Data Scientist and Machine Learning Engineer2018 - PRESENTSelf-employed
- Implemented a reinforcement learning framework for algorithmic trading of cryptocurrencies.
- Implemented chatbots from scratch using NLP state-of-the-art methods, based on Transformers (BERT).
- Executed chatbots using Google Dialogflow and Google Cloud.
- Implemented a framework for automated relation extraction from technical documents.
- Implemented a module for estimating product repurchase-rate for an eCommerce client. In the same context, wrote algorithms for identifying abnormal purchase rates.
- Worked on a machine learning-based solution for pattern detection in trading data (financial domain). Wrote heuristics as a semi-automated procedure for producing labeled data.
Lead Scientist | Text Analysis2012 - 2018Ontotext Ad
Technologies: Ontologies, RDF, Java, R
- Developed ML models for NLP, including methods for domain adaptation, methods for automated feature selection, methods for optimization of F-measure. Applied models such as logistic regression, SVM, CRF for both classification and sequence tagging.
- Developed a machine learning model for classification of tweets as either rumor/not rumor in R.
- Acquired in-depth knowledge in relational databases, ontologies, and linked data. Implemented a classification model written in Java, that automatically categorizes Wikipedia pages as either belonging to the topic "Food and Drink" or not.
- Experimented with topic models with LDA in order to help with a reccommender system for a large publishing company.
- Built prototypes for training word-vectors embeddings and graph embeddings.
- Developed models for sentiment analysis for English and Bulgarian, in R and Java. The methods were supervised for English and unsupervised for Bulgarian.
- Acquired significant experience with automated and semi-automated integration of various RDF resources as DBpedia and Geonames.
PhD2006 - 2012Max-Planck Institute für Informatik
Technologies: Python, R
- Gained expertise in cancer genetics, with a focus on copy number aberrations and acquired additional in-depth knowledge in domains like epigenetics, transcriptomics, and viral genomes.
- Used supervised and unsupervised machine learning methods for modeling cancer genetic data. The supervised methods used were: logistic regression, elastic net, SVM, decision trees, and random forest.
- Wrote machine learning models in the statistical language R and acquired in-depth expertise with visualization techniques in R.
- Acquired solid experience with presenting complex AI models to non-experts (medical doctors), by giving the intuition behind the mathematical models.
- Performed feature selection with various methods: filters with statistical tests, penalty methods for linear models, and pruning.
- Acquired solid knowledge in computational statistics and statistical learning. This includes statistical tests, statistical distributions, estimators, and bias-variance decomposition.
- Wrote scientific papers and learned how to deliver high-quality presentations in conferences and in front of clients.
- Worked closely with medical doctors in hospitals. Conducted interdisciplinary communication with medical doctors, in order to maximize the benefit of the machine learning solutions for their patients.