Experience

Angel Investor

• United States
• Venture Capital and Private Equity Principals
• 1 - 100 Employee

• Angel Investor

  • Aug 2017 - Present

  Actively participate in the strategic decision making and strategy design of the investee company. Providing consulting services to the investee companies. Helping to recruit management personnel for portfolio companies. Assisting in public relations; designing exit channels and organizing corporate exits with professional experience profile to find and identify future developable projects and understand them. Have scientific thinking, compound thinking, entrepreneurial thinking, blueprint thinking, with certain industry research, technology and policy research ability Areas of expertise. Vision and strategic planning, marketing research, new product development, sales and marketing, customer engagement, operations management, leadership development, change management, quality assurance, business development, contract negotiation, public presentations, board membership. Show less



Synchron

• United States
• Medical Equipment Manufacturing
• 1 - 100 Employee

• Senior Director of Neuroscience

  • Jun 2014 - Present

  Computational neuroscience focuses on the detailed implementation of computation, studying neural codes, dynamics and circuits. Artificial neural networks tend to avoid precisely designed codes, dynamics or circuits, favoring strong optimization of cost functions (violent search), usually using simple and relatively homogeneous initial architectures, using structured architectures, including for attention mechanisms, recursion and various forms of short- and long-term memory Second, the cost function and the training process become more efficient. Second, cost functions and training processes become more complex and evolve over time. Here we think about the brain in light of these ideas. We assume that the brain optimizes the cost function, the cost function is diverse and different at different locations in the brain at different stages of development, and the optimization operation is performed within a framework that is pre-structured by behavior and matches the corresponding computational problem. In support of these hypotheses, we argue that a series of implementations of Credit Assignment via multilayer neurons is compatible with our current knowledge of neural circuits and that some specialized systems of the brain can be interpreted to achieve efficient optimization for specific problems. Through a series of interacting cost functions, such non-uniformly optimized systems make the learning process data efficient and precisely tailored to the needs of the organism Show less