Speakers

Bert Kappen completed his PhD in theoretical particle physics in 1987 at the Rockefeller University in New York. From 1987 until 1989 he worked as a scientist at the Philips Research Laboratories in Eindhoven, the Netherlands. Since 2004 he is full professor on machine learning and neural networks at the science faculty of the Radboud University. Bert Kappen conducts research on neural networks, Bayesian machine learning, stochastic control theory and computational neuroscience. His research has made significant contributions to approximate inference in machine learning using methods from statistical physics; he has pioneerd the field of path integral control methods for solving large non-linear stochastic optimal control problems and their relation to statistical physics. He also develops a medical diagnostic decision support system to assist doctors with the diagnostic process in the emergency room of the Erasmus Medical Center. He co-developed the world's smallest neural network, where neurons are modelled as individual atoms. Currently, he is investigating ways to use quantum mechanics for a new generation of quantum machine learning algorithms and control methods for quantum computing; he is interested in possible applications of quantum physics in the brain and the mystery of consciousness. He co-founded in 1998 the company Smart Research that commercializes applications of neural networks and machine learning. Smart Research has developed forensic software for DNA matching used by the Dutch Forensic institute (MH17 plane crash over Ukraine in 2014), Interpol, the Spanish government for analysis of victims of the Spanish Civil War and the Australian Police force.

Arno Siebes, serving as a Professor in Algorithmic Data Analysis at Utrecht University, stands as a prominent figure in the realm of data mining and machine learning. Throughout his distinguished career, Siebes has demonstrated a steadfast commitment to advancing our understanding and utilization of sophisticated data mining techniques. His research pursuits are deeply rooted in unraveling the intricacies of pattern recognition, employing innovative methodologies such as the Minimum Description Length principle to distill complex datasets into actionable insights. Siebes' expertise extends across a diverse array of domains, ranging from bioinformatics to security and social media analysis, showcasing his versatility and adaptability in tackling multifaceted data challenges. Driven by a passion for bridging theory with real-world applications, Siebes' work holds profound implications for industry and academia alike. By empowering organizations to extract meaningful patterns from vast and heterogeneous datasets, he enables informed decision-making and facilitates the discovery of hidden insights that drive innovation and progress. Beyond his research contributions, Siebes is also recognized for his leadership and mentorship within the academic community. Through his guidance and collaborative efforts, he inspires and nurtures the next generation of data scientists, fostering a culture of exploration and excellence that propels the field forward. Arno Siebes' enduring legacy is marked by his unwavering dedication to pushing the boundaries of possibility in data-driven research. His impactful contributions continue to shape the trajectory of algorithmic analysis, paving the way for transformative advancements that redefine our understanding of the digital landscape.