EDBT 2026 Demo / reviewers in the wild / expert
Frank Chang
dblp:183/1454
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 91% Human-AI interaction · 9% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
affective interaction |
0.9 | 1 | 2025 | From Interaction to Relationship: The Role of Memory, Learning, and Emotional Intelligence in AI-Embodied Human Engagement · HRI 2025 |
Human-robot interaction
emotional intelligence |
0.9 | 1 | 2025 | From Interaction to Relationship: The Role of Memory, Learning, and Emotional Intelligence in AI-Embodied Human Engagement · HRI 2025 |
Human-robot interaction
long-term interaction |
0.9 | 1 | 2025 | From Interaction to Relationship: The Role of Memory, Learning, and Emotional Intelligence in AI-Embodied Human Engagement · HRI 2025 |
Methods — techniques the papers use, named apart from their topics
generative AI · 0.9attention mechanism · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | From Interaction to Relationship: The Role of Memory, Learning, and Emotional Intelligence in AI-Embodied Human EngagementabstractThe evolution of artificial intelligence (AI) has profoundly reshaped human-AI interactions, transitioning from rule-based systems to advanced machine learning algorithms capable of nuanced tasks. This paper introduces a novel architecture for embodied AI in human-robot interaction (HRI), designed to foster meaningful, long-term relationships. Central to the approach is the integration of persistent memory, enabling AI systems to recall past interactions, build continuity, and deliver personalised, contextually aware responses. Additionally, the architecture incorporates emotional intelligence to allow AI to recognise, interpret, and respond to human emotions, enhancing emotional engagement and trust. Key contributions of this paper include a unified approach that links attention mechanisms, memory, and generative AI to support dynamic, context-sensitive interactions; a focus on embodiment as a critical factor in HRI, highlighting its role in grounding interactions within physical and social contexts; and theoretical and practical advancements that extend existing attention-based systems by incorporating persistent memory and emotional intelligence for deeper human-AI connections. This work lays the foundation for developing AI systems capable of forming enduring, meaningful relationships with users, setting a new benchmark for human-centered AI design. Frank Chang, Damith Chandana Herath |
HRI | 1 |
| 2025 | Why is Rigorous PCIe LTSSM Testing a Key to Robust and Reliable Systems?abstractPCI Express (PCIe) is a high-speed interconnect technology that plays crucial role in modern computing systems, connecting key components such as storage devices, network interfaces, accelerators, and GPUs. Ensuring robust interoperability is essential to maintaining device compatibility, link stability, and overall system reliability across different configurations and environments. Rigorous PCIe LTSSM testing helps identify and mitigate link training intermittent failures, signal integrity issues, and protocol mismatches early in development, reducing system downtime, preventing performance degradation, and enhancing user experience. This paper presents a case study on PCIe LTSSM testing in data center class platforms, highlighting real-world test methodologies and issues uncovered in systems. Sean Chen, Amarildo Garcia, Frank Chang, Joe Obedowski, Victor Castillo |
ITC | 3 |