Pinned
Most of what I actually need help with, I never think to tell a model. But why is it on me to remember?
Our new paper asks: what if AI could proactively specialize to individuals and the tasks they’re carrying out at this very moment? 🧵
- Say hello to us at UIST in Detroit!! See you at the Tues afternoon coffee break to chat about research, PhD apps, and maybe weigh in on my lab name 👀Interested in a PhD in HCI at UT Austin CS? Meet us at #UIST2026! Join Karthik Mahadevan (@karthikm0), Michelle Lam (@michelle123lam), and me to chat about research and ask questions about PhD applications. Our interests span a range of technical HCI areas (see the flyer).
- After over 10 years at Stanford, it's time to leave :) I will be joining UT Austin's CS department as an assistant professor in fall 2027! If you're excited to envision the future of interaction with AI, I'm recruiting PhD students this cycle. Come join me!
- Could we redesign social media AIs to promote societal values? Why do we want this? Is it even possible? Come hear from @JiaChenyan + me at our #CSCW24 talk—Wed 11/13, 9:30am (Session 1d: Understanding and Shaping Online Behavior)!Best Paper award winner AND an Impact Recognition for the same paper at #cscw2024! Amazing—congratulations @JiaChenyan and @michelle123lam!QuoteDec 8, 2023Michelle Lam@michelle123lamToday's social media AIs encode values—can we mitigate societal harms by making these values explicit and tuneable? Excited to share our #CSCW24 paper introducing societal objective functions, which translate social science constructs into algorithmic objectives for social media!
- How can end users more powerfully shape LLM behavior? DITTO lets a user provide just a handful of demonstrations to align a language model to their needs—and users much prefer these results over those of baseline methods and self-authored prompts!LLMs sound homogeneous *because* feedback modalities like rankings, principles, and pairs cater to group-level preferences. Asking an individual to rank ~1K outputs or provide accurate principles takes effort. What if we relied on a few demos to elicit annotator preferences?
