Verified Expert in Engineering
Machine Learning Developer
Pawel is an experienced data-scientists and machine learning professional. He has worked for Fortune 100 companies, and he has an academic background in the field. Before moving to data science, he was a former lead architect in Samsung R&D Center. Pawel holds a Ph.D. in knowledge representation and reasoning as well as a master's degree and a bachelor of science degree in computer science.
The most amazing...
...thing I've coded is a Clinical Decisions Support System implementing ESMO guideline for cancer treatment.
Senior Machine Learning Engineer
- Recommended systems, image processing, NLP, and deep learning to the production.
- Created machine-learning models using Sklearn and Tensorflow for Fortune 100 customer in the area of trade promotion optimization.
- Created a cognitive programming language that makes AI programming easy allowing mixing reasoning with machine learning, used in a fraud detection system for a public institution.
- Designed and implemented controlled natural language for formalizing the knowledge around lung cancer, used by the oncologist to formalize ESMO guidelines.
- Created affective-computing AI models that are combining both expert knowledge and their intuitions, to calculate the quality score of complex decisions.
- Created the novel, automated user interface synthesis algorithm in which a set of requirements is automatically translated into a working application, currently used by 30+ clinical centers and biggest telecon in Australia.
- Created an NLP classification algorithm for legal documents corpora based on the NLTK library, constructed using mixed feature-extraction techniques: POS-Tagging, noun-phrase extraction, collocations and NER (named entity recognition), followed by Tf/Idf, feature reduction and finally the classification with Passive-Aggressive, scalable classifier.
- Created a critical part of a tax-fraud detection system was based on natural language rules enabling decision makers and specialists to manage a tax fraud knowledge base. The stream-based reasoner allows discovering fraudulent activities in the stream of 5 million invoices per day.
Gdansk University Of Technology, Department of Applied Informatics in Management
- Reviewed “Government Information Quarterly, An International Journal of Information Technology Management, Policies, and Practices," IF=2.515, 5Y IF=3.161.
- Acted as an academic visitor at the University of Newcastle, Australia.
- Participated as a member of the EU Maria-Courie research project "Smart multipurpose knowledge administration environment for intelligent decision support systems development."
- Reviewed and contributed to the “18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems."
- Served as a member of the international BRIDGE project: "CDSS for Oncology."
- Taught the following classes: R Programming, Introduction to DataScience, Business Intelligence and BigData Processing, Software Development Process Methodology and Tools.
- Led design and implementation of an industrial software stack for digital television receivers.
- Led design and implementation of a set-top-box device emulator for efficient application level testing purposes.
- Designed and implemented automated smoked test system with ASP.Net, MSMQ, image recognition, and remote controller emulation.
- Technically managed a team of 30+ programmers.
- Conducted training for newcomers about advanced multithreaded design patterns in C++.
CDSS - Clinical Decision Supporting System
We organized available data into the knowledge of the diagnostic process, based on many sources like studies, publications, recommendations, so it supports doctors decisions. We also developed a central registry for collecting patient’s clinical data from over 70 oncological institutions in Poland. In production since 2016.
The results were published in Expert Systems With Applications that is currently ranked number 1 in the Google Scholar h-index listed under the top publications of artificial intelligence.
Trade Promotion Optimization
- Can we lower overall costs by optimizing products volume sales and its promotion strategy by anticipating a promotion calendar for a given period?
- Can we predict using key indicators when and which sales pattern is the most effective and can be used to increase volume sales?
- Can we set up a useful promotion calendar for “slow-moving products”?
- Can we optimize budget KPIs when planning the next sales period?
In our case, the mis-forecasting (avg. the error was around 20%) led to budget reduction (across multiple stages within a whole supply chain). To solve the problem, we combined business knowledge of subject matter experts with historical sales data that we received. We also took into account their anomalies and outliers.
The solution allowed the company to increase its accuracy in prediction by up to 10% of volume planning.
Tax-fraud Detection on VAT
Automated Decision Making System
The tool is allowing customers to perform guided cyber-security health check, and after the health-check is completed, the detailed report (diagnosis) is generated allowing the customer to understand the current state of the company’s cybersecurity maturity level and understand the weak points. The estimation of the potential cost of the Problem is also provided.
Apache Jena, Ontology Framework, TinkerPop, .NET
Natural Language Toolkit (NLTK), OWL API, TensorFlow, Scikit-learn, Keras, NumPy, Pandas, PyTorch, PySpark, SymPy, SciPy
Protégé, SikuliX, Microsoft Visual Studio, Git, Jira, OpenLink Virtuoso, Apache Solr
Data Science, Anomaly Detection, BPMN, Scrum
WordNet, Genetic Algorithms, Natural Language Processing (NLP), Machine Learning, GPT, Generative Pre-trained Transformers (GPT), Recurrent Neural Networks (RNNs), Deep Learning, Classification Algorithms, Regression Modeling, Clustering Algorithms, Bayesian Inference & Modeling, Logistic Regression, Decision Trees, Random Forests, Markov Model, Ensemble Methods, Evolutionary Algorithms, Sesame, Data Visualization, Scalable Architecture, Time Series Analysis, Principal Component Analysis (PCA), Simple Knowledge Organization System (SKOS), Embedded Systems, Schema.org
Azure, Amazon EC2, Jupyter Notebook, Amazon Web Services (AWS), RStudio
Cassandra, Titan Graph, Oracle SQL, MySQL
Ph.D. in Computer Science
Gdansk University of Technology - Gdańsk, Poland
Master of Engineering Degree in Computer Science
Wroclaw University of Technology - Wrocław, Poland
Bachelor of Engineering Degree in Computer Science
Wroclaw University of Technology - Wrocław, Poland
Deep Learning Specialization
Convolutional Neural Networks
Structuring Machine Learning Projects
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Neural Networks and Deep Learning
Oracle Certified Professional, Java SE 5 Programmer
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