Jungah S
- Research Program Mentor
PhD at University of California Santa Barbara (UCSB)
Expertise
Affective computing, creativity tool, computational tool, image processing, virtual reality
Bio
Hello, my name is Jungah Son. I received my PhD degree at the University of California, Santa Barbara studying Media Arts and Technology. I graduated from Korea University in 2010 with a degree in Electrical Engineering. I am very passionate about creativity tools research. Creativity tool is a type of tools that makes more people more creative more often, enabling them to successfully cope with a wider variety of challenges and even straddle domains (Shneiderman, 2002). In my research, I collaborate with artists, engineers, and scientists to develop new tools for both experts and non-experienced users. I also received a MS in Radiation Applied Life Sciences to improve the medical images using image processing techniques. Outside of research, I really enjoy spending time drawing/painting, reading books, and hanging out with my friends. I look forward to meeting you and exploring your research project together :)Project ideas
Computational Tools for Media Arts
With advancements in digital tools, artists ranging from Hayao Miyazaki to Memo Akten have been creating new types of art, e.g. computer-generated imagery and AI art, that were previously nonexistent. As these tools can provide opportunities to facilitate creativity and support innovation, it is becoming more important to make good use of them. Therefore, we will explore how software influences the visual arts in this project. For practice, students will go through many code examples from Form + Code [1]. Students can choose a subtopic from the examples and dig deeper to develop a new computational tool or improve the existing one. This project will be divided in two major parts: 1. Creating a software system for the visual arts, especially one involving computations. Writing a report on the steps of implementation. What opportunities a computational tool can give for artists and creators? 2. Evaluation of the software based on design goals and discussion on how we can generate a good questionnaire and interview questions for user feedback. What will you learn or gain from this project? • Nowadays technologies cannot be divorced from the process of creation for many kinds of art. You will learn how computers can assist and augment human creativity. • How creativity tools are supporting the users in producing creative work, and what the goals of the software design are. • Learning about programming languages used in the field, and possibility of integrating some components using an interactional approach. • How to write manuscripts for publications, and perhaps your name on a publication from this project. [1] Reas, Casey, and Chandler McWilliams. "Form+ Code: in design, art, and architecture." (No Title) (2010).
Noise Reduction in Medical Images
Reducing noise for improving medical images has been a challenge for medical imaging scientists. Most of the time, noise is commonly caused either by the acquisition process or malfunction of the sensor and other hardware. As the quality of medical images is critical for accurate diagnosis [1], it is important to model image noise and develop appropriate algorithms. In this project, we will examine well-known filtering methods such as mean and median filter, morphological filter, wavelet filters, and diffusion filters. What is the cause of increased noise in medical imaging? What is the relationship between noise reduction and filter parameters? We will explore these questions and examine how we might improve signal to noise ratio (SNR). Anticipated Schedule Students will spend about 30% of their time learning about image processing algorithms with the remaining time dedicated to practical hands-on programming and implementation. Students will have weekly research paper discussions/presentations to gain some familiarity with research implementation at the graduate level. A rough breakdown of weekly schedule: Weeks 1 – 3: Gain an understanding of image processing and how to implement frequently used algorithms. • 50% of the time spent on image processing fundamentals and algorithms. This will include self-study and mentor-led discussions. • Along with the theory, students will practice programming in MATLAB to process medical data. Weeks 3+: A comparison with state-of-the-art model and dataset using their own developed techniques. • 20% medical imaging fundamentals and 80% project work. • Different evaluation methods of noise reduction will be explored. [1] Florez-Aroni, Sussana M., Mijail A. Hancco-Condori, and Fred Torres-Cruz. "Noise Reduction in Medical Images." arXiv preprint arXiv:2301.01437 (2023).