Profile image for Pei-Wei Tsai

Dr Pei-Wei Tsai

Lecturer, Computer Science and Software Engineering


Pei-Wei Tsai received his PhD in Electronic Engineering in 2012 in Taiwan. He was one of the invited speakers in 2008 in the Global COE Program at Tohoku University in Japan. He is currently a lecturer at the Swinburne University of Technology in Australia. He was a visiting scholar in 2011 in the Center of Excellence in Information Assurance (CoEIA) at King Saud University in Saudi Arabia. He was the Program Committee Co-chair of the 13th International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2017) in 2017 and the Enabling and HCI Technologies (HCI) track co-chair of the 36th IEEE International Conference on Consumer Electronics (ICCE) in 2018. He is also the Associated Editor of Journal of Intelligent & Fuzzy Systems, the Associated Editor of Applied Computing and Intelligence, and the Executive Editor of Journal of Network Intelligence. His research interests include artificial intelligence, data analytics, and machine learning.

Research interests

Intelligent Optimisation; Data Analytics; Machine Learning

PhD candidate and honours supervision

Higher degrees by research

Accredited to supervise Masters & Doctoral students as Principal Supervisor.

PhD topics and outlines

Federated Learning Security Issue Study: Federated learning is a relatively new field in Machine Learning. Participants can keep their data private while the central model can be updated by the collected gradient information. However, the aggregator cannot know whether the received gradient is legal or being revised intentionally. This topic aims to provide solutions to ensure the correctness and usability of the received information.


Available to supervise honours students.

Honours topics and outlines

Crowd-based Delivery Optimisation in City Logistcs

Intelligent Optimisation in the Federated Environment: Conventional optimisation techniques require the data to be centralised for discovering the optimal feasible solutions. This topic works on intelligent optimisation techniques under the federated environment, which isolates the data of each participant but the interactions between participants can get the optimal result based on partial revealed information.

Minimum Information Revealing in Bilevel Optimisation for the Competitive Cooperation Environment: Competition is common between different businesses categorised in the same field. However, cooperation between the competitors may possibly create greater value than running solo. This topic provides a solution to break the barrier of revealing information to the competitors for cooperation and achieve equilibrium.

Fields of Research

  • Machine Learning - 461100
  • Artificial Intelligence - 460200
  • Operations Research - 490108

Teaching areas

Data Science;Big Data Archeture


  • 2016, Other, Best Paper Award, The 9th International Conference on Genetic and Evolutionary Computing
  • 2015, Other, Best Paper Award, The 10th International Conference on Innovative Computing, Information and Control
  • 2010, Other, Best Paper Award, International Conference on Genetic and Evolutionary Computing
  • 2007, National, Master Thesis Best Paper Award, Taiwanese Association for Artificial Intelligence (TAAI)
  • 2006, National, Technology and Research Scholarship, China Technical Consultants Incorporated Foundation in Taiwan
  • 2002, National, Superior Award of the 90th Academic Year University and Junior College Communication Technology Monographic Study Competition, Ministry of Education in Taiwan


Also published as: Tsai, Pei-Wei; Tsai, P.
This publication listing is provided by Swinburne Research Bank. If you are the owner of this profile, you can update your publications using our online form.

Recent research grants awarded

  • 2023: Privacy Preservation in Deep Generative Networks *; Commonwealth Scientific & Industrial Research Organisation (CSIRO)
  • 2023: Solar-driven Mobile Charging Pole Design Parameter Optimisation and Prototype Implementation *; FUSION CO FUND SCHEME
  • 2021: Integrated Instrument Service Delivery system *; iMOVECRC
  • 2020: Australian Freight Carbon Calculator *; iMOVECRC
  • 2020: Dashboard of Intelligent Last Mile Delivery in City Logistics by Integrating Multiple Crowd Movements by Parcel Lockers *; iMOVECRC
  • 2020: Data-driven Operation Research for Cleaner Production in Logistics and Transportation *; iMOVECRC
  • 2020: Smart City Logistics based on Minimal Information Sharing for Maximising Returns and Reputation of Business Alliance *; iMOVECRC
  • 2019: Development of an IoT solution for counting and scaling of logs for export Supply Chains *; Swinburne Research, DVCR&D - Internal contributions
  • 2019: Parcel Movement Optimisation *; Passel_PT

* Chief Investigator