As the Team Leader, my mission is to understand and transform project details utilizing creative thinking while encouraging feedback from project stakeholders. I lead the team is able to scope a business problem and strategy that has not well defined. I assumed the helm and mastered a number of leadership roles including data scientist, project manager, and team management.

Provides internal customers and stakeholders a proven approach to transforming, optimizing, and modeling their existing data to enhance decision making. In addition, offers backend setup - data pipeline and frontend setup - data visualization.

Our group objective is to maximize the process yield to enable high-volume manufacturability of the next generation microprocessors that will power the computers of tomorrow. The responsibilities by collecting and analyzing data across the multiple steps of the fabrication process.PROJECTS 1) Yield Forecasting Model: A predictive model project focused on using electrical measurement results and current understanding of process setting as inputs to predict product yield. Results give the direction how to modify the experiment, and address process problems that allowing the manager to plan resource in developing the product. Furthermore, the results also give an estimation about when factory could reach yield target and release new technology.a. Classification correction: A classification feature to identify wafers’ similarity by, process setting, tool conditions, and wafer radius.b. Time-related correction: The feature is to identify time-related event in any giving process tool Related ML models: weighted linear regression, decision tree, and random forest2) Automation Review System: The system monitoring entire production line, and make sure production error can be explained. The system will auto-query the latest measurement data and identify correlation to explain the failure reasons (root cause analysis), and auto-generate reports for engineers to further review and investigate the data. Moreover, the system is able to identify outliers based on statistical analysis (F-ratio, ANOVA) and enhance database knowledge3) Failure Image Recognition: Image recognition project is comparing wafer's yield image and database yield image to provide the possible failure pattern and estimate failure impacts. Related ML models: k-means clusteringAWARDS 2015: Yield Forecast Model Award, Logic Technology Development PTD Yield Development 2014: Employee Recognition Rewards, Intel Corporation

My group is interested in energy efficient solar cell applications using nitride compounds. Since photovoltatic (PVs) devices are similar to LEDs in terms of structure, and operation power, it is possible to take benefits of the existing technical knowledge and experience learned from LEDs and apply them to PVs. However, these are still the major technological challenges of III-nitride PV devices, the lack of a defect-free substrate and low density of extended defects in MQWs. My research focuses on 1)examined the InGaN nanowire synthesis parameters for enhancing InGaN solar cell conversion efficiency; and 2)model a molecular movement in any given condition by Interpreting experimental results with the particle distribution probability and uncertainty principle.PROJECTS Examined InGaN nanowire synthesis parameters for enhancing InGaN solar cell conversion efficiency. Modeled molecular movement in any given condition by interpreting experimental results with the particle distribution probability and uncertainty principle.  Controlled growth conditions, defect free and vertically aligned bulk InGaN nanowires (NWs) grown by MOCVD for the first time. The InGaN NWs effectively relax the strain and enabled defect-free growth on lattice mismatched substrates resulted in enabling efficient energy conversion in semiconductor lighting. Algorithm: Based on molecular inter-attraction and environment boundary condition, InGaN nanowire synthesis mechanism can be explained and the simulation results match with experimental results.

• Teaching assistant for Electrical Engineering and Computer Science Department for introduction to Electric circuits, Systems, and Applications, which covers topics in electric circuits, electronics, and control systems. Lead two labs and one discussion sessions per week.

• Developed and applied a novel growth and process method to low specific contact resistivity to p-type GaN on semipolar and m-plane substrate.Worked within the Solid State Lighting and Energy Center (SSLEC) on developed a novel stack layers for reducing contact resistance to p-type GaN substrates. Examined the impacts of surface treatments, annealing effects, and metallization; and analyzed mass sequencing data for changing devices process procedures. Most contributions are in improving the efficiency of blue LED and CW/ridge green laser diode.PROJECTS Analyzed luminous intensity and emission spectrum data of LED devices to improve device performance. Based on experimental results, developed novel LED device structure increasing output power and life time.  Developed novel stack layer for reducing contact resistance to p-type GaN substrates. Examined impacts of surface treatments, annealing effects, and metallization; and analyzed mass sequencing data for changing devices process procedures. Contributions improved efficiency of blue LED and CW/ridge green laser diode.

• Evaluated the performance and reliability of infrared sensors in medical devices, also be responsible for functional testing and PCB layout.