My research is focused on fundamentals and applications of computational intelligence approaches that mimic nature for problems solving. I am particularly interested in evolutionary computation methods, which are computer-based systems that use computational models of biological evolutionary processes as a key element of their design. Some well known evolutionary techniques are evolutionary algorithms, swarm intelligence, artificial immune systems, and differential evolution. Widely known evolutionary algorithms are genetic algorithms, evolutionary programming, evolution strategies, genetic programming, and learning classifier systems.

One of the fastest growing application areas of evolutionary computation is multi-objective optimization, where evolutionary approaches have been successfully applied to solve multi-criteria optimization problems, especially in the case of two and three objectives problems. Engineering design problems can often be conveniently formulated as multi-criteria optimization problems and there is a growing interest in applying evolutionary algorithms to solve them. However, these problems often consist of a relatively large number of objectives (many objectives), constrained spaces, and non-linear correlations among the variables we want to optimize (epistasis).

An important aim of my work is the development of evolutionary algorithms that can operate effectively in many objectives spaces with non-linear landscapes. The key to a successful design of this new generation of algorithms is a research effort that includes work on fundamentals of evolutionary computation to best match the evolutionary principles with classes of highly dimensional problem we aim to solve. Population search, solutions ranking, selection, and genetic operators are all important issues that need to be analyzed carefully in non-linear fitness landscapes of high dimensional spaces in order to achieve best performance.

Research on fundamentals includes:
- Find ways to appropriately perform selection, recombination, and mutation in multiple and many objectives landscapes
- Understand the effects of epistasis in multiple and many objectives optimization
- Parallelization of evolutionary algorithms

Applications I am working on include:
- Information security
- Automatic generation of cryptographically strong functions
- Computer networks intrusion detection
- Image and video processing for information hiding and authentication

I am also interested on applying evolutionary principles to
- Data mining and classification
- Model inference
- Circuits & systems design and synthesis

The tenure track position gives us the resources and the opportunity to pursue our own line of research independently. My hope for the next years is to build on this unique opportunity to lay the foundations and work to establish a world leading laboratory in the field of computational intelligence. Regarding education, I am looking forward to help educating the next generation of leaders in engineering at Shinshu University.