Nicklas Werge

Nicklas Werge is a Postdoctoral Fellow at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on machine learning, optimization algorithms, and stochastic processes, with applications in financial forecasting and reinforcement learning.

• Institution: University of Southern Denmark
• Department: Mathematics and Computer Science
• Rank: Researcher

Werge's research interests span machine learning, online algorithms, and statistical methods for large-scale data optimization. His work explores stochastic modeling in dynamic environments, including:

• Volatility prediction in financial forecasting
• Pessimism and optimism dynamics in reinforcement learning
• Bayesian-optimistic algorithms for non-stationary bandits
• Nonconvex convergence analysis of SGD

The 10 publications listed demonstrate expertise in:

• Stochastic optimization (54% of collaborative work)
• Deep reinforcement learning (36% of collaborative work)
• Continuous control systems (27% of collaborative work)
• Asymptotic analysis techniques
• Approximation algorithms
• PAC-Bayes uncertainty modeling

Contact: werge@sdu.dk | ORCID