Artificial Intelligence Group
Department of Computer Science and Technology
University of Cambridge
15 JJ Thomson Avenue
Cambridge CB3 0FD
I am a final-year PhD student within the Artificial Intelligence Group at the University of Cambridge.
My research is focused on understanding and improving out-of-distribution generalisation and robustness in machine learning. I investigate these topics across several domains: learning from multiple distributions, group-wise fairness, emergent communication and medical data. 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, I have been organising the 5th Workshop on Emergent Communication @ ICLR 2022 and the Oxbridge Women in Computer Science Conference 2022.
I am also a Scientific Program Officer at the ML in PL conference, where I organised the first Women in ML in PL workshop (2021) and keynote talks.
I hold an MSc with Distinction in Operational Research with Data Science from the University of Edinburgh (a joint programme between the School of Mathematics and the 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 interned and worked part-time as a Data Scientist and Software Engineer 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, reading and Andrej Ivašković.
Contact: agnieszka [dot] slowik [at] cl [dot] cam [dot] ac [dot] uk
|Jun 3, 2021||I gave a short talk with an overview of my last ~year of research on learning from multiple data distributions. The extended version is going to be presented at the Institute of Mathematics of the Polish Academy of Sciences (IMPAN RL Seminar).|
|May 18, 2021||I am organising the first Women in ML in PL workshop at the ML in PL conference. Stay tuned!|
|May 3, 2021||My presentation on Invariant Risk Minimization: a starting point for the discussion at the Causality & Domain Adaptation Reading & Work Group.|
|Apr 13, 2021||Structural Inductive Biases will be presented at CogSci 2021! 🎉|
|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 automation 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). 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!|