FODI LAB
FODI LAB
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From Observed Data to Identifiability
From Observed Data to Identifiability
What can data tell us,
and under what conditions?
What can data tell us,
and under what conditions?
We develop statistical approaches to overcome data limitations that constrain scientific inquiry, with a focus on what can be reliably learned from imperfectly observed information.
WHAT WE WORK ON
WHAT WE WORK ON
Many scientific questions remain unanswered not because suitable methods are unavailable,
but because existing data are incomplete, indirect, fragmented, or collected at insufficient resolution.
At FODI Lab, we investigate these limitations and develop statistical strategies to overcome them, enabling more reliable prediction, inference, and identification.
Statistical Learning under Data Limitations
Statistical Learning under Data Limitations
How can we overcome limitations in data quality, resolution, and observability through statistical learning and modeling to address questions that conventional analyses cannot adequately answer?
Causal Inference
with Incomplete Information
Causal Inference
with Incomplete Information
What causal questions remain answerable when treatment, outcome, confounding, or temporal information is imperfectly observed?
Identification from Partial
& Distributed Information
Identification from Partial
& Distributed Information
What can be identified when information needed for inference is only partially observed or distributed across data sources and populations?
DATA WE WORK WITH
DATA WE WORK WITH
JOIN FODI
JOIN FODI
Bring us a question that does not have an obvious answer.
We welcome students and collaborators who want to develop statistical ideas around challenging questions in human data.
For prospective students, we are more interested in how you challenge an open problem than in a polished statement about yourself.
FODI LAB
College of Natural Sciences, Chungbuk National University
S1-5, Room 106 · Chungdae-ro 1, Seowon-gu, Cheongju, Chungbuk 28644, Korea
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