Andreas Bedorf
The van der Schaar Lab is excited to welcome three new PhD students to its team of researchers, joining us to work on impactful projects and continue our work on achieving impact at major conferences and revolutionising machine learning.
Heading into the 2023 academic year, Julianna Piskorz, Kasia Kobalczyk, and Max Ruiz Luyten will join us in Cambridge. Each of them will bring a new perspective, fresh ideas, and an exceptional academic and professional record to the lab’s ongoing development of world-leading techniques in the field of machine learning and AI for healthcare. The lab’s three new researchers are introduced below.
Kasia Kobalczyk
PhD student – joined the lab in 2023
Kasia joined our lab after completing her MASt in Mathematical Statistics (Part III) at the University of Cambridge. Prior to this, she earned her BSc in Mathematics and Statistics from the University of Warwick, where she was recognised for her top-of-class performance and outstanding results in mathematics-oriented subjects with the Warwick Statistics Prize and the Institute of Mathematics and its Applications (IMA) Prize.
During her bachelors, Kasia cultivated her passion for machine learning and statistics by coordinating and engaging in multiple research projects as the Research and Education Lead of Warwick Data Science Society. In her second year of undergraduate, she received a research grant to study Graphical Models, leading to her first publication in a scientific journal. Kasia has also gained a comprehensive professional experience through several internships in data science and quantitative research roles.
Equipped with the blend of academic and practical experiences, Kasia is eager to contribute to the research endeavours at the van der Schaar Lab. She is especially keen to work on human- and reality-centric AI. Her research is dedicated to understanding the process of knowledge acquisition, belief formation, and human decision-making, ultimately contributing to the advancement of personalised education and healthcare. She aspires to develop AI models that empower individuals’ creativity, thus fostering innovation. Within her research pursuits, Kasia particularly enjoys working with Bayesian methods, imitation and representation learning.
Kasia’s studentship is sponsored by Eedi, where she collaborates with industry experts to enhance the effectiveness of studying and teaching mathematics among school-age children. This involves the development of novel machine learning models aimed at understanding students’ misconceptions and guiding their learning paths.
Max Ruiz Luyten
PhD student – joined the lab in 2023
Max graduated from the Interdisciplinary Higher Education Centre at the Polytechnic University of Catalonia with a Bachelor’s degree in Mathematics and another in Physics Engineering. He was a graduate visiting student in AI at MIT, and his work resulted in a publication in Nature Communications that was highlighted in the Editors’ Highlights webpage of recent research for “Applied Physics and Mathematics,” which showcases the 50 best papers recently published in an area.
He then worked at Meta in Instagram’s recommender systems before joining the van der Schaar Lab. In his own words, he was drawn by the “impact-centered culture in the group and our common interest in seeking the breakthroughs that currently separate ML and unsolved societal problems, commonplace in healthcare.” He feels that in academic research, it is “unfortunately too easy to detach from the test of reality,” which he wants to avoid and was a critical factor in choosing the van der Schaar lab with its reality-centric agenda.
Max aims to tackle temporal control tasks effectively by integrating multiple domain data while handling different resolution scales and uncertainty. This would apply to help clinicians effectively provide personalised medicine from a holistic view of the patient’s history but would also support economic or environmental policies, traffic control, and many other critical tasks. He thus seeks to focus his research around the components needed to that end, from modelling and learning the system’s structure to control-like strategies such as RL.
Max’ work is funded by AstraZeneca
Julianna Piskorz
PhD student – joined the lab in 2023
Julianna is a graduate of the BSc in Mathematics and Statistics at Imperial College London. Her desire to study the foundations of Machine Learning motivated her to undertake the MSc in Statistical Science course at the University of Oxford, which she has recently completed. In her Master Thesis, supervised by Prof. Patrick Rebeschini, she explored and compared different supervised learning methods derived from the infinite-width limits of neural networks.
Prior to joining the lab, Julianna has also completed an internship at Apple, during which she designed a solution which combined large volumes of diverse data from across different systems and departments to provide the Search Engine Optimisation team with smart and informed insights. She has also worked with a Venture Capital startup to create a tool allowing to calculate the similarity in the investing patterns and the dependencies between different VC firms, and visualise these in a clear and informative way.
Julianna’s studentship is founded by Astra Zeneca.
Mihaela van der Schaar
