Excited to share our paper! Due Process on Hold: A Queueing Framework for Improving Access in SNAP arxiv.org/abs/2605.15165 Millions of Americans interface with the social safety net via call centers that are too congested. In Holmes v. Knodell, bad operations = procedural due…
Who's Who of AI
angela zhou
Why they matter
Researcher with public evidence across AI research, Compute & infrastructure, Responsible AI.
- AI signals
- 4 past 30d
- Sources
- 3 distinct domains
- Discussões
- 8 past 30d
- Latest signal
- 8d ago
assistant prof at USC Data Sciences and Operations and Computer Science; phd Cornell ORIE.
data-driven decision-making, operations research/management, causal inference, algorithmic fairness/equity
bureaucratic justice warrior
angelamzhou.github.io
What they're sharing
[2605.15165] Due Process on Hold: A Queueing Framework for Improving Access in SNAP arxiv.org
Offline Preference-Based Trajectory Evaluation arxiv.org
Ask for More Than Bayes Optimal: A Theory of Indecisions for Classification arxiv.org
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Bridging Prediction and Intervention Problems in Social Systems arxiv.org
Bridging Predictions and Interventions: An Integrated Framework for Automated Decision-Systems arxiv.org
Designing Persuasive Experiments arxiv.org
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Building the AI-native hospital | Percepta percepta.ai
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teaching: intro to nlp | Are.na are.na
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Call for the Special Track on AI for Social Impact - AAAI aaai.org
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Starbucks Sold Its AI on a 99% Accuracy Number. Nine Months Later, 11,000 Stores Are Counting Milk by Hand. thestateofbrand.com
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Articles & links
angela zhou reposted
Please try to look deeper than success rate for agent evaluation. Thank you. arxiv.org/abs/2606.17541
AI Weekly's analysis
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- Fernando Diaz argues success-only metrics tie agent comparisons on roughly 75% of instances, gutting statistical power.
- His preference-based trajectory evaluation compares progress and time-to-return profiles, cutting ties to roughly 35%.
- The paper suggests apparent benchmark saturation may reflect the evaluation measure, not exhausted data or problems.
Read full analysis →
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angela zhou reposted
arXiv📈🤖 Ask for More Than Bayes Optimal: A Theory of Indecisions for Classification By Ndaoud, Radchenko, Rava
This is a bit of a "hard launch" for our series of workshops and whitepapers with a multidisciplinary crew of amazing folks! Longer paper: arxiv.org/abs/2507.05216 Shorter perspective: arxiv.org/abs/2606.25668 Your predictive ADS is actually an organizational process, decision…
This is a bit of a "hard launch" for our series of workshops and whitepapers with a multidisciplinary crew of amazing folks! Longer paper: arxiv.org/abs/2507.05216 Shorter perspective: arxiv.org/abs/2606.25668 Your predictive ADS is actually an organizational process, decision…
angela zhou reposted
arXiv📈🤖 Designing Persuasive Experiments By Adusumilli, Vemulapati
angela zhou reposted
At Percepta, we've helped Summa Health automate the process of fairly scheduling nurse shifts. This is the type of work I'm really proud of, clearly using ML to make folks lives easier and better www.percepta.ai/blog/buildin...
angela zhou reposted
I also keep a list of courses here: are.na/maria-antoni... I think @anamarasovic.bsky.social’s course and the Stanford course by Yejin and Diyi were the best/most helpful for me!
angela zhou reposted
More broadly, the call emphasizes a range of ways AI work can have impact -- through field deployments, informing policy, changing what practitioners do, etc -- and we encourage authors to articulate a "theory of change" along any of these or other axes. aaai.org/conference/a...
angela zhou reposted
"Starbucks Sold Its AI on a 99% Accuracy Number. Nine Months Later, 11,000 Stores Are Counting Milk by Hand" <<- aka, it worked in the lab so roll it out everywhere and let the big bang magic happen! www.thestateofbrand.com/news/starbuc...
Their own posts
Recent commentary
i don't think we should waste real expert reviewer's time reviewing mediocre AI-written papers and having real expert reviewer provide valuable feedback that, presumably, will just get sent to LLMs to complete. at that point the reviewers might as well be authoring along with the authors
need an ai agent to pm me on my multiple projects (or keep me from adding too many projects to my stack)
i feel like prototyping an ai product or workflow to Work Well and Add Value instead of becoming More Workslop is like cooking. you need to be tasting as you go, and even as models are getting better, you need someone to be tasting as you go along (where tasting = looking at the data)
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