Research
Papers I have written or co-authored. First-party work can be read right here as HTML or downloaded as PDF.
Source-Bounded Exact Recovery for Docker Log Followers
Kelvin Amoaba
Preprint 2026
Defines source-bounded exactness — every retained, distinguishable Docker source record eventually appears exactly once in durable collector output — with a generation-aware multiset oracle that separates source truncation from collector omission. Across 120 collector-runs, a fixed LogDeck revision was exact in 60/60 trials while unmodified Grafana Alloy 1.18.0 was exact in 20/60, showing that lifecycle reacquisition, not a persisted read position alone, determines exact recovery within the retained-source horizon.
CoFEE: Reasoning Control for LLM-Based Feature Discovery
Maximilian Westermann, Ben Griffin, Aaron Ontoyin Yin, Zakari Salifu, Yagiz Ihlamur, Kelvin Amoaba, Joseph Ternasky, Fuat Alican, Yigit Ihlamur
arXiv 2026
A framework for automating feature discovery from unstructured data using LLMs with cognitive constraints, inducing reasoning behaviors like backward chaining from outcomes and verification against data leakage criteria. Achieves 15.2% higher success rate, 29% fewer features generated, and 53.3% cost reduction over vanilla LLM approaches.
VCBench: Benchmarking LLMs in Venture Capital
Rick Chen, Joseph Ternasky, Afriyie Samuel Kwesi, Ben Griffin, Aaron Ontoyin Yin, Zakari Salifu, Kelvin Amoaba, Xianling Mu, Fuat Alican, Yigit Ihlamur
arXiv 2025
The first benchmark for predicting founder success in venture capital, providing 9,000 anonymized founder profiles. State-of-the-art LLMs like DeepSeek-V3 deliver over 6x baseline precision, with most models surpassing human benchmarks.
From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital
Mihir Kumar, Aaron Ontoyin Yin, Zakari Salifu, Kelvin Amoaba, Afriyie Kwesi Samuel, Fuat Alican, Yigit Ihlamur
arXiv 2025
A framework for predicting rare, high-impact outcomes by integrating large language models with a multi-model machine learning architecture for venture capital decision-making.