Conveners
Award session
- Jacqueline Asscher (Kinneret College, Technion)
Cellwise outliers, introduced by Alqallaf et al. (2009), represent a shift from the traditional rowwise approach in robust statistics by focusing on individual anomalous data cells rather than entire observations. This paradigm offers significant advantages, such as pinpointing which variables cause outlying behavior and preserving more usable data, particularly in high-dimensional settings...
I will tell the story of a social media influencing mission to empower every scientist and engineer in the world with the tools of Statistical Design and Analysis of Experiments. I'll share compelling examples, talk about why I started this, and how I did it. Using visual explorations of impressions data you will see what we can learn about using online channels to promote the value of statistics.
In the digital era, artificial intelligence (AI) is transforming how industries operate—powering breakthroughs in image analytics, large language models (LLMs), deep learning, reinforcement learning, hybrid modeling, and real-time decision-making.
This talk will reframe the narrative around AI, not as a hype or threat, but as an opportunity for the statistics community to lead with purpose...