I am a final-year PhD student at the Department of Computer Science and Technology, University of Cambridge, supervised by Pietro Liò and a member of King’s College. My research focuses on learning multimodal and graph-structured representations of the world. Until November 2020, I was a Research Scientist intern at DeepMind, hosted by Piotr Mirowski in the Robotics, Embodied Agents and Lifelong learning (REAL) team led by Raia Hadsell.
My professional experience includes undergraduate Software Engineering internships at Google and Facebook. During the PhD, I was a Research Intern at Mila and an AI Resident at X. In 2020, I briefly worked for Relation Therapeutics as a (graph) ML consultant.
I have a great passion for teaching and am constantly involved in academic and departmental life - supervising undergraduate courses, final year projects and Master’s research projects (+200h and ~60 students), interviewing CS applicants, chairing women@CL and introducing professionals to Machine Learning concepts as a Cambridge Spark Teaching Fellow. I also helped prepare and deliver Master’s courses, both on the practical (L42 Neural Networks in 2018 and 2019) and theoretical side (R250 Graph Neural Network seminars in 2020 and 2021).
Outside work, I love rowing with the Women’s First Boat in King’s College, travelling, playing the piano/guitar/singing in a rock band and chasing my favourite bands on tour. 🎼 I’ve also recently revived an old habit of writing poetry!
PhD in Machine Learning, 2021 (expected)
University of Cambridge
MPhil in Advanced Computer Science, 2017
University of Cambridge
BA in Computer Science, 2016
University of Cambridge
Recent advancements in graph representation learning have led to the emergence of condensed encodings that capture the main properties of a graph. However, even though these abstract representations are powerful for downstream tasks, they are not equally suitable for visualisation purposes. In this work, we merge Mapper, an algorithm from the field of Topological Data Analysis, with the expressive power of graph neural networks to produce hierarchical, topologically-grounded visualisations of graphs. These visualisations do not only help discern the structure of complex graphs, but also provide a means of understanding the models applied to them for solving various tasks. We further demonstrate the suitability of Mapper as a topological framework for graph pooling by showing an equivalence with soft-cluster assignment pooling methods (minCUT, DiffPool). Building upon this framework, we introduce a novel pooling algorithm based on PageRank, which obtains competitive results with state-of-the-art methods on graph classification benchmarks.
Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based solely on its egocentric input to answer a given question. The desired outcome is that the agent learns to combine capabilities such as scene understanding, navigation and language understanding in order to perform complex reasoning in the visual world. However, initial advancements combining standard vision and language methods with imitation and reinforcement learning algorithms have shown EQA might be too complex and challenging for these techniques. In order to investigate the feasibility of EQA-type tasks, we build the VideoNavQA dataset that contains pairs of questions and videos generated in the House3D environment. The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task. We investigate several models, adapted from popular VQA methods, on our benchmark. This establishes an initial understanding of how well VQA-style methods can perform within the novel EQA paradigm.
Master’s research projects: Goal-Conditioned Reinforcement Learning in the Presence of an Adversary (Carlos Purves, 2019-20) (87⁄100), Representation Learning for Spatio-Temporal Graphs (Felix Opolka, 2018-19) (85⁄100) (presented at ICLR RLGM), Dynamic Temporal Analysis for Graph Structured Data (Aaron Solomon, 2018-19) (presented at ICLR RLGM)
Computer Science Tripos Part II projects: Benchmarking Graph Neural Networks using Wikipedia (Péter Mernyei, 2019-20, Novel Applications spotlight talk at ICML GRL+), Multimodal Relational Reasoning for Visual Question Answering (Aaron Tjandra, 2019-20), The PlayStation Reinforcement Learning Environment (Carlos Purves, 2018-19) (80⁄100) (presented at NeurIPS Deep RL), Deep Learning for Music Recommendation (Andrew Wells, 2017-18) (76⁄100).
Undergraduate courses for Murray Edwards, King’s, and Queens’ Colleges: AI, Databases, Discrete Mathematics, Foundations of Computer Science, Logic and Proof, Machine Learning and Real-world Data.