CV

Education

• 2020-present: Bocconi University (Milan, Italy), PhD in Statistics and Computer Science
• 2018-2020: Bocconi University (Milan, Italy), M.Sc. in Business Analytics and Data Science
  • Grade: 110/110 “Cum Laude”
  • Research Thesis on “Constructing protein embeddings using TWEC model”
• 2014-2018: Higher School of Economics (Moscow, Russia), B.Sc. in Economics
  • Grade: 9.04/10 “Cum Laude”
  • Research Thesis on “Predicting time series data with hybrid models based on LSTM and wavelet transform”

Summer schools and workshops

• May 2024: Youth In High Dimensions conference, Trieste, Italy, (ML & statistical physics), poster presentation
• Aug 2023: Cargese workshop, Corsica, France, (ML \& statistical physics), poster presentation
• July 2023: SigmaPhy conference, Heraklion, Greece, (statistical physics), poster presentation
• Dec 2022: CompBio workshop, Milan, Italy (computational biology \& ML)
• Aug 2022: ESSLLI summer school, Galway, Ireland (logic, NLP and ML)
• Jun 2022: Como summer school, Italy (probability and causal methods)

Teaching assistance

• Feb 2024 - June 2024: “Machine Learning”, MSc program
• Feb 2024 - June 2024: “Machine Learning and AI”, BSc program
• Sep 2023 - Dec 2023: “Computer Programming in C and Python”, BSc program
• Feb 2023 - June 2023: “Machine learning”, BSc program
• Feb 2023 - June 2023: “Artificial intelligence”, BSc program
• Sep 2022 - Dec 2022: “Computer Programming in C and Python”, BSc program
• Sep 2022 - Dec 2022: “Game theory”, BSc program
• Feb 2022 - Jun 2022: “Machine Learning”, MSc program
• Sep 2021 - Dec 2021: “Computer Programming in C and Python”, BSc program

Work experience

• Nov 2022 - Feb 2023: Special collaboration project at Alkemy S.p.A.
  • developed an autonomous anomaly detection pipeline in Python with MLOps using statistical methods
• Sep 2019 - June 2020: Research Assistant in ARTLAB Bocconi
  • ran experiments on alternative optimization techniques and analyzed their effects on robustness against adversarial examples of images
  • improved the robustness of Deep Q-Learning methods
• Nov 2018 - Mar 2019 Intern Data Scientist in Quick Algorithm company
  • developed predictive models for the Scops analytical platform

Skills

• Programming Languages: Python (PyTorch/Keras)/Julia (Flux)/C/bash/R
• HPC (parallelization over GPU/CPU), git, SQL

Languages:

• Russian (Native)
• English (Advanced)
• Italian (Upper-Intermediate)
• French (Intermediate)

Publications

• Pittorino, Fabrizio, et al. “Entropic gradient descent algorithms and wide flat minima.” Journal of Statistical Mechanics: Theory and Experiment 2021.12 (2021) (ICLR conference)

• Demyanenko, Elizaveta, et al. “The twin peaks of learning neural networks.” Machine Learning: Science and Technology (2024) (presented at the Cargese workshop)

• Demyanenko, Straziota, et al. “Sampling through Algorithmic Diffusion in Non-Convex Perceptron Problems” (2024) (preprint, presented at the Youth In High Dimensions conference)