A Summer of Julia 2019

Every summer, we welcome a large group of students working on Julia and its packages via the Google Summer of Code program. Last year, we had 22 amazing students, working on diverse topics from machine learning to graphs to differential equations, many of whom continue to be valued contributors to our ecosystem. We are incredibly grateful to Google for the amazing opportunity it provides through GSoC to both the Julia ecosystem as a whole and to the students who are selected.

This year, we recieved an even larger number of very high quality applications, but were offered only 15 slots for GSoC. Not wanting to lose some very impressive students and their exciting projects, we decided to supplement the program with the Julia Season of Contributions (JSoC), using some community funds. Details on the program were announced here: https://discourse.julialang.org/t/julia-seasons-of-contributions-to-supplement-gsoc/23922

So we are excited to see what our impressive set of students achieve this summer. Here is a list of all the projects for GSoC and JSoC 2019

  Name Project Blog
1 Aadesh Deshmukh Improved flowpipe/guard intersections for hybrid reachability using Taylor models ✔️
2 Adam Jozefiak Extending the DiffEqOperators.jl Package  
3 Ching-Wen Cheng Practical implementation of BERT models for Julia ✔️
4 Tushar Sinha Implementing Blossom V Algorithm for Computing Minimum Cost Perfect Matching in a General Graph  
5 Jerry Chen Heterogeneous Computing in Julia: MAGMA binding ✔️
6 Shashank Shekhar Gaussian process integration between Turing and Stheno  
7 Elisabeth Rosch Fitting Neural Differential Equations in Julia  
8 Divyanshu Gupta Quantum Algorithms for Differential Equations ✔️
9 Langwen Huang Implicit Runge-Kutta algorithms with more robust Jacobian reuse mechanism and sparse Jacobian support  
10 Kartikey Gupta Reinforcement Learning environments for Julia ✔️
11 Koustav Chowdhury Implementation of Robin Hood Hashing scheme in Julia ✔️
12 Kirill Zubov Implement package for solving high-dimensional partial differential equations using Neural Networks ✔️
13 Ludovico Bessi Accelerating optimization via machine learning with different surrogate models ✔️
14 Pankaj Mishra Automatic Computation of Sparse Jacobians ✔️
15 Sharan Yalburgi Variational Inference Methods in Turing.jl ✔️
16 Yashvardhan Sharma Implementing Charibde: The Hybrid Algorithm for constrained Interval Optimisation ✔️
17 Shivin Srivastava Efficient Finite Difference Discretizations of Partial Differential Operators  
18 Saurabh Agarwal Implementing Parallel Extrapolation Algorithms  
19 Sumegh Roychowdhury Special Functions ✔️
20 Yash Raj Gupta Standard Compliant Interval Arithmetic Library in Julia ✔️
21 Yash Patel ULMFiT:Universal Language Model Fine-Tuning for Text Classification and Sentiment Analysis ✔️
22 Akshay Jain Trace Estimation of Matrix on Analytic Functions such as Matrix Inverse and Log-Determinant ✔️
23 Saumya Shah Model Zoo for Turing.jl ✔️
24 Arda Akdemir De-Bruijn Graph Constructor Package for De-novo Genome Assembly ✔️
25 Andreas Peter Differentiable Tensor Networks ✔️
26 Manjunath Bhat Enriching Model Zoo with Deep Learning Models ✔️
27 Abhinav Mehndiratta GraphBLAS Implementation ✔️
28 Kanav Gupta Performance Enhancements and General Fixes ✔️
29 Tor Fjelde Variational Inference for Turing.jl  
30 Brandon Taylor Query.jl to SQL translation  
31 Raghvendra Gupta Sparsifying Neural Networks using Sensitivity driven Regularization ✔️
32 Avik Pal Differentiable Ray Tracer in Julia ✔️
33 Ayush Kaushal Practical Models for Named Entity Recognition and Part-of-Speech Tagging ✔️
34 Shreyas Kowshik Addition Of Baseline Models To Model Zoo ✔️
35 Deepesh Thakur Native Julia ODE, SDE, DAE, DDE, and (S)PDE Solvers ✔️
36 Tejan Karmali Differentiable Duckietown  
37 Morten Piibeleht Rejuvenating Documenter ✔️
38 Johnny Chen Towards Better Images.jl Ecosystem ✔️