Bio

Adriano Koshiyama is a Research Fellow in Computer Science at University College London. He is the Co-founder of Holistic AI, a start-up focused on providing assurance of AI systems. He was part of The Alan Turing Institute as an Enrichment Scheme Student during 2018-2019. He has held many roles as a Data Scientist in Retail, Finance, Recruitment, and R&D companies over the last 7 years. Academically, he has published more than 30 papers in international conferences and journals. His main research topics are related to Machine Learning, Finance, Trustworthy AI. He also holds BSc in Economics and an MSc in Electrical Engineering. 

Research interests

During his PhD, Adriano is focusing on the application of computational statistics and machine learning as building blocks for profitable and reliable investment decisions. Specifically his thesis considers three complementary topics: a) devising a new trading recommendation system that supports decision-making processes of a trader looking for derivatives-based strategies; b) putting forward a complete framework for assessment of algorithmic trading strategies; and c) establishing a protocol to certificate a predictive model performance before deployed in a real setting.

However, due to his unorthodox academic background, Adriano has been always involved in different topics of research over the years. During his Bachelor's, he has been very active in understanding the economic, health & safety and welfare factors that impact beekeepers in Brazil. While completing his Master's Degree, he was involved in projects for the power supply and oil companies, involving new methods in neural networks, evolutionary computing, and fuzzy logic. During a spell in the industry and a fertile interaction with some collaborators, Adriano expanded his research interest in concept drift detection, entrepreneurship and teams assessment, and multi-modal optimisation. Overall, he is very passionate about the applications of data science methods (stats, machine learning, heuristics, etc.) to solve real-world problems.