Dr. Christian G. Liu (刘玄睿, formerly 刘钢 / Gang Liu) is a postdoctoral researcher in the Faculty of Computer Science at Dalhousie University and an Adjunct Postdoctoral Scholar at the same institution. His research focuses on software engineering for distributed systems, particularly the integration of artificial intelligence, business process modeling, and blockchain technologies.
Dr. Liu received his Ph.D. in Computer Science from Dalhousie University and holds master's degrees from both Dalhousie and Concordia University. His research develops model-driven frameworks for generating secure and scalable blockchain applications. In particular, his DE-HSM multi-modal framework enables automated transformation of BPMN models into executable smart contracts, supporting long-term transactions, modular upgrades, and collaborative distributed processes.
Prior to academia, Dr. Liu accumulated more than 20 years of industry experience with companies including Ericsson, CGI, IBM, and Samsung, leading projects in enterprise software systems, telecommunications infrastructure, and AI-enabled blockchain integration.
At Dalhousie University and Saint Mary's University, he has taught courses in databases, cloud computing, business intelligence, and data analytics, emphasizing practical, AI-driven approaches to modern data systems. His research has been published in venues including IEEE, ACM, Springer, and Elsevier.
Education
Doctor of Philosophy in Computer Science
— Dalhousie University, Halifax, Canada (2021–2024) Thesis:Supporting Long-term Transactions in Smart Contracts Generated from Business Process Model and Notation (BPMN) Models
Master of Computer Science
— Dalhousie University, Halifax, Canada (2019–2020) Thesis:FSM for Modeling for Off-Blockchain Computation
Master of Applied Science in Computer Science
— Concordia University, Montreal, Canada (2005–2007) Course-based degree
Professional Experience
Academic
Adjunct Postdoctoral Scholar — Dalhousie University (4/2026 – Present, affiliated research appointment)
Postdoctoral Fellow — Dalhousie University (5/2024 – 4/2026), under supervision of Drs. Ye and Bodorik Project: Adapting the TABS tool for SMEs to generate smart contracts for sustainability and safety data of their products
Instructor — Dalhousie University & Saint Mary's University (1/2018 – Present)
CSCI 2133 – Rapid Prototyping (2019) — Dalhousie University
CSCI 4140 – Advanced Database Systems (2017, 2018, 2021) — Dalhousie University
MCDA 5560 – Business Intelligence (2018) — Saint Mary's University
CSCI 5408 – Data Warehousing (2018) — Dalhousie University
Samsung (Nanjing) Research Centre (5/2018 – 8/2018) — Senior Lab Manager Led four AI research teams for Samsung mobile platform; supervised research tasks, proposed features, managed project timelines.
IBM Canada Ltd., Halifax, NS (4/2015 – 5/2018) — Senior IT Specialist Full-stack development, coding documentation, and test analysis; collaborated with business and test analysts.
In our previous research, we addressed the problem of automated transformation of models, represented using the Business Process Model and Notation (BPMN) standard, into the methods of a smart contract. The transformation supports BPMN models that contain complex multi-step activities supported using our concept of multi-step nested trade transactions. In this paper, we present a methodology for repairing a smart contract that cannot be completed due to unanticipated events. The repair process starts with the original BPMN model fragment causing the issue and amends the pattern based on successful completion of previous activities. This paper describes the tool TABS+R, developed by extending TABS+, to allow repair of smart contracts.
Research on blockchains addresses multiple issues, with one being automated creation of smart contracts. We report on a new approach to develop smart contracts with the objective to automate the process to increase developer efficiency and reduce risks. We use Business Process Model and Notation (BPMN) to describe an application targeting the trade vertical. The system transforms a BPMN model into a multi-modal model combining Discrete Event (DE) modeling with Hierarchical State Machines (HSMs), then further transforms the DE-HSM model into smart contract methods. The system lets the modeler decide which independent patterns should be deployed on a sidechain for cost reduction and/or privacy.
The power and correctness of smart contracts have been the focus of much research. We propose a new approach for developing smart contracts that uses multi-modal modeling to represent the application logic for the trade domain. We use discrete events modeling for concurrency combined with FSM modeling to use concurrent FSMs to simplify the design process, scale blockchain applications, and facilitate identifying parts of a smart program suitable for off-chain processing on a sidechain that also provides privacy.
Scalability, privacy, and interoperability are major issues in blockchain research. We concentrate on the trade finance vertical, developing a new modeling approach for automatic transformation of a BPMN application into a smart contract deployed on a blockchain. We describe how the BPMN model is transformed into a multimodal model combining DE-HSM modeling, and how the DE-HSM model is automatically transformed into deployable smart contracts that interact to form a distributed application, providing interoperability and privacy via private sidechains.
This paper proposes a new algorithm for blockchain software developers and architects to determine what computations of a smart contract can be effectively done off-chain without loss of trust. Our algorithm uses FSMs or HSMs to create smart contract patterns using graphs, then uses pattern recognition to identify which parts should be moved off-chain. Expert software developer inspection, in the context of a Trade Finance use case, validates our algorithm's ability to find optimal patterns.
This paper proposes a new approach and tool for blockchain software developers to determine which computations of a smart contract can be effectively done off-chain without loss of trust, and how they can be moved off-chain automatically. Our approach uses FSM and HSM modeling to create smart contract patterns, then uses pattern properties to identify candidates for off-chain execution.
