Zoa Research YCアプリケーション

Summer 2024Active

Zoa ResearchはSummer 2024バッチでB2B企業としてY Combinatorを卒業しました。以下：会社の事業内容、創業者、そして実際のYCフォームで質問ごとに再構築されたYCへの応募内容。

YCバッチ: Summer 2024
業界: B2B
チーム規模: 5
ステータス: Active
ローンチ済み: 2024

創業者

Greg VolynskyFounder
Sam DamashekFounder

Zoa Researchの事業内容

Historically, quantitative models are domain specific. Brilliant people spend their best years testing features, tuning hyperparameters, and iterating architectures within a narrow domain. But scale is the panacea: large models will find patterns people, and specialized models, could not. Forecasting generalizes. Zoa trains cross-domain event forecasting engines. *Automating Iteration* LLMs - embedded in multi-agent optimization loops and evaluated against fixed policies - can automate the build-test-improve modeling cycle. Think AlphaEvolve for forecasting problems. *Sample-Efficient General Models* Today’s forecasting models are narrowly crafted with deep human priors. But larger models will outperform state-of-the-art specialized models. Unlike existing event models, our models leverage data from across contexts and rely less on human intuition. And compared to LLMs, our models are built with more inductive priors and rely more heavily on inference-time compute - improving sample efficiency. *Why It Matters* In the real economy, our models could be useful for forecasting supply chain volatility, energy supply and demand, even earthquake risk. Science is, Ian Hacking writes, the taming of chance. It is the process of iteratively updating priors (something like: identify uncertainty, conceive experiment to reduce uncertainty, execute, update). If science is uncertainty-reduction, forecasting is a critical measure of progress. Better forecasting improves our ability to select interesting experiments (roughly those with greatest expected uncertainty reduction) and update priors. Our models will be used by labs and academics in data-heavy domains. Sam's ex-girlfriend introduced him to Greg back at Carnegie Mellon in 2017, and while that relationship didn't last, their friendship has. After college, Greg went to Harvard Law School, while Sam worked for three years at Jane Street on their Options desk, building & leading a satellite dev team.

Zoa ResearchのYCアプリケーション、再構築済み

Y Combinator 申請

Zoa Research 様、Summer 2024 バッチ。公開データから回答された、実際のフォームの質問。

Founders2
Company7
Progress7
Idea4
Equity3
Curious2
Batch Preference1

Company

Company name

Zoa Research

Describe what your company does in 50 characters or less.

Powerful quantitative forecasting models

What is your company going to make? Please describe your product and what it does or will do.

We build generalizable event forecasting models that train across diverse domains rather than specializing narrowly. Our models automate the build-test-improve cycle by embedding LLMs in multi-agent optimization loops. This extends forecasting beyond traditional domain-specific models, making predictions about supply chains, energy, and risk using data integration and sample-efficient inference.

Where do you live now, and where would the company be based after YC?

We live in New York, NY, and the company is based here now and would remain so after YC, at least initially.

Explain your decision regarding location.

Most of our experience and network is in New York. We don't see a pressing reason to relocate; YC is remote-friendly and we can engage with the network while continuing to build where we are.

フルアプリケーションで21件の追加回答

はフルバージョンにあります。無料アカウントを作成して、他のすべてのYCバッチの横にあるYCインサイトで開いてください。

申請内容全体を見る

よくある質問

Zoa Researchの実際のYCアプリケーションですか？

いいえ。Y Combinatorは提出されたアプリケーションを非公開にしています。これは再構築されたものです。実際のYCフォームの質問に、Zoa Researchの公開データ（ローンチノート、創業者経歴、製品説明）から回答しています。Zoa Researchのような会社がYCにどのようにアピールするかを示しており、彼らが提出したテキストではありません。