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Neven Pičuljan

Neven Pičuljan

Zagreb, Croatia
Member since September 19, 2016
Neven is a passionate deep learning/machine learning research and development engineer with six years of experience. He has extensive experience working with cutting-edge technologies and a strong ability to understand and solve problems efficiently. He communicates extremely well and is looking for additional freelance projects to challenge himself with.
Neven is now available for hire
  • Python, 6 years
  • C++, 6 years
  • C, 6 years
  • Django REST Framework, 4 years
  • OpenCV, 2 years
  • TensorFlow, 2 years
  • BLAS, 2 years
  • PyTorch, 1 year
Zagreb, Croatia
Preferred Environment
Linux, PyCharm, CLion, Git
The most amazing...
...thing I've built is a face recognition system; I scraped the data set online, trained the model in Torch, and wrote the neural network inference engine in C.
  • Research Engineer
    Visage Technologies
    2016 - 2017
    • Collected the data set for building a face recognition system.
    • Built a training tool and trained a face recognition neural network model using Torch and TensorFlow.
    • Created a testing framework.
    • Coded the neural network inference engine in C/C++.
    • Cross-compiled the neural network inference engine.
    Technologies: Linux, PyTorch, Torch, TensorFlow, C, C++, BLAS, Microsoft Visual Studio, PyCharm, CLion, OpenCV, Robot Operating System, Android Studio
  • Django Developer
    Mobilne Aplikacije d.o.o.
    2015 - 2015
    • Developed Django applications and REST web services.
    • Created database models.
    • Scraped data from the internet.
    Technologies: Django, Python, Linux, Django REST Framework, PyCharm, MySQL
  • Machine Learning/Data Mining Intern
    2015 - 2015
    • Collected data to create a named entity recognizer for the Croatian language.
    • Trained a named entity recognizer for Croatian language.
    • Created a testing framework.
    • Made a web service to expose the named entity recognizer.
    • Crawled various types of data from the internet.
    Technologies: Linux, Python, C, C++
  • Software Engineering Intern
    Visage Technologies
    2014 - 2014
    • Developed a video face annotator.
    • Created tests for the face annotator.
    • Created a user’s manual for the face annotator.
    Technologies: Linux, Windows, C/C++, OpenCV, Microsoft Visual Studio
  • Teaching Assistant on Probability and Statistics
    University of Zagreb, Faculty of Electrical Engineering and Computing
    2014 - 2014
    • Prepared students for the exams.
    • Created assignments for the students.
    • Corrected students' exams.
    Technologies: Productivity Software
  • Deep Visual Biometrics (Development)

    I created a feasibility study and demo for a face recognition system that I developed at Visage Technologies. The demo was written in C/C++ and Python. I collaborated with the Swedish Police, the Swedish National Forensic Centre, and the Swedish Defence Research Agency on this project.

  • Neural Network for Function approximation Using Levenberg-Marquardt Algorithm in Torch Framework (Development)

    A neural network for function approximation using the Levenberg-Marquardt algorithm. I tested the code on various functions and used Torch framework and Python.

  • Credit Card Application Classifier (Development)

    A simple classifier in R for credit card applications. I was given a data set with users’ interactions and experimented with various machine learning algorithms: SVMs, decision trees, random forests, logistic regression, etc.

  • Clustering (Development)

    A project in data mining. I was given a data set with detailed information about interactions of visitors with different stations at Copernicus Science Centre in Warsaw, Poland. The goal was to characterize the flow of visitors through these stations and to segment visitors into separate categories/segments. I used R.

  • Contour Detection (Development)

    A system for detection and localization of a 2D contour (human head) in an image, where many such contours of different size could exist. For this purpose, I applied the generalized Hough Transform (GHT). The system was written in Python.

  • Operations on Graphs in LISP (Development)

    An implementation of various operations on graphs in LISP: finding cycles in graphs, finding paths from one node to another in graphs, checking if the binary tree is symmetric, depth-first order graph traversal, finding maximum depth of a binary tree, and finding a leaf with a maximum value in a binary tree.

  • Expert System in Prolog (Development)

    An expert system created in Prolog for animal identification.

  • Face Recognition (Development)

    A face recognition system. It was trained and tested in Torch framework. The data set was made of publicly available data sets.

  • Deep Regression for Face Alignment (Development)

    Research conducted on different algorithms for face alignment.

  • Answer Selection in Community Question Answering (Development)

    A system to automate the classification of Stack Overflow's posts in the answer thread into three categories: One for those that answer the question well. Another for those that can be potentially useful to the user (e.g., because they can help educate him/her on the subject). Lastly, group those that are just bad or useless.

    I experimented with various machine learning algorithms (scikit-learn): Gaussian naive Bayes, SVMs, and random forests.

  • Pedestrian Detection in Urban Environments Using Detectors Based on Contours (Development)

    A system to do pedestrian detection in urban environments using contour based detection. It was written in Python using Numpy, Scikit-learn, and OpenCV.

  • Performance-driven Animation as a Web Application (Development)

    Performance-driven animation as a web application. Face tracking was used to gain motion in the face of an animated virtual character. The graphics system used to build the application was Three.js based on WebGL. The face-tracking system used to build the application was Visage|SDK.

  • SkyRail Computer Game Controlled with Head Movements (Development)

    A computer game controlled with head movements. It was written in C# using Unity game engine.

  • Introduction to Deep Learning Trading in Hedge Funds (Publication)
    In this article, Toptal Freelance Software Engineer Neven Pičuljan introduces you to the intricacies of deep learning in hedge funds and finance in general.
  • Languages
    C, C++, Python, Perl, JavaScript, Prolog, Bash, Lisp, R, Java
  • Frameworks
    Django REST Framework, Caffe, Django, Robot Operating System
  • Libraries/APIs
    TensorFlow, OpenCV, BLAS, PyTorch
  • Tools
    CLion, Microsoft Visual Studio, PyCharm, Android Studio, Subversion (SVN), Git
  • Platforms
    Linux, Android, Windows
  • Other
    Deep Learning, Torch, Machine Learning (ML)
  • Storage
    MySQL, MongoDB
  • Master's degree in Computer Science
    Warsaw University of Technology - Warsaw, Poland
    2015 - 2016
  • Master's degree in Computer Science
    University of Zagreb, Faculty of Electrical Engineering and Computing - Zagreb, Croatia
    2014 - 2016
  • Bachelor's degree in Computer Science
    University of Zagreb, Faculty of Electrical Engineering and Computing - Zagreb, Croatia
    2011 - 2014
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