ASEF Podcast (SLO): From Ljubljana to Stanford: Machine Learning, Artificial Intelligence, and Research with Valter Hudovernik and Mark Žnidar [Episode #55]
In this episode of the ASEF Podcast, host Gal Gantar welcomes ASEF Fellows Valter Hudovernik and Mark Žnidar, who spent last summer as visiting researchers at Stanford University under the mentorship of Prof. Dr. Jure Leskovec.
Valter holds a master’s degree in Data Science from the Faculty of Computer and Information Science at the University of Ljubljana and works as a Machine Learning Engineer at KumoAI. Mark is a graduate of Computer Science and Mathematics at the University of Ljubljana and is currently pursuing a master’s degree in Computer Science at the University of Oxford. Both work at the intersection of machine learning, data science, and modern approaches to analyzing complex data systems.
The conversation begins with their academic journeys and early interests in computer science, mathematics, and robotics, which led them toward data science and advanced research in artificial intelligence. Drawing on their personal experiences, they discuss the differences between studying in Slovenia and abroad, particularly at Oxford, where the academic environment places a strong emphasis on independent work and research.
The central part of the episode focuses on the foundations of modern machine learning. The guests explain the basic principles of neural networks, the importance of data in developing AI systems, and the broader shift toward foundation models that can perform a wide range of tasks using unified architectures. They also discuss the challenges posed by computational resources, energy consumption, and access to infrastructure, as well as the role of open-source models in democratizing AI research.
A significant portion of the discussion is dedicated to relational machine learning and the use of graph-based and relational data. Valter explains how these approaches are used to model complex relationships in real-world systems, while Mark highlights the importance of benchmarks and evaluation standards, which have become essential for both academic research and industrial applications of artificial intelligence.
Later in the episode, the conversation turns to practical applications of AI in industry, ranging from advertising systems to predictive models, as well as Valter’s experience working in an industrial setting. The guests also reflect on their research visit to Stanford, their work in Prof. Dr. Jure Leskovec’s laboratory, and the dynamics of a research environment where academic inquiry is closely connected with real-world technological challenges.
The episode further explores life in Silicon Valley, the Slovenian community in California, hackathons, and the broader technology culture surrounding Stanford and San Francisco, characterized by rapid experimentation, collaboration, and a strong connection between research and entrepreneurship. In the concluding part of the discussion, both guests reflect on the impact of the ASEF Fellowship on their academic and professional development and on how international research experiences have shaped their future career aspirations.