CTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
With background in formal logic, our laboratory is seeking for new computational models, developing new methods for analyzing and verifying such models, and implementing new tools for analysis and verification. While our main target is software systems and programming languages, we also deal with molecular and biological systems, and we are currently doing research on molecular computing.
We study theoretical foundations for software, and their applications to programming languages, program verification, and program transformation. The recent topics include automated program verification based on higher-order model checking.
We study various topics in machine learning.
Our research is focused on natural language processing and computational linguistics. Examples include:
Computational science is a new approach in the science after the experimental and the theoretical approaches and has developed with the exponential evolution of computers. By utilizing the first-principles electronic structure calculation as a core, the laboratory carries out the educations and researches in the interface region between computer science and compuational science.
Our research interests include operating systems, real-time systems, and parallel and distributed systems, with a particular emphasis on resource management for heterogeneous computing environments composed of many cores, GPUs, and FPGAs. We also work on parallel processing on those processors and its performance analysis. Furthermore, we develop autonomous vehicles, high-definition 3D maps, and online machine learning frameworks.
The center of our research interests is computer architecture. We are pursuing various researches on highly-efficient next generation computers, such as custom computing employing FPGA and domain-specific hardware, algorithm/hardware co-design for machine learning, and high-level synthesis compiler for productive hardware design environment.
We study the following topics on machine learning.
Quantifying uncertainty in predictions, learning feature representations of data, learning with human-in-the-loop, learning from biased data, and theoretical analysis for learning models including deep learning.
As an application, we have developed analysis and support systems for the medical field.
We study image processing and spatio-temporal data analysis. Our research topics include:
The aim of our research is to realize human-like natural language understanding systems by integrating logic-based approaches with machine learning-based approaches. Recent topics: