JuliaCon 2019 streaming live

JuliaCon 2019 is streaming live on the Julia Channel on Youtube. Every video has its own livestream and will be linked on the channel page as it goes live.

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I just want to take a minute to appreciate the quality of the livestreams this year. I am glad you guys got this YouTube thing all figured out :slight_smile:

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With some help from @cormullion’s gist I put together a spreadsheet where I’m adding links to live streams, slides, repos, other supporting material etc. I’ll update it as I watch the talks or if anyone points me to relevant material. By the time the whole thing is over I think we should have a pretty comprehensive list :slight_smile:

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The code I used earlier this year (YouTube views and likes) to scrape viewcounts and stats doesn’t work today (Google knows why :)), so the viewcounts are obstinately stuck at 0. Will update once or twice more perhaps, rate-caps permitting.

You can use this link to directly see the current live streams and upcoming streams.

My sheet of livestream links is now more tweetable: https://tiny.cc/jc19talksummary. I’m not on Twitter so someone might want to get that out there if it seems useful. I’ll continue to update it with links to people’s slides, repos, papers etc as I see them.

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@swt30 Thanks a lot.

Yesterday, I took advantage that my girlfiend was out, and I pass the majority of the afternoon watching JuliaCon 2019 talks :slight_smile: . There are very interesting talks, and you can learn a lot (even about solving cryptic crosswords :stuck_out_tongue: ).

I have scraped the Youtube data on all the Juliacon 2019 talks and have created a “score” to help me give a quality indications of all the talks so I can prioritise which to watch. The score is based on some implementation of adjusted likes/views - adjusted dislikes/views.

Base on current data “Mining Imbalanced Big Data with Julia” is the best talk at the Juliacon 2019!

Code is here https://github.com/xiaodaigh/juliacon_youtube

| titles | rank | score | views | likes | disklikes |
| – | – | – | – | – | – | – |
| Mining Imbalanced Big Data with Julia | 1 | 0.04201735687533807 | 538 | 30 | 0 |
| Using Julia in Secure Environments | 2 | 0.04117365656271626 | 305 | 18 | 0 |
| A case study of migrating Timelineapp.co to the Julia language | 3 | 0.03711281221940532 | 148 | 9 | 0 |
| Scientific AI: Domain Models with Integrated Machine Learning | 4 | 0.034864841138641495 | 1199 | 52 | 0 |
| Probabilistic Biostatistics: Adventures with Julia from Code to Clinic | 5 | 0.033460508243300054 | 209 | 11 | 0 |
| How We Wrote a Textbook using Julia | 6 | 0.03241592676757127 | 1071 | 44 | 0 |
| JuliaCN: A Community Driven Localization Group for Julia in China | 7 | 0.030525716747163846 | 109 | 6 | 0 |
| The Unreasonable Effectiveness of Multiple Dispatch | 8 | 0.03006807234238759 | 1419 | 53 | 0 |
| Gaussian Process Probabilistic Programming with Stheno.jl | 9 | 0.029833286180076902 | 286 | 13 | 0 |
| IVIVC.jl: In vitro - In vivo correlation module for pharmaceutical modeling in Pumas | 10 | 0.028661262049042575 | 116 | 6 | 0 |
| Intelligent Tensors in Julia | 11 | 0.028339287934526523 | 676 | 26 | 0 |
| Yao.jl: Extensible_ Efficient Quantum Algorithm Design for Humans | 12 | 0.028268918689989896 | 247 | 11 | 0 |
| MLJ - Machine Learning in Julia | 13 | 0.028167125632644348 | 831 | 31 | 0 |
| SIMD and Cache-Aware Sorting with ChipSort.jl | 14 | 0.028082308415941157 | 276 | 12 | 0 |
| Brain Tumour Classification with Julia | 15 | 0.026700591619932837 | 205 | 9 | 0 |
| Towards Faster Sorting and Group-by operations | 16 | 0.02669812286817836 | 233 | 10 | 0 |
| Interval methods for scientific computing in Julia | 17 | 0.026523838839529135 | 411 | 16 | 0 |
| Porting a Massively Parallel Multi-GPU Application to Julia | 18 | 0.025832857587495713 | 391 | 15 | 0 |
| Multi-threading in Julia with PARTR | 19 | 0.025742754214679534 | 1622 | 52 | 0 |
| Sponsor Address: Intel | 20 | 0.025143704515017885 | 308 | 12 | 0 |
| OmniSci.jl: Bringing the open-source_ GPU-accelerated relational database to Julia | 21 | 0.024864883639827706 | 220 | 9 | 0 |
| Differentiate All The Things! | 22 | 0.024665462154187846 | 1264 | 40 | 0 |
| TrajectoryOptimization.jl: A Testbed for Optimization-Based Robotic Motion Planning | 23 | 0.024160897717104314 | 196 | 8 | 0 |
| Array Data Distribution with ArrayChannels.jl | 24 | 0.023166022251449876 | 236 | 9 | 0 |
| Turing: Probabalistic Programming in Julia | 25 | 0.022880830939287222 | 618 | 20 | 0 |
| Neural Ordinary Differential Equations with DiffEqFlux | 26 | 0.022578150248787062 | 447 | 15 | 0 |
| Analyzing and updating code with JuliaInterpreter and Revise | 27 | 0.022316070798761998 | 488 | 16 | 0 |
| Machine Learning for Social Good | 28 | 0.022308660357245717 | 245 | 9 | 0 |
| SemanticModels.jl: Not Just Another Modeling Framework | 29 | 0.022235301142680228 | 348 | 12 | 0 |
| Prototyping Visualizations for the Web with Vega and Julia | 30 | 0.021746415944636523 | 464 | 15 | 0 |
| High-Performance Portfolio Risk Aggregation | 31 | 0.021373590041710782 | 435 | 14 | 0 |
| Sponsor Address: Julia Computing | 32 | 0.021259591655261086 | 257 | 9 | 0 |
| Transducers: data-oriented abstraction for sequential and parallel algorithms on containers | 33 | 0.021259591655261086 | 257 | 9 | 0 |
| Smart House with JuliaBerry | 34 | 0.021121725888545103 | 294 | 10 | 0 |
| “Online” Estimation of Macroeconomic Models | 35 | 0.020899194534159053 | 192 | 7 | 0 |
| Keynote: Professor Steven G. Johnson | 36 | 0.02085869181885888 | 1980 | 56 | 2 |
| XLA.jl: Julia on TPUs | 37 | 0.020501332958295607 | 434 | 16 | 1 |
| Differentiable Rendering and its Applications in Deep Learning | 38 | 0.020293526665246087 | 558 | 19 | 1 |
| Why writing C interfaces in Julia is so easy | 39 | 0.020205860025377845 | 460 | 14 | 0 |
| Guaranteed Constrained and Unconstrained Global Optimisation in Julia | 40 | 0.019956206939761637 | 201 | 7 | 0 |
| What’s Bad About Julia | 41 | 0.01994441206273697 | 3064 | 78 | 2 |
| Debugging Code with JuliaInterpreter | 42 | 0.019824192414789395 | 1093 | 29 | 0 |
| Queryverse - Under the Hood | 43 | 0.01978644467759544 | 352 | 11 | 0 |
| Generating Documentation: Under the Hood of Documenter.jl | 44 | 0.019614705458022682 | 293 | 12 | 1 |
| Let’s Play Hanabi! | 45 | 0.0195567379410433 | 178 | 9 | 1 |
| Cleaning Messy Data with Julia and Gen | 46 | 0.019224183872736767 | 650 | 18 | 0 |
| Symbolic Manipulation in Julia | 47 | 0.018908342997449187 | 618 | 17 | 0 |
| Heterogeneous Agent DSGE Models in Julia at the Federal Reserve Bank of New York | 48 | 0.018607092558516208 | 254 | 8 | 0 |
| Gen: A General-Purpose Probabilistic Programming System | 49 | 0.018341942294716116 | 637 | 17 | 0 |
| Opening Remarks | 50 | 0.0181714455971159 | 1161 | 33 | 2 |
| Analyzing Social Networks with SimpleHypergraphs.jl | 51 | 0.01714382984641421 | 193 | 6 | 0 |
| The Linguistics of Puzzles: Solving Cryptic Crosswords in Julia | 52 | 0.01714382984641421 | 193 | 6 | 0 |
| Fitting Neural Ordinary Differential Equations with DiffeqFlux.jl | 53 | 0.017018682687722348 | 454 | 12 | 0 |
| A General-Purpose Toolbox for Efficient Kronecker-Based Learning | 54 | 0.01700598255375005 | 248 | 10 | 1 |
| Keynote: Dr. Steven Lee | 55 | 0.01663208160225778 | 328 | 9 | 0 |
| TimerOutputs.jl - a cheap and cheerful instrumenting profiler | 56 | 0.01663208160225778 | 328 | 9 | 0 |
| Julia + JavaScript = :heart: | 57 | 0.01595981109195069 | 408 | 13 | 1 |
| Non-Gaussian State-Estimation with JuliaRobotics/Caesar.jl | 58 | 0.015760250628697365 | 167 | 5 | 0 |
| A Probabilistic Programming Language for Switching Kalman Filters | 59 | 0.01544632538582526 | 601 | 14 | 0 |
| DataKnots.jl - an extensible_ practical and coherent algebra of query combinators | 60 | 0.015235633617972836 | 217 | 6 | 0 |
| Recommendation.jl: Building Recommender Systems in Julia | 61 | 0.015095616120611157 | 219 | 6 | 0 |
| Julia User and Developer Survey (2019) | 62 | 0.015086588503018798 | 913 | 22 | 1 |
| Julia’s Killer App(s): Implementing State Machines Simply using Multiple Dispatch | 63 | 0.01489234969034202 | 467 | 11 | 0 |
| State of the Data: JuliaData | 64 | 0.014694928683545166 | 371 | 9 | 0 |
| Soss.jl: Probabilistic Metaprogramming in Julia | 65 | 0.014694928683545166 | 371 | 9 | 0 |
| Modia3D: Modeling and Simulation of 3D-Systems in Julia | 66 | 0.014693887875667087 | 338 | 11 | 1 |
| GigaSOM.jl: Huge-scale_ high-performance cytometry clustering in Julia | 67 | 0.01443243749382738 | 229 | 6 | 0 |
| MendelIHT.jl: Generalized Linear Models for High Dimensional Genetics (GWAS) Data | 68 | 0.014216724282161097 | 185 | 5 | 0 |
| Sponsor Address: J P Morgan Chase & Co. | 69 | 0.013751546044083098 | 291 | 7 | 0 |
| Keynote: Professor Heather Miller | 70 | 0.013709913600450875 | 1600 | 34 | 2 |
| PackageCompiler | 71 | 0.013391445788579481 | 1177 | 22 | 0 |
| A New Breed of Vehicle Simulation | 72 | 0.013102752852745161 | 360 | 8 | 0 |
| Simulation and Estimation of Nonlinear Mixed Effects Models with PuMaS.jl | 73 | 0.013055878886543004 | 253 | 6 | 0 |
| Slow images_ fast numbers: Using Julia in biomedical imaging and beyond | 74 | 0.013013065676093082 | 202 | 5 | 0 |
| A Showcase for Makie | 75 | 0.012759007888944247 | 858 | 21 | 2 |
| Generic Sparse Data Structures on GPUs | 76 | 0.012758894593484046 | 206 | 5 | 0 |
| Re-designing Optim | 77 | 0.012230516982381081 | 327 | 7 | 0 |
| Electrifying Transportation with Julia | 78 | 0.012164884910696554 | 216 | 5 | 0 |
| Sponsor Address: Relational AI | 79 | 0.011659132102660237 | 170 | 4 | 0 |
| If Runtime isn’t Funtime: Controlling Compile-time Execution | 80 | 0.011261696652033528 | 355 | 7 | 0 |
| Sponsor Address: University of Maryland | 81 | 0.010702165845826657 | 77 | 2 | 0 |
| Differential Programming Tensor Networks | 82 | 0.01058755139214851 | 248 | 5 | 0 |
| Mimi.jl – Next Generation Climate Economics Modeling | 83 | 0.01033693503177368 | 133 | 3 | 0 |
| Keynote: Professor Madeleine Udell | 84 | 0.01026458508311841 | 1469 | 33 | 5 |
| The Julia Language Ephemeris and Physical Constants Reader for Solar System Bodies | 85 | 0.00975922570948214 | 338 | 6 | 0 |
| TSML (Time Series Machine Learning) | 86 | 0.009536645328341163 | 440 | 10 | 1 |
| FilePaths: File System Abstractions and Why We Need Them | 87 | 0.009440037740742483 | 278 | 5 | 0 |
| Keynote: Dr. Ted Rieger | 88 | 0.009331741510062316 | 663 | 10 | 0 |
| Ultimate Datetime | 89 | 0.009079225514211092 | 289 | 5 | 0 |
| Julia web servers deployment | 90 | 0.00880390894590882 | 298 | 5 | 0 |
| Keynote: Arch D. Robison | 91 | 0.008551177352391089 | 710 | 15 | 2 |
| Concolic Fuzzing – Or how to run a theorem prover on your Julia code | 92 | 0.008424745245149775 | 163 | 3 | 0 |
| Building a Debugger with Cassette | 93 | 0.00817072656667643 | 321 | 5 | 0 |
| Solving Delay Differential Equations with Julia | 94 | 0.008141756341118082 | 243 | 4 | 0 |
| Geometric algebra in Julia with Grassmann.jl | 95 | 0.0073797503065606085 | 467 | 9 | 1 |
| Implicit Geometry with Multi-Dimensional Bisection Method | 96 | 0.006520349333162488 | 126 | 2 | 0 |
| Polynomial and Moment Optimization in Julia and JuMP | 97 | 0.006229686541624899 | 321 | 7 | 1 |
| Pkg_ Project.toml_ Manifest.toml and Environments | 98 | 0.005997236604208287 | 574 | 9 | 1 |
| Computational topology and Boolean operations with Julia sparse arrays | 99 | 0.00554718742442667 | 148 | 2 | 0 |
| Modeling in Julia at Exascale for Power Grids | 100 | 0.004698835954581646 | 277 | 6 | 1 |
| The Climate Machine: A New Earth System Model in Julia | 101 | 0.004047753860282182 | 321 | 6 | 1 |
| Pyodide: The Scientific Python Stack Compiled to WebAssembly | 102 | 0.00403454584465295 | 494 | 7 | 1 |
| Counting On Floating Point | 103 | 0.0038316015655475164 | 214 | 2 | 0 |
| Targeting Accelerators with MLIR.jl | 104 | 0.0035799702677530194 | 229 | 2 | 0 |
| Open Source Power System Production Cost Modeling in Julia | 105 | 0.003182061062326278 | 112 | 1 | 0 |
| Static walks through dynamic programs – a conversation with type-inference. | 106 | 0.0028852133057565205 | 284 | 2 | 0 |
| Writing Maintainable Julia Code | 107 | 8308806183545021e-20 | 807 | 15 | 5 |
| LightQuery.jl | 108 | -0.0019332550507556367 | 326 | 3 | 1 |
| Formatting Julia | 109 | -0.005804844313789168 | 466 | 6 | 3 |
| Julia for Battery Model Parameter Estimation | 110 | -0.012547780555374439 | 131 | 1 | 1 |
| Raising Diversity & Inclusion among Julia users | 111 | -0.03639844881267043 | 293 | 13 | 12 |

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Nice work. The trouble with the likes and dislikes is that it’s YouTube… :slight_smile: Most disliked video? “Raising Diversity and Inclusion”…

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The problem with that may be that the video is 2 hours long but video only starts one hour in.

Weird: How is “The Unreasonable Effectiveness of Multiple Dispatch” with a huge number of views and the largest number of likes number 8?

Based on the data, the ratio of likes to view is not as high as number 1.

I see now: it is the ratio (ratios) that play a role.

Nice job! Fun to see that people are eager to know what’s bad about Julia.

These are the top videos by views

How about ranking them simply on the basis of views? I find that #views also intuitively also makes sense.

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What I was going for was to uncover interesting content that may not have been that popular looking at the title or because presenter is not very well-known.

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The formula needs to be more thought out for sure.

Hmm, that seems too simple. You should make a neural net that classifies that topic based on views, likes, and the number of vowels. Then rank on predictive accuracy. This is Julia, come on!

Made a table that actually has links to every video

https://github.com/xiaodaigh/juliacon_youtube/tree/master

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