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The AI Lab
The R&D guideline of Suoxinda AI Lab comprises 1 core and 3 pillars. The core is to develop a model factory (modern production line for data analysis and modeling) for the financial industry. And the 3 pillars for commercial application refer to innovation-oriented (model automata machine - automated machine learning, model interpreter - interpretable machine learning, micro-segmentation divider - customer micro-segmentation, finite state machine - augmented learning), applicability-oriented (feature platform, model management platform), and capability-oriented (training system for leading analysts). This is an inexhaustible source of creativity and growth for our clients in the financial sector.
Research projects of Suoxinda AI Lab are closely aligned to financial practices. In response to the business needs and scenarios of the financial industry, it explores cutting-edge AI technologies and their application, including intelligent marketing, intelligent risk control and intelligent decision making. Its development outcomes have generated quick wins in financial scenarios, helping clients improve performance, efficiency and customer experience at lower cost and risks.
M.S., Dalian University of Technology
VP
Ph.D. in applied computer science, China Academy of Sciences
Chief Scientist
M.S. in probability and mathematical statistics, Jinan University
Senior Data Scientist
Ph.D. in Engineering, Clermont-Ferrand Second University
Data Scientist
Ph.D. in finance,Durham University
Data Scientist
Ph.D. in physics,Chinese Academy of Sciences
Data Scientist
Ph.D. in Science, Peking University
Data Scientist, Suoxinda
M.S., Shenyang Aerospace University
Data Scientist
M.S., The University of Nebraska
Data Scientist
PhD in Management, Lancaster University
Data Scientist
Ph.D. in biomedical engineering,UTS
Data Scientist
M.S. in Statistics,Temple University
Data Scientist
CORE MODULE
INNOVATION ORIENTED
PRACTICAL ORIENTATION
ABILITY ORIENTED