• Parallel computing: OpenCL 1.2, CUDA, OpenMP
• Machine learning & scientific computing: MATLAB, Python, Tensorflow, PyTorch, NumPy, SciPy, scikit-image, scikit-learn
• Programming languages: C, C++, Java, PHP
• Graphics programming: OpenGL, Vulkan, path tracing, bounding volume hierarchy
• Server side programming, databases: SQL, SQL Server, Java EE, Spring MVC, Object Relational Mapping.
• Version control systems: Git, SVN.
• Operating systems: Linux (debian derivatives)
• Writing research papers, giving presentations, efficient collaboration and communication.

Machine learning engineer, applied scientist (deep neural networks for microscopy image analysis), part time
Single-Cell Technologies Ltd.
2022-

• Efficient implementation of deep neural network based computer vision algorithms for instance segmentation and classification of microscopy and pathology images in 2D and 3D with Tensorflow C API and C++, OpenCV, CUDA.

Researcher in biomedical image analysis and systems biology
Biological Research Centre, Biological Image Analysis and Machine Learning Group (BIOMAG - Peter Horvath's lab)
2016-

• Image analysis pipelines (instance segmentation, classification, tracking) for high-throughput microscopy images of single-cells in 2D and 3D for several systems biology research projects.
• Building deep neural networks & training object recognition pipelines for microscopy image analysis from scratch (Tensorflow, PyTorch)
• Ultra-fast deep learning based single-cell segmentnation and tracking for 3D multiphoton microscopy images.
• Efficient implementation of a differential intensity contrast microscopy image reconstruction algorithm with OpenCL.
• Development of synthetic data generation pipelines with image style transfer and GANs for 2D instance segmentation in human tissues.
• Development of a novel 3D active contour method for nuclei segmentation in 3D, numerical approximation with finite differences, efficient implementation in CUDA and C++, 3D visualization with MITK, Qt, visualization with vispy.
• Worked on the statistical analysis of multi-omics data (genomics, proteomics) in several projects, used several machine learning techniques for feature selection (in a large population health database) such as Lasso and ElasticNet.
• Collaboration in several international research consortiums.

Research Assistant in data mining and natural language processing, part time
University of Szeged, Department of Artificial Intelligence
2014-2015

• Analysis of social networks using graph algorithms and machine learning: personalized PageRank, power iteration, random walks, logistic regression, sentiment analysis.
• Analysis of social media posts using conditional random fields, discriminative modeling, feature extraction, n-grams, L-BFGS.

Software Engineer, full stack developer (Java Enterprise Edition)
EPAM Systems, Inc
2011-2012

• Development of content management systems with 3-tier architecture in Java Enterprise Edition (EJB 3.0)
• Planning and implementation of entity relationship mappings, development of database schemas in SQL Server, schema normalization, refactoring T-SQL stored procedures, caching, load balancing, http proxy servers.
• Development of web interface with sping MVC, HTML, CSS and jQuery.

Education