Keynote Speakers
Prof. Eko Kuswardono Budiardjo, University of Indonesia, Indonesia
Prof. Eko K. Budiardjo has been the faculty member of the Faculty of Computer Science, Universitas Indonesia since 1985. Teaching, research, and practical services are aligned; give result in a full spectrum of academic achievement. Majoring in Software Engineering as a professional track record, he has made some scientific contributions such as Software Requirement Specification (SRS) patterns representation method, R3 Method, ZEF Framework, FrontCRM Framework, SCRM-HE Framework, and ScrumBoosterTM. Graduated from Bandung Institute of Technology (ITB) in 1985, holds Master of Science in Computer Science from the University of New Brunswick - Canada in 1991, and awarded Philosophical Doctor in Computer Science from Universitas Indonesia in 2007. He is a member of the Association for Computing Machinery (ACM), a member of the International Association of Engineers (IAENG). Currently, he is the Head of the Reliable Software Engineering (RSE) Lab. Faculty of Computer Science Universitas Indonesia, and Chairman of The Indonesian ICT Profession Society (IPKIN).
Speech Title: Visioning the Future of Artificial Intelligence for Software Engineering Process (AI4SE-Proc)
Speech Abstract: Artificial Intelligence (AI) is rapidly transforming diverse industries, with Software Engineering (SE) undergoing a similar paradigm shift. AI provides potent tools and methodologies to enhance and automate various stages of the Software Engineering Process (SEP), demonstrably increasing efficiency, improving software quality, and accelerating development cycles, thereby facilitating the creation of novel software solutions. This transformative influence of AI is anticipated to pervade all phases of the SEP through task automation, intelligent support systems, and data-driven decision-making, empowering software engineering teams to achieve superior software quality with enhanced efficiency. Although implementation challenges persist, the potential benefits of AI integration within SEP are substantial, driving ongoing research and innovation in this field. Therefore, it is critical for software engineers and organizations to proactively explore and adopt these advancements, strategically integrating AI to optimize processes and advance the state of software engineering.
Prof. Kenji Doya, Okinawa Institute of Science and Technology Graduate University, Japan
Kenji Doya is a Professor of Neural Computation Unit, Okinawa Institute of Science and Technology (OIST) Graduate University. He studies reinforcement learning and probabilistic inference, and how they are realized in the brain. He took his PhD in 1991 at the University of Tokyo, worked as a postdoc at U. C. San Diego and the Salk Institute, and joined Advanced Telecommunications Research International (ATR) in 1994. In 2004, he was appointed as a Principal Investigator of the OIST Initial Research Project and as OIST established itself as a Graduate University in 2011, he became a Professor and served as the Vice Provost for Research till 2014. He served as a Co-Editor in Chief of Neural Networks from 2008 to 2021 and the Chairperson of Neuro2022 in Okinawa, and currently serves as the President of Japanese Neural Network Society (JNNS). He received INNS Donald O. Hebb Award in 2018, JNNS Academic Award and APNNS Outstanding Achievement Award in 2019, and the age-group 2nd place at Ironman Malaysia in 2022.
Speech Title: Reinforcement Learning, Bayesian Inference and the Digital Brain
Abstract: In this lecture, I will introduce our theory-driven and data-driven approaches to brain functions.Bayesian inference is a standard way of handling uncertainties in sensory perception and While they are used in combination for perception and action in uncertain environments, the similarity of their computations has been formulated as the duality of inference and control, or control as inference.I will first review the theoretical frameworks, their possible implementation in the cerebral cortex and the basal ganglia, and common circuit architectures of the sensory and motor cortices.
In the second round of Japan’s brain science program, Brain/MINDS 2.0 (https://brainminds.jp/en/), a remarkable feature is that the Digital Brain is supposed to play a central role in integrating structural and dynamic brain data from multiple species for understanding brain functions and tackling neuropsychiatric disorders. I will discuss what is the Digital Brain of Brain, how we can build that, and how we can use that.
Prof Yukari SHIROTA, Gakushuin University, Japan
Prof Yukari SHIROTA (Professor of Gakushuin University) graduated from the Department of Information Science, Faculty of Science, the University of Tokyo, and then received a D.Sc. in computer science in 1998. As a researcher in the private sector, she conducted research for 13 years and then in 2001 she was involved in Faculty of Economics, Gakushuin University, Tokyo as Associate Professor. In 2002, she became a Professor, Faculty of Economics at Gakushuin University. In 2006 to 2007, she stayed at University of Oxford, Oxford, UK as an academic visitor. She is a Fellow of the Information Processing Society of Japan, a Board Member of the Japan Society of Business Mathematics, and a Board Member of the Japanese Operations Management and Strategy Association. Research fields are industry analysis by AI, data visualization on the web, social media analysis, and visual education methods for business mathematics. She has read the paper in the top conference of the “AI in Finance” field: “An Analysis of Political Turmoil Effects on Stock Prices – a case study of US-China trade friction –“ (ACM AI in Finance 2020). She organized the special session titled “Awareness Technology for Economic and Social Data Analysis” in IEEE iCAST in 2019 and 2020, so that they can discuss the economics/social themes with the latest machine learning technologies.
Speech Title: "Revolutionizing Education with AI: Solving Complex Mathematics Problems and Generating Educational Graphics Using ChatGPT"
Speech Abstract: In my keynote speech, I will explore the feasibility of using ChatGPT to solve financial mathematics word problems and generate educational graphics as teaching materials. ChatGPT, an AI based on statistical methods, leverages deductive reasoning capabilities derived from large-scale language model training to solve financial mathematics problems by combining formulas deductively. This capability is further enhanced by incorporating AI for symbolic processing, such as Wolfram Cloud, allowing ChatGPT to provide perfect solutions to fundamental word problems without assistance. Additionally, ChatGPT can illustrate the deductive reasoning process as a graph, enabling the efficient generation of visual teaching aids. This approach has already been implemented in my financial mathematics classes, where students are provided with deductive reasoning graphs generated by ChatGPT as part of their learning materials. These graphs not only facilitate understanding but also enable students to perform answer verification, enriching their learning experience through active engagement.