Position: Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 3


Projects

Pulmonary fibrosis
Description: having unstructured list of patients’ visits info and 3d CT scans of their lungs, the goal was to predict the lungs’ capacity by the certain date and give the probability of the prediction. Tabular data was normalised and concatenated with the flatten output of the scan after putting it into efficientnetB6. The success metric was Laplacian.
Stack: efficientnet, EDA, sklearn, numpy, pandas, tensorflow, keras, matplotlib, seaborn, PIL.

G2Net signal detection.
Description: Waves from 3 different detectors (like LIGO) were given and the task was to identify, whether the signal existed among these or not - binary classification.
Stack: keras, sklearn, scipy (signal processing, CQT,CWT filters), math, pandas, numpy.

ReIdentification:
Description: Detecting and tracking people on the video in real time and identify them. Stack: yolov5, deepsort, pytorch, flask\dash.

Text generation:
Description: using trainable text networks to generate compliments for the certain field of the LinkedIn profile: languages, certifications, summary, experience etc.
Stack: t5, gpt2, spacy.

Lead message generator (Leadbot)
Description: having proposal on the upwork, task was to generate the relevant lead message to a customer. Message consisted field for filling by the text networks and templates given from the DB.
Stack: flask, gpt2, nltk.

Matching system
Description: the task was to match cv’s and job description according to the keywords, those were extracted and where. Paragraphs were categorised and splited by t5 network. Keywords were taken from sql tables and filtered in order to increase quality of info extraction. 
Stack: t5, sql, nltk, spacy, NER, OOP.

Text Classification
Description: the list of the transactions were inputs and their tax categories were output. Several approaches were tested: tf-idf classification, word embeddings, sentence embeddings. 
Stack: fastText, sklearn.

Formulas’ photos to latex code transformer
Description: Inputs were images and output should be the formulas as a sequence of latex tokens - Image to text generation. End-to-end networks were implemented, as Image transformers. The best decision was to mask and detect symbols separately, classify their BB’s and write rule-based system for transferring their coordinates and classes into sequence of latex tokens.
Stack: pytorch, tensorflow, U-net, SVM, huggingface.