Advisor: Prof. Cristina (Crista) Lopes - Trustworthy LLMs & Coding Agents | LLM, Coding Agent, AI4SE

• Research on trustworthy large language models and coding agents, focusing on reasoning quality, failure analysis, and reliability in coding and software engineering tasks.
• Develop benchmarks, evaluators, and analysis frameworks to assess how coding agents and LLMs solve complex coding tasks, identify reasoning failures, and improve safety, robustness, and solution efficiency — first-authored “Beyond Output Correctness” (CodeRQ-Bench + VERA).
• Research on LLM transparency and explainability for code generation, interpreting internal mechanisms to improve code safety; member of the UCI Institute for Software Research (ISR).

Open-source agent evaluation with the Terminal-Bench / Harbor team | Coding Agent, Agent Evaluation, RL Environments

• Terminal-Bench 3 contributor (2k+ stars) — tasks and datasets for benchmarking agents on hard, realistic terminal workflows.
• Harbor Adapters Core Contributor (1.6k+ stars) — a framework for running agent evaluations and creating and using RL environments.

Collaborators: Dr. Jiechao Gao & Prof. Michael D. Lepech - Causal Reasoning & Interpretable ML | LLM, LoRA/DPO, Tsetlin Machines

• Finetuned (LoRA, DPO, etc.) open-source LLMs on the first self-constructed dataset with causal structures, improving causal reasoning and mitigating hallucinations — offering the first empirical evidence linking causality to hallucination.
• Developed an LLM-guided semantic bootstrapping method that distills LLM knowledge into interpretable Tsetlin Machines, achieving BERT-level accuracy without embeddings or inference while preserving clause-level transparency — accepted at Findings of ACL 2026.

Advisor: Prof. Yue Zhao - Trustworthy LLMs & Anomaly Detection | LLM, Anomaly Detection, Causal Reasoning, PyTorch

• Proposed CDCR-SFT, a novel fine-tuning method that internalizes causal DAG-based reasoning, boosting causal reasoning to 95.3% (from 72.9%) on the CLADDER benchmark and reducing hallucinations by 11% on HaluEval — accepted at AAAI 2026.
• Proposed NLP-ADBench, the first comprehensive NLP anomaly detection benchmark, establishing a standardized foundation by showing the strong effectiveness of LLM-based features across 19 methods and 8 datasets — accepted at EMNLP 2025 Findings.
• Developed AD-LLM to evaluate LLMs for NLP anomaly detection, showing strong generalization in zero-shot detection, data augmentation, and model selection — accepted at ACL 2025 Findings.
• Designed a multi-step reasoning framework for LLMs to simulate voter decisions, cutting election prediction error by 77% and exposing bias and overfitting issues in models like GPT-4o and LLaMA 3.1.

Advisor: Dr. Zhaoyuan Su, Prof. Yue Cheng - AutoML & MLSys (Inference) | LLM Inference, Trustworthy AI Systems

• Researched automated methods for generating optimal LLM inference compression strategies for user-specific tasks.

Advisor: Dr. Jiechao Gao, Prof. Brad Campbell - Federated Learning for IoT & Healthcare | Federated Learning, PyTorch, Sparse Networks

• Proposed H-FedSN, a personalized sparse network method for hierarchical federated learning in IoT, reducing communication cost by up to 238× while preserving accuracy via structured masking and Bayesian aggregation.
• Developed FedMetaMed, integrating federated learning and meta-learning to enhance personalized medication strategies across distributed healthcare systems, improving model adaptability and privacy preservation.
• Single-handedly developed Fed-LDR, a federated learning algorithm that employs GCN to enhance spatio-temporal data analysis through local data integration and node-centric optimization.
• FedMetaMed and Fed-LDR were accepted by IEEE BIBM and ICDM respectively, and I gave talks on these research.

Advisor: Dr. Iordanis Fostiropoulos - Distributed ML Execution Framework | MLSys, AutoML, Ray, Optuna, Docker, PyTorch, Pytest

• Contributed to an open-source project for distributed AutoML execution used by 30+ researchers at USC.
• Developed Ablator, a deep learning framework for horizontal scaling of ablation experiments, enabling large-scale experiments with Ray and hyperparameter tuning with Optuna, across 50 pull requests.
• Constructed CI pipelines with GitHub Actions, enhancing code quality and package management; authored docs and unit tests with Pytest, increasing test coverage to 95%+.
• Created and published python-rclone on PyPI, simplifying cross-platform use of RClone without pre-installation and with automatic binary selection.

Advisor: Prof. Guoquan Wu - Automated Testing Platform Development | Docker, Node.js, JSON, Vue.js, RobotFramework

• Contributed to the R&D of a web-based automated testing tool using Record and Playback technology, significantly enhancing test case management by enabling streamlined recording, editing, execution, analysis, and result generation. This implementation boosted end-to-end testing efficiency by 300% and saved over 15 hours per week.
• Independently developed a script parser using Node.js that converts user actions recorded in JSON format into executable Robot Framework and Selenium scripts, enabling the replay and repeated execution of these user actions.
• Single-handedly created innovative UI components using Vue.js and AceEditor, orchestrated the optimal containerization of the program with Docker, and automated the DevOps pipeline to maximize development efficiency.

Generative AI Team | Coding Agent, Agentic AI, Complex Task Automation

• Working on coding agents and agentic AI systems for complex task automation at Kilby Labs, TI’s central research organization.

“Hexagon Adventure” 2D Game | Unity, C#, Firebase, Python, Data Analytics

• Implemented automated data collection in Unity using C# to gather critical player behavior and game state data, stored in Firebase Realtime Database to support decision-making.
• Analyzed complex game data using Python, focusing on key metrics like player progression, health point loss, and power-up usage, contributing to a 40% increase in player engagement.
• Drove game design enhancements and level mechanics refinements through data analysis and playtesting sessions, aligning the game with audience preferences.

SaaS Platform Development | Kubernetes, Docker, Go, CI/CD, CRD, Operator-SDK, Helm3, Prometheus, Grafana, ELK

• Developed “RocketMQ as a Service”, mirroring “RabbitMQ as a Service” from AWS Marketplace: fully managed RocketMQ clusters with robust support, easy monitoring, convenient scaling, and seamless updates.
• Utilized Operator SDK and Custom Resource Definitions (CRD) to construct a Kubernetes-based RocketMQ Operator, automating lifecycle management and minimizing manual intervention — leading to 50% faster RocketMQ service creation, automated error recovery, and improved reliability.
• Employed Helm3 to package components into Helm charts, simplifying Kubernetes deployment; integrated Prometheus and Grafana for real-time monitoring of key metrics and node health.
• Automated key workflows including unit tests, container image builds, and Helm3 Chart updates via GitLab CI/CD, speeding up development processes.

Text Translation Platform Development | Java, SpringBoot, Spring, Java Persistence API, Maven, Nginx, MySQL, Git

• Developed an AI document translation system with Java/Spring/Maven; independently responsible for the parsing and translation of PDF, XML, and EML documents, attracting 30,000 MAUs.
• Used Nginx for reverse proxy and load balancing, and managed version control with Git.

Recruitment Data Mining System | Python, Spring Boot, ReactJS, NodeJS, MongoDB, MySQL, Docker

• Led the development of a data mining system to gather recruitment data and conduct multi-dimensional statistical analysis; earned “National Level Innovative Excellence Project” recognition and was adopted by the school.
• Utilized web scraping to extract 10 million+ recruitment records from major job sites with Python, lxml, and XPath; used Bloom Filter for deduplication and Docker with CI/CD for project automation.
• Employed Spring Boot, ReactJS, NodeJS, AntV, and SQL to build automated reports, dashboards, and data visualizations; automated ETL processes with numpy and pandas, storing data in MongoDB and MySQL.