# Aashish Panta - Ph.D. Candidate | AI/ML & Large Data Specialist | Petascale Data Visualization
Source: https://hello.cv/aashishpanta
Salt Lake City

## Links
- LinkedIn | https://linkedin.com/in/aashishpanta
- GitHub | http://aashishpanta0.github.io/
- Email

## About
Ph.D. Candidate in Computer Science with extensive experience in developing advanced AI/ML frameworks and high-performance data visualization solutions for petascale scientific datasets. Specializes in leveraging cloud platforms and cutting-edge technologies like RAG and Large Language Models to drive impactful research in climate science and geoscience, contributing significantly to national labs and federal agencies.

## Work
### Software Engineer | VISOAR LLC
Enhanced database performance and scalability by migrating backend systems and integrating advanced visualization tools for large-scale data analysis.
- Migrated backend database from SQLite to MongoDB, significantly improving database performance and scalability for critical operations.
- Implemented and integrated MongoDB Atlas cloud service for robust database hosting and management, refactoring backend API and ORM logic for seamless system compatibility.
- Integrated Jupyter notebooks and visualization dashboards using Panel and Bokeh into the web application, enabling large-scale data analysis and insights.
### Graduate Research Assistant | Scientific Computing and Imaging Institute
Conducts research with Professor Valerio Pascucci on developing cyberinfrastructure for efficient visualization and analysis of petabyte-scale datasets.
- Developed cyberinfrastructure for efficient visualization of large-scale datasets (terabytes to petabytes) in collaboration with Professor Valerio Pascucci's team at SCI.
- Designed a novel data model incorporating efficient progressive streaming, compression, and automated data reduction frameworks, in collaboration with national labs and federal agencies.
### Visiting Scholar | NSF NCAR
Spearheaded the development of advanced web-based dashboards and containerized workflows for petabyte-scale climate data analysis, enhancing accessibility and reproducibility for domain scientists.
- Developed interactive web-based dashboards for multi-terabyte to petabyte-scale climate datasets, eliminating lossy resampling and enabling real-time analysis in NSF NCAR's Research Data Archive.
- Engineered scalable containerized dashboards and Python notebook workflows on the CISL CIRRUS system, facilitating exploration of CESM2-LENS and ERA5 data in NetCDF and Zarr formats.
- Optimized ingestion pipelines for large geoscience datasets in collaboration with NCAR researchers, ensuring efficient deployment, reproducibility, and usability for domain scientists.
### Machine Learning Intern | NASA Jet Propulsion Lab
Designed and deployed a unified AI framework for Earth Science Intelligence, integrating RAG and LLMs to enable interactive, high-accuracy analysis of massive climate datasets.
- Developed a unified AI framework for Earth Science Intelligence, integrating Retrieval-Augmented Generation (RAG) with Large Language Models for interactive, high-accuracy analysis of massive climate datasets.
- Built modular tools including a climate data assistant, multi-model AI comparison interface, and multilingual voice-enabled dashboard, to support diverse research workflows.
- Deployed the AI framework on Microsoft Azure, utilizing Blob Storage, AI Search, and Foundry services, with OpenAI's Whisper for multilingual speech-to-text, ensuring scalability and reproducibility.
### Software Engineer Intern | NASA Jet Propulsion Lab
Optimized Mars rover data processing workflows and enhanced system functionalities for in-depth statistical analysis of surface parameters.
- Collaborated with the M2020 Perseverance Robotic Arm team, identifying data trending gaps and resolving unnoticed bugs, enhancing the system with functionalities for in-depth statistics of Mars rover surface parameters.
- Analyzed and graphically represented Mars 2020 rover's trending data over time to detect proximity to fault limits, facilitating preventative measures against system failures.
- Optimized the Mars rover backprocessing workflow, achieving a substantial reduction in processing time and improving operational efficiency.

## Education
### The University of Utah | Computer Science
### The University of Mississippi | Computer Science
3.84 GPA

## Awards
### CCGRID International Scalable Computing Challenge (SCALE) Finalist
CCGRID | 2025-01-01
Recognized as a finalist in the prestigious international challenge for scalable computing.
### Best Paper Award, IEEE Large Scale Data Analysis and Visualization (LDAV) symposium
IEEE LDAV | 2024-01-01
Received a best paper award at a leading symposium for contributions to large-scale data analysis and visualization.

## Publications
### Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics
IEEE Transactions on Visualization and Computer Graphics
A. Panta, A. Sahistan, X. Huang, A. A. Gooch, G. Scorzelli, H. Torres, P. Klein, G. A. Ovando-Montejo, P. Lindstrom, and V. Pascucci, 'Expanding access to science participation: A fair framework for petascale data visualization and analytics,' IEEE Transactions on Visualization and Computer Graphics, pp. 1–16, 2025.
### A Voice-Enabled AI Agent for Interactive Visualization and Analysis of NASA's Downscaled Dataset
American Geophysical Union (AGU)
Presented at American Geophysical Union (AGU) conference.
### Large Data Acquisition and Analytics at Synchrotron Radiation Facilities
IEEE International Conference on Big Data (IEEE Big Data)
A. Panta, G. Scorzelli, A. Gooch, W. Sun, K. Shanks, S. Sarker, D. Bougie, K. Soloway, R. Verberg, T. Berman, G. Tarcea, J. Allison, M. Taufer, and V. Pascucci, 'Large data acquisition and analytics at synchrotron radiation facilities,' in Proceedings of the IEEE International Conference on Big Data (IEEE Big Data), Macau, China, 2025.
### From Validation to Societal Value: A User-Centric Framework for Evaluating Regional Climate Models
American Geophysical Union (AGU)
Presented at American Geophysical Union (AGU) conference.
### Climate Data for Power Systems Applications: Lessons in Reusing Wildfire Smoke Data for Solar PV Studies
Annual Hawaii International Conference on System Sciences
A. Salinas, I. Sohail, V. Pascucci, P. Stefanakis, S. Amjad, A. Panta, R. Schigas, T.C.Y. Chui, N. Duboc, M. Farrokhabadi, and R. Stull, 'Climate Data for Power Systems Applications: Lessons in Reusing Wildfire Smoke Data for Solar PV Studies,' in 2026 Proceedings of the Annual Hawaii International Conference on System Sciences, 2026.
### Scalable Web-Based Exploration and RAG-enhanced Insights for NASA's Downscaled Climate Data
Cloud-Native Geospatial (CNG) Conference
Presented at Cloud-Native Geospatial (CNG) Conference.
### Scalable Climate Data Analysis: Balancing Petascale Fidelity and Computational Cost
IEEE 25th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)
A. Panta, A. Gooch, G. Scorzelli, M. Taufer, and V. Pascucci, 'Scalable climate data analysis: Balancing petascale fidelity and computational cost,' in 2025 IEEE 25th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW), 2025, pp. 245–248.
### Leveraging National Science Data Fabric Services to Train Data Scientists
SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis
M. Taufer, H. Martinez, A. Panta, P. Olaya, J. Marquez, A. Gooch, G. Scorzelli, and V. Pascucci, 'Leveraging national science data fabric services to train data scientists,' in SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2024, pp. 355-362.
### Web-based Visualization and Analytics of Petascale Data: Equity as a Tide that Lifts All Boats
IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV)
A. Panta, X. Huang, N. McCurdy, D. Ellsworth, A. A. Gooch, G. Scorzelli, H. Torres, P. Klein, G. A. Ovando-Montejo, and V. Pascucci, 'Web-based visualization and analytics of petascale data: Equity as a tide that lifts all boats,' in 2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV), 2024, pp. 1-11.
### Enhancing Scientific Research with FAIR Digital Objects in the National Science Data Fabric
Computing in Science & Engineering
M. Taufer, H. Martinez, J. Luettgau, L. Whitnah, G. Scorzelli, P. Newell, A. Panta, P.-T. Bremer, D. Fils, C. R. Kirkpatrick, and V. Pascucci, 'Enhancing scientific research with fair digital objects in the national science data fabric,' Computing in Science & Engineering, vol. 25, no. 5, pp. 39–47, 2023.

## Skills
### Cloud Platforms & Services
- Azure Services
- MongoDB Atlas
- Blob Storage
- AI Search
- Foundry services
- OpenAI Whisper
### Methodologies & Tools
- Agile
- GIT
- Containerization
- ORMs
### Web Development
- Express
- NodeJS
- MongoDB
- MySQL
- React
- HTML5
- CSS
- JavaScript
### Data Visualization
- Paraview
- 3D Slicer
- Panel
- Bokeh
- Jupyter Notebooks
### Programming Languages
- Python
- PHP
- SQL
### Scientific Computing
- Petascale Data
- Geoscience Datasets
- Climate Modeling
- Cyberinfrastructure
- Data Reduction Frameworks
- High-Performance Computing
### Machine Learning & AI
- Retrieval-Augmented Generation (RAG)
- Large Language Models (LLMs)
- Deep Neural Networks
- AI Agent Development
- Climate Data Assistant
- Multi-model AI Comparison

## Projects
### OpenVisus: Large-scale Scientific Visualization Tool
Developed and optimized Python APIs and data access plugins for OpenVisus to enable high-performance streaming, multiresolution visualization, and analysis of large-scale datasets across research domains including synchrotron facilities, materials science, and data commons. Collaborated with scientists at NASA Jet Propulsion Laboratory (JPL) to develop scalable cyberinfrastructure supporting data streaming, subsetting, and real-time visualization of climate datasets within the AIST OCW framework.
### Dynamic Super-Resolution for Large Multi-variate Climate Dataset
Designed and implemented a deep neural network architecture integrating convolutional layers, upsampling, and multi-quality inputs to achieve dynamic super-resolution and efficient analysis of large multivariate datasets, as part of research at the University of Utah.
### NASA ARSET Program: Assessing Extreme Weather Statistics using NEX-GDDP-CMIP6
Co-led a NASA ARSET program tutorial on assessing extreme weather statistics using NASA Earth eXchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6), engaging 700 participants from 93 countries.
### A Unified and Interactive Framework for Data Intelligence
Developed an interactive data intelligence framework integrating Azure AI Search, Azure OpenAI, and cloud-hosted datasets, enabling natural language querying, dynamic subsetting, and on-demand analytics directly from the deployed interface, in collaboration with NASA JPL and Microsoft.
### Tutorial: Strategies for Large-Scale Data Analysis with the National Science Data Fabric (NSDF)
Co-led a tutorial on strategies for large-scale data analysis with the National Science Data Fabric (NSDF) at IEEE IPDPS, 2025.
### Invited Presentation: OpenVisus for Petascale Scientific Visualization (NCAR Earth System Data Science)
Presented 'OpenVisus for Petascale Scientific Visualization' at the NCAR Earth System Data Science (ESDS) Initiative, remotely.
### Invited Presentation: OpenVisus for Petascale Scientific Data (NASA Earth Exchange)
Presented 'OpenVisus for Visualization of Petascale Scientific Data' at the NASA Earth Exchange (NEX) Biweekly Meeting, remotely.
### Tutorial: Enabling Scientific Discovery with National Science Data Fabric
Co-led a tutorial at IEEE VIS, 2024, focusing on harnessing the power of the National Science Data Fabric for large-scale data analysis to enable scientific discovery.
### Tutorial: Using NSDF Services for End-to-End Analysis and Visualization of Large Scientific Data
Co-led a tutorial as part of the NSDF Webinar Series, 2024, on utilizing NSDF services for end-to-end analysis and visualization of large scientific data.

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