CS Events

Qualifying Exam Human-AI Collaboration for Cyber-Physical Systems
Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Wednesday, December 20, 2023, 09:30am - 10:30am

Speaker: Zejun Xie

Location : CoRE 301

Committee: 

Associate Professor Desheng Zhang

Assistant Professor Dong Deng

Assistant Professor Qiong Zhang

Assistant Professor Yongfeng Zhang

Event Type: Qualifying Exam

Abstract: In the domain of Cyber-Physical Systems, the integration of human intelligence and artificial intelligence plays a key role. This talk highlights a practical application of this integration in the context of last-mile delivery services, which include various delivery stations segmented into distinct delivery areas. Each area is assigned a courier, responsible for all deliveries within it. The necessity for data-driven methods to assess delivery area difficulty is well-recognized, yet the significant expenses associated with precise workload measurement curtail the availability of dependable ground truth data, constraining the scalability of current machine learning solutions. In this work, we turn to a frequently overlooked resource—the couriers’ firsthand knowledge of their delivery areas. In this paper, we design \texttt{$AD^2I$} (Assessing Delivery Area Difficulty Isotonic) Framework, which includes two modules: (i) a Preference Rank Aggregation module, which collects individual courier preferences and enriches them with historical delivery data to assess their familiarity with each delivery area; (ii) an Isotonic Integration module, which combines the aggregated preference with the assessment of existing machine learning model through isotonic regression to enhance the accuracy of delivery area difficulty assessments. Our \texttt{$AD^2I$} framework, tested on six-month data from a major logistics company, demonstrated a 13.3% accuracy increase and 0.175 Kendall tau improvement. Its adoption improved income fairness, reducing the Gini coefficient by 0.29 for courier salaries and increasing on-time delivery rates by 1.67%.

: 

Contact  Associate Professor Desheng Zhang

Zoom Link:
https://rutgers.zoom.us/j/98002068848?pwd=SDk5Q3VpaU53U29oQjJpdTlqUGJrQT09