Quality Control and Reliability Engineering (QCRE) Division Data Challenge Competition

Sponsored by ProcessMiner

On behalf of the Quality Control & Reliability Engineering (QCRE) community, we would like to invite you to participate in the 2023 QCRE Data Challenge Competition at the IISE Annual Conference. This year's QCRE Data Challenge problem is sponsored by ProcessMiner Inc., an industry expert that delivers predictive analytics and optimization for complex manufacturing processes.

In this data challenge, a research problem of Fungal Spores Concentration Prediction is posed to participants. More details regarding the competition tasks can be found on the next page.

The submission deadline is April 16th, 2023. The organizing committee will hold a Q&A session through Zoom from 3 pm to 4 pm, EST, on March 7th, 2023. Zoom link: https://ncsu.zoom.us/j/96738008960?pwd=R2NYdkxIZDJPc3llQnZPSnJ5QndVdz09

The competition organizing committee will select a maximum of four finalist teams. Those finalist teams will be required to present their work at the 2023 IISE Annual Conference and winners will be determined by invited judges.

The first prize comprises a plaque and a cash award of $1,000.


  • Individuals or teams of a maximum of four members.
  • At least one of the participants from each team should be a QCRE member.
  • Each team can only participate once.

Important Dates

  • Results submission deadline: Sunday, April 16th.
  • Notification of finalists: Sunday, April 23rd.
  • Presentation: IISE Annual Conference & Expo, May 19 – 22, 2023

Competition Chairs

Praneeth Reddy
Data Scientist
ProcessMiner Inc.
715 Peachtree St. NE
Suite 100
Atlanta, GA 30308

Shancong Mou
Ph.D. Candidate
The H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology

Xiaolei Fang
Assistant Professor
Edward P. Fitts Department of Industrial and Systems Engineering
North Carolina State University xfang8@ncsu.edu

2023 Winners

Bo Shen, Raghav Gnanasambandam, Jihoon Chung
Sequence-to-Sequence LSTM for Fungal Spores Concentration (FSC) Prediction