Alex Pang

Dr. Alex Pang conducts research on the cutting-edge areas of virtual reality interfaces, uncertainty visualization, scientific visualization, and tensor visualization. As a Professor in the Computer Science Department at the University of California, Santa Cruz, he is deeply invested in advancing the field of visualization collaboration software. His work focuses on developing innovative methods and tools that enhance the way complex data is visualized and interpreted, making significant contributions to both academic research and practical applications. In his teaching role, Dr. Pang is dedicated to educating the next generation of computer scientists. He currently teaches CMP 12A: Introduction to Programming, CMP 12B: Data Structures, and CMP 161: Introduction to Data Visualization. These courses are designed to equip students with foundational skills in programming and data visualization, preparing them for careers in technology and research. His teaching philosophy emphasizes hands-on learning and real-world applications, ensuring that students gain both theoretical knowledge and practical experience. Dr. Pang has also taught a variety of other courses, including CMP 160: Introduction to Computer Graphics, CMP 162: Advanced Graphics and Animation, and CMPS 290B: Topics in Scientific Visualization. Through these courses, he has guided students in exploring the complexities of computer graphics and animation, as well as specialized topics in scientific visualization. His commitment to education is reflected in his efforts to continually update course content to reflect the latest advancements in the field. Beyond his teaching and research, Dr. Pang is actively involved in collaborative projects that bridge academia and industry. His work often involves interdisciplinary teams, leveraging diverse expertise to tackle complex visualization challenges. By fostering collaboration and innovation, he aims to push the boundaries of what is possible in the realm of data visualization and virtual reality, ultimately contributing to a deeper understanding of complex scientific phenomena.