Dr. Jolanta Tańcula is an expert in computer science and mathematics, specializing in technical sciences and cybersecurity. She earned a PhD in Computer Science from the Polish Academy of Sciences in 2014, following master’s degrees in both Mathematics and Computer Science. Since 2018, she has served on the Scientific Committee for the International Conference on Applied Informatics (ICDD). Her international experience includes lecturing on distributed systems at Hochschule Ostwestfalen-Lippe in Germany and advanced security training at Graz University of Technology in Austria. Most recently, she contributed to the CyberEva project, where she developed a proprietary evaluation system for IoE/IoT cybersecurity.

Dr Jixin Ma is a Full Professor of Computer Science (Artificial Intelligence) and the Director of PhD/Postgraduate Research Programme in the School of Computing and Mathematical Sciences at University of Greenwich, U.K. He has been the Director of the Centre for Computer and Computational Science and the Lead of Artificial Intelligence Research Group. Professor Ma is also a Visiting Professor of Beijing Normal University, Hainan University, Anhui University, Zhengzhou Light Industrial University and Macau City University.

Professor Ma obtained his BSc and MSc of Mathematics in 1982 and 1988, respectively, and PhD of Computer Sciences in 1994. His main research areas include Artificial Intelligence, Data Science, and Information Systems, with special interests in Temporal Logic, Information Security, Machine Learning, Case-Based Reasoning and Pattern Recognition. Professor Ma has been a member British Computer Society, American Association of Artificial Intelligence, ACIS/IEEE, World Scientific and Engineering Society, and Special Group of Artificial Intelligence of BCS.

He has also been the Editor of several international journals and international conference proceedings, Conference/Program Chair, and Invited Keynote Speakers of many international conferences. Professor Ma has published more than 200 research papers in peer-reviewed international journals and conferences.

Biography: Rakesh More Program Lead – AI and Finance Portfolio & Application Management A.J. Gallagher | Dec 2021 – Present Lead enterprise AI portfolio strategy integrating machine learning and advanced analytics across insurance business functions. Direct large-scale AI and cloud transformation programs supporting risk assessment, fraud detection, and financial analytics platforms. Implement responsible AI governance frameworks aligned with regulatory standards, including NAIC and data privacy requirements. Manage cross-functional teams delivering enterprise AI platforms across multiple business units.

PROFESSIONAL SUMMARY

AI and machine learning leader with 20+ years of experience delivering enterprise-scale intelligent systems in the insurance and financial services sectors. Specializing in responsible AI, generative AI governance, and large-scale AI transformation programs across underwriting, claims, fraud detection, and risk analytics. Active researcher in generative AI safety with work focusing on the integration of Retrieval-Augmented Generation (RAG) architectures with guardrails frameworks to improve trustworthiness and reliability of large language models in enterprise environments.

KEY CONTRIBUTIONS

Led enterprise AI strategy initiatives integrating machine learning and generative AI across insurance operations, including underwriting, claims, and fraud detection. Conducted research on the integration of Retrieval-Augmented Generation (RAG) with guardrails frameworks to mitigate hallucinations in enterprise LLM systems. Developed evaluation approaches for LLM reliability, hallucination reduction, and responsible AI deployment in regulated industries. Led cross-functional AI initiatives involving data science, engineering, legal, and compliance teams to deploy responsible AI frameworks aligned with regulatory requirements.

Ravi Mandal is a Ph.D. Scholar at the Hindustan Institute of Technology & Science in Bangalore, India. His research focuses on the intersection of data integrity and automated governance. Specifically, his interests include the development of scalable frameworks for data quality assurance, the application of unsupervised machine learning for outlier analysis, and the optimization of data pipelines in enterprise environments. His current work aims to reduce manual intervention in data validation through advanced statistical profiling and anomaly detection engines.