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Dr Vitaliy Milke

Senior Lecturer in Computing and Artificial Intelligence

Faculty:
Faculty of Science and Engineering
School:
Computing and Information Science
Location:
Cambridge
Areas of Expertise:
Artificial Intelligence , Computing and technology
Research Supervision:
Yes

Vitaliy teaches advanced machine learning and AI methods and has extensive practical experience in the financial industry. He conducts research in applied AI in finance and new mathematical methods for Artificial General Intelligence (AGI).

[email protected]

LinkedIn

Background

Before returning to academia, Vitaliy had a successful career in the financial industry as a Chief Financial Officer (CFO) of three financial and brokerage companies, a Chief Executive Officer (CEO) of OTKRITIE Bank in Moscow and CEO of an Asset Management Company. Vitaliy began his career in finance as a bank analyst and financial auditor. Vitaliy joined Anglia Ruskin University in Cambridge (School of Computing and Information Science) in 2020. Previously, he was a Professor in AI at Bauman Moscow Technical University and a visiting lecturer at Skolkovo Business School and Moscow Institute of International Relations. Vitaliy seeks to use his previous professional experience to provide realistic, stimulating, practice-oriented research and teach students advanced AI practices.

Research interests

  • Applied Machine Learning and Artificial Intelligence in Finance
  • Artificial General Intelligence (AGI)
  • Time Series Analysis
  • Math Modelling
  • Mathematical Methods for Training Computer Systems
  • Large Language Models (LLMs)
  • Computer Vision

Area of Expertise:

  • Advanced Machine Learning in Finance
  • Applied Artificial Intelligence
  • Artificial General Intelligence (AGI)
  • Large Language Models, Computer Vision
  • Time Series Analysis.

Areas of research supervision

  • Machine Learning and AI in Finance
  • Time Series Analysis for Securities Markets
  • Progressive Mathematical Methods for AGI

Teaching

  • Neural Computing and Deep Learning
  • Advanced Machine Learning
  • Principles of Data Mining and Machine Learning
  • Applications of Machine Learning
  • Semantic Data Technologies
  • Core Mathematics for Computing

Qualifications

  • PhD in Computer Science and Machine Learning (Quant Developing: “Intraday Machine Learning for the Securities Market”), Anglia Ruskin University, Cambridge, UK.
  • Executive MBA, Kingston University London, UK.
  • MSc in Computer Science (Distinction), ARU, Cambridge, UK.
  • MSc in Mechanical and Rocket Engineering (Distinction), Bauman Moscow Technical University, Russia.
  • BEng (Hons) in Mechanical (Distinction), Bauman Moscow Technical University, Russia.
  • Certificate of Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore.
  • Postgraduate Certificate in Astronomy, Lomonosov State Moscow University, Russia.
  • Postgraduate Certificate in Industrial Management, Plekhanov Russian University of Economics.

Recent presentations and conferences

Milke, V., Luca, C., & Wilson, G., 2024. Reduction of financial tick big data for intraday trading. Expert Systems, e13537. https://doi.org/10.1111/exsy.13537

Milke, V., 2023, November. AIJ Conference. Backpropagation Alternatives and an Artificial Scientist as the Next Possible Steps Toward AGI. Available at: https://aij.ru/eng/archive?albumId=2&videoId=308

Devarajula, S.; Milke, V. and Luca, C., 2023. Using Neural Network Architectures for Intraday Trading in the Gold Market. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1; ISSN 2184-433X, pages 885-892. DOI: 10.5220/0011794400003393.

Milke, V., 2021, November. Artificial Intelligence Journey -Sber. Challenges of training large-scale neural networks as a significant barrier on the way to Artificial General Intelligence. Available at: https://www.youtube.com/watch?v=mM5uCXzg6Ok

Milke, V., 2020, October. Artificial Intelligence Journey -Sber. Artificial General Intelligence (AGI) from Theory to Practice. First Draft of the Road Map. Available at: <https://www.youtube.com/watch?v=3hSzLTowZRA&feature=emb_logo> [Accessed: 27 November 2022]

Milke, V., Luca, C., Wilson, G. and Fatima, A., 2020. Using Convolutional Neural Networks and Raw Data to Model Intraday Trading Market Behaviour. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7; ISSN 2184-433X, pages 224-231. DOI: 10.5220/0008992402240231

Milke, V., 2020. Ethics in the National Strategy for Artificial Intelligence. In: RANEPA, ed.(2020), Ethics and digital: ethical issues of digital technologies. RANEPA. Moscow, Russia, (pp. 79-80).

Milke, V., Luca, C. and Wilson, G., 2017, May. Minimisation of parameters for optimisation of Algorithmic Trading Systems. In 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP) (pp. 1114-1119). IEEE.