Yunkai (James) Zhan
CS + Applied Math @ USC | ML / LLM Post-Training
Education
University of Southern California, Viterbi School of Engineering
Aug 2023 — May 2027Bachelor of Science, Computer Science | Applied Math (double major)
Experience
Currents AIMachine Learning Engineer (Internship) — Social World Model, Post Training
Oct 2025 — Present- Building an LLM-based Celebrity Simulator to simulate the top 100 political, business, and finance figures' tone, knowledge, belief system, and decision making with a post-training pipeline and agentic framework.
- Establishing a post-training pipeline with multi-source data collection and processing, Supervised Fine-Tuning (SFT) on Qwen, and reinforcement learning with a generator-discriminator framework.
- Participating in the company's web agent extension launch, including test script writing and marketing planning.
Alibaba CloudSoftware Engineer (Internship) — Knowledge Graph, RAG
Jun 2024 — Aug 2024- Pioneered the industry's first RAG multi-hop question set auto-generator for the Chinese finance sector; evaluated and improved the model's retrieval accuracy by 20%.
- Developed a Python pipeline based on Knowledge Graph (Neo4j), vector embeddings, and prompt engineering to auto-generate more challenging questions for the RAG system.
- Applied the pipeline to Alibaba's finance AI assistant Qwen Dianjin's internal RAG evaluation, improving product satisfaction among 30+ leading Chinese financial companies.
Projects
Multimodal LLM Adaptive ThinkingAcademic Research — MLLM Post-Training
Feb 2026 — Present- Post-trained multimodal LLMs (Qwen3-4B-VL) across diverse vision-language tasks under both thinking and non-thinking modes; identified a bidirectional performance gap where thinking mode outperforms by ~15% on geometry and math but underperforms by 5–10% on VQA and counting tasks.
- Designing GRPO-based training with mode-conditioned reward shaping to unify reasoning capabilities, enabling a single model to adaptively leverage extended or compressed thinking without sacrificing performance in either regime.
Research Assistant — USC HUMANS LabAcademic Research — Social Simulation, Agent-Based Modeling, RL
May 2025 — Present- Contributed to a WWW 2026 submission on simulating LLM-driven influence operations (IO) using multi-agent generative modeling in social media environments.
- Helped implement a Generative Agent-Based Model (GABM) of 40 organic and 10 IO agents using AutoGen and Llama 3.3-70B, exploring coordination under varying operational awareness levels.
- Researching the application of reinforcement learning to multi-agent social media simulation to improve simulated online marketing engagement rate and purchasing intent uplift.
Research Assistant — USC Media Communication LabAcademic Research — RL, Green Learning
May 2025 — Present- Conducting research with Prof. Jay Kuo on the intersection of Reinforcement Learning and Green Learning — an explainable, energy-efficient alternative to conventional deep learning pipelines.
- Surveyed and reproduced 10+ papers on main approaches in Green Learning on standard vision benchmarks, focused on feature engineering (variants of PCA, Sparse Coding) and XGBoost.
- Researching the application of the Green Learning pipeline on Policy Gradient as an alternative to Deep Policy Networks with high interpretability, efficiency, and superior performance on small datasets.
LLM-Powered Learning AssistantProject — Next.js, TypeScript, MongoDB, RAG, LLM
May 2025 — Aug 2025- Engineered a production-ready Retrieval-Augmented Generation (RAG) pipeline using FastAPI, embedding course materials with Voyage-3 into an L2-normalized FAISS vector index for high-speed semantic search.
- Orchestrated a multi-model LLM strategy, leveraging Claude 3.5 Haiku for efficient document summarization and Claude 3.7 Sonnet for context-aware answers, achieving 99% concept accuracy.
- Built a full-stack interface featuring an interactive document viewer and persistent chat backed by MongoDB for scalable storage of large PDFs.
Skills
Coding:
Python (NumPy, Pandas, Scikit-Learn, PyTorch), C++, Java, MATLAB
Web Development:
React, Node.js, HTML, CSS, TypeScript, JavaScript, Spring Boot, MySQL, Git, Docker
ML / AI:
LLM Post-Training (SFT, RLHF, GRPO), RAG, Knowledge Graphs (Neo4j), Vector Embeddings (FAISS), Multi-Agent Systems (AutoGen), Green Learning
Relevant Coursework:
Algorithms, Data Structures, Software Engineering, Machine Learning, Linear Algebra, Probability and Statistics, Data Science