Outreach & Education
We are at a unique point in time when we can address ML robustness before it is widely deployed and exploited in critical systems. Towards this goal, our Center will establish a research community focused on trustworthy machine learning that will address this issue and continue to thrive long after our frontier project ends. The resulting science and arsenal of defensive techniques developed within this project will provide the basis for building future systems in a more safe and secure manner.
The Center PIs are working towards achieving this goal via an extensive joint outreach effort, including an annual conference, broad-based educational initiatives, and speaking to a variety of government officials. The PIs are using the Center to further their ongoing efforts at broadening participation in computing through a joint summer school on trustworthy ML aimed at under represented groups, and by engaging in activities for high school students across the country.
Below you can find more information about our programs and opportunities.
Recent PhD graduate, Nicolas Papernot, performing cutting edge research on machine learning security.
Opportunities
Our PIs are active in teaching many graduate and undergraduate courses and are excited to share their information with you.
Please see the following:
- Adversarial Machine Learning Webinar on Youtube
- Everyday Ethics and Quotidian Quandaries for Computer Scientists at UVA
- Robust and Nonparametric Statistics at UC Berkeley
- Data, Inference, and Decisions at UC Berkeley
- Robust Statistics at UC Berkeley
- Security and Privacy of Machine Learning at UVA
- Pavilion Seminar: How will Artifical Intellegence change Humanity? at UVA
- Mini-course of Trustworthy Machine Learning at the 19th International School of Foundations of Security Analysis & Design
Dr. McDaniel will be participating in Penn State’s Game Makers, Game Changers summer camp for middle-schoolers being held virtually June 21-25, 2021. Please go here for more information and to apply.
The summer schools for graduate students and researchers is meant to provide exposure to this exciting topic. It’s modeled after the recent DeepSpec Summer School at Penn (as part of an NSF Expeditions project). The basic format is to bring researchers at one place and have course capsules on various topics (e.g., training-time attacks).
The primary focus is on developing curricula to rapidly provide a broad mix of researchers with the background they need in both security and machine learning to contribute to this area.
Our Center encourages young women to participate in trustworthy machine learning through a variety of venues. We recommend the following as good resources for high school and undergraduate women looking for networking opportunities.
Girls who Code Association for Women in Computing Anita B Women in Science & Engineering Leadership Institute