Michalina Wanda PacholskaI’m quite bad at following a simple path. I studied inter-faculty math and science studies (mostly mathematics, some physics with just a pinch of chemistry and biology) at University of Warsaw. In 2016 I finished Masters with specialisation in topology and set theory. My master thesis was in (mathematical) signal processing, and I’ve been under guidance of Computational Biology Group. Then I have done an internship at Google Mountain View where I worked on C data processing pipeline. Came back to Europe to do PhD in computer science at EPFL, where I tried working on physically based rendering only to go back to settle in LCAV. Signal processing led me to Fourier optics and playing with lasers in the Galata Laboratory, but as cool it sounds, I wandered off towards localisation and SLAM. Oh, and I had a break last year, I went for an internship to DeepMind to fold some proteins.In the free time I draw cows for Helvetic Coding Contest :)
Utku NormanUtku Norman is a doctoral assistant at CHILI Lab, and a PhD student in Computer Science at EPFL, Switzerland. He is driven by a desire to help build a better future, and understand the world along the way: His chosen course for how is advancing machine intelligence to develop systems that try to understand us. Utku is curious about how humans, unlike robots, come to be so highly skilled in understanding each other, and detecting and addressing misunderstandings. One way humans do so is by representing whether the others understood what we said or did by using the complex cognitive ability of mutual modelling, i.e. the reciprocal ability to construct a mental representation of the other, by attributing beliefs, desires and other mental states to the other. This ability is critical in order for humans to comprehend each other and react appropriately in their interactions. Thus, the main goal of my PhD is to equip a robot with mutual modelling ability, and use this ability in an educational activity in order to improve the quality of the interactions between the robot and a learner and (hopefully) the learning outcomes.
