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brainnets's Introduction

drawing

Hey there 👋, I'm Makis!

If I were to name a favorite book, that would be Gibson's "Neuromancer"; hackers jacking into cyberspace by projecting their consciousness into a shared virtual space, a hidden layer augmenting the perceived reality. That was cyberpunk at its most refined form; brains interfacing with computers and enhanced cognitive capabilities. Now I am positioned to realize the blur between science fiction and reality. Ideas and notions that were previously thought to be science fiction are becoming a reality, and that is something that overwhelms me.

Quoting Gibson, "My problem is that all things are increasingly attractive to me".
My unique interest and enthusiasm for Data Engineering (and ML as an afterthought) came organic from my previous professional experiences in SWE and my PhD research. Specifically, I tackled challenges related to analyzing big data in the latter one (Apache Spark enters the chat).

What's up?

👨‍💻 My current role is that of Senior Big Data Engineer (DE).
🌍 Interested in green/energy tech and sustainability.

My professional skills and interests include

🐍 Python
🚀 Scala
🌊 DE — Azure/AWS and Databricks/Spark/SQL (batch & stream)
🤖 MLE — k8s/kubeflow and Tensorflow
🎯 Graph Theory
❌ C++/Qt — not anymore

... and messing around with

🎓 In-depth theoretical topics on DE
🧊 Engineering tools; Flink, Iceberg, Nessie, Datahub, etc
🦀 Rust, Go, Mojo
🕸️ Graph databases like neo4j

Academic background

🧠 PhD Candidate in Cognitive Neuroscience at Maastricht University; focused on Brain Connectivity (functional) & Network Neuroscience.

💁 I did pursue a PhD in Cognitive Neuroscience (after 4+ years, countless hours of researching and publishing) just for fun and a hobby. I reached all the requirements to defend it but just I decided that I was not really interested in it anymore.

That's also me 🤓

⚡ neurobastard.io 💬 LinkedIn 🌱 ResearchGate
🔭 OrcID 🎓 Lifelong learning 📓 notion

brainnets's People

Contributors

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Stargazers

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Watchers

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Forkers

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brainnets's Issues

clang: error: unable to execute command: Segmentation fault: 11

Trying to compile on macOS Big Sur 11.6.5 (20G527) with Qt 6.2 and Boost v1.78.0 and Eigen v3.4.0 I am getting the following compilation error:

cd brainnets/ && ( test -e Makefile || /usr/local/bin/qmake -o Makefile /Users/wizofe/Projects/neuro/brainnets/brainnets/brainnets.pro ) && /Library/Developer/CommandLineTools/usr/bin/make -f Makefile
cd brainnets/ && ( test -e Makefile || /usr/local/bin/qmake -o Makefile /Users/wizofe/Projects/neuro/brainnets/brainnets/brainnets.pro ) && /Library/Developer/CommandLineTools/usr/bin/make -f Makefile
/Library/Developer/CommandLineTools/usr/bin/clang++ -c -pipe -stdlib=libc++ -std=c++11 -O2 -std=gnu++1z  -arch x86_64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk -mmacosx-version-min=11 -Wall -Wextra -fPIC -DBRAINNETS_LIBRARY -DQT_NO_DEBUG -I../../brainnets -I. -I../../../third-party -I../../../third-party/boost -I../../../third-party/eigen3 -I/usr/local/share/qt/mkspecs/macx-clang -o ../../builds/release/brainnets-tmp/networks_mst.o ../networks_mst.cpp
In file included from ../networks_mst.cpp:10:
In file included from ../../../third-party/boost/boost/graph/prim_minimum_spanning_tree.hpp:15:
../../../third-party/boost/boost/graph/dijkstra_shortest_paths.hpp:100:62: warning: unused parameter 'u' [-Wunused-parameter]
    template < class Edge, class Graph > void tree_edge(Edge u, Graph& g) {}
                                                             ^
../../../third-party/boost/boost/graph/dijkstra_shortest_paths.hpp:100:72: warning: unused parameter 'g' [-Wunused-parameter]
    template < class Edge, class Graph > void tree_edge(Edge u, Graph& g) {}
                                                                       ^
../../../third-party/boost/boost/graph/dijkstra_shortest_paths.hpp:253:40: warning: unused parameter 'g' [-Wunused-parameter]
        static type build(const Graph& g, const IndexMap& index,
                                       ^
../../../third-party/boost/boost/graph/dijkstra_shortest_paths.hpp:254:43: warning: unused parameter 'array_holder' [-Wunused-parameter]
            boost::scoped_array< Value >& array_holder)
                                          ^
../../../third-party/boost/boost/graph/dijkstra_shortest_paths.hpp:302:40: warning: unused parameter 'g' [-Wunused-parameter]
        static type build(const Graph& g, const IndexMap& index)
                                       ^
clang: error: unable to execute command: Segmentation fault: 11
clang: error: clang frontend command failed due to signal (use -v to see invocation)
Apple clang version 13.0.0 (clang-1300.0.29.30)
Target: x86_64-apple-darwin20.6.0
Thread model: posix
InstalledDir: /Library/Developer/CommandLineTools/usr/bin
clang: note: diagnostic msg:
********************
PLEASE ATTACH THE FOLLOWING FILES TO THE BUG REPORT:
Preprocessed source(s) and associated run script(s) are located at:
clang: note: diagnostic msg: /var/folders/kt/4jcwh7_x3d3bnyblfzk4945w0000gn/T/networks_mst-12f2b5.cpp
clang: note: diagnostic msg: /var/folders/kt/4jcwh7_x3d3bnyblfzk4945w0000gn/T/networks_mst-12f2b5.sh
clang: note: diagnostic msg: Crash backtrace is located in
clang: note: diagnostic msg: /Users/wizofe/Library/Logs/DiagnosticReports/clang_<YYYY-MM-DD-HHMMSS>_<hostname>.crash
clang: note: diagnostic msg: (choose the .crash file that corresponds to your crash)
clang: note: diagnostic msg:

********************
make[2]: *** [../../builds/release/brainnets-tmp/networks_mst.o] Error 254
make[1]: *** [sub-brainnets-make_first] Error 2
make: *** [sub-brainnets-make_first] Error 2

Any idea why?

Possible to attach an example fMRI code?

I try to compile this project in order to calculate some graph metrics and get the correlation matrix and adjacency matrix of a resting-state fMRI (4D). I am not sure how to implement though the NIfTI reading, and what input is getting used by brainnets? Is it the raw 4D matrix from NIfTI? E.g. in my case that is a (45,51,45,167) matrix.

Is it possible to attach an example of a use case of reading an input fMRI and calculating some metrics (also pinpointing where the correlation matrix/adjacency matrix are saved?)

Have you tried a multithreading approach as well?

Thank you for your time.

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