CHARM

Previous spotlights

• Chunggi Lee on AI Assistants August, 2026 ▾

  In science fiction, people talk to the world around them and it responds: they ask a question aloud, and the room, headset, or glasses seem to understand what they mean. Chunggi Lee works toward a grounded version of this idea. He builds situated AI: everyday assistants that understand what people say, what they are looking at, and what they are doing, and work with them rather than simply acting for them. He designs both the AI models behind these systems and the ways people interact with them, with the goal of strengthening human perception, learning, and judgment while keeping people actively involved.

  His recent work shows what this can look like. In Who’s That Player? (IEEE VIS 2026), he developed an XR sports system that lets viewers ask spoken questions about a game and clear up any misunderstanding when they are misheard. In ViSTAR (CHI 2026), he developed an augmented reality coaching system with 3D avatars that supports people as they practice and improve physical skills themselves. He also developed DETRAM (ECCV 2026), a computer vision model that turns people in real-world video into trackable 3D representations that can support richer XR experiences. Together, these projects connect advances in AI models with tools that help people access information, learn, and engage with the world around them.

  Chunggi’s current work sits at the intersection of human-AI interaction and situated AI systems. He is exploring more reliable spoken interaction, AI-generated experiences that adapt to individual preferences, and assistance that responds to people’s situations. Across these projects, his focus is not on doing tasks for people but on extending what they can perceive, understand, and do for themselves, treating AI as a partner in the moment rather than a replacement for their own judgment. The promise of a world that responds when we speak to it is not just convenience. Chunggi’s aim is technology that meets people in the moment and leaves them stronger for the next one, more able to learn, decide, and act for themselves.

• Esther Brown on Wearable Technology July, 2026 ▾

  Nearly one in three Americans wear a fitness tracker every day, yet most people barely glance beyond their step count. These devices quietly collect a rich stream of physiological data: heart rate, sleep quality, stress indicators, and more. The question is how this information can be used to improve people’s health in more ways than just exercise.

  Esther’s research takes that question seriously. She is building a visual framework that layers people’s physiological data alongside the events of their daily lives, whether that be a stressful meeting, a skipped lunch, or a late workout, using lightweight, user-driven tagging. The insight behind her work is that current tools ask people to input a lot of information while giving very little back: the result is generic graphs that are easy to ignore. Esther’s approach aims to make personal data feel genuinely personal, surfacing patterns that are meaningful and actionable.

  The goal is to give people a clearer window into their own health, one where they can spot, for example, that their sleep suffers after late-night screen time, or that their stress peaks mid-week before they even feel it. By putting richer, more contextual insights directly in users’ hands, Esther’s work moves wearable technology closer to what it has always promised: helping people make real, informed decisions about their own wellbeing.

• Ziwei Gu on AI-Resiliency May, 2026 ▾

  As generative AI produces information—code, essays, analysis, videos—at an unprecedented scale and speed, we face a critical bottleneck: the human capacity to verify alignment and make sense of it all. To address this, Ziwei is moving the paradigm from “AI that generates” toward “AI that co-creates.” A key theme of his research is AI-resiliency, the design of interfaces that ensure potential AI errors or contextually inappropriate decisions are easily noticed, judged, and recovered from.

  This vision has taken shape in his recent publications. In his CHI 2024 work, Ziwei proposes GP-TSM, a novel LLM-powered text rendering technique that improves reading efficiency while minimizing the risks of AI-generated summaries. He further expanded this to large-scale document analysis with AbstractExplorer (UIST 2025), which leverages LLMs and human cognitive theories to help users navigate patterns across massive text corpora without losing the context of individual documents.

  Currently, Ziwei is rethinking the fundamental interfaces of human-AI interaction. He is developing generative controls to allow users to intuitively audit and refine AI outputs. He is also extending the AI-resiliency framework to autonomous agents. By designing mechanisms to help users anticipate the consequences of an agent’s actions before they are finalized, he aims to empower users to remain in control in increasingly complex, agent-driven workflows.

• Hongjin Lin on AI in Climate Science April, 2026 ▾

  Over the next decade, communities around the world will decide which neighborhoods get protected from floods, which farms get water in a drought, and which areas are left most exposed to heat waves. The World Meteorological Organization reports that the last 11 years have been the warmest on record, so the choices we make now about how to respond to climate change will shape our future for generations. AI is becoming more involved in these decisions– raising questions about whether we should trust these algorithms to help make choices that can change people’s lives and environments.

  Hongjin’s research looks closely at what happens when AI is used “for social good” in real communities, especially around climate change. Instead of seeing AI as a neutral tool, she studies AI for Social Good (AI4SG) projects as socio‑technical systems: who designs them, who benefits, and whose voices get left out. Drawing on Human‑Computer Interaction for Development (HCI4D) and Data Feminism, she investigates how power imbalances can cause community organizations’ goals to be sidelined, even in projects that claim to help them.

  To address this, Hongjin is looking to work with community groups to co‑design and co‑evaluate new technologies for social good. In practical terms, that means bringing educators, activists, and local residents into the design process, and asking what kinds of AI tools would actually serve their priorities. One direction explores the idea of giving “a voice to nature”: for example, AI systems that translate data about rivers, forests, or air quality into stories, visualizations, or conversations that people can understand and act on.