Keynote Speaker

Danilo Pelusi, Ph.D.

Danilo Pelusi, Ph.D.
Associate Professor of Computer Science at the Department of Communication Sciences, University of Teramo

Danilo Pelusi received the degree in Physics from the University of Bologna (Italy) and the Ph.D. degree in Computational Astrophysics from the University of Teramo (Italy). Currently, he is an Associate Professor of Computer Science at the Department of Communication Sciences, University of Teramo. Editor of Springer, Elsevier and CRS books, and Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence (2017-2020), IEEE Access (2018-present), IEEE Transactions on Neural Networks and Learning Systems (2022-present), IEEE Transactions on Intelligent Transportation Systems (2022-present) and IEEE Transactions on Fuzzy Systems (2024-present) he is Guest Editor for Elsevier, Springer and MDPI journals. Keynote speaker, Guest of Honor and Chair of IEEE conferences, he is inventor of international patents on Artificial Intelligence. World’s 2% Top Scientist 2021 and 2022, his research interests include Fuzzy Logic, Neural Networks, Information Theory, Machine Learning and Evolutionary Algorithms.

Suitable Ways to Combine Fuzzy Techniques and Evolutionary Algorithms

Although the computational power of computer is increased, finding the solution of huge complexity problems is a hard task. The application of exact methods leads to unreasonable waiting times. Therefore, suitable intelligent strategies have be useful to obtain optimal solutions in reasonable times. Among pillars of artificial intelligence, fuzzy logic and evolutionary algorithms can be combined to solve high complexity problems. The speech will illustrate suitable ways to combine fuzzy logic and evolutionary computation to solve complex problems. The challenge is to find the suitable trade-off between the knowledge supplied by fuzzy logic and biological evolution of evolutionary algorithms.

Dr. Kemas Muslim Lhaksmana

Dr. Kemas Muslim Lhaksmana
Assistant Professor of Informatics, School of Computing, Telkom University
Director of Research and Community Services, Telkom University

Kemas Muslim received his bachelor’s degree in informatics from the Institut Teknologi Bandung, Bandung, Indonesia, in 2005, his master’s degree in information systems development from the HAN University of Applied Sciences, Arnhem, The Netherlands, in 2009, and his Ph.D. in social informatics from Kyoto University, Kyoto, Japan. He has been a lecturer in the Department of Informatics at Telkom University, Indonesia, since 2011. His research interests include text mining, services computing, and multiagent systems. Since 2021, he has served as the Director of Research and Community Services at Telkom University. Additionally, he was the Treasurer of the IEEE Indonesia Section from 2019 to 2022.

Humanity-Centric Machine Learning to Address Global South Challenges

Since its inception in the 1950s, AI has regained momentum in the 2000s with the advent of Big Data and rapid advances in computational power. The following decade saw breakthroughs in deep learning, leading to the emergence of conversational AI and large language models (LLMs). In the near future, advancements in AI, coupled with other computing technologies such as quantum computing, will likely accelerate, making AI even more ubiquitous and impactful for humanity.

Despite these promising achievements, the world continues to face serious challenges, particularly in the Global South. Recent reports on the progress of Sustainable Development Goals (SDGs) in 2023 indicate that most goals are alarmingly off track. While there have been efforts to harness technology to address issues in the Global South, much work remains to be done. Our research aims to explore how AI, and specifically machine learning, can help address these critical issues in the Global South and beyond. This aligns with Telkom University’s aspiration to become a world-class entrepreneurial university known for advanced humanity-centric innovations.

Although there is debate surrounding the term “human-centered” machine learning, we use the term “humanity-centric” to encompass not only the individual human but also the collective human society. Our approach to machine learning is twofold. First, we investigate how machine learning can be applied to solve humanity’s challenges. Second, we address AI ethics to ensure that every stage of AI development and use is guided by principles that protect humanity from potential harm. Our research includes projects on people analytics, conversational AI for education, Quranic computing, and AI ethics in the Global South. These research directions focus on, but are not limited to, addressing local problems that are also pertinent to the Global South. By adopting a humanity-centric approach, we aim to leverage machine learning to create solutions that benefit both individuals and society as a whole, ensuring ethical considerations are integral to our innovations.