Artificial Intelligence Group
Department of Computer Science and Technology
University of Cambridge
15 JJ Thomson Avenue
Cambridge CB3 0FD
My PhD topic: Understanding and improving out-of-distribution generalisation in machine learning. I use inductive biases to devise models for multi-agent communication, visual reasoning and causal discovery. Follow @slowiika
Apart from research, I teach maths, programming and applied machine learning at Cambridge Spark. I also do my best to contribute to the departmental activities as well as to the broad machine learning research community (most recently, as a reviewer for AISTATS 2021, ICLR 2021, ICML 2020, EMNLP 2020 and NeurIPS 2020).
I hold an MSc with Distinction in Operational Research with Data Science from the University of Edinburgh (a joint programme between School of Mathematics and School of Informatics). In my MSc thesis I proposed a new approach to relational reasoning with convolutional neural networks. I also hold a BSc degree in Computer Science from the Jagiellonian University. In my BSc thesis I investigated probability distributions defining random projections in extreme learning machines under supervision of Wojciech Czarnecki.
Before my PhD, I had an opportunity to intern in data science and software engineering at IBM, Architech and Barclays UK.
I’m a traveller at heart: I visited 35 countries and lived in five so far. On that note, I had a chance to learn four foreign languages, which allows me to throw awkward sentences in attempts at icebreaking to this day. I also love mountains and Andrej Ivašković.
Contact: agnieszka [dot] slowik [at] cl [dot] cam [dot] ac [dot] uk
|Feb 15, 2021||In February and early March, I’m mentoring PhD students and post-docs from the University of Cambridge as part of the Data for Science Residency Programme. We discuss their datasets and applications of machine learning to scientific discovery (e.g., building tools for automatisation of literature review, optimising data analysis in neuroscience and chemistry, long-term prediction in materials science, anomaly detection in cloud microservices).|
|Feb 14, 2021||In the next months, I’m going to teach Maths for Data Science (Advanced) to professionals via Cambridge Spark (full-day online workshops). On a related note, I will also supervise undergraduate courses in Artificial Intelligence and Introduction to Probability at the Department of Computer Science and Technology.|
|Jan 23, 2021||Neural Function Modules accepted at AISTATS 2021! 🎉|
|Oct 30, 2020||Our benchmark Linear unit-tests for invariance discovery was accepted at the NeurIPS Workshop on Causal Discovery and Causality-Inspired Machine Learning! See the code and spotlight talk.|
|Oct 24, 2020||Our paper on Inductive Bias and Language Expressivity in Emergent Communication was accepted at the 4th NeurIPS Workshop on Emergent Communication: Talking to Strangers: Zero-Shot Emergent Communication! See the paper and the code.|
|Jul 28, 2020||The project I supervised as a TA at AI4Good Summer Lab was presented at the AI4Good Demo Day and it won in the final competition as the best Academic project. The students were awarded a stipend and additional mentoring from academia. Congrats!|
|May 20, 2020||I joined AI4Good Summer Lab as a Teaching Assistant! In May and June 2020 I taught intensive modules on Linear Algebra, Introduction to Probability, Calculus, Neural Networks and Reinforcement Learning to undergraduate students in STEM.|
|Mar 25, 2020||Our paper on Analyzing Structural Priors in Multi-Agent Communication accepted as a spotlight talk at the Workshop on Adaptive and Learning Agents at AAMAS 2020 (ALA 2020)! See the slides and the recording.|
|Mar 13, 2020||I presented a poster at the 14th Annual Machine Learning Symposium!|
|Feb 17, 2020||I joined the Mila AI Institute of Quebec as a Research Intern supervised by Yoshua Bengio.|