Taking a broad view, my research interests concentrate around the theory and practice of machine learning in situations when we have limited knowledge of the quality of "feedback" available. In seeking strong yet transparent guarantees, one inherently runs into a deep and fundamental entanglement between the statistical and computational aspects of the learning problem. Put simply, I am interested in methodologies that enable us to reliably design efficient algorithms that work for reasons we understand.
For research papers:
I try to ensure all the work I have published is freely available to the public. Please check out my publications page for details.
Please see my repositories on GitHub (username: feedbackward). To ensure that the work done by myself and my collaborators is both reproducible and transparent, I am enforcing a "designed to go public" approach to my experimental methodology. The hope is that instead of slicing off a tiny, over-polished segment of code as a demo, more substantial content can be shared in an accessible way that benefits a wide audience.
For a more detailed CV:
Feel free to inquire by email, or see my researchmap page, in either English or Japanese, as you prefer.