CV

Download CV (PDF)

Education

• Ph.D. in Computer Science, University of Maryland, College Park, 2023 – 2027
  • Dean’s Fellowship recipient. Advisor: Professor Hal Daumé III.
  • Dissertation focus: Safety, Robustness and Trustworthiness in AI Systems.
• M.S. in Computer Science, University of Southern California, Los Angeles, 2018 – 2021
  • Concentration: Data Science.
• B.S. in Applied Mathematics & Public Health, University of California, Irvine, 2013

Publications

Selected work is listed on my publications page; see my Google Scholar for the full list.

Research Experience

• Doctoral Student & Research Assistant, University of Maryland, College Park, 2023 – 2027
  • AI Oversight and Governance: Design oversight methods to detect and mitigate misaligned or deceptive behaviors in deployed AI systems.
  • LLM Safety & Alignment: Develop data-efficient, self-correcting LLM frameworks using preference optimization to improve robustness in content moderation.
  • Bias in AI Systems: Mitigate social and systemic biases in LLMs for employment, education, and healthcare applications using causal inference.
  • Causal Fairness: Develop counterfactual fairness methods with respect to protected attributes, enhancing transparency in AI decision-making.
  • Explainable AI: Leverage causal inference and in-context learning to reduce stereotypes in generative explanations from large language models.
• CBAI Research Fellow, Cambridge Boston Alignment Initiative, Boston, Winter 2026
  • Mentor: Professor Dylan Hadfield-Menell (MIT).
  • AI Technical Governance: Developing a Model Spec Sheet as a blueprint to map components of the training pipeline to downstream behaviors.
• MATS 9.0 & 9.1 Fellow, Berkeley, Spring 2026
  • Mentor: Dr. Noam Kolt.
  • AI + Legal Alignment: Develop audit methods to evaluate, predict, and orient LLM and AI tools to legal requirements.
• Researcher, Information Sciences Institute, Marina del Rey, 2019 – 2023
  • Cryptocurrency Fraud Detection: Developed deep CNN & RNN models achieving <6% error margin in detecting pump-and-dump frauds using social and financial data.
  • Content Moderation: Created crowdsourced annotation frameworks and improved anti-Asian hate speech detection by 8% F1-score using BERT & RoBERTa models.
  • Meta-Learning for NLP: Designed Prototypical-MAML classifiers achieving >70% accuracy in cross-dataset offensive speech detection with only 100 labels.
• Data Scientist & Team Lead, Children’s Data Network, Los Angeles, 2017 – 2023
  • Predictive Modeling for Social Good: Led development of Random Forest & XGBoost models to optimize California’s child welfare system, ensuring model transparency through SHAP and LIME interpretability tools.
  • Large-Scale Data Mining: Analyzed historical administrative databases to build predictive classifiers for child abuse prevention, directly informing state and federal policy.
  • Healthcare Analytics: Conducted mental health analyses using ICD-9/10 codings to improve Medicaid-insured children’s services.

Industry Experience

• Research Intern, AI Applied Science, Oracle, New York City, NY, 2026
  • Designed a novel QUR evaluation framework for RAG artifacts to categorize risk profiles for downstream performance, leading to insights for optimization and system selection.
  • Adapted QUR to agentic RAG systems to augment reliable contextual engineering.
• GenAI Research Intern, Oracle Cloud Infrastructure (OCI), Burlington, MA, 2025
  • Built an iterative alignment framework for conversational medical assistants using preference optimization methods.
  • Improved harmful query detection by up to 42% in harmful query detection on the CARES-18K benchmark across multiple LLMs.
• Graduate Intern, Capital One, McLean, VA, 2024
  • Implemented alignment techniques (SFT, preference optimization, KTO) to strengthen LLM safety guardrails against adversarial attacks.
  • Deployed LLM prototypes (Llama 2/3, Mixtral), reducing inference latency by 75%.
  • Manager-nominated Leadership Award for initiating impactful research collaborations.
• Data Programmer, Edwards LifeSciences, Irvine, CA, 2015 – 2017
  • Maintained ORACLE-based clinical database systems for FDA-compliant clinical trials.
  • Created machine learning models to automate health record analysis and reporting.

Honors & Awards

• MATS 9.0 & 9.1 Scholar – Acceptance rate below 7%, 2026
• Outstanding Reviewer Award – IJCNLP-AACL 2025
• Best Paper Award – ML4H Symposium, 2025
• Outstanding Paper Award – AAAI PDLM Workshop, 2025
• Leadership Award – Capital One (Manager-nominated), 2024
• Dean’s Fellowship – University of Maryland, College Park, 2023 – 2027
• Finalist – Data Science for Social Good Fellowship, Carnegie Mellon University, 2021
• Awardee – UCSD Summer Training Academy for Research Success, 2021
• Head of Programs – USC Graduate and Rising in Information and Data Science, 2020
• Finalist – Center for Knowledge-Powered and Interdisciplinary Data Science, 2019
• 2nd Place – USC–Boeing Inaugural Data Hackathon, 2018

Technical Skills

• Programming: Python, SQL, SAS, JavaScript, MATLAB
• ML/AI Frameworks: PyTorch, TensorFlow, Keras, Hugging Face Transformers
• LLM Techniques: Fine-tuning (SFT, DPO, GRPO, KTO), Alignment, RLHF, vLLM serving
• ML Methods: Deep Learning (CNN, LSTM, RNN, GAN), Causal Inference, Meta-Learning
• Data & Analytics: Spark, AWS, Kubernetes, Git, Tableau, Statistical Analysis
• Specialized: SHAP/LIME Interpretability, Crowdsourcing (AMT), Clinical Data Systems

Teaching Experience

• Research Assistant, Personalization of LLM-generated content, 2025 – 2026
• Teaching Assistant, CMSC 839E: Uncertainty Communication for Decision-making, Fall 2024
• Teaching Assistant, CMSC 116: You, Me and Generative AI, Spring 2024
• Teaching Assistant, CMSC 320: Introduction to Data Science, Fall 2023

Service

Committee

• Junior Chair, Bias and Fairness Area, ML4H Symposium, 2025

Reviewing

• Reviewer – NeurIPS, ICLR
• ACL Rolling Review (ARR) – ACL, EMNLP, AACL
• Trustworthy NLP Workshop
• ACM Transactions on Intelligent Systems and Technology
• Journal of Medical Internet Research
• Machine Learning 4 Health (ML4H)
• Expert Systems with Applications

Invited Talks

• HireWatch: Measuring LLM Compliance to US Labor Law, MATS 9.0 Symposium, Winter 2026
• “You gotta be a Doctor Lin”: The Risk of Bias in LLM, UMD INST 414, Spring 2025
• A Tutorial on Trustworthiness and Bias in LLMs, UMD Values-centered AI Institute, Fall 2024
• Summer Education Program for High Schools in STEM, Summer 2023