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Incomming PhD

University of California, Los Angeles (UCLA)

Mobility Lab

Undergraduate

University of California, Los Angeles (UCLA)

B.S. in Computational Mathematics, B.S. in Statistics and Data Science

• Cumulative GPA: 3.823/4.0
• Dean's Honors List (Fall 2021, Winter/Spring/Fall 2022, Winter/Spring 2023)

High School

American Community School of Athens (ACS Athens)

• Weighted Cumulative GPA: 4.886/4.0
• Final IB Score: 44/45

InSPE: Rapid Evaluation of Heterogeneous Multi-Modal Infrastructure Sensor Placement

Submitted to International Conference on Computer Vision

Co-firsr Author. This paper introduces InSPE, a framework for evaluating heterogeneous multi-modal infrastructure sensor placement by integrating metrics like sensor coverage, occlusion, and information gain, supported by a new dataset and benchmarking experiments to optimize perception in intelligent intersections.

AgentAlign: Misalignment-Adapted Multi-Agent Perception for Resilient Inter-Agent Sensor Correlations

Submitted to International Conference on Computer Vision

Second Author. This work presents AgentAlign, a real-world multi-agent perception framework that mitigates multi-modality misalignment in cooperative autonomous systems using cross-modality feature alignment and introduces the V2XSet-Noise dataset for robust evaluation.

RelMap: Enhancing Online Map Construction with Class-Aware Spatial Relation and Semantic Priors

Submitted to International Conference on Computer Vision

Second Author. RelMap is an online HD map construction framework that enhances vectorized map generation using class-aware spatial relations and semantic priors, significantly improving accuracy and data efficiency on nuScenes and Argoverse 2 datasets.

V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion for Multi-Agent Perception and Prediction

Submitted to International Conference on Computer Vision

Sixth Author. V2XPnP proposes a spatio-temporal fusion framework for multi-agent perception and prediction, leveraging a Transformer-based architecture and a novel sequential dataset to benchmark when, what, and how to fuse information in V2X scenarios.

V2X-ReaLO: An Open Online Framework and Dataset for Cooperative Perception in Reality

Submitted to International Conference on Computer Vision

Eighth Author. V2X-ReaLO is an open online cooperative perception framework that enables real-time V2X fusion in real-world urban environments, introducing a large-scale benchmark dataset and validating online intermediate fusion for practical deployment.

Researcher

HKU Summer Research Program

Leveraged Large Language Models (MiniGPT-4) for multi-modality brain tumor segmentation, integrating four distinct MRI modalities (T1c, T1w, T2c, and FLAIR) onto a common space to enhance segmentation accuracy.

• Awarded Best Presenter and received a PhD offer with a Presidential Scholarship.

Research Assistant

Mobility Lab, UCLA

Contributed to multi-agent perception, sensor fusion, and infrastructure-aware autonomous driving, co-authoring five papers on multi-modal sensor placement (InSPE), misalignment adaptation in cooperative perception (AgentAlign), class-aware map construction (RelMap), spatio-temporal fusion for V2X perception (V2XPnP), and real-world cooperative perception datasets (V2X-ReaLO).

• Participating in the U.S. DOT Intersection Safety Challenge and won $750,000

Research Assistant

Vwani Roychowdhury's Lab, UCLA

Contributed to the implementation and deep learning models of Hilbert (HIL) detector of PyHFO, a multi-window desktop application providing time-efficient HFO detection algorithms for artifact and HFO with spike classification

• Reduced the detection run-time by 50 times compared to state-of-the-art with comparative study to ensure correctness.

AI/Data Analyst Intern

Office of Palo Alto Councilmember Greg Tanaka

Analyzed voter data from public social media, HubSpot, and voter profiles within a California congressional district, identifying trends and developing predictive models to anticipate voting behavior

• Utilized LLMs to generate personalized campaign emails and campaign services, increasing efficiency.

Data Analysis Intern

Uber, Hong Kong

Participated in the facial mask recognition project during the COVID-19 pandemic for backend utilities

• Performed in-depth analysis and demand forecasting for Uber's regional operations, evaluating the influence of factors such as humidity, wind, time of day, as well as origin and destination