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Show Notes:

• (2:27) Alex talked about his undergraduate experience studying Applied Mathematics at the Kiev Polytechnic Institute.
• (3:15) Alex quickly went over his time working remotely as a Machine Learning Engineer for a US-company called Inma AI during university.
• (4:24) Alex mentioned his decision to pursue a Master’s degree in Mathematics at the University of Verona.
• (6:21) Alex went over the Math graduate classes that he took for his degree, including differential geometry and optimization theory.
• (7:58) Alex talked about his experience working at Mlvch to create the best solutions on the market related to visual style transfer and image enhancement.
• (10:01) Alex shed some light on his Master’s thesis work called “Boosting financial models calibration with deep neural networks” (Read his post "Meta-learning in finance: boosting models calibration with deep learning”).
• (11:39) Alex talked about the applications of meta-learning, a powerful technique to train deep neural networks, in physics and biology.
• (13:15) Alex shared his experience working as a partner and solutions architect at Mawi Solutions, a Ukraine-based hardware-and-software company that is disrupting the market of wearable preventive healthcare.
• (16:29) In reference to blog post “Deep learning: the final frontier for signal processing and time series analysis?,” Alex discussed ways to apply deep learning to model time series (Watch his talk at PyCon Italia 2019 as well).
• (18:52) Alex is currently the Co-Founder and CTO of Neurons Lab, an innovative European AI boutique based out of London that serves clients in financial tech, marketing tech, and medical tech areas.
• (22:41) Alex is well-known for a series of blog posts that experiment neural networks for algorithmic trading time series forecasting (Check out his Deep Trading code repo).
• (26:51) Alex emphasized the benefits of using multi-task learning, in reference to his post “Multitask learning: teach your AI more to make it better” (check out his experiments for 4 different use cases).
• (29:35) Alex advocated for the need of using generative models in applied AI (Read his 2 blog posts “GANs beyond generation: 7 alternative use cases” and “Generative AI: A key to machine intelligence?”).
• (34:02) Alex talked about a new approach called disentangled representation learning, which combines the best of classical math and machine learning modeling, in reference to his article “GANs” vs “ODEs”: the end of mathematical modeling? (See his Code and Talk).
• (36:36) In reference to his post “Fantastic data scientists: where to find them and how to become one,” Alex described the type of data scientist he identifies with as well as the skills that he is looking to improve upon.
• (42:08) Alex went over the evolution of algorithms for asset portfolio management, referring to his piece “AI for portfolio management: from Markowitz to Reinforcement Learning” (See his Code Repo).
• (45:09) Alex gave his advice for people who want to get into blogging and public speaking. 
• (48:00) Alex gave his AI predictions for the year 2020 (Read his 2018 predictions for researchers and for developers).
• (49:54) Alex shared his thoughts regarding the data science community in East Europe vs West Europe.
• (52:10) Closing Segment.

His Contact Info:

• LinkedIn
• Medium
• Twitter
• GitHub

His Recommended Resources:

• Quantopian Lectures on Quantitative Investment
• Successful Algorithmic Trading Ebook from QuantStart
• Advanced Algorithmic Trading Ebook from QuantStart
• "Python for Finance” by Yves Hilpisch
• "Derivatives Pricing in Python” by Yves Hilpisch
• "Advances in Financial Machine Learning” by Marcos Lopez de Prado
• “Pattern Recognition and Machine Learning” by Christopher Bishop
• Machine Learning and Reinforcement Learning in Finance in Coursera
• OpenAI
• DeepMind
• Salesforce Einstein
• Facebook AI
• Google AI