RESEARCH / ONE CONNECTED PROGRAM
Perceive broadly.
Compute deliberately.
Act reliably.
My research begins with a simple systems question: how can multimodal intelligence preserve the right evidence, spend computation where it matters, and remain useful under real-world constraints?
INTERACTIVE RESEARCH MAP
A research system, not a keyword list.
Select a node to trace its questions, methods, papers, and projects.
Multimodal Intelligence
Select a direction or a keyword to inspect its evidence.
Established RESEARCH THEME
Multimodal Perception
How can heterogeneous sensors describe the same identity across viewpoints, spectra, and environments?
I align RGB, near-infrared, thermal, text, and aerial-ground observations into identity-aware representations that remain dependable in complex scenes.
- Cross-modal alignment
- Modality-specific representation
- Text-guided fusion
Growing RESEARCH THEME
Efficient Multimodal Large Models
How can multimodal foundation models preserve useful evidence while spending less compute?
I study visual token selection, semantic priors, state-space modeling, and efficient adaptation for multimodal models.
- Visual token pruning
- Vision-language adaptation
- State-space aggregation
Emerging RESEARCH THEME
Efficient Agent Systems
How can multimodal agents perceive, remember, and act under strict latency, memory, and device constraints?
My emerging agent direction connects visual evidence management with reliable, deployment-aware GUI agent systems.
- Evidence-aware inference
- Visual memory efficiency
- Device-aware deployment
RESEARCH PHILOSOPHY
Four principles behind the work.
Perceive beyond pixels.
Multimodal systems should preserve the evidence unique to each sensor while discovering the semantics they share.
Spend compute where it matters.
Efficiency is an information-design problem: select useful tokens, route useful features, and reuse useful memory.
Design for the device.
A method is more valuable when latency, memory, and deployment constraints influence the research question from the beginning.
Connect perception to action.
The next step is not only to understand multimodal environments, but to help agents make reliable decisions within them.
WHERE I’M GOING
From efficient models to reliable agent systems.
The next research questions build directly on the same through-line: multimodal evidence, efficient computation, and deployment-aware decisions.
Current · Now
Efficient multimodal models
Reduce token, feature, and memory redundancy while preserving cross-modal understanding.
Next · Near term
Long-horizon GUI agents
Build agents that perceive interfaces, retain useful visual history, and act under real device budgets.
Future · Research vision
Cloud–edge multimodal agent systems
Coordinate perception, reasoning, memory, and action across device and cloud for reliable autonomous decision-making.
COLLABORATE