We discuss exciting applications of data science techniques or promising methodological developments Journal Club of the Data Science for SHaPE group is a monthly meeting for anyone with an interest in Data Science and its applications to societally beneficial causes. We cover a very wide range of topics but try to always provide appropriate context and assumptions, and highlight the relevant high-level message. We find that coming from a range of backgrounds and being a fairly interdisciplinary audience makes for very interesting discussions - so anyone welcome to join!If you would like to attend, please contact us at datascienceunit@inf.ed.ac.uk Next Journal Club Insights into population dynamics: A foundation model for geospatial inference Paper: https://research.google/blog/insights-into-population-dynamics-a-foundation-model-for-geospatial-inference/Presenter: Sean Ó HéirDate: 09/05/205 List of previously discussed papers Mamba: Linear-Time Sequence Modeling with Selective State Spaces Paper: https://arxiv.org/abs/2312.00752Presenter: Karthik MohanDate: 11/04/205 Causal Machine Learning: A survey and Open Problems Paper: https://arxiv.org/pdf/2206.15475Presenter: Sohan SethDate: 14/03/2025 A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views Paper: https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202401754Presenter: Paola GaldiDate: 14/02/2025 A Unified Approach to Interpreting Model Predictions Paper: https://arxiv.org/pdf/1705.07874Presenter: Jorge GaeteDate: 08/11/2024 Inferring Cultural Landscapes with the Inverse Ising Model Paper: https://www.mdpi.com/1099-4300/25/2/264Guillermo RomeroDate: 11/10/2024 A Foundation Model for Image Segmentation: Segment AnythingPaper(s): Segment Anything, Segment Anything in Medical ImagesPresenter: Karthik MohanDate: 09/02/2024Hierarchical Transformer-Based Model for Accurate Prediction of Clinical EventsPaper(s): Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health RecordsPresenter: Iris HoDate: 12/01/2024Large Lanuage ModelsPaper(s): Improving Language Understanding by Generative Pre-Training, GPT-4 Technical Report, A Survey on Evaluation of Large Language ModelsPresenter: Lara JohnsonDate: 08/12/2023If your eyes are glassing over XAI, maybe you need interpretable glass-box AI?Paper(s): One Explanation Does Not Fit All, Stop explaining black box machine learning models for high stakes decisions and use interpretable models insteadPresenter: Daga PanasDate: 10/11/2023Application of Approximate Bayesian Computation in epidemiologyPaper(s): A Real Time Regional Model for COVID-9: Probabilistic situational awareness and forecasting (if you are interested in reading more about Approximate Bayesian Computation beforehand, these are useful: 1, 2 and 3)Presenter: Kieran RichardsDate: 13/10/2023Supplementary Reading: Approximate Bayesian Computation tutorials / overviews: 1, 2 and 3Applications of Large Language Models in Information RetrievalPaper(s): ttps://arxiv.org/pdf/2304.13157.pdf, http://marksanderson.org/publications/my_papers/SIGIR_23_GPT.pdf and potentially also https://arxiv.org/pdf/2304.09161.pdfPresenter: Anirban ChakrabortyDate: 09/06/2023Everything is Connected: Graph Neural Networks Paper(s): https://arxiv.org/pdf/2301.08210.pdfPresenter: Sohan SethDate: 12/05/2023Patterns of genetic association and protective effects in COVID-19Paper(s): https://www.nature.com/articles/s41588-022-01042-x#Sec9Presenter: Nikos AvramidisDate: 14/04/2023Physics-Informed Neural Networks for structural analysisPaper(s): https://www.science.org/doi/10.1126/sciadv.abk0644 and https://www.sciencedirect.com/science/article/abs/pii/S0021999118307125Presenter: Daga PanasDate: 10/03/2023Presentation: Link to the PowerPoint [UUN login required]Semi-supervised clusteringPaper(s): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3979639/ and https://dl.acm.org/doi/10.1145/1015330.1015360Presenter: Luwei WangDate: 10/02/2023Survival Machine LearningPaper(s): https://link.springer.com/chapter/10.1007/978-3-031-08341-9_35Presenter: Sury ManochaDate: 13/01/2023Evaluation metrics and sources of harm in Machine LearningPaper(s): https://www.nature.com/articles/s41598-022-09954-8 and https://dl.acm.org/doi/abs/10.1145/3465416.3483305Presenter: Chima EkeDate: 11/11/2022Causal structure learningPaper(s): https://papers.nips.cc/paper/2018/hash/e347c51419ffb23ca3fd5050202f9c3d-Abstract.html and https://proceedings.neurips.cc/paper/2021/hash/e987eff4a7c7b7e580d659feb6f60c1a-Abstract.htmlPresenter: Oisín NolanDate: 09/09/2022Supplementary reading: https://list01.bio.ens.psl.eu/wws/d_read/machine_learning/BayesianNetworks/koller.pdf and https://www.bradyneal.com/Introduction_to_Causal_Inference-Dec17_2020-Neal.pdfParadigm shift in the Information Retrieval communityPaper(s): https://arxiv.org/abs/1711.08611, https://dl.acm.org/doi/10.1145/3269206.3271800 and https://dl.acm.org/doi/10.1145/3397271.3401075Presenter: Anirban ChakrabortyDate: 12/08/2022Supplementary reading: https://nlp.stanford.edu/IR-book/Attention and Vision TransformersPaper(s): https://doi.org/10.48550/arXiv.1706.03762, https://doi.org/10.48550/arXiv.2010.11929 and https://doi.org/10.1109/jtehm.2021.3134096Presenter: Karthik MohanDate: 08/07/2022 This article was published on 2024-11-22
