Women in the Smart World Forum

Biography

She received her B.Sc. in Electronics Technology from the National University of Defense Technology, China, in 1982, and her Ph.D. in Computer Science and Mathematics from the University of the West of England, UK, in 1993. She is a full professor at Hosei University, Japan since 2003. She served as the head of the Department of Computer Science from 2008 to 2010 and currently holds the position of Deputy Director at Hosei University Library.

Inclusive IoT: Building a Smart World for Everyone

The Internet of Things (IoT) is rapidly transforming how we live, work, and interact with technology. Yet, as cities and systems become smarter, ensuring inclusivity, diversity, accessibility, and cultural relevance remains a critical challenge. This presentation explores how IoT can be designed with context-awareness—adapting to users' environments, cultural norms, and diversity—to serve different communities and bridge socio-economic gaps.

Drawing on research in smart cities, environmental monitoring, and AI-enabled sensor networks, this talk will share inclusive projects ranging from a postpartum mental health app leveraging IoT and LLMs, to international methane emission detection frameworks, the SCIRA smart city initiative for interoperable urban systems, and a participatory air quality sensing that empowers citizens through IoT-enabled volunteer sensing. These case studies illustrate how multidisciplinary collaboration can create IoT ecosystems that are not only intelligent but also inclusive, equitable, and truly human-centered—empowering all members of society to benefit from the smart technologies shaping our future.

Biography

Sara Saeedi is an Assistant Professor in Electrical and Software Engineering at the University of Calgary. Her research focuses on AI, IoT, digital twins, and smart geospatial technologies for applications in agriculture, health, and climate resilience. She actively contributes to advancing ethical AI standards in engineering and technology fields.

AI and Ethics: Preparing the Next Generation for Responsible Decision-Making

This presentation emphasizes the critical need for human oversight in AI decision-making processes to ensure that ethical considerations are consistently supported. The insights shared are informed by extensive, hands-on experience in academic settings, within the context of a smart world ecosystem. In education, fostering critical thinking and ethical decision-making when working with AI applications is essential. By teaching students to critically assess the impact of AI on society and engage with it responsibly, we are preparing them to navigate an increasingly AI-driven world while upholding values of fairness, transparency, and accountability.

Biography

Rossitza Marinova has been a faculty member at Concordia University of Edmonton since 2004. Before joining Concordia, she worked as a research scientist in the software development industry in Canada and in a government research laboratory in Japan. Dr. Marinova is a Senior Member of the IEEE.

Leadership in Convergence: How Human-AI Partnerships and Ecosystem Thinking are Driving Global Robotics

The global expansion of small and medium-sized enterprises (SMEs) in the robotics sector is frequently hindered by a critical bottleneck: the scarcity of localized, high-fidelity data required for robust algorithm training and validation. This presentation introduces 'Ecosystem Leadership,' a novel framework that addresses this challenge by forging deep Human-AI partnerships.

We present a case study demonstrating how, through this model, we leverage NVIDIA's Isaac Sim and OpenUSD platforms not merely as tools, but as collaborative 'AI Partners'. This partnership generates synthetic data that is both technically precise and culturally localized, effectively overcoming the data dilemma and enabling robotics SMEs to de-risk development, accelerate international market entry, and build foundational local trust. The talk concludes by arguing that this leadership model—defined by its human-centric focus, collaborative nature, and sustainable mindset—represents a powerful and effective approach often championed by women in technology. It provides a tangible blueprint for how we can lead the convergence of minds and disciplines to build a more inclusive and resilient intelligent future.

Biography

With a Ph.D. from the University of Nottingham and an M.Sc. from Vanderbilt, Rosy Luo is a visionary leader in AI and robotics. She has led roles as an AI Architect in autonomous driving, Head of Perception Algorithms for UBTECH’s humanoid robot Walker, and Technical Lead at SANY International Research Institute. A pioneer in China's robotics scene, her teams have won RoboCup awards, and her work has advanced synthetic data use in mass production. In 2024, she founded Raygen Innovation to drive human-robot collaboration and explore transformative AI applications across industries.

A trajectory generation for an indoor space

Human mobility simulation and trajectory generation are critical for urban planning, transportation management, and privacy-preserving mobility analysis. While deep generative models, such as GANs and VAEs, can synthesize mobility data, they often fail to capture complex environmental constraints and diverse spatiotemporal patterns, thereby limiting the realism of pedestrian trajectory simulations. In this presentation, I will share a diffusion-based generative framework to adapt the U-Net architecture for noise prediction across diffusion timesteps and to incorporate indoor map and spatial density encoders to capture fine-grained spatiotemporal characteristics in indoor environments.

Biography

Kyoung-Sook Kim is Deputy Director at the Intelligent Platforms Research Institute, AIST Japan. Her work focuses on spatiotemporal data platforms, big data analysis, and AI/data quality. She leads the ISO/IEC 5259-2 project and contributes to international standards on AI data ecosystems and smart cities. Previously, she was a researcher at NICT Japan (2007-2014). She co-chairs the Moving Features SWG, GeoAI DWG, and Urban Digital Twins DWG at the Open Geospatial Consortium (OGC), and actively participates in ISO/IEC JTC 1/SC 42, IEC SyC Smart Cities, and ISO/TC 204 working groups.

AI and Education: From AI support to AI-human collaboration

AI has tremendous potential, but its greatest potential is not to do work for us, but with us. In this talk, we will dive into the area of AI and education and explore how AI can support learners and educators as well as work in partnership with them to improve learning and teaching. When building effective and smart learning environments that are inclusive and consider the needs of all learners, diverse perspectives are of utmost importance, particularly in the design and evaluation of the underlying AI approaches. In this talk, we will discuss how AI can help to consider each individual learner's needs by analyzing behavioral and other data from learners and educators to find characteristics of learners as well as behavior patterns that lead to success or failure. In addition, we will introduce several smart learning environments that consider such individual differences, provide personalized recommendations, and encourage learners and educators to reflect on their activities. We will start with learning environments that focus on supporting learners and/or educators through personalized recommendations and adaptive interfaces and then investigate smart learning environments that work together with learners or educators to better understand their needs and provide better personalization.

Biography

Sabine Graf is a Full Professor in Canada whose research focuses on making learning and information systems more personalized, intelligent, and data-driven. Her research aims at making information systems, especially learning systems, more personalized, intelligent and adaptive, as well as enabling those systems to make use of the huge amounts of collected data to provide decision makers (e.g., students, teachers, etc.) with actionable information. Her expertise includes adaptivity, personalization, AI, user modeling, and learning analytics. She has authored over 150 peer-reviewed publications with 10,000+ citations, including four best paper awards. Dr. Graf is frequently invited to present her work at international conferences, universities, and companies.