Network for the full particle field and a one-dimensional CNN for the surface, reducing runtime from hours to seconds with ~0.4 mm error; designed a Python pipeline for a reverse-fault dataset of ~60 million particles across 18,000 steps, converting ~3.6 TB of raw output into ~120 GB of GPU-ready per-step files and documenting data provenance and uncertainty.

Research Fellow at Cyberinfrastructure Research for Societal Advancement (TACC UT Austin)

June 1, 2024 - August 1, 2024

Conducted research at the NSF-funded Texas Advanced Computing Center (TACC), utilizing Graph Neural Networks to predict granular flow dynamics; generated pointed clouds for 3D reconstruction and analysis using multi-view cameras and LiDAR with COLMAP; built a mixed reality demo with human-in-the-loop controls performing object detection and particle interactions in near real time; ran experiments on Lonestar6 and linked reconstructed scenes to hazard simulation outputs to support preparedness and recovery planning.

Research Assistant at Medical Imaging Research

February 1, 2024 - June 1, 2024

Developed a novel calibration framework for deep learning models in medical imaging, improving calibration metrics by 35.83%; integrated evaluation into the pipeline with clear metrics, plots, and deployment checks, and built visualizations that translated performance data into actionable feedback for clinical use; co-authored an EMBC 2024 paper documenting the approach and linking gains to trustworthy clinical decision support.

Project Lead at Personal Project: Toxicity Comment Detector

January 1, 2024 - May 1, 2024

Led a deep neural network project to identify toxicity in comments with a focus on content related to immigrants and undocumented individuals; built text preprocessing and vectorization pipelines, tuned network architectures, evaluated with standard metrics, and developed a Gradio interface so users can test comments and view scores, treating the system as a research tool rather than an automatic filter.

Research Assistant at Data Analyzation & Visualization Research

November 1, 2023 - February 1, 2024

Analyzed 40+ years of Bexar County crash records; built a Python and GIS pipeline to clean data, join Census and roadway layers, and produce interpretable visuals; created an interactive map of San Antonio and identified crash hotspots; linked patterns to infrastructure and socioeconomic factors in immigrant and underrepresented neighborhoods; designed an interactive results brief and demo for stakeholders, earning first place at the university’s 10th Annual Research Symposium.