AI Systems / ML Engineer

Tianao (Owen) Zeng

I build production-oriented AI systems across LLMs, generative models, and multimodal perception. My full-stack product background gives me the infrastructure instincts to turn research ideas into reliable systems people can use.

Resume GitHub LinkedIn Scholar Email

Training

Education

Carnegie Mellon University

Master of Science, Artificial Intelligence Engineering - Electrical and Computer Engineering

Expected Dec. 2026

Pittsburgh, PA | GPA 4.0/4.0

Coursework: Large Language Models, Deep Generative Models, GPU Acceleration, Computer Vision

University of Southern California

Bachelor of Science, Electrical and Computer Engineering

Jan. 2023 - May 2025

Los Angeles, CA | GPA 3.76/4.0

Coursework: Verilog, Computer Systems, Networks, Electromagnetism, Embedded Systems, Internet of Things

Full-stack systems depth

Work Experience

Rally

Sep. 2024 - Sep. 2025

Co-founder and Full Stack Developer | Los Angeles, CA

Built a college-marketplace product across mobile, backend, infrastructure, payments, trust, and admin workflows.

Architected Dockerized Node.js microservices on RDS and Nginx with JWT auth, HTTPS, health checks, and operational separation.
Built Android/iOS search and media workflows with PostgreSQL full-text search, filters, S3 signed uploads, compression, and Rekognition.
Customized Vue admin tooling with RBAC, analytics, ticket transfers, proof galleries, and zoomable receipt review.

DetailsReact NativeNode.jsPostgreSQLAWS

XPENG Motors

May 2024 - Aug. 2024

Software Engineer Intern | San Diego, CA

Worked on infrastructure observability, runtime visualization, OCR triage, and development pipeline isolation.

Visualized infrastructure runtime across Kubernetes clusters and reduced idle PostgreSQL database connections by 70 percent.
Built OCR-based text detection and triage from input video using OpenCV, then improved runtime with multiprocessing.
Implemented a development pipeline that separated testing workflows from production runs.

KubernetesPostgreSQLOpenCVmultiprocessing

Applied ML research

Research Experience

Khan Lab at USC

Jun. 2023 - Aug. 2025

Individual Researcher under Dr. Khan, Ming Hsieh Department of Electrical and Computer Engineering | Los Angeles, CA

Built software, firmware, data processing, and ML analysis workflows for EEG-based cognitive and affective-state experiments.

Developed an EEG software interface with Stroop, math, and video-feedback tests in Python.
Designed and tested firmware for a custom EEG collection ADC board in C++.
Conducted experiments with mentors, collected data through custom EEG software, and processed results in Jupyter Notebook.

Details Khan LabEEGwearablesPythonC++

Research through an engineering lens

The Khan Lab work adds a signal-processing and biomedical-ML thread to the site: experiment software, embedded/firmware support, structured data cleaning, and classical ML modeling on physiological signals.

AI systems focus

Featured Projects

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2026

Asynchronous LLM Reinforcement Learning Under Constrained Hardware

A from-scratch AReaL-style async RL system for Qwen 2.5-0.5B on 2 V100 GPUs, studying bounded staleness, policy lag, and learning efficiency under constrained hardware.

LLM systems
reinforcement learning
distributed training
infrastructure

2026

OmniPaint Reproducibility Study for Object-Oriented Diffusion Editing

An ICCV 2025 reproducibility study of OmniPaint, validating removal metrics on the public benchmark and exposing insertion benchmark sensitivity with public substitute data.

generative models
diffusion
reproducibility
image editing

2026

Robust Perception for Autonomous Vehicles: Camera-Only vs. Camera+LiDAR Fusion

A CARLA closed-loop robustness study comparing YOLOv8n camera-only perception with RGB+LiDAR PointPainting-style fusion under weather, viewpoint, and adversarial stress.

multimodal perception
autonomous vehicles
sensor fusion
robustness

Publication

Selected Publication

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2022Published conference chapter

EMG-Based View Controller Using VR Applications

Jingcheng Zhao, Anjun Zhang, Tianao Zeng, Hao Cai, et al.

Proceedings of the 5th International Conference on Signal Processing and Information Communications

DOI: 10.1007/978-3-031-13181-3_4

Built and evaluated an EMG-driven VR view controller with real-time Unity3D testing, achieving approximately 96.5 percent initial classification accuracy across five head-movement directions.

DOI Google Scholar