Dr Vitaliy Milke

Dr Vitaliy Milke 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]

Connect with Vitaliy on LinkedIn

Background

Vitaliy began his career in finance as a Bank Analyst and Financial Auditor, rising to 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 joined ARU in 2020 and is a member of the Computing, Informatics and Applications Research Group. 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.

Spoken Languages

• Russian
• English

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)
• Generative AI
• Computer Vision

Area of Expertise:

• Artificial General Intelligence (AGI)
• Machine Learning in Finance
• Large Language Models (LLMs) & Generative AI
• 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

• Machine Learning in Finance
• 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.
• • PGCert in Learning and Teaching in Higher Education, 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

Etim, U.E., Milke, V. and Luca, C., 2025. An Evaluation of ChatGPT’s Reliability in Generating Biographical Text OutputsAn Evaluation of ChatGPT’s Reliability in Generating Biographical Text Outputs. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence, Volume 3 (pp. 993-1000), ISBN 978-989-758-737-5, ISSN 2184-433X. DOI: 10.5220/0013248400003890

Keta, E. and Milke, V., 2025. Deep Learning Techniques for Enhanced Extraction of Gravitational Waves from Binary Black Hole Mergers. Available at SSRN 5226929. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5226929

Etim, U.E., Milke, V. and Luca, C., 2025. Ensemble of Neural Networks to Forecast Stock Price by Analysis of Three Short Timeframes. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART), at: Porto, Portugal, Volume 3 (pp. 813-820), DOI: 10.5220/0013183600003890

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.