Pratheepa Jeganathan

We're thrilled to announce our distinguished Chief Guest, Sabesan Sithamparanathan, PhD, OBE, Founder & President of PervasID Ltd., UK. Don't miss his insightful talk on "Passive IoT Powered by AI" at 9 AM on July 6th. #TASME2024 #Conference #TorontoEvents #RFID #Technology

Join us at the 28th Annual TASME Conference! Free for virtual non-presenting attendees, with in-person tickets starting at just $28.52! 📅 July 6-7, 2024 🏫 University of Toronto, Scarborough Limited spots. Click this link to book your spot now! 🚀 - shorturl.at/LOIRx

Are you looking to apply Large Language Models to biomedical questions? You may want to take a look at our new framework (biochatter.org) and the updated preprint (arxiv.org/abs/2305.06488). 🧵👇

Prof. Emily Hector from @NCState will dive into "Distributed Model Building and Recursive Integration for Modeling Big Spatial Data" @CANSSIOntario #ARESeminars. #StatisticalSciences 🗓️ March 11, 2024 | 3:30-4:30 pm ET 🔗 Register here: bit.ly/3HOdnih

Submit your presentation abstract for the @CSSC_CCES 2024 by March 1: bit.ly/3O4Igm7

Make time on Mar 1 for "Design and Analysis of Experiments for Data Science," a New England Statistical Society webinar chaired by Devon Lin @queensu featuring Peter Chien @uwisconsin, Nathaniel T. Stevens @UWaterloo & John Stufken @GeorgeMasonU. Details: nestat.org/news/2024/01/w…

The Department of Statistics at UBC is organizing the 2024 Constance van Eeden seminar! Title: Ethical AI is More than Loss Functions Date: Thursday, April 4, 2024 - 3 to 5 pm PT Speaker: Dr. Sherri Rose, Professor, Stanford University Registration: forms.gle/KXavxHurpGVxuX…

People often ask me what is the best way to start with time series and forecasting in 2023. Well the answer is still the same as in 2022 and 2021, the best way to start with forecasting in 2023 is to learn the fundamentals from a great book by @robjhyndman otexts.com/fpp3/ #timeseries #forecasting #machinelearning

Microbiome + transfer function + intervention analysis + boosting + inference with mirror statistics "Microbiome Intervention Analysis with Transfer Functions and Mirror Statistics" arxiv.org/abs/2306.06364

🏆 Congratulations to our four Bioconductor 2023 Award winners!! 🎉 Matt Ritchie @mritchieau, Susan Holmes, Constantin Ahlmann-Eltze @const_ae, Simone Bell Thank you for your outstanding contributions to the Bioconductor community! #Bioinformatics #RStats

Shiheng Huang will present about combining cell features and spatial transcriptomics to uncover spatial structure. @Bioconductor #BioC2023 Short Talks - Spatial Transcriptomics (Thurs).

I'm thinking about moving from RStudio to VSCode. #rstats folks: What are good resources to make the transition as smooth as possible? Any obstacles I should be aware of?

Here are my 15 Zotero tutorials — all in one place 🧵

Starting 4 January running until 30 June @NewtonInstitute “The mathematical and statistical foundation of future data-driven engineering” with satellite workshops @turinginst newton.ac.uk/event/dde/ @RAEngNews @DCE_Journal @Cambridge_Eng @DigitalTwinHub @DCE_Journal

Deep Learning with PyTorch - University of Amsterdam(UvA) A fantastic series of tutorials covering a wide array of topics from PyTorch basics, basics of neural nets, architectures(CNNs, transformers, GNNs), generative networks, and contrastive learning. uvadlc-notebooks.readthedocs.io/en/latest/

DAGs, Golems, and Owls: Statistical Rethinking 2023 Lecture 1 (of 20). No hard work in this introductory lecture, just a conceptual outline and some dank memes. Lecture 2 later this week introduces Bayesian inference. Globes will be tossed. youtube.com/watch?v=FdnMWd…

Excited to be a part of the CANSSI-funded CRT project!

The newest versions of #cytomapper and #imcRtools are now released on @Bioconductor. These #RStats packages support #spatial #singlecell and #multiplexed #imaging analysis. See the newest features below 👇

arxiv.org/abs/2208.09978 New preprint from Mengying on scalable modeling of (multidimensional) spatiotemporal data. We introduce Bayesian Complementary Kernelized Learning, combing two popular grid-based probabilistic missing data imputation techniques, MF/TF and GP, in one model.