Meet the Team
Yu Sun - Founder and CEO
Yu Sun, founder and CEO, holds an MBA degree and a Master’s degree in Electrical Engineering. She has 20 years of industrial experience in product development, during which she established connections with various contacts. She has worked as an embedded system design engineer and has led project teams comprising more than ten people. She routinely collaborates with cross- functional teams (including engineering, marketing, manufacturing, customer services, etc.) on the full lifecycle of product development and delivery of business solutions. The products for which software was developed by her team, have been sold in high volume to critical and demanding customers such as Wal-Mart.
Ms. Sun is responsible for the company daily operation, and oversees overall technical direction of projects.
Giorgian Borca-Tasciuc - Research Engineer
Giorgian is a Research Engineer here at Sunrise Technology. He started in January 2021 while working on his B.E. in Electrical Engineering and Computer Science at Stony Brook University. After finishing his degree he then went on to complete a M.S in Computer Science also at Stony Brook. He currently works on machine learning based trigger development for the sPHENIX project and embeddings generation of X-Ray scattering images. His research interests include but are not limited to the development of new machine learning architectures and training techniques for improving classification performance.
Kevin Mahon- Embedded Software Engineer
Kevin is an embedded software engineer working on the Autonomous Driving Project and Laser Feedback Control. He started working for Sunrise Technology in January 2022. He graduated from Stony Brook with a B.E. in Computer Engineering and Mathematics and an M.S. in Computer Engineering. He’s currently interested in improving hardware acceleration for machine learning models and optimizing inference pipelines.
Dantong Yu - Guest Speaker
Professor Yu is an accomplished educated and researcher in his field. He is currently the Hurlburt Chair Professor at Martin Tuchman School of Management at NJIT. He is an Associated Professor in Data Science and Computer Engineering and draws on his expertise in the field to teach both data mining and machine learning. His research interests include: Data Mining, Machine Learning, Deep Neural Networks Algorithms, Business Data Science that applies AI/ML to business domains: finance, FinTech, biomedical, and healthcare. He is the instructor for our Summer Camp on Machine Learning for Autonomous Driving. To learn more check out his website here
Zeyu Dong - Research Project Assistant
Zeyu is currently a PhD student in Stony Brook University in the Applied Math and Statistics Department. He began working at Sunrise in July 2021, since then he has worked on numerous projects. He currently works on researching deep reinforcement learning methods for an orbit feedback control system in the National Synchrotron Light Source II. He also works on the autonomous driving system based on the Robot Operating System (ROS) for real time data aquisition, processing, decision making with deep learning algorithms.
Hao Quan- Machine Learning Intern
Hao is currently working on his PHD in Applied Mathematics and Statistics at Johns Hopkins University. He worked at Sunrise Technology for the Summer of 2022 into Fall 2022. His work on the mobile development for our Autonomous Driving project was pivotal to the advancement of the project. His research interests include turbulence, partial differential equations, and fluid dynamics.
Arghya Bhattacharya - Machine Learning Intern
Arghya is working as an intern at Sunrise Technology Inc. He is a Ph.D. candidate in Computer Science at Stony Brook University. He has worked on interpretability of graph neural networks for trigger detection under the sPHENIX project. He is currently working on terabits/s data transfer from ultra-high rate detectors at x-ray light sources to DoE supercomputers. His thesis sheds light on designing ML-advised algorithms and cache- and memory-friendly algorithms that breed theoretical and practical advantages for shared-memory and cloud systems.