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my CV

my 2024 study plan
Month focus
Jan - Feb: Blender python, CUDA
March - April (passover break): Intermediate Rust - Bindgen, Rustonomicon, RataTUI, CXX, Tokio, PyO3, WASM, Data Structures and Algorithms
May: Rust Leptos, Rust Macros, Zero to Production in Rust
June: Rust Bevy, NeoVim, Kitty
July: Rust SeaORM, DataFusion, Effective Rust; terminal Foo, Vulkan
Aug: Rust internals, LazyVim, eBPF, WASM, WebGPU, Hyprland
Sept: C++ refresher, CMake, SIMD
Oct: JAX NumPyro, Scipy
Nov: C++ CGAL
Dec: LLVM / MLIR

Skills

Engineering Leadership:

  • Team management, planning, mentoring & training, code-review, remote outsource coordination

Rust:

  • foundations: std, cargo, rustc, async, design patterns, data structures & algorithms
  • Macros
  • FFI: unsafe, Bindgen, Py03, CXX
  • RataTUI
  • Bevy ECS game engine
  • fullstack: Tokio + Axum / Tonic, SeaORM + SQLX, Leptos, Tracing

Artificial Intelligence:

  • Deep Learning: Transformers, Reinforcement Learning, Math foundations, MLOps pipelines, GenAI
  • SDKs: JAX, TensorFlow, HuggingFace, LangChain, Ray + RLLib
  • GOFAI / Algorithms: Search, Planning, Probabilistic Logic, Semantic MediaWiki / SparQL, Spacy NLP, SKLearn classic ML, Causality / Bayesian Inference
  • Misc: AGI + Philosophy, Mathematics, Program Synthesis, Protein Structural Bioinformatics

Currently self-learning:

  • Advanced AI + Mathematics: Graph Neural Nets, Numerical Methods, Monte Carlo Sampling, Stochastic Calculus, Numpyro, Differential Geometry, NeRFs, Tensorflow Lite, MediaPipe, JAX ecosystem
  • CUDA + NVidia SDKs: Triton, TensorRT-LLM, NeMo, DeepStream, Isaac
  • Compilers: LLVM, MLIR, OpenXLA
  • 3D: Vulkan, Blender API, WebGPU, glTF, Omniverse
  • Internals: Linux, NVim, Jupyter, CPython, eBPF, ELF dissasembly, AVX SIMD, GTK, Wayland

Architectural Technologies:

  • WebDev FullStack: TypeScript, React, Redux, NextJs, Deno, CSS, WebPack, Apollo + GraphQL
  • Modern C++: CMake, HPC {MPI, OpenMP}, JNI + NDK, Qt, JVM project Panama FFM, Cython
  • Distributed Systems + DBs: Scala + Akka, Iceberg + Trino, Neo4J, PostgresSQL, Redis, Kafka, gRPC, Streaming
  • Google Cloud: BigQuery, DataFlow (Beam), GKE, VertexAI
  • Data Science Stack: KubeFlow, Numpy/Scipy, Dask, RAPIDS, Milvus (VectorDB), Pandas, NetworkX, MatplotLib, FastAPI, SqlAlchemy, Poetry, PyEnv
  • Kubernetes DevOps Ecosystem: Linux + Bash, Docker, GitHub + CI/CD, Istio, Knative, Tekton, Prometheus, Kyverno, Helm, Operators
  • Data Visualization: WebGL / ThreeJs, SVG, Metabase, Dash / Plotly
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Josh Reuben's Projects

btgym icon btgym

OpenAI Gym environment for Backtrader trading platform

bulbea icon bulbea

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

casadi icon casadi

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

cdk icon cdk

The Chemistry Development Kit

cgal icon cgal

The public CGAL repository, see the README below

cleveralgorithms icon cleveralgorithms

An open source book that describes a large number of algorithmic techniques from the the fields of Biologically Inspired Computation, Computational Intelligence and Metaheuristics in a complete, consistent, and centralized manner such that they are accessible, usable, and understandable.

cppcoreguidelines icon cppcoreguidelines

The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++

cs229 icon cs229

Stanford CS229 (Autumn 2017)

css-keylogging icon css-keylogging

Chrome extension and Express server that exploits keylogging abilities of CSS.

cuda-samples icon cuda-samples

Samples for CUDA Developers which demonstrates features in CUDA Toolkit

darkflow icon darkflow

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

datalab-notebooks icon datalab-notebooks

This repository includes end-to-end labs on how to use GCP for applied data science

decay-book icon decay-book

Resources for the book "Finite Difference Computing with Exponential Decay Models" by H. P. Langtangen

deep-coder icon deep-coder

Re-implement DeepCoder (https://openreview.net/pdf?id=ByldLrqlx)

deep-learning-nlp icon deep-learning-nlp

:satellite: Organized Resources for Deep Learning in Natural Language Processing

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