Hi 👋, my name is Sadamori Kojaku. I am a network scientist with a background in computer science. My current work involves developing computational tools that utilize Network Science and Machine Learning to study complex systems. I hold the position of Assistant Professor at the Thomas J. Watson College of Engineering and Applied Science (Department of Systems Science and Industrial Engineering), Binghamton University (State University of New York).


The invention of the telescope in the 17th century revolutionized astronomy and led to groundbreaking discoveries, challenging prevailing beliefs and paving the way for modern understanding of the solar system.

My research focuses on a computational macroscope that points towards our society. Our society is a small universe but highly connected complex universe, where various elements, ranging from microorganisms like viruses and proteins to individuals and nations, constantly influence one another. As our society is highly interconnected, the macroscope should be able to capture not only the individual elements but also the intricate connections between them.

As an essential foundation for the computational macroscope, my main focus is on representation learning, also known as embedding, which involves projecting complex systems onto a high-dimensional vector space. Neural networks, which are the basis of modern artificial intelligence, naturally create an embedding. We can consider this embedding as an internal representation that allows AI to perceive our world. By comprehending this representation, we can achieve a profound understanding of the underlying logic behind powerful yet blackbox AIs, and, more importantly, translate machine understanding into human understanding.

Developing instruments needs good data. And I consider science as a great source of good data. In many ways, science is a reflection of our society; it is a collaborative and dynamic system that consists of many different parts including people, ideas and organizations that are connected in a complex way. The greater availability and transparency of data about science have paved the way for the development of the macroscope, allowing us to observe and analyze large-scale patterns and trends. By harnessing the power of representation learning, we can unlock valuable insights and accelerate scientific progress.