From curiosity to contribution
Daniel’s PhD will build on this foundation, combining theoretical depth with practical relevance.
“One concept I’m particularly interested in exploring further is how to dynamically adjust the capacity of deep neural networks in continual learning, so that we can balance performance with computational cost.”
For Daniel, research impact is about influence rather than visibility.
“To me, impact means contributing ideas that become part of how others think and work.”
Looking ahead, he hopes his research will offer structured guidance for both academics and practitioners.
“I’d like my work to provide principled frameworks that don’t just advance theory, but also offer clear approaches for people tackling real-world machine learning problems.”
Opening doors through NextGen
The G-Research graduate scholarship allows Daniel to focus fully on his doctoral research while staying connected to both academia and industry.
“The scholarship gives me the freedom to dedicate myself to research without financial constraints, attend leading conferences and connect with mentors working at the intersection of machine learning and quantitative finance.”
As part of the scholar community, Daniel is particularly looking forward to being surrounded by people who value depth and application in equal measure.
“Being part of a community that values rigorous academic work as much as real-world impact is something I’m genuinely excited about.”
