Rogel Mari Sese
Regulus SpaceTech, The Philippines
Dr. Sese obtained his undergraduate degree in Applied Physics at the University of the Philippines Los Baños (UPLB) campus and his Master’s degree in Physics at the Diliman campus. He finished his Doctorate in Physics in 2009 at the University of Tsukuba in Japan with a specialization in computational astrophysics focusing on the radiative transfer processes in circumstellar disks around massive stars.
In 2011, Dr. Sese returned to his original post as an Assistant Professor of Physics at UPLB and spearheaded the creation of the first Astrophysics Research Laboratory in the country. He was also appointed as the Government Focal Person of the Philippine Space Science Education Program under the Science Education Institute of the Department of Science and Technology. He is active in promoting space science education and development in the Philippines through various initiatives such as the Universe Awareness Program and the Galileo Teacher Training Program. His research interests stellar astrophysics, astrophysical instrumentation, nano-satellite development and space science education. Recently, he was a participant in the 4th CanSat Leadership Training Program and was grantee of the 2012 Emerging Space Leaders Grant Program of the International Astronautical Federation.
Dr. Sese is involved with several international organizations such as the Space Generation Advisory Council and World Space Week Organization. He is also the current Chairman of the Southeast Asian Young Astronomers Collaboration and President of Regulus SpaceTech, a space technology R&&D company based in the Philippines.
Rosario Ang holds a Bachelor of Science degree in Geodetic Engineering and a Master of Science degree in Remote Sensing both from the University of the Philippines Diliman. She is currently a faculty member of the UP Department of Geodetic Engineering. Her research interests include 3D modeling using close range photogrammetry, remote sensing and GIS applications in atmospheric sciences, climate change studies and renewable energy resource assessment and modeling natural processes using machine learning algorithms.