CV

Updated June 3, 2026

Research Interests

Network Science, Complex Systems, Computational Social Science, Text Analytics, Statistical Modeling of Network Data

Education

University of Southern California
Los Angeles, CA, USA

• Ph.D. Student, Industrial and Systems Engineering (Aug. 2023 - Present)
• MSc, Analytics (Aug. 2021 - Dec. 2022)
  • Advisor: Mayank Kejriwal
  • Key Courses: Design of Experiments and Quality Engineering, Stochastic Processes, Decision Analysis, Reinforcement Learning, Linear Optimization, Stochastic Optimization, Data Management, Data Mining, Text Analytics

The Chinese University of Hong Kong, Shenzhen
Shenzhen, China

• BSc, Statistics (Sep. 2016 - May 2020)
  • Key Courses: Probability and Statistics, Regression Analysis, Statistical Inference, Survey Sampling, Nonparametric Statistics, Categorical Data Analysis, Statistical Modelling in Financial Markets, Stochastic Calculus

Awards

• Jenny Wang Excellence in Teaching Award (USC) (2024)
• ISE Department Outstanding Teaching Assistant of the Year (USC) (2024)

Publications

• Sun, Y., He, D., & Kejriwal, M. (2026). Examining persistent inequities in financial complaint resolution for older Americans and veterans in the United States. PNAS Nexus.
• Sun, Y., & Kejriwal, M. (2026). Structural modeling of campaign finance decisions in the U.S. House of Representatives. PLOS Complex Systems.
• Sun, Y. (2025). Scalable estimation of exponential random graph models on large networks. The Web Conference (TheWebConf).
• Gawin, C., Sun, Y., & Kejriwal, M. (2025). Navigating semantic relations: Challenges for large language models in abstract common-sense reasoning. The Web Conference (TheWebConf).
• Sun, Y., & Kejriwal, M. (2025). Backlash or reinforcement? Donald Trump’s 2017 inauguration and shifting climate beliefs in the United States. NPJ Climate Action.
• Sun, Y., & Kejriwal, M. (2024). A study of firm-switching of inventors in Big Tech using public patent data. Chapter 12 in Springer Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection.
• Sun, Y., & Kejriwal, M. (2023). A structural study of Big Tech firm-switching of inventors in the post-recession era. Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 670-677.
• Sun, Y., & Kejriwal, M. (2023). DeepGraph: Multi-cluster interactive visualization of complex networks in a learned representation space. Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 427-430.

Under Review

• Sun, Y., & Kejriwal, M. (2026). Network structure and market concentration of commercial sex work in the Caribbean. Under review.
• Sun, Y., & Kejriwal, M. (2025). Discovery of theoretically grounded Exponential Random Graph Models for complex networks with large language models. Under review at The Web Conference (TheWebConf) 2026.
• Sun, Y., & Kejriwal, M. (2025). Mapping the growth and geography of online sex work in Southeast Asia. Under review at Humanities and Social Sciences Communications.
• Sun, Y., & Kejriwal, M. (2025). Modeling complex associations between climate change policies and sociodemographic factors using conditional inference trees. Under review at Humanities and Social Sciences Communications.
• Patil, V., Bacha, S. V., Yamani, R., Sun, Y., & Kejriwal, M. (2024). Beyond the star rating: A scalable framework for aspect-based sentiment analysis using LLMs and text classification. Under review at Journal of Computational Social Science.

Presentations

• Poster Presentation: “Mapping Collaboration in AI-Driven Firms: A Co-Inventor Dataset (2020-2025)”, International Conference of the Computational Social Science Society of the Americas (CSSSA), Washington, D.C., Nov. 6-9, 2025.
• Poster Presentation: “Reviewer Rating Variability, Reviewer Confidence, and Language Model-Based Sentiment Prediction of Machine Learning Conference Submissions”, 10th International Congress on Peer Review and Scientific Publication, Chicago, IL, Sep. 3-5, 2025.
• Oral Presentation: “Collaboration Diversity and Paper Success: A Network Analysis of Co-authorship in a Major Machine Learning Conference”, INSNA Sunbelt Conference, Paris, France, Jun. 23-29, 2025.
• Poster Presentation: “Scalable Estimation of Exponential Random Graph Models on Large Networks”, The Web Conference (TheWebConf), May 13, 2025.
• Oral Presentation: “Navigating Semantic Relations: Challenges for Large Language Models in Abstract Common-Sense Reasoning”, The Web Conference (TheWebConf), May 13, 2025.
• Oral Presentation: “AI Reveals Systemic Financial Inequities: A Study of CFPB Complaints for Older Adults and Veterans”, USC ShowCAIS Symposium, Apr. 2025.
• Oral Presentation: “Understanding the Drivers of Human Trafficking Using Massive Multi-Partite Network Modeling and Analytics”, USC Cloudwalkers Premiere, Mar. 2025.
• Poster Presentation: “Modelling campaign finance data using network science to map structures and flows of influence”, Southern California Applied Mathematics Symposium (SOCAMS), Apr. 22, 2023.
• Oral Presentation: “A structural study of Big Tech firm-switching of inventors in the post-recession era”, Advances in Social Networks Analysis and Mining (ASONAM), Nov. 7, 2023.
• Oral Presentation: “DeepGraph: Multi-Cluster Interactive Visualization of Complex Networks in a Learned Representation Space”, Advances in Social Networks Analysis and Mining (ASONAM), Nov. 6, 2023.

Teaching Experience

University of Southern California

• Teaching Assistant:
  • ISE 547: Applied Generative Artificial Intelligence for Enterprises (Fall 2025)
  • ISE 529: Predictive Analytics (Fall 2025)
  • ISE 561: Economic Analysis of Engineering Projects (Summer 2025)
  • ISE 540: Text Analytics (Fall 2023, Spring 2025, Summer 2025)

Mentoring

• John Lim (REU Summer program; Northwestern University) (Summer 2023)
• Yining Ma (Directed Research; USC) (Spring 2024)
• Revanth Yamani (Directed Research; USC) (Spring 2024)
• Shree Vaishnavi Bacha (Directed Research; USC) (Spring 2024 - Present)
• Vishal Patil (Directed Research; USC) (Spring 2024 - Present)
• Daiqi He (Directed Research; USC) (Summer 2024 - Present)
• Herry Wang (Directed Research; USC) (Summer 2024)
• Erica Okeh (Viterbi SURE Program; Howard University) (Summer 2024)
• Richard Joungyoon Kim (CURVE Program; USC) (Fall 2024 - Present)
• Angel Otters (Directed Research; USC) (Fall 2024)

Professional Service

• Journal Reviewer for:
  • Social Network Analysis and Mining (Springer)
  • Frontiers in Political Science
  • Frontiers in Sociology

Industry Experience

USC Information Sciences Institute
Marina del Rey, CA, USA

• Research Engineer (Jan. 2023 - Aug. 2023)
  • Conducted applied research in the Artificial Intelligence and Complex Systems group.
  • Designed and ran controlled experiments, implemented model variants and evaluation pipelines, and contributed to academic publications through manuscript drafting and preparation of experimental results.

Datago Technology Limited
Hong Kong

• Quantitative Researcher Intern (May 2021 - Sep. 2021)
  • Built data pipelines to systematically extract and standardize text features required by sentiment-based factors.
  • Implemented factor construction logic from raw forum data.
  • Ran signal diagnostics and walk-forward backtests to evaluate predictive strength, decay, and out-of-sample performance.

Ping An Insurance
Shenzhen, China

• Data Analyst Intern (Jun. 2019 - Sep. 2019)
  • Assisted in designing and executing A/B tests for a new version of the online banking portal.
  • Analyzed key performance indicators, checked sample balance, and prepared diagnostic reports to support product decisions.

Guosen Securities
Shenzhen, China

• Algorithmic Trader Intern (Jun. 2018 - Sep. 2018)
  • Assisted in automating and optimizing intraday trading strategies, including signal preprocessing, rule refinement, and basic execution tuning.
  • Supported backtesting and parameter checks to improve stability and reduce latency-related errors.

Technical Skills

• Machine Learning & AI: Supervised and Unsupervised Learning, Deep Learning, Large Language Models, Graph-Based Learning
• LLM & Agentic Systems: Prompt Engineering, Retrieval-Augmented Generation (RAG), Multi-Agent Coordination, LLM Orchestration, Evaluation Pipelines
• Network Science & Graph Analytics: Graph Theory, Community Detection, Centrality Analysis, Graph Embeddings, Structural Modeling
• Natural Language Processing: Embedding Models, Semantic Search, Text Classification, Sequence Labeling, Topic Modeling
• Programming & Tools: Python (PyTorch, TensorFlow, Scikit-Learn, Transformers), R, Julia, SQL, JavaScript (React.js, D3.js)
• Data Engineering & Storage: PostgreSQL, Neo4j, Spark, Snowflake
• Data Visualization: Matplotlib, Plotly, Seaborn, Altair, Tableau, Gephi
• Cloud & Systems: AWS, Linux, Distributed Computing, Workflow Automation