Hi, I’m Kenny. My goal for the last 20+ years has been to accelerate the advancement of AI. I studied math and computer science at CMU, followed by Ph.D. studies at USC (A.B.D.). I started Algorithmia to solve the problem of hosting and distribution of ML models running on GPUs. I am currently founder and CEO of Hyperparam, building tools to make ML dataset curation orders of magnitude more efficient.
At Hyperparam, we help engineers explore and curate massive ML datasets through a scalable, browser-based UI combined with machine learning techniques for dataset evaluation. Our mission is to empower teams to engineer the highest quality datasets, enabling the creation of better models through interactive and efficient data analysis. Learn more at hyperparam.app.
We are pushing the limits of big data in the browser. Check out our open-source contributions on GitHub.
Founded Algorithmia in 2014 to democratize access to machine learning by creating a marketplace where developers can discover, share, and deploy algorithms. Algorithmia was an early leader in the AI infrastructure space, long before ChatGPT existed.
I have been a significant contributor to a number of open source projects. Explore my contributions on GitHub.
My research interests include Algorithmic Mechanism Design, Artificial Intelligence, Algorithms, Cryptography, and Economics. During my academic career, I published works that have been cited over 1,500 times, and was first author on a paper that won the AAAI Classic Paper Award. My experiences at USC directly inspired me to co-found Algorithmia, where I translated theoretical insights into practical applications.
I’ve had the privilege of delivering talks on various topics, including tools and processes for the machine learning lifecycle. Here’s some examples of my work:
We talk with Kenny Daniel, founder of Hyperparam, to explore why actually looking at your data is the most high-leverage move you can make for building state-of-the-art models. It used to be that the first step of data science was to get familiar with your data. However, as modern LLM datasets have gotten larger, dataset exploration tools have not kept up. Kenny makes the case that user interfaces have been under-appreciated in the Python-centric world of AI, and new tools are needed to enable advances in machine learning. Our conversation also dives into new methods of using LLM models themselves to assist data engineers in actually looking at their data.
Traditional software development has a Software Development Life Cycle (SDLC) built around a set of tools and processes. In contrast, machine learning is a tangle of tools, languages, and infrastructures, with almost no standardization.
To build and deploy enterprise-ready machine learning models that generate real value, organizations need to consider a standard ML focused life cycle that supports IT's Operations Management and Infrastructure groups.
On a personal note, I created BASEline to help skydivers and BASE jumpers improve wingsuit flight performance through data analysis and in-flight feedback. BASEline is the premiere data analysis tool used by professional wingsuit BASE jumpers around the world. BASEline combines cutting-edge technology with a user-friendly interface to enhance safety and performance in extreme sports.