Past Summer Projects

CICF Student Fellows have the option to apply for a hands-on project-based learning experience at either an MF or a CI Compass institution during the summer. Spaces are very limited, so summer placement is not guaranteed and requires an additional application process that takes plce during the Spring Program.

Here is a list of past summer projects:

Summer 2026

Diya Adhikari - CICF ‘26 - MagLab

This summer, I had the opportunity to work at the National High Magnetic Field Laboratory (MagLab) as a CI Compass Fellow. Throughout my time there, I contributed to SciKG, a provenance-aware knowledge graph connecting researchers, publications, instruments, and experimental data for the lab's FT-ICR and proteomics research. I developed a data pipeline to extract, normalize, validate, and load information from FT-ICR publication records, and raw-data files into a single Neo4j graph. My work spanned from fetching raw data files and normalizing them into controlled, canonical vocabularies, generating entities and relationships from that data, validating and loading the resulting graph, and verifying it back against the source data to catch discrepancies in instruments and researcher names. Through collaboration with my fellow teammate, Veronika Saiadian and our research advisor, David Butcher, I gained valuable experience in graph databases, data provenance, and building tools to support scientific data discoverability at a research facility

Je-Yu “Leo” Chou - CICF ‘26 - NCAR

This summer, I had the opportunity to work at the NSF National Center for Atmospheric Research (NCAR) in the Climate & Global Dynamics division as an NSF CI Compass Fellow. My research focused on evaluating the computational and performance trade-offs between legacy Fortran and JAX frameworks across CPU and GPU architectures. Building upon the Fortran-to-JAX translation and unit testing work completed by Aya Lahlou and Linnia Hawkins, my main contribution centered on parallelizing a multi-layer canopy fork of the Community Land Model (CLM-ml). I implemented OpenMP directives to transform the serial Fortran code into a multi-threaded parallel workflow capable of running site ensembles across 128 threads on NCAR's Derecho supercomputer. I then systematically benchmarked the parallelized Fortran code against JAX CPU and GPU runs, revealing that OpenMP-driven CPU execution remains significantly more time- and cost-effective for current ensemble sizes. Through this experience, I was a lot more confident and fluent in understanding and writing Fortran code as well as understanding high performance computing, including how to navigate and execute jobs. I have also learned a lot in climate modeling as well as parallel computing and how to optimize programs with OpenMP.

Jeremiah David - CICF ‘26 - NCAR

This summer I had the opportunity to work at the NSF National Center for Atmospheric Research (NCAR), contributing to research on the stability and future of Thwaites Glacier in West Antarctica. Thwaites Glacier is nicknamed the "Doomsday Glacier" because it is one of the most unstable glaciers in the world and poses a significant threat to global sea levels if it were to collapse. I ran ice sheet simulations using CISM (Community Ice Sheet Model) within the CESM framework on NCAR's Derecho supercomputer, analyzing key variables such as ice thickness, surface mass balance, basal melt rate, and ice velocity in the Thwaites region. Using Python in JupyterHub, I developed notebooks to process and visualize large scale NetCDF output files, applying a glacier basin mask to isolate Thwaites for targeted analysis. I also helped conduct a Perturbed Parameter Ensemble (PPE) study, running 50 CISM simulations with different combinations of 8 key physics parameters generated through Latin Hypercube sampling, to quantify uncertainty in predictions of Thwaites Glacier instability and sea level rise. Through this work I gained valuable experience in scientific computing, climate data analysis, ice sheet modeling, and high performance computing.

Md. Naeem Islam - CICF ‘26 - OOI

This summer, I worked with the NSF Ocean Observatories Initiative (OOI) on a project building an artificial intelligence pipeline in a JupyterHub environment to make retrieving datasets much easier. I worked with the Cyberinfrastructure Development team to connect traditional systems with modern AI agents including Claude and opencode. I built a context engine and wrote automated scripts to map out all the OOI sensors, sites, and variables. I mapped out OOI research themes to get a good overview so the AI could brainstorm effectively. I also set up a Model Context Protocol server so the AI could understand the complex data structures. To retrieve the information, the AI learned to pull from sources like the M2M API, THREDDS servers, and mounted kdata. To test my work, I fed the AI research paper abstracts from the OOI Science Highlights page to see if the system could get the right data products used for the research and write the correct Python code. The tests were a success, and the AI consistently pulled the right datasets with room for improvement. This project taught me a lot about artificial intelligence engineering, complex problem solving, and how to build reliable tools for science.

Robert “Cole” Mullis - CICF ‘26 - NCAR

This summer I worked with the NSF National Center for Atmospheric Research (NCAR) in their CGD group, focusing on load-balancing an emerging Atmosphere-Ocean coupled model. I set up scripts for metric collection and workflow-streamlining to facilitate the project, and ran numerous tests with their high-performance computer -- Derecho. I compared the timings of the individual runs to find a balanced configuration and compare its performance to the standalone model's runtime, to compute the added cost of coupling. My results show a negligible amount of overhead and gave credence to the viability of the project. This opportunity has introduced me to many aspects of research computing and the efforts required in an academic research institution.

Rosio Rodriquez - CICF ‘24 - NCAR (Remote)

This summer, I had the opportunity to work with NSF NCAR on scientific computing and research software infrastructure for atmospheric modeling. My project focused on CAM-SIMA, CIME testing workflows, and the structure of related GitHub repositories. I learned how model cases are created, configured, built, submitted, and inspected on Derecho, and I explored how CAM-SIMA regression tests use before-and-after NetCDF snapshot files to validate physics outputs. I also contributed to a GitHub Actions workflow that automatically validates XML suite definition and scheme name list files during pull requests. Toward the end of the project, I began using Python and Xarray to inspect NetCDF snapshot files and better understand how model-state variables are organized. This experience helped me build confidence working with HPC systems, GitHub, terminal-based workflows, scientific data formats, and the software tools that support climate and atmospheric model development.

Veronika Saiadian - CICF ‘26 - MagLab

This summer, I worked at the National High Magnetic Field Laboratory (MagLab) on SciKG-FT-ICR, a Neo4j knowledge graph built over the lab's FT-ICR mass spectrometry publications. My main focus was the PDF extraction pipeline, the stage that reads the papers themselves to recover metadata that citation databases don't carry. I built it by pairing Docling for document parsing with LangExtract and a locally hosted Llama 3.1 model, running the jobs on our university's HPC cluster to pull six gap fields from each paper: instrument, ionization method, sample type, facility, software tools, and dataset accession. Only 55 papers could be pulled automatically, since most of the field's core work sits behind paywalls, so I expanded the corpus to 378 by acquiring the rest by hand and wrote integrity checks that caught DOI mismatches, duplicates, and papers the extractor had silently skipped. A firm rule on our project is that we never fabricate metadata without a traceable source. I also wrote a script to extract the metadata stored inside the experiment's raw instrument files, which exists only in the files themselves, and used it to enrich and connect information in the graph. This was an initial set of 46 files, and Diya later collected many more. On the database side, I wrote the Neo4j connection layer that enforces the uniqueness constraints the graph loads against. The project taught me how to submit batch Slurm jobs to a supercomputer, work with APIs, and troubleshoot through an iterative process. I also learned about ontologies and schemas, why metadata and provenance matter, how to automate their collection, and how much more there is to do in extraction.

Anshuraj Sedai - CICF ‘25 - OOI (Remote)

This summer at the NSF Ocean Observatories Initiative (OOI), I worked toward a tool that lets people ask an AI assistant how to find and use OOI's ocean data inside its JupyterHub research platform. Because a general AI doesn't know OOI's specific instruments, data formats, or quality-control conventions, my work focused on building the trustworthy, well-documented groundwork such an assistant would rely on. I first built a notebook that cross-validates in-situ sea-surface temperature from a coastal mooring's CTD sensor against NASA's satellite MUR temperature product for the same place and time, cleaning the buoy data with its QARTOD quality flags first. I then turned that notebook into a reusable template with a swappable instrument designator, and switched its satellite access to NASA's native earthaccess path so it runs both on the JupyterHub and on a personal computer. In parallel, I tested whether OOI's machine-to-machine (M2M) data delivery returns consistent results by repeatedly requesting identical data across several fluorometer models and a CTD. Together, this work validates how OOI's data behaves and lays the foundation for an AI assistant that can help users trust and correctly use it. This project was a great learning experience to understand how a major facility works and how the scientists think about different kinds of problems and how they navigate their way to a solution. I am incredibly grateful to CICF and OOI for this opportunity.

Bibhuti Shah - CICF ‘26 - UNDERC

This summer, I had the opportunity to work as a research intern mentored by Dr. Nathan G. Swenson at the University of Notre Dame Environmental Research Center (UNDERC) in Wisconsin/Michigan, which is also a National Ecological Observatory Network (NEON) site. My central research question was to compare the functional and phylogenetic diversity of trees measured directly by forest ecologists in the field against the predictions of a machine learning model developed by forest ecologists across the US. To do this, I used Geographic Information System (GIS) files and coordinate data to trace out field plots and ran analyses including Principal Component Analysis on trait matrices. Through this project, I learned to work extensively in R (RStudio), manipulating community and trait matrices using ecology packages like vegan and picante to build interpretable ecological insights from raw field data and the predicted data. I used three complementary approaches to compare the field data against the model's predictions: functional diversity, phylogenetic diversity, and community-weighted mean, applying each to trees categorized by diameter width of > 10 cm, > 15 cm and > 20 cm. I also gained experience analyzing beta diversity across the selected region. These insights can be used to identify pain points of the machine learning model and improve its accuracy so it can aid ecologists in the process of tree identification and trait quantification in diverse forest types. My biggest learning experience was seeing how a single question can be examined from multiple analytical angles, for instance, in the PCA, we separated out individual calculated axes and ran analyses on each one independently, in addition to considering them together as a whole. By the end of the summer, I had developed a deeper appreciation for the work of ecologists, statistical analyses, and the critical role cyberinfrastructure plays in transforming massive ecological datasets into meaningful insights. I am very grateful to NSF CICF and UNDERC for giving me this wonderful opportunity!

Fioni Thakker - CICF ‘26 - CHESS

Machine Learning for Parameter Tuning in High-Energy Diffraction Microscopy Analysis

This summer, I am conducting research at the Cornell High Energy Synchrotron Source (CHESS), where I am developing machine learning approaches to improve parameter tuning for High-Energy Diffraction Microscopy (HEDM). HEDM is a technique used to characterize the internal structure of polycrystalline materials such as metals and ceramics, but reconstructing experimental data requires scientists to iteratively tune more than 20 parameters through trial and error. My project focuses on developing and training a machine learning model to assist with parameter selection and tuning, with the goal of making HEDM analysis more efficient and reducing the number of reconstruction iterations. I am grateful for the opportunity to work alongside scientists at CHESS and to deepen my understanding of machine learning, scientific research, and synchrotron-based materials characterization through the CI Compass Summer Program.

Summer 2025

Ejay Aguirre - OOI

This summer, I worked with Ocean Observatories Initiative (OOI), where I investigated ocean temperature variability and its connection to El Niño and La Niña events. Using Python in a Jupyter Notebook environment, I integrated long-term datasets from OOI, Ocean Networks Canada (ONC), and the National Ecological Observatory Network (NEON), where I employed multiple data retrieval methods, including ERDDAP, NEON’s Data API, and direct file downloads to compile a consistent time series of data. Ensuring thorough documentation, I compared and analyzed seawater temperature and air temperature across the East-Central Tropical Pacific region whilst correlating them with El Niño and La Niña events. Through this project, it has deepened my understanding of differences in data infrastructures, particularly in instrumentation granularity, data accessibility, and sensitivity to ENSO signals across regions.

Macy Callahan - NSF NCAR

This summer, I had the opportunity to work at the National Center for Atmospheric Research (NCAR). Throughout my time there, I contributed to the CESM (Community Earth System Model) Unified Postprocessing and Diagnostics (CUPiD) package. I developed and modified Python scripts to process and visualize large-scale earth system model outputs using high-performance computing resources. My work involved generating diagnostics for high-resolution CESM simulations, exploring methods for remapping data to different grids, and improving workflow automation. Through collaboration with scientists and software engineers, I gained valuable experience in scientific computing, climate data analysis, and the development of tools to support ongoing research.

Raymond James Gallant - STEMSEAS (CICF 2024)

Over the past year, I’ve completed three cybersecurity internships that shaped my perspective on what it means to grow in this field. I started in summer 2024 at the NSF National Center for Atmospheric Research, where I worked on high performance computing and parallel workflows with the CESM team. I came in with no programming background and felt completely out of place, but my mentor helped me learn on the job and build a technical foundation from scratch. In spring 2025, I joined the UNOLS-MATE SECURE-IT Cyber Training Cruise aboard the R/V Sikuliaq. I deployed Fortinet firewalls, worked with OpenRVDAS, and saw firsthand how scientific infrastructure is secured in harsh, real-world environments. Across these experiences, I’ve learned that success in cybersecurity isn’t about being the most technical person in the room. It’s about staying adaptable, asking questions, and learning through discomfort.

Dylan Hermosillo - OOI

During my time at the Ocean Observatories Initiative, my project focused on applying convolutional neural networks (CNNs) to detect and classify ice floes using digital stills captured by a surface buoy at the Global Irminger Sea Array. To process the raw image data stream, I implemented unsupervised pretrained models and Vision Transformers to isolate specific features. In the latter half of the project, I extended the pipeline to classify cloud formations using the same image dataset. I utilized the PyTorch framework for model development and DINOv2 and FAISS for image embedding and clustering during the initial processing stage.

Aiden Kenney - NCAR

During the summer I had the pleasure of working at NCAR, working on their global fish population model, FEISTY (A Fisheries Size and Functional Type Model). During my time, I debugged a glitch in switching between an old and a new python environment with updated versions and new libraries. In my work, I delved deep into learning the structure of the model, the importance of various variables, and the relevance and accuracy of produced results. My biggest breakthrough was in comparing generated metadata from running both environments, and seeing an inconsistency between how data was temporarily chunked. An update in the Xarray library caused this, and my fix was to force the model to chunk in a specific way no matter the version. This solved most issues, and results produced were identical. This experience was critical in expanding my knowledge in python, jupyter notebooks, and debugging.

Naomi Kolodisner - Globus/UChi

This summer, I was an intern with Globus Labs at the University of Chicago, where I worked on converting a bioinformatics pipeline into an agent-based scientific workflow using the Academy framework. My project focused on transforming a viral detection genomics workflow, originally implemented as a linear sequence of Slurm batch scripts, into a modular, adaptive system composed of autonomous agents. The original workflow processes metagenomic samples to identify viral sequences, assess quality, dereplicate and cluster them, and annotate them using various bioinformatics tools. I reworked the stages of my workflow into discrete, parallelizable agents and integrated decision logic that dynamically selects the best viral detection tool based on performance metrics. Along the way, I also explored parallel task execution with Parsl and ran scaling experiments on an HPC cluster to optimize resource use. This internship was an incredibly rewarding experience that challenged me to grow and learn in many ways. I especially valued the hands-on work with computing systems and workflow design, which are skills highly relevant to my future career goals. I’m grateful for the opportunity to learn from and collaborate with such a supportive and knowledgeable team!

Tamara Segal - NCAR

This summer, I worked at NSF NCAR in the Climate and Global Dynamics Lab. I utilized a large language model to help in converting antiquated code into modern languages and architectures. This code deals with converting ice sheet data during the process of running NCAR’s atmospheric models. In the end, I was able to create a draft of the new, updated script and ran tests to evaluate its functionality. Through this experience, I learned a great deal about file management and version control, using tools like Linux and GitHub. I was also able to explore the various possibilities of AI in software development and inspire new possibilities at NCAR!

Jasmine Turner - NCAR

I am beyond grateful for the opportunity to be at NSF NCAR for my summer internship. I was able to work with and learn from some extremely talented scientists and software engineers. Specifically I focused on improving part of the CESM (Community Earth System Model) post-processing workflow. This involved using NCAR’s high-performance computing systems, Derecho and Casper, to run simulations and process large climate datasets. My work centered on enhancing the CUPiD (CESM Unified Postprocessing and Diagnostics) framework by integrating diagnostics from both the Atmosphere Diagnostics Framework (ADF) and the International Land Model Benchmarking (ILAMB) package into a single, unified summary page. By consolidating outputs from these two tools, the project aimed to make it significantly easier for scientists to access, interpret, and compare complex model evaluation results.

Priscilla Zavala - NCAR

During this summer, I had the privilege of working at NCAR under the guidance of expert scientists at the Mesa Lab. I was introduced to new topics within the Community Earth System Model (CESM) and CESM Unified Postprocessing and Diagnostics (CUPiD) via JupyterHub on their high-performance computing (HPC) cluster, Casper. Through this experience, I deepened my understanding of parallel computing and acquired new skills in Python libraries NumPy, Pandas, Xarray, and Dask beyond what I had encountered in my university coursework. The first half of my internship focused on conducting in-depth research and experimenting with difference plots to compare fields of data and apply these computations to extreme weather tracking software. Based on the uniqueness of the best approach for memory optimization with the intended softwares, the second half was more focused on working with Dask separately to understand the intricacies of dask parallelism for large-scale data analysis.

Summer 2024

Ingrid Carlson - NSF NCAR

I had an educational and enjoyable summer working with NCAR. The summer started with me learning about their Community Earth System Model (CESM) and CESM Unified Postprocessing and Diagnostics (CUPiD). The goal of my project was for CUPiD to run automatically with the CESM workflow. After several weeks of learning new skills and working with scientists at NCAR, I was able to successfully complete my project and my changes to the CESM workflow will hopefully be added in the near future.

Matthew Chung - Globus Labs

During the summer, I had the opportunity to work at Globus Labs where I got a chance to take a deep dive into Parsl, a Python based parallel scripting library used to parallelize workloads on supercomputers. My project was focused on researching and developing workload distribution algorithms in order to maximize the efficiency of Parsl’s High Throughput Executor. During this project, I learned how to develop, test and deploy the various algorithms using realistic workloads.

Cailin Cobey - OOI

This summer, I had the opportunity to work with WHOI on a project where I utilized HTML, CSS, and Beaver Builder within WordPress to create a professional dashboard that integrated data from Google Sheets and Google Drive. My project involved developing a centralized resource where users could access a calendar and view important datasets. By writing custom HTML and CSS, I was able to design a clean, responsive layout that enhanced the user experience. I used the Google Sheets API to pull real-time data into the dashboard, which was then displayed in tables and charts for easy analysis. The calendar was embedded using an iframe, ensuring seamless integration with Google Calendar. Overall, this project allowed me to blend front-end development skills with data integration, resulting in a polished and functional tool for users. I really enjoyed my time working with WHOI’s team and am very grateful for the opportunity!

Ellie Fahey - NSO (CICF 2023)

This summer, I was an intern for the development calibration team at the National Solar Observatory (NSO) data center in Boulder, Colorado. The data center team works alongside scientists on the Daniel K. Inouye Solar Telescope (DKIST), to create and maintain the data pipelines necessary to make solar observations available to scientists worldwide. Within DKIST, there are four (eventually five) instruments which all process different aspects of solar information, ranging from near-infrared to spectrography and visual imaging. Each instrument requires its own unique set of calibrations, visualization tools, and data pipeline in order to result in workable data for scientific use. It is the job of the development calibration team to write the code that will calibrate for mechanical effects on each instrument's data. My work focused on the Diffraction Limited Near-Infrared Spectropolarimeter, or DL-NIRSP, and writing code that does detector image correction to account for stray light on the instrument’s detector.

Raymond James Gallant - NSF NCAR

This summer, I had the incredible opportunity to work with scientists and software developers at the NSF National Center for Atmospheric Research in the Climate and Global Dynamics Lab. I gained a broad learning experience, exploring topics such as software development, parallel computing, and containerization, along with other areas that were new to me. I worked with Python libraries like Pandas, Dask, and NumPy, which enriched my understanding. This experience has significantly impacted my career goals, sparking new interests in areas that were previously unfamiliar and not covered in my regular university studies.

Mark Onders - NSF NCAR (CICF 2023)

I had an amazing internship this summer at NSF NCAR! I was so fortunate to be placed right on CU Boulder’s campus and able to work in person at the MESA facility. Not only is the MESA lab itself beautiful, but I really enjoyed getting to meet lots of people who work for NCAR and experience, first hand, the collaborative work atmosphere there. I was even able to go the the Research Aviation Facility and see NSF NCAR’s C130 airplane! This in itself was an incredible experience, but it was even more interesting because the research I did this summer directly relates to this plane!

This summer I worked within the Application Scalability and Performance (ASAP) team, which is located within the Computational and Information and Systems Lab. My team is currently working on code that will be used alongside NSF NCAR’s new Airborne Phased Array Radar (APAR) system. APAR will be placed on the outside of the C130 plane to allow it to gather observational meteorological data while in flight, which is pretty awesome! The code that my team is working on is called SAMURAI, or more formally known as Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation. During my summer, I was able to learn valuable skills which allowed me to submit jobs from this code on NCAR’s supercomputers, Derecho and Casper. I was also able to learn how to benchmark, or essentially time, this code’s performance and create several PRs on github to help progress this project. I also researched other tools and frameworks that were being considered added to SAMURAI.

Overall, this was a great experience, and I was able to learn valuable skills that I am grateful to have in my career going forward. I am grateful for CI Compass, NSF NCAR, and the National Science Foundation for making this opportunity possible!

Palina Pauliuchenka - OOI

Over this summer, I worked on two projects with OOI. For the first project, I learned a lot about data from OOI, NEON, ONC, AOOS, and other programs. I pulled data for specific instruments and parameters, such as CDT and sea water temperature, using their APIs and integrated all the data in a Jupyter Notebook. I correlated the data by determining sampling rates and coordinating timestamps, and documented the notebooks thoroughly so others could use them.

For my second project, I analyzed trends over time and correlated them with El Niño and La Niña events. This project allowed me to apply my knowledge in research and data analysis, and I learned a lot about El Niño, La Niña, and other environmental events in the process.

Quan Quy - MagLab

This summer at MagLab (the National High Magnetic Field Laboratory) has been an incredible learning experience for me. I developed a predictive model to forecast power supply failures for magnets at the MagLab facility, focusing on key features that could affect the magnets' health and failures. I also delved into time series forecasting to predict coil resistance deviation, which indicates the health of the magnets' coils, experimenting with various statistical and machine learning techniques to enhance model accuracy. It was eye-opening to see the real-world impact of this work on helping the facility with planning and maintenance scheduling. Additionally, I improved an audio classification program intended to detect hazardous conditions in magnet operations through audio frequency analysis and computer vision, making it more efficient and functional.

Mahee Shah - NSF NCAR(CICF 2023)

During my second summer at NCAR, I had the opportunity to work on the Community Earth System Model (CESM) with the Land and Ice team. My focus was on gaining proficiency with NCAR's newest HPC system, Derecho. This involved learning the intricacies of data management and job scheduling, crucial for running large-scale climate simulations.

In the latter half of my summer, I concentrated on running a simulation using the land and ice topography updater, a tool critical for tracking ice sheets in Greenland and Iceland. This involved configuring and executing a T-compset simulation forced by a B-compset output, focusing on the period from 2015 to 2100. The primary goal was to analyze the impact of atmospheric forcing on the Greenland Ice Sheet under a specific scenario. Through this process, I not only honed my skills in setting up and running CESM cases but also gained valuable insights into the dynamics of ice sheet changes and their implications for future climate scenarios.

Lisa Schulz - UNDERC

This summer I worked with the University of Notre Dame Center for Research Computing (CRC) and the University of Notre Dame Environmental Research Center (UNDERC). The overarching goal for my project was to create a lake water budget model that could be applied to the lakes of the UNDERC property to see how the lake volumes change over time. I had to learn how to create a digital terrain model from LiDAR data, and how to put together a mathematical model using equations from other scientist's previous efforts on related topics that I fit to match the needs and constraints of my project. I also learned how to use ArcGIS and I significantly improved my R programming skills, since R was the software I used to gather and interpolate data we needed for the model, and then construct the functions we need to run the program. The UNDERC property is in an area with one of the highest densities of lakes in the world, so a hydrological model like the one I am creating gives insight to water and nutrient flows which ultimately dictate the health of this critical ecosystem.

Connor Vessely - OOI

I primarily worked on computer vision projects during my internship at OOI. My first project was automating video summarization for over 46,000 videos captured by OOI’s video camera. In this project, I developed a process to select 9 frames from each video to represent the breadth of the video’s contents. I also developed a pipeline to identify blank images and work towards cleaning up the images in the OOI database. Most recently, I have been working on utilizing the newly operational GPUs at OOI.

Summer 2023 with NCAR

Raja Ali (CICF 2023)

This summer at NCAR has been nothing but transformative for me! I've been involved in enhancing the Ionosphere Dynamo Model, a sizable Fortran codebase set to be incorporated into WACCM-X, an advanced Earth atmosphere model. It's been an eye-opening journey learning the ins and outs of working in a research setting at a place as significant as NCAR.

From setting up a CI/CD Pipeline from scratch for the model for automated testing purposes to creating a namelist file that boosted the model's computational efficiency by 117% by eliminating recompilation time, I've gotten a real deep dive into the technologies used in Climate and Atmospheric Science, as well as what common technologies are being used in the cyberinfrastructure space by most major facilities. It's been a pleasant surprise to experience the sense of collaboration at NCAR. Everyone's eager to pool their knowledge and lend a hand, which creates a supportive environment despite the technically demanding nature of our work.

Edward Lin (CICF 2022)

At NCAR I was able to immerse myself in a research environment through meetings and activities. Interacting with scientists and engineers provided me with insight into what a potential career in research could look like. On top of that, I worked on a project involving the classification of weather tweets which combined my interests in natural language processing and the research done at NCAR.

Eddie Mayor (CICF 2023)

I have been observing and participating in the NCAR_NEON project, which connects powerful models with NEON's measurement network, bringing together ecology and earth science. The goal is to predict how Earth's systems will impact ecosystems. I’ve learned how NCAR gathers NEON data sets, processes them for model input, and uses data analysis and visualization techniques so that various audiences can understand their research.

Bhagya Ram (CICF 2023)

I'm working at the Atmospheric Chemistry Observations and Modeling division at NCAR. At my internship, I'm working on MusicBox, a web-based model that simulates the chemistry and transport of trace gases and aerosols in the atmosphere. I'm working on the model's front-end, utilizing Javasript, Django and React. I'm very thankful for this opportunity. I'm learning a lot, and I'm excited to draw on my experience in the future!

Mahee Shah (CICF 2023)

During the summer, I had the privilege of working at NCAR to explore the field of machine learning. The first part of my summer was dedicated to in-depth research and experimentation with various modeling softwares. The second half was spent deconstructing a project on Natural Disaster Tweets Classification, with the goal of comprehensively understanding its intricacies while attempting to replicate its outcomes.

Summer 2023 with OOI

Gareth Oram (CICF 2023)

I worked for WHOI designing a “scraping tool” to extract desired information from data logs. The data logs were transmitted through wires and wirelessly from buoys that collect oceanographic data. My program also functioned as a sorting tool to sort the desired information into four different categories.

Calloway Sutton (CICF 2023)

I worked on two main projects while at WHOI, upgrading the document management system and working on a way to search through OOI’s large video dataset. For the first project I got to learn about the various different DMS’ on the market such as Alfresco, Mayan, and SmartyPants as well as some potential alternatives to a DMS like Gitlab, Onbase and the large list of cloud drive providers. For the second project, I had the opportunity to use my previous knowledge in machine learning to work on making a system which could efficiently search through petabytes worth of videos with little compute.