Jun Otsuka

I primarily work in the philosophy of biology, statistics, machine learning, and causality, with a central focus on the problem of inductive reasoning. Induction sits at the intersection of science, mathematics, and philosophy, and I am fascinated by how this blend manifests in both theoretical frameworks and practical applications across scientific fields.

Below are examples of the topics and questions I have explored and continue to investigate:

Philosophy of Biology

The causal foundations of evolutionary theory
- Causal Foundations of Evolutionary Genetics
- Ontology, Causality, and Methodology of Evolutionary Research Programs
Causal modeling of various evolutionary dynamics and concepts
- Evolutionary game theory of continuous traits from a causal perspective
- A generalised approach to the study and understanding of adaptive evolution
How and why does mathematics help us understand evolution?
- The Role of Mathematics in Evolutionary Theory

Causality

Abstraction of causal models
- On the Equivalence of Causal Models: A Category-Theoretic Approach
Modeling the process theory of causality using category theory
- Process theory of causality: A category-theoretic perspective

Philosophy of Statistics & Machine Learning

Philosophical (ontological, semantic, and epistemological) foundations of statistics and machine learning
- Thinking About Statistics: The Philosophical Foundations
Concepts and representations in machine learning
- What Machine Learning Tells Us About the Mathematical Structure of Concepts
The Grue paradox and category-theoretic modeling of statistics
- A Categorical Solution to the Grue Paradox

See here to find more.