Arm
Senior Fae Manager
Tensilica Aug 2000 - Sep 2003
Applications Engineer
Vitesse Semiconductor Mar 2000 - Aug 2000
Product Marketing Manager
Hitachi Semiconductor May 1997 - Mar 2000
Product Manager
Datalogic Nov 1993 - May 1997
Design Engineer
Education:
Cornell University
Masters, Electrical Engineering, Engineering
University of Dayton
Master of Science, Masters
Xiamen University
Bachelors, Bachelor of Science, Physics
Skills:
Soc Asic Semiconductors Arm Mixed Signal Ic Eda Processors Embedded Systems Microprocessors Product Marketing Fpga Semiconductor Industry Verilog Device Drivers Hardware Architecture Application Specific Integrated Circuits System on A Chip
Ford Motor Company
Lead Analytics Scientist and Analytics Supervisor
Ford Motor Company Jun 1, 2015 - Sep 2017
Analytics Scientist
Northwestern University Jun 2010 - Jun 2015
Research Assistant
Northwestern University 2012 - 2015
Teaching Assistant, Grader
Ford Motor Company Jun 2012 - Aug 2012
Product Development Summer Intern
Education:
Northwestern University 2010 - 2015
Doctorates, Doctor of Philosophy, Design, Philosophy, Mechanical Engineering
Fudan University 2006 - 2010
Bachelors, Bachelor of Science
Fujian Normal University
Skills:
Matlab Statistics Mathematical Modeling Numerical Analysis Simulations Mathematica Research Latex Finite Element Analysis Abaqus Materials Science Design of Experiments Minitab Characterization Ansys Monte Carlo Simulation Stata Labview Statistical Data Analysis Optimization Teaching Data Analysis Machine Learning
The disclosure generally pertains to systems and methods for assigning travel routes to vehicles. An example method may involve evaluating a first vehicle for deploying on a first travel route and a second vehicle for a second travel route. Evaluating the first vehicle may include determining a first probability that the first vehicle will need a first energy replenishment operation during deployment on the first travel route, and also determining a first deployment cost for the first vehicle. The first deployment cost can include a first energy replenishment cost based on the first probability. Evaluating the second vehicle may include determining a second deployment cost for the second vehicle, the second deployment cost including a second energy replenishment cost. The first vehicle is assigned to the first travel route and the second vehicle to the second travel route if the first deployment cost is less than the second deployment cost.
A computer-implemented method for generating recommendations for vehicle powertrain changes is described herein. Disclosed systems and methods include receiving operational data associated with one or more vehicles in a fleet of networked vehicles, and receiving maintenance data associated with the vehicles. The maintenance data can include a vehicle parts history associated with one or more fleet vehicle parts. A machine learning analytical model is described that determines, based in part on the operational data and the maintenance data, a total cost of ownership for the vehicles, and generates, based at least in part on the total cost of ownership for the vehicles, a vehicle recommendation indicative of a powertrain changes for the vehicles in the fleet. Aspects of the present disclosure may provide automated sources of crowdsourced vehicle fleet information with which vehicle fleet managers can make actionable decisions for fleet powertrain upgrades.
Systems And Method For Ridesharing Using Blockchain
Systems, methods, and computer-readable media are disclosed describing ridesharing using blockchain. Example methods may include determining a state associated with a user of a ridesharing vehicle from at least one first device associated with the ridesharing vehicle; identifying a confirmation of the state by one or more second devices; and adding a transaction to a blockchain, the transaction comprising a description of the state, the confirmation, and a link to a trip footage of the state.
- Dearborn MI, US Zhen JIANG - Mountain View CA, US Yan FU - Bloomfield Hills MI, US
International Classification:
G07C 5/08 G07C 5/00 B60W 40/04
Abstract:
A transportation mobility system includes a data storage configured to maintain vehicle data indicating fuel consumption and count of passengers for vehicles of a transportation system, and user data describing movements of the passengers within the transportation system. The system also includes an emissions monitoring portal, programmed to provide, for vehicles of a fleet, estimates of pollutant emissions for the fleet and a percent share of miles completed by zero-emissions transportation for the fleet.
System And Method For Automated Vehicle Performance Analytics
A vehicle includes a controller programmed to activate a fuel savings feature upon satisfaction of transition conditions and inhibit the transition according to satisfaction of inhibit conditions. The controller is further programmed to accumulate data indicative of the inhibit conditions and a time associated with the conditions being satisfied over a drive cycle.