Research Inspirations
Journal Club
In our weekly team meetings, we host a journal club where each lab member is expected to present a research paper in turn. This forum encourages critical thinking and keeps our team updated on recent advancements in the field. Below is our current journal club rotation schedule, organized by date and topic.
| Date | Category | Topic | Paper |
|---|---|---|---|
| 26-Jan | LLM post-training | SFT | Instruction Tuning for Large Language Models: A Survey |
| 2-Feb | LLM post-training | PEFT 1: LoRA | LoRA: Low-Rank Adaptation of Large Language Models |
| 9-Feb | LLM post-training | PEFT 2: QLoRA | QLoRA: Efficient Finetuning of Quantized LLMs |
| 16-Feb | LLM post-training | RLHF 1: RL basics | Dynamic Programming Principles |
| 23-Feb | LLM post-training | RLHF 2: DPO | Direct Preference Optimization: Your Language Model is Secretly a Reward Model |
| 2-Mar | LLM post-training | RLHF 3: PPO | Proximal Policy Optimization Algorithms |
| 9-Mar | LLM post-training | RLHF 4: GRPO | DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models |
| 23-Mar | LLM application | RAG 1: Basics | Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks |
| 30-Mar | LLM application | RAG 2: Advancements | Retrieval-Augmented Generation for Large Language Models: A Survey |
| 6-Apr | LLM application | RAG 3: GraphRAG | From Local to Global: A Graph RAG Approach to Query-Focused Summarization |
| 13-Apr | LLM application | Agent 1: Overview | Large Language Model Agent: A Survey on Methodology, Applications and Challenges |
| 20-Apr | LLM application | Agent 2: Planning | Tree of Thoughts: Deliberate Problem Solving with Large Language Models |
| 27-Apr | LLM application | Agent 3: Planning | ReAct: Synergizing Reasoning and Acting in Language Models |
| 4-May | LLM application | Agent 4: Planning | Reflexion: Language Agents with Verbal Reinforcement Learning |
| 11-May | LLM application | Agent 5: Memory | Generative Agents: Interactive Simulacra of Human Behavior |
| 18-May | LLM application | Agent 6: Memory | MemGPT: Towards LLMs as Operating Systems |
| 1-Jun | LLM application | Agent 7: Tool | Toolformer: Language Models Can Teach Themselves to Use Tools |
| 8-Jun | LLM application | Agent 8: Tool | Gorilla: Large Language Model Connected with Massive APIs |
| 15-Jun | LLM application | Agent 9: Tool | HuggingGPT: Solving AI Tasks with ChatGPT and Its Friends |
| 22-Jun | LLM application | Agent 10: Multi-Agent Systems | Generative Agents: Interactive Simulacra of Human Behavior |
| 29-Jun | LLM application | Agent 11: Multi-Agent Systems | CAMEL: Communicative Agents for “Mind” Exploration |
| 6-Jul | LLM application | Agent 12: Multi-Agent Systems | MetaGPT: Meta Programming for a Multi-Agent Collaborative Framework |
| 13-Jul | LLM application | Agent 13: Multi-Agent Systems | AutoGen: Enabling Next-Gen LLM Applications |
| 20-Jul | LLM application | Agent 14: Multi-Agent Systems | ChatDev: Communicative Agents for Software Development |
| 27-Jul | LLM application | Agent 15: Multi-Agent Systems | AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors |
Paper Reading List
The reading list below is tailored for new lab members, featuring a selection of the latest papers from leading journals. These publications reflect a broad spectrum of topics that are loosely connected to our main research interests. All new members are encouraged to explore these articles to stay at the forefront of emerging trends and developments in our field:
Acute Kidney Injury:
- Feng Y, Wang AY, Jun M, et al. Characterization of Risk Prediction Models for Acute Kidney Injury A Systematic Review and Meta-analysis. JAMA Network Open. 2023;6(5). doi:10.1001/jamanetworkopen.2023.13359
- Haredasht FN, Vanhoutte L, Vens C, et al. Validated risk prediction models for outcomes of acute kidney injury: a systematic review. BMC Nephrology. 2023;24(1). doi:10.1186/s12882-023-03150-0
- Hu J, Xu J, Li M, et al. Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study. eClinicalMedicine. 2024;68. doi:10.1016/j.eclinm.2023.102409
- Rank N, Pfahringer B, Kempfert J, et al. Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance. npj Digital Medicine. 2020;3(1). doi:10.1038/s41746-020-00346-8
- Wu C, Zhang Y, Nie S, et al. Predicting in-hospital outcomes of patients with acute kidney injury. Nature Communications. 2023;14(1). doi:10.1038/s41467-023-39474-6
Dialysis:
- Cooper BA, Branley P, Bulfone L, et al. A Randomized, Controlled Trial of Early versus Late Initiation of Dialysis. New England Journal of Medicine. 2010;363(7):609-619. doi:10.1056/nejmoa1000552
- Hemmige V, Deshpande P, Norris KC, et al. Geographic Dialysis Facility Density and Early Dialysis Initiation. JAMA Network Open. 2024;7(1):E2350009. doi:10.1001/jamanetworkopen.2023.50009
- McCoy IE, Weinhandl E, Hussein W, Hsu CY. Initial Management and Potential Opportunities to Deprescribe Dialysis among Patients with AKI-D Patients after Hospital Discharge. Journal of the American Society of Nephrology. 2023;34(12):1949-1951. doi:10.1681/ASN.0000000000000225
- The STARRT‐AKI Investigators. Timing of Initiation of Renal-Replacement Therapy in Acute Kidney Injury. New England Journal of Medicine. 2020;383(3):240-251. doi:10.1056/NEJMoa2000741
Transfer Learning:
- Ebbehoj A, Thunbo MØ, Andersen OE, et al. Transfer learning for non-image data in clinical research: A scoping review. PLOS Digital Health. 2022;1(2):e0000014. doi:10.1371/journal.pdig.0000014
- Gao Y, Cui Y. Deep transfer learning for reducing health care disparities arising from biomedical data inequality. Nature Communications. 2020;11(1). doi:10.1038/s41467-020-18918-3
- Qi T, Wu F, Wu C, et al. Differentially private knowledge transfer for federated learning. Nature Communications. 2023;14(1). doi:10.1038/s41467-023-38794-x
- Radhakrishnan A, Ruiz Luyten M, Prasad N, et al. Transfer Learning with Kernel Methods. Nature Communications. 2023;14(1). doi:10.1038/s41467-023-41215-8
- Zhao Z, Alzubaidi L, Zhang J, et al. A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations. Expert Systems with Applications. 2024;242. doi:10.1016/j.eswa.2023.122807
Federated Learning:
- Dayan I, Roth HR, Zhong A, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine. 2021;27(10):1735-1743. doi:10.1038/s41591-021-01506-3
- Kaissis G, Ziller A, Passerat-Palmbach J, et al. End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence. 2021;3(6):473-484. doi:10.1038/s42256-021-00337-8
- Zhang D, Tong J, Jing N, et al. Learning competing risks across multiple hospitals: one-shot distributed algorithms. Journal of the American Medical Informatics Association. 2024;31(5):1102-1112. doi:10.1093/jamia/ocae027
- Zhang D, Tong J, Stein R, et al. One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites. Journal of Biomedical Informatics. 2024;150. doi:10.1016/j.jbi.2024.104595
Causal Learning:
- Feuerriegel S, Frauen D, Melnychuk V, et al. Causal machine learning for predicting treatment outcomes. Nature Medicine. 2024;30(4):958-968. doi:10.1038/s41591-024-02902-1
- Madumal P, Miller T, Sonenberg L, Vetere Victoria F. Explainable Reinforcement Learning through a Causal Lens. www.aaai.org
- Prosperi M, Guo Y, Sperrin M, et al. Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nature Machine Intelligence. 2020;2(7):369-375. doi:10.1038/s42256-020-0197-y