Survey Scope: Traced why LLM post-training moved from supervised fine-tuning to preference-based RL, reviewing the RLHF pipeline (SFT, reward modeling, PPO with KL regularization) through foundational works including InstructGPT
Comparative Analysis: Compared RLHF with PPO and DPO across supervision, optimization complexity, and on-policy signal; identified where the literature agrees, conflicts, and remains open
Dataset Integration: Added full SUPPORT2 dataset support (dataset class, YAML config, documentation, test data) with a test suite following PyHealth standards, strengthening dataset coverage for open-source clinical ML research
Task Development: Implemented a task class with feature extraction across six clinical feature groups; included a demo example illustrating the complete workflow from dataset loading to ML-ready samples
Deep Learning Research Reproduction: Survival Analysis
Framework Replication: Reproduced 15 core functionalities of the Auton-Survival framework integrating survival regression, patient phenotyping, and evaluation metrics for censored time-to-event data in healthcare
Ablation & Validation: Conducted two ablation studies (mixture components, architecture depth) and a cross-dataset validation; clarified how design choices affect performance and identified feature-dependency limitations in cross-domain transfer
Full-Stack Development: Developed a web-based analytics dashboard for prospective graduate applicants to explore academic programs, compare universities, and identify prominent researchers through interactive visualizations
Cloud Infrastructure: Deployed a multi-database architecture using Aiven (MySQL), MongoDB Atlas, and Neo4j Aura as data stores; ensured continuous availability via PythonAnywhere hosting and GitHub Actions–based keep-alive scheduling
