# Experience Canonical page: https://yiziruifang.com/experience/ Website owner: Yizirui Fang Yizirui Fang’s engineering and research experience in LLM agents, embodied AI, uncertainty quantification, and human-AI collaboration. ## Experience ### Software Dev Engineer — Amazon.com Applied science and engineering focus: - Designed and productionized data and training pipelines for code-generation agents targeting domain-specific languages, using AWS Bedrock, SageMaker, LangChain, and Python. - Collaborated with cross-functional engineering and science partners to improve agent tool-use workflows, retrieval quality, and evaluation coverage for production-facing code generation. ### Research Assistant — Social Cognitive AI (SCAI) Lab at Johns Hopkins University Applied science and research focus: - Studied embodied assistance and spoken instruction following with theory-of-mind models, Bayesian inverse planning, and simulated/real-world robot task data. - Contributed to research artifacts on embodied AI and human-centered AI, including SIFToM and UnclearInstruct, advised by Prof. Tianmin Shu at Johns Hopkins University. ### Research Assistant — Learning to Defer with an Uncertain Rejector via Conformal Prediction Applied science and research focus: - Proposed an uncertainty-aware, distribution-free post-training rejector for learning to defer, improving human-AI collaboration on object recognition and hate-speech detection tasks. - Developed surrogate losses and baselines for learning-to-defer experiments with Wide ResNet, human expert simulators, data augmentation, and PyTorch. - Built experiment pipelines for uncertainty quantification and distribution shift, including batch ensembles, SNGP, MC-Dropout, Bayesian neural networks, CIFAR-10/100 corruption tests, and GPU/HPC automation with Slurm, Shell, and Docker. ### Research Assistant — Augmentation for Distribution Drift in Credit Scoring Applied science and research focus: - Proposed data augmentation algorithms with kernel density estimation to reduce distribution drift in credit scoring models, improving AUC from 0.73 to 0.85 under varying economic factors. - Benchmarked gradient boosting and neural-network credit risk models with Python, PyTorch, LightGBM, NumPy, Pandas, and matplotlib. - Built large-scale financial time-series datasets with Spark and SQLAlchemy across approximately 2B data points. ## Education - MS Eng in Computer Science, Johns Hopkins University - Applied Statistics and Machine Learning, Imperial College London - BSc Computer Science, University of Nottingham --- [All papers](https://yiziruifang.com/publication/) · [Research](https://yiziruifang.com/research/)