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Guilherme Dinis Chaliane Junior is a PhD candidate in the Data Science Group within the Department of Computer and Systems Science at Stockholm University. His research focuses on bridging the gap between theoretical knowledge and practical applications to solve real-world decision-making problems, particularly in the realm of sequential decision-making where actions have implications for future situations and outcomes. He is investigating challenges associated with learning from delayed feedback and the scarcity of feedback signals, particularly in environments where feedback is slow to manifest. Additionally, Dinis Junior's work includes the development of solutions for common issues in recommender systems, including bias in learning data, limited exploration budgets, and handling large action spaces. His research interests also encompass distributed systems, multi-agent problems, and representation learning.
Spotify • Stockholm
Working as a Machine Learning engineer focused on applying ML techniques to solve real-world decision-making problems.
Includes Analytical Chemistry, Organic Chemistry, Biochemistry, and Sustainable Chemistry.