Katharine Jarmul

Just what does Claude Code need access to and why? In this talk, we'll dive into privacy and security concerns when using LLMs, code assistants and AI agentic workflows. Aside from making you more paranoid, you'll leave with some practical tips on how to cover your prompts in tin foil and avoid...

Can deep learning/AI models forget? In this talk, you'll explore the realm of machine unlearning, where researchers and practitioners aim to remove memorized examples from machine learning models.

With increased ease of smaller "AI" models, better chips and on-device learning, is it possible now to build and train your own models for your own use? In this keynote, we'll explore learnings of small, medium and large-sized model personalization, but driven by yourself and for yourself.

For the third year in a role, the PyLadies Panel at PyCon PyData engages with a broader audience on critical issues related to gender disparities, ethics, and the ongoing importance of women-focused tech groups.

I know you probably don't want to hear about it, but your deep learning model probably memorized some of its training data. In this talk, we'll review active research on deep learning and memorization, particularly for large models such as large language and multi-modal models.

If you are curious about the field of cryptography and what it has to offer data science and machine learning, this talk is for you! We'll dive into the field of encrypted computation, where decryption isn't needed in order to perform calculations, transformations and operations on the data.

Many of us have heard terms like Data for Good, Ethical Machine Learning, Human-Centric Product Design, but those words also bring forward questions -- if we need "Ethical ML" what is the rest of machine learning?

As a Staff-Plus Engineer, I’ve had plenty of learning moments in my career where I realized my view of a problem was hindered by my own desire to solve it with technology.