CV

Education

• Ph.D. Candidate, Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS), Berlin, Germany
  • Topics: End-to-End Management of Data Science Processes, Research Reproducibility
• M.Sc. in Data Science, University of Skövde, Sweden
  • 120 ECTS, GPA: 3.25/4.00
  • Visby Programme scholarship holder, funded by the Swedish Institute
  • Global Swede Award 2018 for excellent social, innovative and entrepreneurial performances, issued by the Swedish Ministry of Foreign Affairs
  • Thesis: Finding Early Signals of Emerging Trends in Text through Topic Modeling and Anomaly Detection
• B.Sc. in Information Systems and Technologies, Moscow Engineering and Physics Institute (NRNU MEPhI), Moscow, Russia
  • 240 ECTS, GPA: 3.74/4.00
  • Specialization: Mathematical support, hardware and software for IS
  • Thesis: Sequential Traversing Method for Generalized Search Trees with Concurrent Access for PostgreSQL Database Management System

Research Experience

• Research Associate @ Database Systems and Information Management group (DIMA), TU Berlin (October 2019 – present)
  • Research reproducibility, end-to-end management of data science processes
  • Data management, experimentation in software engineering
• Research Assistant @ University of Skövde, Sweden (August 2017 – May 2018)
  • Project: Dataflow – Finding Patterns in New Data Flows, funded by the Swedish Governmental Agency for Innovation Systems (VINNOVA)
    • Data management, exploratory data analysis, production line simulation
    • Decision support systems to be used in metal industry
  • Project: DREAM – Development of Robot-enhanced Therapy for Children with Autism Spectrum Disorders, funded by the 7th Framework Programme of the EU, grant #611391
    • Algorithms for automated annotation generation for video-recorded therapeutic interventions
    • Deep Learning algorithms for facial expression classification

Research Interests

• End-to-End Management of Data Science Processes
• Research Reproducibility
• Automation in Data Science
• Interpretability in ML
• Biologically- and Neuroscience Inspired ML

Industrial Experience

• Summer Intern @ SAP Labs LLC, Moscow, Russia (July 2015 – August 2015)
  • Application design and development within the area of precision agriculture
  • Data retrieval, analysis and visualization using SAP HANA Cloud Platform and SAP Fiori

Teaching

Skills

• Programming Languages: Python, Scala, PL/pgSQL, Ruby, Java, C/C++
• Machine Learning Tools: Keras, scikit-learn, Numpy, Pandas [python]
• Big Data Frameworks: Apache Spark
• Database Management Systems: PostgreSQL, MongoDB, Oracle

Publications

Automated Documentation of End-to-End Experiments in Data Science

S. Redyuk (2019). Automated Documentation of End-to-End Experiments in Data Science. In Ph.D. Symposium track, IEEE 35th International Conference on Data Engineering (ICDE’19), Macau, China

Learning to Validate the Predictions of Black Box Machine Learning Models on Unseen Data

S. Redyuk, S. Schelter, T. Rukat, V. Markl, F. Biessmann (2019) Learning to Validate the Predictions of Black Box Machine Learning Models on Unseen Data. HILDA’19, Amsterdam, Netherlands

Towards Unsupervised Data Quality Validation on Dynamic Data

S. Redyuk, V. Markl, S. Schelter (2020) Towards Unsupervised Data Quality Validation on Dynamic Data. ETMLP’20, Copenhagen, Denmark

Automating Data Quality Validation for Dynamic Data Ingestion

S. Redyuk, Z. Kaoudi, V. Markl, S. Schelter (2021) Automating Data Quality Validation for Dynamic Data Ingestion. EDBT’21, Nicosia, Cyprus

Projects

Talks

Service and leadership

• Chairperson @ NFGL Local Network Skövde (October 2016 - June 2018)
  • Financial management, communication, event management
  • Covering topics in interdisciplinary nature of data science and sustainable development