Aparna Dhinakaran
where else to find them
what they talk about
all 5 in the filters
where they keep going back
no series more than once — all 4 in the filters
on a stage with
- Colleen Collonce
- Elenore Bastianonce
- Sophia Vargasonce
who talks about the same things
- Aman Khan6 talks
- Phil Hetzel4 talks
- Ara Khan2 talks
- Shishir G Patil5 talks
- Dat Ngo3 talks
- Ohans Emmanuel3 talks
| title | conference | date |
|---|---|---|
| The Future of Evals: From LLM as a Judge to Agent as a Judge | Observe 2026 | 2026-06-04 |
Arize reports running 100 million evals per month and sees teams run about 12 eval jobs on average with top teams at 3,800 evaluators, and argues evals must… | ||
| The Unreasonable Effectiveness of Prompt Learning | Observe 2026 | 2026-06-04 |
Claims a feedbacK-driven system-prompt learning loop that applies RL-style updates to prompts not weights and auto-tunes an agents.md. Uses unit tests +… | ||
| Break It 'Til You Make It: Building the Self-Improving Stack for AI Agents | AI Engineer World's Fair 2025 | 2025-06-03 |
Claims a deployed agent needs a continuous monitoring and improvement stack that actually connects evals, tracing, observability, experimentation and… | ||
| EmpowerUs | KubeCon + CloudNativeCon North America 2021 | 2021-10-11 |
Panelists argue concrete tactics for influence and combating imposter syndrome: cite Stack Overflow numbers (developers who are women ~5% globally, ~9% in the… | ||
| Ray + Arize - Why you Need Model Observability | Ray Summit 2020 | 2020-09-30 |
Demo shows a Lending Club loan-initiation fraud classifier served with Ray Serve and logged to Arize, capturing inputs, predictions and delayed actuals with… | ||
| nothing matches | ||