Selected media coverage and applied work in visual intelligence, multimodal AI, and evidence analysis.
My applied work translates multimodal foundation models, embodied reasoning, 3-D reconstruction, and public safety sensing into trustworthy AI for noisy, incomplete, and consequential evidence.
Some of this work supported Washington Post investigations recognized in its Pulitzer-winning public-service coverage.
Contributed crowd-density estimation, spatial registration, and movement-flow analysis for The Washington Post's January 6 Capitol attack reconstruction, linking visual evidence with radio traffic, dispatch records, movement patterns, and event timelines.
Methods: crowd counting, density estimation, spatial registration, temporal alignment.
Role: Listed second among experts in The Washington Post Visual Forensics credits.
Assisted with multi-camera crowd-density reconstruction and spatial alignment for venue safety analysis. The work helped compare local crowd conditions across footage and ground-plane views.
Methods: camera alignment, ground-plane mapping, density estimation, synchronized review.
Role: Lead for CMU analysis credited in the Washington Post video.
CMU LTI: Reported the lab's Astroworld analysis for The Washington Post.
View coverage Visual Forensics video Credit timestamp CMU LTI report
Contributed audio-visual analysis for timing, localization, and sequence reconstruction. The work helped compare citizen videos across camera motion, occlusion, and partial viewpoints in noisy public settings.
Methods: event timing, temporal alignment, localization, audio-visual checks.
Role: Team member under Prof. Alex Hauptmann's supervision.
Supported audio-visual reasoning about gunshot timing, camera geometry, trajectories, and location bounds, cross-checking footage, timestamps, and viewpoints while connecting visual evidence with scene constraints under uncertainty and incomplete observations.
Methods: audio-visual sync, camera geometry, trajectory analysis, localization.
Role: Team member under Prof. Alex Hauptmann's supervision.
Applied 3-D reconstruction and geometry-aware modeling to analyze scene layout, possible viewpoints, and line-of-sight constraints for a contested public event with incomplete visual evidence, using camera calibration, spatial consistency checks, and trajectory reasoning to support evidence interpretation.
This case illustrates how spatial reasoning, camera geometry, and interpretable visual evidence analysis can support human-centered investigation when direct observations are incomplete or conflicting.
Methods: 3-D modeling, camera geometry, spatial reasoning, visual evidence.
Role: Team member under Prof. Alex Hauptmann's supervision.
Developed an early public-safety prototype that used multiple smartphone videos from distributed devices for event localization, supporting real-time emergency situational awareness through video calibration, multimodal sensing, and audio-visual alignment.
Methods: multimodal sensing, video calibration, audio-visual localization.
Role: Team member under Prof. Alex Hauptmann's supervision.
Covered the prototype's technical foundations, including calibration, robustness, and deployment constraints. The work focused on localization from distributed video sources.
Methods: video calibration, multi-view alignment, event localization.
Role: Team member under Prof. Alex Hauptmann's supervision.
Provided technical perspective on metadata, archival sourcing, and responsible verification. The work supported careful interpretation of sensitive visual materials and provenance evidence.
Methods: provenance analysis, metadata review, privacy-aware handling.
Role: Team member under Prof. Alex Hauptmann's supervision.
Note. These projects involve consequential public settings. Analysis supports journalistic verification, legal review, domain expertise, and human judgment.