Suren Byna | UCSC OSPO

AI Data Readiness Inspector (AIDRIN)

Fri, 30 Jan 2026 10:15:00 -0700

Garbage In, Garbage Out (GIGO) is a widely accepted quote in computer science across various domains, including Artificial Intelligence (AI). As data is the fuel for AI, models trained on low-quality, biased data are often ineffective. Computer scientists who use AI invest considerable time and effort in preparing the data for AI.

AIDRIN (AI Data Readiness INspector) is a framework that provides a quantifiable assessment of data readiness for AI processes, covering a broad range of dimensions from the literature. AIDRIN uses metrics from traditional data quality assessment, such as completeness, outliers, and duplicates, to evaluate data. Furthermore, AIDRIN uses metrics specific to assessing AI data, such as feature importance, feature correlations, class imbalance, fairness, privacy, and compliance with the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles. AIDRIN provides visualizations and reports to assist data scientists in further investigating data readiness.

AIDRIN Multiple File Formats

The proposed work will include improvements in the AIDRIN framework to (1) add support for new file formats such as Zarr, ROOT, and HDF5; and (2) to allow providing custom data ingestion mechanisms.

Topics: data readiness, AI, data analysis
Skills: Python, C/C++, data analysis, good communicator
Difficulty: Moderate
Size: Large (350 hours)
Mentors: Jean Luca Bez and Suren Byna

AIDRIN Privacy-Centric Enhancements: Backend & UX Upgrades

Fri, 25 Jul 2025 00:00:00 +0000

⏱️ Reading time: 5–6 minutes

Hey everyone,

If you’ve ever wondered what it takes to make AI data pipelines not just smarter, but safer and more transparent, you’re in the right place. The last few weeks working on AIDRIN for GSoC have been a deep dive into the engine room of privacy and backend systems that power the AIDRIN project. My focus has been on building out the core privacy infrastructure and backend features that power AIDRIN’s ability to give users real, actionable insights about their data. It’s been challenging, sometimes messy, but incredibly rewarding to see these changes make a tangible difference.

Having Dr. Jean Luca Bez and Prof. Suren Byna as mentors, along with the support of the entire team, has truly made all the difference. Their guidance, encouragement, and collaborative spirit have been a huge part of this journey, whether I’m brainstorming new ideas or just trying to untangle a tricky bug.

Privacy Metrics: Making Data Safer

A major part of my work has been putting data privacy at the front and center in AIDRIN. I focused on integrating essential privacy metrics like k-anonymity, l-diversity, t-closeness, and more, making sure they’re not just theoretical checkboxes, but real tools that users can interact with and understand. Now, these metrics are fully wired up in the backend and visualized in AIDRIN, so privacy risks are no longer just a vague concern. They are something AI data preparers can actually see and act on. Getting these metrics to work seamlessly with different datasets and ensuring their accuracy took some serious backend engineering, but the payoff has been worth it.

Speeding Things Up (So You Don’t Have To Wait Around)

As AIDRIN started handling bigger datasets, some of the calculations can be time-consuming because data has to be accessed every time a metric is computed. To address this, I added caching for previously computed metrics, like class imbalance and privacy checks, and set up asynchronous execution with Celery and Redis. This should make the app super responsive. Rather than waiting for heavy computations to finish, one can start taking notes about other metrics or explore different parts of the app while their results are loading in the background. It’s a small change, but it helps keep the workflow moving smoothly.

Small Touch Ups That (Hopefully) Make a Big Difference

I also spent time on the details that make the app easier to use. Tooltips now explain what the privacy metrics actually mean, error messages are clearer, and there’s a new cache info page where you can see and clear your cached data. The sensitive attribute dropdown is less confusing now, especially if you’re working with quasi-identifiers. These tweaks might seem minor, but they add up and make the app friendlier for everyone.

Docs, Docs, Docs

I’m a big believer that good documentation is just as important as good code. I updated the docs to cover all the new features, added citations for the privacy metrics, and made the install process a bit more straightforward. Hopefully, this means new users and contributors can get up to speed without too much hassle.

Huge Thanks to My Mentors and the Team

I really want to shine a light on Dr. Bez, Prof. Byna, and the entire AIDRIN team here. Their encouragement, practical advice, and collaborative spirit have been a huge part of my progress. Whether I’m stuck on a bug, brainstorming a new feature, or just need a second opinion, there’s always someone ready to help me think things through. Their experience and support have shaped not just the technical side of my work, but also how I approach problem-solving and teamwork.

What’s Next?

Looking ahead, I’m planning to expand AIDRIN’s support for multimodal datasets and keep refining the privacy and fairness modules. There’s always something new to learn or improve, and I’m excited to keep building. If you’re interested in data quality, privacy, or open-source AI tools, I’d love to connect and swap ideas.

Thanks for reading and for following along with my GSoC journey. I’ll be back soon with more updates!

This is the second post in my 3-part GSoC series with AIDRIN. Stay tuned for the final update.

Improving AI Data Pipelines in AIDRIN: A Privacy-Centric and Multimodal Expansion

Thu, 12 Jun 2025 00:00:00 +0000

⏱️ Reading time: 4–5 minutes

Hi 👋

I’m Harish Balaji, a Master’s student at NYU with a focus on Artificial Intelligence, Machine Learning, and Cybersecurity. I’m especially interested in building scalable systems that reflect responsible AI principles. For me, data quality isn’t just a technical detail. It’s a foundational aspect of building models that are reliable, fair, and reproducible in the real world.

This summer, I’m contributing to AIDRIN (AI Data Readiness Inspector) as part of Google Summer of Code 2025. I’m grateful to be working under the mentorship of Dr. Jean Luca Bez and Prof. Suren Byna from the Scientific Data Management Group at Lawrence Berkeley National Laboratory (LBNL).

AIDRIN is an open-source framework that helps researchers and practitioners evaluate whether a dataset is truly ready to be used in production-level AI workflows. From fairness to privacy, it provides a structured lens through which we can understand the strengths and gaps in our data.

Why this work matters

In machine learning, one principle always holds true:

“Garbage in, garbage out.”

Even the most advanced models can underperform or amplify harmful biases if trained on incomplete, imbalanced, or poorly understood data. This is where AIDRIN steps in. It provides practical tools to assess datasets across key dimensions like privacy, fairness, class balance, interpretability, and support for multiple modalities.

By making these characteristics measurable and transparent, AIDRIN empowers teams to make informed decisions early in the pipeline. It helps ensure that datasets are not only large or complex, but also trustworthy, representative, and purpose-fit.

My focus this summer

As part of my GSoC 2025 project, I’ll be focusing on extending AIDRIN’s evaluation capabilities. A big part of this involves strengthening its support for privacy metrics and designing tools that can handle non-tabular datasets, such as image-based data.

The goal is to expand AIDRIN’s reach without compromising on interpretability or ease of use. More technical insights and updates will follow in the next posts as the summer progresses.

What comes next

As the AI community continues to evolve, there’s a growing shift toward data-centric practices. I believe frameworks like AIDRIN are essential for helping us move beyond the question of “Does the model work?” toward a deeper and more meaningful one: “Was the data ready in the first place?”

Over the next few weeks, I’ll be working on development, testing, and integration. I’m excited to contribute to a tool that emphasizes transparency and reproducibility across the AI lifecycle, and to share lessons and ideas with others who care about responsible AI.

If you’re exploring similar challenges or working in the space of dataset evaluation and readiness, I’d love to connect and exchange thoughts. You can also read my full GSoC 2025 proposal below for more context around the project scope and vision:

👉 Read my GSoC 2025 proposal here

This is the first in a 3-part blog series documenting my GSoC journey with AIDRIN. Stay tuned for technical updates and behind-the-scenes insights as the summer unfolds!

AI Data Readiness Inspector (AIDRIN)

Tue, 11 Feb 2025 10:15:00 -0700

Garbage In Garbage Out (GIGO) is a universally agreed quote by computer scientists from various domains, including Artificial Intelligence (AI). As data is the fuel for AI, models trained on low-quality, biased data are often ineffective. Computer scientists who use AI invest considerable time and effort in preparing the data for AI.

AIDRIN (AI Data Readiness INspector) is a framework that provides a quantifiable assessment of the readiness of data for AI processes, covering a broad range of readiness dimensions available in the literature. AIDRIN uses metrics in traditional data quality assessment, such as completeness, outliers, and duplicates, for data evaluation. Furthermore, AIDRIN uses metrics specific to assess data for AI, such as feature importance, feature correlations, class imbalance, fairness, privacy, and FAIR (Findability, Accessibility, Interoperability, and Reusability) principle compliance. AIDRIN provides visualizations and reports to assist data scientists in further investigating the readiness of data.

AIDRIN Visualizations and Science Gateway

The proposed work will include improvements in the AIDRIN framework to (1) enhance, extend, and optimize the visualizations of metrics related to all six pillars of AI data readiness and (2) set up a science gateway on NERSC or AWS cloud service.

Topics: data readiness AI
Skills: Python, C/C++, good communicator
Difficulty: Moderate
Size: Large (350 hours)
Mentors: Jean Luca Bez and Suren Byna

h5bench with AI workloads

Tue, 11 Feb 2025 10:15:00 -0700

h5bench is a suite of parallel I/O benchmarks or kernels representing I/O patterns that are commonly used in HDF5 applications on high performance computing systems. h5bench measures I/O performance from various aspects, including the I/O overhead, and observed I/O rate.

Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputers. With massive amounts of data produced or consumed by compute nodes, high-performant parallel I/O is essential. I/O benchmarks play an important role in this process; however, there is a scarcity of I/O benchmarks representative of current workloads on HPC systems. Toward creating representative I/O kernels from real-world applications, we have created h5bench, a set of I/O kernels that exercise HDF5 I/O on parallel file systems in numerous dimensions. Our focus on HDF5 is due to the parallel I/O library’s heavy usage in various scientific applications running on supercomputing systems. The various tests benchmarked in the h5bench suite include I/O operations (read and write), data locality (arrays of basic data types and arrays of structures), array dimensionality (1D arrays, 2D meshes, 3D cubes), I/O modes (synchronous and asynchronous). h5bench measurements can be used to identify performance bottlenecks and their root causes and evaluate I/O optimizations. As the I/O patterns of h5bench are diverse and capture the I/O behaviors of various HPC applications, this study will be helpful to the broader supercomputing and I/O community.

h5bench with AI workloads

The proposed work will include (1) analyzing and characterizing AI workloads that rely on HDF5 datasets, (2) extracting a kernel of their I/O operations, and (3) implementing and validating the kernel in h5bench.

Topics: I/O HPC benchmarking
Skills: Python, C/C++, good communicator
Difficulty: Moderate
Size: Large (350 hours)
Mentors: Jean Luca Bez and Suren Byna