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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
Portfolio
Experience
  • Python, 6 years
  • C++, 6 years
  • C, 6 years
  • OpenCV, 4 years
  • Django REST Framework, 4 years
  • TensorFlow, 3 years
  • PyTorch, 3 years
  • BLAS, 2 years
Zagreb, Croatia
Availability
Part-time
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.
Employment
  • CEO
    2018 - PRESENT
    Pičuljan Technologies, Artificial Intelligence Research & Development and Consulting
    • Created models for time series analysis, computer vision, and NLP.
    • Built an AI library and associated products.
    Technologies: Python, PyTorch, OpenCV, C/C++
  • Co-founder/AI Engineer
    2017 - PRESENT
    Poze
    • Created neural network inference engine for Android.
    • Trained pose estimation model.
    • Created a testing framework for pose estimation model.
    • Created pose estimation C/C++ library.
    • Talked to the clients.
    Technologies: Python, Tensorflow, OpenCV, C/C++
  • Artificial Intelligence Specialist
    2017 - PRESENT
    Toptal
    • Working on various AI projects (computer vision, time series analysis, NLP, ...).
    • Implemented computer vision algorithms.
    • Worked with time series data.
    • Implemented server for AI models.
    • Implemented data visualization web application.
    Technologies: Python, PyTorch, Caffe, TensorFlow, OpenCV
  • Research Engineer
    2016 - 2017
    Visage Technologies
    • 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
    2015 - 2015
    Mobilne Aplikacije d.o.o.
    • 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
    Bisnode
    • 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
    2014 - 2014
    Visage Technologies
    • 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
    2014 - 2014
    University of Zagreb, Faculty of Electrical Engineering and Computing
    • Prepared students for the exams.
    • Created assignments for the students.
    • Corrected students' exams.
    Technologies: Productivity Software
Experience
  • Deep Visual Biometrics (Development)
    http://www.visualsweden.se/aktuella-projekt/forstudie-deep-visual-biometrics/

    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)
    https://www.youtube.com/watch?v=QrsVpX5-LXo

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

  • Schooling Flappy Bird: A Reinforcement Learning Tutorial (Publication)
    Leveraging DeepMind's breakthrough AI approaches takes some work, but the results are astounding. In this article, Toptal Freelance Deep Learning Engineer Neven Pičuljan guides us through the building blocks of reinforcement learning, training a neural network to play Flappy Bird using the PyTorch framework.
  • 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.
Skills
  • Languages
    Python, C++, C, Bash, Java, Prolog, JavaScript, Perl, R, Lisp
  • Frameworks
    Machine Learning, Django REST Framework, Core ML, Caffe, Django, Robot Operating System
  • Libraries/APIs
    PyTorch, BLAS, OpenCV, TensorFlow, LSTM, Stanford NLP, Quandl API
  • Tools
    AWS-CLI, AWS CLI, Microsoft Visual Studio, PyCharm, Android Studio, CLion, NER, Stanford NER, Subversion (SVN), AWS ECS, Git
  • Paradigms
    Data Science
  • Platforms
    AWS EC2, Linux, Android, Windows
  • Storage
    AWS S3, MongoDB, MySQL
  • Other
    Recurrent Neural Networks, Statistics, Computer Vision Algorithms, Text Analytics, Text Classification, Classification, Linear Regression, Logistic Regression, Decision Tree Regression, Decision Tree Classification, Decision Trees, Random forests, Support Vector Machines (SVM), SVMs, Gated Recurrent Unit (GRU), LSTM Networks, Probability theory, Sentiment Analysis, Deep Learning, Torch, Natural Language Processing (NLP), Deep Neural Networks, Computer Vision, Artificial Intelligence (AI), Reinforcement Learning, Deep Reinforcement Learning
Education
  • Master's degree in Computer Science
    2015 - 2016
    Warsaw University of Technology - Warsaw, Poland
  • Master's degree in Computer Science
    2014 - 2016
    University of Zagreb, Faculty of Electrical Engineering and Computing - Zagreb, Croatia
  • Bachelor's degree in Computer Science
    2011 - 2014
    University of Zagreb, Faculty of Electrical Engineering and Computing - Zagreb, Croatia
Certifications
  • Artificial Intelligence
    APRIL 2018 - PRESENT
    Toptal
  • Data Science
    APRIL 2018 - PRESENT
    Toptal
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