Biography: Professor Chenguang (Charlie) Yang holds the Chair in Robotics with Department of Computer Science, University of Liverpool, UK. He is leading the Robotics and Autonomous Systems group. He was a Professor of Robotics with University of the West of England, Bristol, leading Robot Teleoperation Group at Bristol Robotics Laboratory. He is a member of European Academy of Sciences and Arts, and holds fellowships of Institute of Electrical and Electronics Engineers (IEEE), Institute of Engineering and Technology (IET), Institution of Mechanical Engineers (IMechE), Aisa-Pacific AI Association (AAIA), and British Computer Society (BCS). He is the corresponding Co-Chair of IEEE Technical Committee on Collaborative Automation for Flexible Manufacturing (CAFM). He was awarded EPSRC Innovation Fellowship (2018-21) and EU FP-7 Marie Curie International Incoming Fellowship grant (2011-13). As the lead author, he received the prestigious IEEE Transactions on Robotics Best Paper Award (2012) and IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award (2022). He previously served as President of the Chinese Automation and Computing Society in the UK (CACSUK) and has organized several conferences as the general chair, including the 25th IEEE International Conference on Industrial Technology (ICIT) and the 27th International Conference on Automation and Computing (ICAC).

 

Assoc. Prof. Ting Zou
Memorial University of Newfoundland, Canada

Biography: I graduated from Xi’an Jiaotong University (China) with the degree of bachelor in electrical engineering in 2005 and master of engineering in automation in 2008. I received my PhD in mechanical engineering from McGill University in 2013, focusing on mechanism design & fabrication of an innovative bi-axial accelerometer based on microelectromechanical systems (MEMS) and its strapdown to ease current rigid-body pose (position and attitude) and twist (velocity and angular velocity) estimation.

After completing my PhD in 2013, I worked as a Post-Doc fellow at the Centre for Intelligent Machines of McGill University. My Post-Doc work was mainly composed of two research projects: optimum design of the next-generation multi-speed transmissions for electric vehicles and nonlinear motion control of autonomous tracked vehicles for mining drilling operations.

Then I joined the Department of Mechanical and Mechatronics Engineering at Memorial University in 2018. My current research focuses on the mechanism design of robotic mechanical systems, biologically inspired robots, mobile robots, nonlinear control and state estimation of robotic systems, advanced human machine interaction, applied machine learning for robotics, intelligent manufacturing, and oceanic robots.

 

Dr. Sritama Sarkar
ABL Group, UK

Speech Title: Integrating Artificial Intelligence for Sustainable and Efficient Dredging

Abstract: Dredging plays a critical role in offshore, coastal/ estuarine and inland environments for infrastructure development, navigational requirements, reclamation purposes and other sedimentation problems. The objectives, challenges and regulatory frameworks differ significantly between these areas.
Traditionally dredging was seen as a mechanically driven industry. In recent years it is undergoing significant transformation in terms of using clean energy, adapting sustainable methodologies and integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms along with Big Data and Internet of Things (IoT). AI-driven dredging optimization is focused on stakeholder and decision maker engagement at appropriate levels, optimization of dredging operations thereby reducing operational costs, selection of suitable dredging window and minimizing environmental impacts.
Smart sustainable dredgers are becoming reality where sensors and real time monitoring along with AI and ML allows the dredger to adapt to changes in hydro-morphologic characteristics, soil conditions and weather in real time thereby increasing efficiency and minimizing environmental footprint. AI can be used to develop predictive maintenance algorithms, thus extending the lifecycle of critical equipment and components by anticipating wear before failure occurs. AI will empower engineers, project managers and environmental scientists to make smarter, faster and more sustainable decisions – turning dredging from a reactive task to a proactive strategy.
This paper presents how AI and ML algorithms along with Big Data and IoT can be integrated with various phases of capital and maintenance dredging in offshore, coastal/ estuarine and inland areas. The challenges of adopting such technologies for capital and maintenance dredging are also presented.

Biography: Dr Ir Sritama Sarkar is a Chartered Engineer (UK) with over 20 years of extensive experience in complex deepwater oil and gas projects, subsea infrastructure/ subsea vehicles and offshore trenching and dredging operations for both hydrocarbon and renewables energy sectors. Sritama’s work bridges the hydrocarbon and renewables sectors with a focus on sediment transport, seabed risk mitigation and environmentally sensitive engineering design.
Her career spans roles across the offshore energy value chain, including inland dredger manufacturing, subsea vehicle design, offshore flexible pipeline systems, installation contracting, and consultancy for offshore operations. She is currently Principal Engineer at ABL Group, where she works as Project Manager or Technical Lead for high-risk marine energy projects.
She holds degrees in geology (bachelors and master’s) from Calcutta University (India), a master’s in mechanical engineering with a focus on dredging technology from TU Delft (The Netherlands), and a PhD in ocean and naval architecture from Memorial University of Newfoundland (Canada). Sritama is known for her risk-based approach to sediment management and her leadership in technically challenging marine environments.