I am studying mathematical models related to "probability," such as dice outcomes from 1 to 6. In particular, I am researching the "Monte Carlo method" for numerically calculating "expected values." I am also challenging applied research to consider various issues arising when implementing them as programs and their solutions.

While working on themes considered difficult even in university mathematics, I acquired the attitude of carefully reading literature, organizing thoughts, and seeking solutions. After graduation, I will work as a programmer, so I want to utilize the "mathematical thinking ability" acquired at university.

I am developing "optical synaptic devices" that mimic the mechanism by which humans learn and memorize from visual information. The goal is to provide functionality that changes the ease of memory based on the repetition and strength of information, just like the human brain. The material uses "two-dimensional materials" obtained by "peeling" layered crystals, which are only a few atomic layers thick.

We have succeeded in developing a device where repeated input of optical signals makes current flow easier, improving information transmission. Currently designing a device that conversely makes current flow more difficult upon signal input, suppressing information transmission. I want to realize machine learning with lower power consumption by applying this research.

I am conducting experiment-based research on fatigue properties of metal materials. Most causes of failure in mechanical parts subjected to repeated loads are metal fatigue. I am working on elucidating fatigue mechanisms by identifying the fatigue life of metal materials and observing microscopic damage (cracks) generated in fatigue tests under a microscope.

Since various materials are used in machines, there are many unclear points in the fatigue fracture mechanism. The materials handled in research are often special, and various challenges are faced in test preparation and subsequent analysis. By utilizing a wide range of mechanical engineering knowledge learned so far to tackle challenges, I acquired qualities as an engineer.

I am researching differences in how people perceive events. For example, among volleyball spectators, fans supporting a team may be disappointed by their team's mistakes, while fans supporting the opposing team might rejoice at the same mistakes. I am verifying through limited-condition simulations the mechanism by which the same event produces different impressions and stories due to cognitive bias.

Currently verbalizing numerical differences extracted from simulations with generative AI and verifying story differences. In graduate research, I plan to conduct research on how stories change when people with different stories converse, and what kind of conversation is necessary to cause intended changes.